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6,400 | 6,400 | 16,277,371 | 2,184 | Monitoring and reporting methods and apparatus include the acquisition of detailed aircraft state and systems data, analysis of the collected data, and transmission of the collected data and/or analysis of the collected data to a destination automatically via a portable electronic device which is carried onto and off of the aircraft by the pilot or another crew member. More particularly, monitoring and reporting methods and apparatus include collecting analog or digital sensor data onboard an aircraft, analyzing the data in real-time, and automatically transmitting the data and/or analysis of the data to a destination including a portable storage device such as a portable computer, electronic flight bag (EFB), or smart phone, by means such as wireless transmission, for automatic transfer to another destination when the portable computer, electronic flight bag (EFB), or smart phone is off of the aircraft. | 1. An apparatus comprising:
means for receiving data; means for recording data; at least one of a Quick Access Recorder (QAR) Board and a Multi Function Data Acquisition Unit (MFDAU); and a means of autonomous transmission of data from the QAR board or the MFDAU to a removable device in an aircraft which can retransmit the data autonomously when removed from the aircraft. 2-10. (canceled) 11. The apparatus of claim 1, wherein the means of transmission is wireless transmission. 12. The apparatus of claim 11, wherein the wireless transmission occurs substantially continuously while the aircraft is in flight or taxiing. 13. The apparatus of claim 11, wherein the wireless transmission occurs substantially continuously while the removeable device is in the aircraft. 14-19. (canceled) 20. The apparatus of claim 1, wherein the data is derived from internal and external sensors of an aircraft, said data comprising:
data acquired from a CV2R Ethernet feed; data from an Ethernet Vibration feed; data a MFDAU Data Feed; and aircraft flight data. 21. The method apparatus of claim 20, wherein data analysis is conducted in real-time. 22. The apparatus of claim 21, wherein the data analysis further comprises comparing the data against a predetermined set of events. 23. The apparatus of claim 21, wherein the data analysis further comprises comparing data in a virtual live basis in flight with Flight Operational Quality Assurance (FOQA). 24-73. (canceled) 74. A method of automatically transmitting QAR data from an aircraft, comprising:
automatically transmitting the QAR data to a portable electronic device which a crew member brings with him onto the aircraft and which will leave the aircraft with the crew member when he leaves the aircraft; and, automatically transmitting the QAR data from the portable electronic device when the electronic device is off of the aircraft and connects to a network. 75. The method of claim 74, wherein the data is wirelessly transmitted to the portable electronic device in the aircraft. 76. The method of claim 74, wherein the data is encrypted before transmission to the portable electronic device. 77. The method of claim 74, wherein the portable electronic device has software that analyzes the data and can send messages to remote locations or simply let those in the aircraft know that there is an issue. 78. (canceled) 79. The method of claim 74, wherein the data is transmitted substantially continuously while the portable electronic device is in the aircraft. 80. The method of claim 74, wherein the data is transmitted substantially continuously while the aircraft is in flight or taxiing. 81-82. (canceled) 83. An apparatus comprising:
a) means for receiving data; b) means for recording data; and, c) at least one of a Quick Access Recorder (QAR) Board and a Multi Function Data Acquisition Unit (MFDAU); wherein the data is transmitted substantially continuously while an aircraft is in flight or taxiing. 84. The apparatus of claim 83, wherein the data is transmitted substantially continuously while a portable electronic device is in the aircraft. | Monitoring and reporting methods and apparatus include the acquisition of detailed aircraft state and systems data, analysis of the collected data, and transmission of the collected data and/or analysis of the collected data to a destination automatically via a portable electronic device which is carried onto and off of the aircraft by the pilot or another crew member. More particularly, monitoring and reporting methods and apparatus include collecting analog or digital sensor data onboard an aircraft, analyzing the data in real-time, and automatically transmitting the data and/or analysis of the data to a destination including a portable storage device such as a portable computer, electronic flight bag (EFB), or smart phone, by means such as wireless transmission, for automatic transfer to another destination when the portable computer, electronic flight bag (EFB), or smart phone is off of the aircraft.1. An apparatus comprising:
means for receiving data; means for recording data; at least one of a Quick Access Recorder (QAR) Board and a Multi Function Data Acquisition Unit (MFDAU); and a means of autonomous transmission of data from the QAR board or the MFDAU to a removable device in an aircraft which can retransmit the data autonomously when removed from the aircraft. 2-10. (canceled) 11. The apparatus of claim 1, wherein the means of transmission is wireless transmission. 12. The apparatus of claim 11, wherein the wireless transmission occurs substantially continuously while the aircraft is in flight or taxiing. 13. The apparatus of claim 11, wherein the wireless transmission occurs substantially continuously while the removeable device is in the aircraft. 14-19. (canceled) 20. The apparatus of claim 1, wherein the data is derived from internal and external sensors of an aircraft, said data comprising:
data acquired from a CV2R Ethernet feed; data from an Ethernet Vibration feed; data a MFDAU Data Feed; and aircraft flight data. 21. The method apparatus of claim 20, wherein data analysis is conducted in real-time. 22. The apparatus of claim 21, wherein the data analysis further comprises comparing the data against a predetermined set of events. 23. The apparatus of claim 21, wherein the data analysis further comprises comparing data in a virtual live basis in flight with Flight Operational Quality Assurance (FOQA). 24-73. (canceled) 74. A method of automatically transmitting QAR data from an aircraft, comprising:
automatically transmitting the QAR data to a portable electronic device which a crew member brings with him onto the aircraft and which will leave the aircraft with the crew member when he leaves the aircraft; and, automatically transmitting the QAR data from the portable electronic device when the electronic device is off of the aircraft and connects to a network. 75. The method of claim 74, wherein the data is wirelessly transmitted to the portable electronic device in the aircraft. 76. The method of claim 74, wherein the data is encrypted before transmission to the portable electronic device. 77. The method of claim 74, wherein the portable electronic device has software that analyzes the data and can send messages to remote locations or simply let those in the aircraft know that there is an issue. 78. (canceled) 79. The method of claim 74, wherein the data is transmitted substantially continuously while the portable electronic device is in the aircraft. 80. The method of claim 74, wherein the data is transmitted substantially continuously while the aircraft is in flight or taxiing. 81-82. (canceled) 83. An apparatus comprising:
a) means for receiving data; b) means for recording data; and, c) at least one of a Quick Access Recorder (QAR) Board and a Multi Function Data Acquisition Unit (MFDAU); wherein the data is transmitted substantially continuously while an aircraft is in flight or taxiing. 84. The apparatus of claim 83, wherein the data is transmitted substantially continuously while a portable electronic device is in the aircraft. | 2,100 |
6,401 | 6,401 | 15,922,631 | 2,178 | There is presented a method and system for providing a compact graphical user interface for flexible filtering of data. The method comprises showing a search interface on a display device for filtering a content set by a plurality of domains, including a first domain, displaying, within the search interface, a first graphical representation of a parameter set of the first domain in response to a selecting of the first domain, receiving a first parameter subset from the first graphical representation, filtering a content set using the first parameter subset to obtain a search result, and displaying the search result on a display device. The search interface includes a temporally visible menu for selecting parameter sets of the domains and a compact single line query box to display graphical representations of parameter sets or to provide a conventional text entry box. | 1-20. (canceled) 21. A method comprising:
showing a search interface on a display device for filtering a content set by a plurality of domains including a first non-textual domain, wherein the search interface comprises a menu to display the plurality of domains for a selection by a user; in response to the selection of the first non-textual domain by the user, receiving and displaying, within the search interface, a first non-textual graphical representation of a digital image set of the first non-textual domain; receiving a first digital image selected by the user from the digital image set of the first non-textual graphical representation; filtering the content set using the first digital image to obtain a search result; and displaying the search result on the display device. 22. The method of claim 21, wherein the receiving further includes receiving a second digital image selected by the user from the digital image set of the first non-textual graphical representation, and the filtering further filters the content set using the second digital image to obtain the search result. 23. The method of claim 21, wherein the digital image set includes character images, personality images and scene images. 24. The method of claim 21, further comprising:
replacing the first non-textual graphical representation in the search interface with a text entry box in response to a selecting of a textual parameter set of the plurality of parameter sets, the textual parameter set of one or more of the plurality of domains; receiving a textual parameter from the text entry box; filtering the content set using the textual parameter to update the search result; displaying the search result on the display device. 25. The method of claim 21, wherein each content in the content set includes a color border, and wherein the method further comprises:
displaying, within the search interface, a second non-textual graphical representation of a color set of a second non-textual domain; receiving a first color selected by the user from the color set of the second non-textual graphical representation; filtering the content set using the first color to obtain a second search result including the content having the color border that matches the first color; and displaying the second search result on the display device. 26. The method of claim 25 further comprises receiving a second color selected by the user from the color set of the second non-textual graphical representation, and the filtering further filters the content set using the second color to obtain the second search result. 27. The method of claim 25, further comprising:
replacing the second non-textual graphical representation in the search interface with a text entry box in response to a selecting of a textual parameter set of the plurality of parameter sets, the textual parameter set of one or more of the plurality of domains; receiving a textual parameter from the text entry box; filtering the content set using the textual parameter to update the second search result; displaying the second search result on the display device. 28. The method of claim 25, wherein the plurality of domains include a third non-textual domain including three-dimensional models. 29. The method of claim 28, wherein the third non-textual domain includes a third non-textual graphical representation including at least one graphical icon. 30. A device comprising:
a processor configured to:
show a search interface on a display device for filtering a content set by a plurality of domains including a first non-textual domain, wherein the search interface comprises a menu to display the plurality of domains for a selection by a user;
in response to the selection of the first non-textual domain by the user, receive and display, within the search interface, a first non-textual graphical representation of a digital image set of the first non-textual domain;
receive a first digital image selected by the user from the digital image set of the first non-textual graphical representation;
filter the content set using the first digital image to obtain a search result; and
display the search result on the display device. 31. The device of claim 30, wherein the receiving further includes receiving a second digital image selected by the user from the digital image set of the first non-textual graphical representation, and the filtering further filters the content set using the second digital image to obtain the search result. 32. The device of claim 30, wherein the digital image set includes character images, personality images and scene images. 33. The device of claim 30, wherein the processor is further configured to:
replace the first non-textual graphical representation in the search interface with a text entry box in response to a selecting of a textual parameter set of the plurality of parameter sets, the textual parameter set of one or more of the plurality of domains; receive a textual parameter from the text entry box; filter the content set using the textual parameter to update the search result; display the search result on the display device. 34. The device of claim 30, wherein each content in the content set includes a color border, and wherein the processor is further configured to:
display, within the search interface, a second non-textual graphical representation of a color set of a second non-textual domain; receive a first color selected by the user from the color set of the second non-textual graphical representation; filter the content set using the first color to obtain a second search result including the content having the color border that matches the first color; and display the second search result on the display device. 35. The device of claim 34, wherein the processor is further configured to:
receive a second color selected by the user from the color set of the second non-textual graphical representation, and wherein the filtering further filters the content set using the second color to obtain the second search result. 36. The device of claim 34, wherein the processor is further configured to:
replace the second non-textual graphical representation in the search interface with a text entry box in response to a selecting of a textual parameter set of the plurality of parameter sets, the textual parameter set of one or more of the plurality of domains; receive a textual parameter from the text entry box; filter the content set using the textual parameter to update the second search result; display the second search result on the display device. 37. The device of claim 34, wherein the plurality of domains include a third non-textual domain including three-dimensional models. 38. The device of claim 37, wherein the third non-textual domain includes a third non-textual graphical representation including at least one graphical icon. | There is presented a method and system for providing a compact graphical user interface for flexible filtering of data. The method comprises showing a search interface on a display device for filtering a content set by a plurality of domains, including a first domain, displaying, within the search interface, a first graphical representation of a parameter set of the first domain in response to a selecting of the first domain, receiving a first parameter subset from the first graphical representation, filtering a content set using the first parameter subset to obtain a search result, and displaying the search result on a display device. The search interface includes a temporally visible menu for selecting parameter sets of the domains and a compact single line query box to display graphical representations of parameter sets or to provide a conventional text entry box.1-20. (canceled) 21. A method comprising:
showing a search interface on a display device for filtering a content set by a plurality of domains including a first non-textual domain, wherein the search interface comprises a menu to display the plurality of domains for a selection by a user; in response to the selection of the first non-textual domain by the user, receiving and displaying, within the search interface, a first non-textual graphical representation of a digital image set of the first non-textual domain; receiving a first digital image selected by the user from the digital image set of the first non-textual graphical representation; filtering the content set using the first digital image to obtain a search result; and displaying the search result on the display device. 22. The method of claim 21, wherein the receiving further includes receiving a second digital image selected by the user from the digital image set of the first non-textual graphical representation, and the filtering further filters the content set using the second digital image to obtain the search result. 23. The method of claim 21, wherein the digital image set includes character images, personality images and scene images. 24. The method of claim 21, further comprising:
replacing the first non-textual graphical representation in the search interface with a text entry box in response to a selecting of a textual parameter set of the plurality of parameter sets, the textual parameter set of one or more of the plurality of domains; receiving a textual parameter from the text entry box; filtering the content set using the textual parameter to update the search result; displaying the search result on the display device. 25. The method of claim 21, wherein each content in the content set includes a color border, and wherein the method further comprises:
displaying, within the search interface, a second non-textual graphical representation of a color set of a second non-textual domain; receiving a first color selected by the user from the color set of the second non-textual graphical representation; filtering the content set using the first color to obtain a second search result including the content having the color border that matches the first color; and displaying the second search result on the display device. 26. The method of claim 25 further comprises receiving a second color selected by the user from the color set of the second non-textual graphical representation, and the filtering further filters the content set using the second color to obtain the second search result. 27. The method of claim 25, further comprising:
replacing the second non-textual graphical representation in the search interface with a text entry box in response to a selecting of a textual parameter set of the plurality of parameter sets, the textual parameter set of one or more of the plurality of domains; receiving a textual parameter from the text entry box; filtering the content set using the textual parameter to update the second search result; displaying the second search result on the display device. 28. The method of claim 25, wherein the plurality of domains include a third non-textual domain including three-dimensional models. 29. The method of claim 28, wherein the third non-textual domain includes a third non-textual graphical representation including at least one graphical icon. 30. A device comprising:
a processor configured to:
show a search interface on a display device for filtering a content set by a plurality of domains including a first non-textual domain, wherein the search interface comprises a menu to display the plurality of domains for a selection by a user;
in response to the selection of the first non-textual domain by the user, receive and display, within the search interface, a first non-textual graphical representation of a digital image set of the first non-textual domain;
receive a first digital image selected by the user from the digital image set of the first non-textual graphical representation;
filter the content set using the first digital image to obtain a search result; and
display the search result on the display device. 31. The device of claim 30, wherein the receiving further includes receiving a second digital image selected by the user from the digital image set of the first non-textual graphical representation, and the filtering further filters the content set using the second digital image to obtain the search result. 32. The device of claim 30, wherein the digital image set includes character images, personality images and scene images. 33. The device of claim 30, wherein the processor is further configured to:
replace the first non-textual graphical representation in the search interface with a text entry box in response to a selecting of a textual parameter set of the plurality of parameter sets, the textual parameter set of one or more of the plurality of domains; receive a textual parameter from the text entry box; filter the content set using the textual parameter to update the search result; display the search result on the display device. 34. The device of claim 30, wherein each content in the content set includes a color border, and wherein the processor is further configured to:
display, within the search interface, a second non-textual graphical representation of a color set of a second non-textual domain; receive a first color selected by the user from the color set of the second non-textual graphical representation; filter the content set using the first color to obtain a second search result including the content having the color border that matches the first color; and display the second search result on the display device. 35. The device of claim 34, wherein the processor is further configured to:
receive a second color selected by the user from the color set of the second non-textual graphical representation, and wherein the filtering further filters the content set using the second color to obtain the second search result. 36. The device of claim 34, wherein the processor is further configured to:
replace the second non-textual graphical representation in the search interface with a text entry box in response to a selecting of a textual parameter set of the plurality of parameter sets, the textual parameter set of one or more of the plurality of domains; receive a textual parameter from the text entry box; filter the content set using the textual parameter to update the second search result; display the second search result on the display device. 37. The device of claim 34, wherein the plurality of domains include a third non-textual domain including three-dimensional models. 38. The device of claim 37, wherein the third non-textual domain includes a third non-textual graphical representation including at least one graphical icon. | 2,100 |
6,402 | 6,402 | 15,201,880 | 2,174 | Disclosed herein is an information processing device including a download executing section downloading application software and patch data, a progress state display section displaying a progress state of the downloading, and an executing section executing an application by using the application software and the patch data, the progress state display section displaying the progress state of the downloading of a combined total of the application software and the patch data. | 1. An information processing device comprising:
a download executing section downloading application software and patch data; a progress state display section displaying a progress state of the downloading; and an executing section executing an application by using the application software and the patch data; the progress state display section displaying the progress state of the downloading of a combined total of the application software and the patch data. 2. The information processing device according to claim 1, wherein
the application software and the patch data each include a plurality of groups, one group of the patch data is associated with one group of the application software, and the executing section can start the application after the download executing section downloads a file belonging to a first group of the application software and downloads a file belonging to a first group of the patch data, and the progress state display section displays the progress state of the downloading of the combined total of the file of the application software, the file belonging to the first group, and the file of the patch data, the file belonging to the first group. 3. The information processing device according to claim 2, wherein
after completion of the downloading of the file of the application software, the file belonging to the first group, and the file of the patch data, the file belonging to the first group, or completion of installation of the file of the application software, the file belonging to the first group, and the file of the patch data, the file belonging to the first group, the progress state display section displays information indicating the completion in a first display region. 4. The information processing device according to claim 3, wherein
the progress state display section displays, in a second display region, the progress state of the downloading of a combined total of files belonging to a second group and subsequent groups of the application software and files belonging to a second group and subsequent groups of the patch data. 5. The information processing device according to claim 4, wherein
the progress state display section displays, in the second display region, the downloading progress state enabling a comparison of an amount of already downloaded data with a total amount of data of the application software and the patch data. 6. The information processing device according to claim 3, wherein
the first display region displaying the information indicating the completion is configured to give an instruction to start the application when selected by a user. 7. The information processing device according to claim 2, further comprising a notifying section that, after completion of the downloading of the file of the application software, the file belonging to the first group, and the file of the patch data, the file belonging to the first group, or completion of installation of the file of the application software, the file belonging to the first group, and the file of the patch data, the file belonging to the first group, makes pop-up display of information indicating the completion on a screen displaying the progress state of the downloading or another screen,
wherein even after completion of the downloading of all of files of the application software and the patch data or completion of installation of all of the files of the application software and the patch data, the notifying section does not make pop-up display of information indicating the completion. 8. A method of displaying a download progress state, the method comprising:
downloading application software and patch data; and displaying a progress state of the downloading; the displaying of the progress state displaying the progress state of the downloading of a combined total of the application software and the patch data. 9. A program for a computer, comprising:
downloading application software and patch data; and displaying a progress state of the downloading; the progress state displaying including
displaying the progress state of the downloading of a combined total of the application software and the patch data. | Disclosed herein is an information processing device including a download executing section downloading application software and patch data, a progress state display section displaying a progress state of the downloading, and an executing section executing an application by using the application software and the patch data, the progress state display section displaying the progress state of the downloading of a combined total of the application software and the patch data.1. An information processing device comprising:
a download executing section downloading application software and patch data; a progress state display section displaying a progress state of the downloading; and an executing section executing an application by using the application software and the patch data; the progress state display section displaying the progress state of the downloading of a combined total of the application software and the patch data. 2. The information processing device according to claim 1, wherein
the application software and the patch data each include a plurality of groups, one group of the patch data is associated with one group of the application software, and the executing section can start the application after the download executing section downloads a file belonging to a first group of the application software and downloads a file belonging to a first group of the patch data, and the progress state display section displays the progress state of the downloading of the combined total of the file of the application software, the file belonging to the first group, and the file of the patch data, the file belonging to the first group. 3. The information processing device according to claim 2, wherein
after completion of the downloading of the file of the application software, the file belonging to the first group, and the file of the patch data, the file belonging to the first group, or completion of installation of the file of the application software, the file belonging to the first group, and the file of the patch data, the file belonging to the first group, the progress state display section displays information indicating the completion in a first display region. 4. The information processing device according to claim 3, wherein
the progress state display section displays, in a second display region, the progress state of the downloading of a combined total of files belonging to a second group and subsequent groups of the application software and files belonging to a second group and subsequent groups of the patch data. 5. The information processing device according to claim 4, wherein
the progress state display section displays, in the second display region, the downloading progress state enabling a comparison of an amount of already downloaded data with a total amount of data of the application software and the patch data. 6. The information processing device according to claim 3, wherein
the first display region displaying the information indicating the completion is configured to give an instruction to start the application when selected by a user. 7. The information processing device according to claim 2, further comprising a notifying section that, after completion of the downloading of the file of the application software, the file belonging to the first group, and the file of the patch data, the file belonging to the first group, or completion of installation of the file of the application software, the file belonging to the first group, and the file of the patch data, the file belonging to the first group, makes pop-up display of information indicating the completion on a screen displaying the progress state of the downloading or another screen,
wherein even after completion of the downloading of all of files of the application software and the patch data or completion of installation of all of the files of the application software and the patch data, the notifying section does not make pop-up display of information indicating the completion. 8. A method of displaying a download progress state, the method comprising:
downloading application software and patch data; and displaying a progress state of the downloading; the displaying of the progress state displaying the progress state of the downloading of a combined total of the application software and the patch data. 9. A program for a computer, comprising:
downloading application software and patch data; and displaying a progress state of the downloading; the progress state displaying including
displaying the progress state of the downloading of a combined total of the application software and the patch data. | 2,100 |
6,403 | 6,403 | 15,588,217 | 2,154 | Surfacing relevant socially trending informational items in response to an exploratory query is provided. A temporally relevant social data search system includes an intelligent assistant, a knowledgebase generation system, and a temporal graph knowledgebase. The knowledgebase generation system builds the temporal graph knowledgebase from entities and relationships detected in social data mined from a plurality of social networking data sources. Responsive to receiving an exploratory query associated with one or more entities, the intelligent assistant queries the temporal graph knowledgebase for information items related to the one or more entities, selects a relevant information item to include in a response, and provides the response to the user. | 1. A method for providing a relevant informational item to a user, comprising:
mining a plurality of social networking data sources for collecting social data items; parsing the collected social data items for detecting entities; generating a graph knowledgebase storing a first node representing a first entity, wherein the first entity is a social data item from the collected social data items, a second node representing a second entity, wherein the second entity is associated with the social data item, and an edge representing a relationship connecting the first node and the second node; responsive to receiving an exploratory query by the user for information associated with the second entity, querying the graph knowledgebase for identifying social data items related to the second entity; selecting a social data item related to the second entity based on a relatedness score, the relatedness score based at least in part on an amount of and recency of social activity associated with the first entity and the second entity; generating a response to the user including the information parsed from the selected social data item; and delivering the response to the user via a communication channel. 2. The method of claim 1, wherein selecting the social data item further comprises:
determining whether the social data item includes information relevant to the user based on personalization information; when the social data item includes information relevant to the user based on personalization information, incrementing the relatedness score; and when the social data item does not include information relevant to the user based on personalization information, decrementing the relatedness score. 3. The method of claim 2, wherein determining whether the social data item includes information relevant to the user comprises determining whether the social data item includes information relevant to the user based on personalization information explicitly defined by the user in a user profile. 4. The method of claim 2, wherein determining whether the social data item includes information relevant to the user comprises determining whether the social data item includes information relevant to the user based on personalization information implicitly defined based on user interaction data. 5. The method of claim 1, wherein generating the graph knowledgebase comprises storing the second node representing the second entity, wherein the second entity is an entity mentioned in the social data item represented by the first node. 6. The method of claim 1, wherein generating the graph knowledgebase comprises storing the second node representing the second entity, wherein the second entity is an author of the social data item represented by the first node. 7. The method of claim 1, wherein calculating the relatedness score comprises calculating the relatedness score based on a number of:
shares; likes, posts, or re-posts. 8. The method of claim 1, wherein querying the graph knowledgebase comprises:
extracting a portion of the graph knowledgebase including the second entity; and traversing the extracted portion of the graph knowledgebase for discovering other entities, relationships, and associated relatedness scores. 9. The method of claim 1, further comprising:
continually mining the plurality of social networking data sources for collecting social data items; parsing the collected social data items for detecting entities; and updating the graph knowledgebase with the detected entities and relationships connecting the entities. 10. The method of claim 1, further comprising:
responsive to a follow-up exploratory query by the user for information associated with the second entity and a third entity, querying the graph knowledgebase for identifying other entities related to the second entity and the third entity; selecting a social data item related to the second entity and the third entity based on a relatedness score; generating a response to the user including the information parsed from the selected social data item; and delivering the response to the user via a communication channel. 11. A system for providing a relevant informational item to a user, comprising:
a processing unit; and a memory, including computer readable instructions, which when executed by the processing unit is operable to provide a temporally relevant social data search system operative to:
mine a plurality of social networking data sources for collecting social data items;
parse the collected social data items for detecting entities;
generate a graph knowledgebase storing a first node representing a first entity, wherein the first entity is a social data item from the collected social data items, a second node representing a second entity, wherein the second entity is associated with the social data item, and an edge representing a relationship connecting the first node and the second node;
calculate a relatedness score between the first entity and the second entity based at least in part on an amount of and recency of social activity associated with the first entity and the second entity;
responsive to receiving an exploratory query by the user for information associated with the second entity, query the graph knowledgebase for identifying social data items related to the second entity;
increase the relatedness score of identified social data items related to the second entity that include information relevant to the user based on personalization information;
select a social data item related to the second entity based on the relatedness score;
generate a response to the user including the information parsed from the selected social data item; and
deliver the response to the user via a communication channel. 12. The system of claim 11, wherein:
the personalization information is explicitly defined by the user in a user profile; or the personalization information is implicitly defined based on user interaction data. 13. The system of claim 11, wherein in calculating the relatedness score, the temporally relevant social data search system is operative to calculate the relatedness score based on a number of:
shares; likes, posts, or re-posts. 14. The system of claim 11, wherein in querying the graph knowledgebase, the temporally relevant social data search system is operative to:
extract a portion of the graph knowledgebase including the second entity; and traverse the extracted portion of the graph knowledgebase for discovering other entities, relationships, and associated relatedness scores. 15. The system of claim 11, wherein the temporally relevant social data search system is further operative to
continually mine the plurality of social networking data sources for collecting social data items; parse the collected social data items for detecting entities; and update the graph knowledgebase with the detected entities and relationships connecting the entities. 16. The system of claim 11, wherein the temporally relevant social data search system is further operative to:
responsive to a follow-up exploratory query by the user for information associated with the second entity and a third entity, query the graph knowledgebase for identifying other entities related to the second entity and the third entity; select a social data item related to the second entity and the third entity based on a relatedness score; generate a response to the user including the information parsed from the selected social data item; and deliver the response to the user via a communication channel. 17. A computer readable storage device including computer readable instructions, which when executed by a processing unit is operable to:
mine a plurality of social networking data sources for collecting social data items; parse the collected social data items for detecting entities; generate a graph knowledgebase storing a first node representing a first entity, wherein the first entity is a social data item from the collected social data items, a second node representing a second entity, wherein the second entity is associated with the social data item, and an edge representing a relationship connecting the first node and the second node; calculate a relatedness score between the first entity and the second entity based at least in part on an amount of and recency of social activity associated with the first entity and the second entity; responsive to receiving an exploratory query by the user for information associated with the second entity, query the graph knowledgebase for identifying social data items related to the second entity; increase a relatedness score of identified social data items related to the second entity that include information relevant to the user based on personalization information; select a social data item related to the second entity based on the relatedness score; generate a response to the user including the information parsed from the selected social data item; and deliver the response to the user via a communication channel. 18. The computer readable storage device of claim 17, wherein:
the personalization information is explicitly defined by the user in a user profile; or the personalization information is implicitly defined based on user interaction data. 19. The computer readable storage device of claim 17, wherein the device is further operative to:
continually mine the plurality of social networking data sources for collecting social data items; parse the collected social data items for detecting entities; and update the graph knowledgebase with the detected entities and relationships connecting the entities. 20. The computer readable storage device of claim 17, wherein in querying the graph knowledgebase, the device is operative to:
extract a portion of the graph knowledgebase including the second entity; and traverse the extracted portion of the graph knowledgebase for discovering other entities, relationships, and associated relatedness scores. | Surfacing relevant socially trending informational items in response to an exploratory query is provided. A temporally relevant social data search system includes an intelligent assistant, a knowledgebase generation system, and a temporal graph knowledgebase. The knowledgebase generation system builds the temporal graph knowledgebase from entities and relationships detected in social data mined from a plurality of social networking data sources. Responsive to receiving an exploratory query associated with one or more entities, the intelligent assistant queries the temporal graph knowledgebase for information items related to the one or more entities, selects a relevant information item to include in a response, and provides the response to the user.1. A method for providing a relevant informational item to a user, comprising:
mining a plurality of social networking data sources for collecting social data items; parsing the collected social data items for detecting entities; generating a graph knowledgebase storing a first node representing a first entity, wherein the first entity is a social data item from the collected social data items, a second node representing a second entity, wherein the second entity is associated with the social data item, and an edge representing a relationship connecting the first node and the second node; responsive to receiving an exploratory query by the user for information associated with the second entity, querying the graph knowledgebase for identifying social data items related to the second entity; selecting a social data item related to the second entity based on a relatedness score, the relatedness score based at least in part on an amount of and recency of social activity associated with the first entity and the second entity; generating a response to the user including the information parsed from the selected social data item; and delivering the response to the user via a communication channel. 2. The method of claim 1, wherein selecting the social data item further comprises:
determining whether the social data item includes information relevant to the user based on personalization information; when the social data item includes information relevant to the user based on personalization information, incrementing the relatedness score; and when the social data item does not include information relevant to the user based on personalization information, decrementing the relatedness score. 3. The method of claim 2, wherein determining whether the social data item includes information relevant to the user comprises determining whether the social data item includes information relevant to the user based on personalization information explicitly defined by the user in a user profile. 4. The method of claim 2, wherein determining whether the social data item includes information relevant to the user comprises determining whether the social data item includes information relevant to the user based on personalization information implicitly defined based on user interaction data. 5. The method of claim 1, wherein generating the graph knowledgebase comprises storing the second node representing the second entity, wherein the second entity is an entity mentioned in the social data item represented by the first node. 6. The method of claim 1, wherein generating the graph knowledgebase comprises storing the second node representing the second entity, wherein the second entity is an author of the social data item represented by the first node. 7. The method of claim 1, wherein calculating the relatedness score comprises calculating the relatedness score based on a number of:
shares; likes, posts, or re-posts. 8. The method of claim 1, wherein querying the graph knowledgebase comprises:
extracting a portion of the graph knowledgebase including the second entity; and traversing the extracted portion of the graph knowledgebase for discovering other entities, relationships, and associated relatedness scores. 9. The method of claim 1, further comprising:
continually mining the plurality of social networking data sources for collecting social data items; parsing the collected social data items for detecting entities; and updating the graph knowledgebase with the detected entities and relationships connecting the entities. 10. The method of claim 1, further comprising:
responsive to a follow-up exploratory query by the user for information associated with the second entity and a third entity, querying the graph knowledgebase for identifying other entities related to the second entity and the third entity; selecting a social data item related to the second entity and the third entity based on a relatedness score; generating a response to the user including the information parsed from the selected social data item; and delivering the response to the user via a communication channel. 11. A system for providing a relevant informational item to a user, comprising:
a processing unit; and a memory, including computer readable instructions, which when executed by the processing unit is operable to provide a temporally relevant social data search system operative to:
mine a plurality of social networking data sources for collecting social data items;
parse the collected social data items for detecting entities;
generate a graph knowledgebase storing a first node representing a first entity, wherein the first entity is a social data item from the collected social data items, a second node representing a second entity, wherein the second entity is associated with the social data item, and an edge representing a relationship connecting the first node and the second node;
calculate a relatedness score between the first entity and the second entity based at least in part on an amount of and recency of social activity associated with the first entity and the second entity;
responsive to receiving an exploratory query by the user for information associated with the second entity, query the graph knowledgebase for identifying social data items related to the second entity;
increase the relatedness score of identified social data items related to the second entity that include information relevant to the user based on personalization information;
select a social data item related to the second entity based on the relatedness score;
generate a response to the user including the information parsed from the selected social data item; and
deliver the response to the user via a communication channel. 12. The system of claim 11, wherein:
the personalization information is explicitly defined by the user in a user profile; or the personalization information is implicitly defined based on user interaction data. 13. The system of claim 11, wherein in calculating the relatedness score, the temporally relevant social data search system is operative to calculate the relatedness score based on a number of:
shares; likes, posts, or re-posts. 14. The system of claim 11, wherein in querying the graph knowledgebase, the temporally relevant social data search system is operative to:
extract a portion of the graph knowledgebase including the second entity; and traverse the extracted portion of the graph knowledgebase for discovering other entities, relationships, and associated relatedness scores. 15. The system of claim 11, wherein the temporally relevant social data search system is further operative to
continually mine the plurality of social networking data sources for collecting social data items; parse the collected social data items for detecting entities; and update the graph knowledgebase with the detected entities and relationships connecting the entities. 16. The system of claim 11, wherein the temporally relevant social data search system is further operative to:
responsive to a follow-up exploratory query by the user for information associated with the second entity and a third entity, query the graph knowledgebase for identifying other entities related to the second entity and the third entity; select a social data item related to the second entity and the third entity based on a relatedness score; generate a response to the user including the information parsed from the selected social data item; and deliver the response to the user via a communication channel. 17. A computer readable storage device including computer readable instructions, which when executed by a processing unit is operable to:
mine a plurality of social networking data sources for collecting social data items; parse the collected social data items for detecting entities; generate a graph knowledgebase storing a first node representing a first entity, wherein the first entity is a social data item from the collected social data items, a second node representing a second entity, wherein the second entity is associated with the social data item, and an edge representing a relationship connecting the first node and the second node; calculate a relatedness score between the first entity and the second entity based at least in part on an amount of and recency of social activity associated with the first entity and the second entity; responsive to receiving an exploratory query by the user for information associated with the second entity, query the graph knowledgebase for identifying social data items related to the second entity; increase a relatedness score of identified social data items related to the second entity that include information relevant to the user based on personalization information; select a social data item related to the second entity based on the relatedness score; generate a response to the user including the information parsed from the selected social data item; and deliver the response to the user via a communication channel. 18. The computer readable storage device of claim 17, wherein:
the personalization information is explicitly defined by the user in a user profile; or the personalization information is implicitly defined based on user interaction data. 19. The computer readable storage device of claim 17, wherein the device is further operative to:
continually mine the plurality of social networking data sources for collecting social data items; parse the collected social data items for detecting entities; and update the graph knowledgebase with the detected entities and relationships connecting the entities. 20. The computer readable storage device of claim 17, wherein in querying the graph knowledgebase, the device is operative to:
extract a portion of the graph knowledgebase including the second entity; and traverse the extracted portion of the graph knowledgebase for discovering other entities, relationships, and associated relatedness scores. | 2,100 |
6,404 | 6,404 | 13,553,599 | 2,156 | Embodiments of the invention relate to a computer-implemented method for generating explanatory data from a personalized recommendations process for a primary user based at least on stored data about the primary user. The method comprises a server computer obtaining data related to one or more users who are relevant to the primary user, then determining at least one group of users relevant to the primary user. The server computer also obtains data related to one or more entities, determines one or more entities relevant to the primary user, and associates the at least one relevant group of users with the one or more relevant entities. One or more potential candidate factors are generated. A set of factors are selected from the one or more potential candidate factors, wherein the potential candidate factors are used as explanatory data to determine recommendations to the primary user. | 1. A computer-implemented method for generating explanatory data from a personalized recommendations process for a primary user based at least on stored data about the primary user, the method comprising:
determining the primary user for whom explanatory data is to be generated, the primary user being one of a plurality of users over which recommendations are considered; obtaining data related to a set of relevant users, wherein the set of relevant users comprises one or more users in the plurality of users that are deemed relevant to the primary user, wherein relevancy of a user in the set of relevant users to the primary user is based, at least in part, on similarities, according to stored user data, between the relevant user and the primary user; obtaining, at the server computer, entity data from a plurality of data sources, wherein the entity data is associated with an entity in a plurality of entities; storing the entity data at the server computer, wherein the entity data associated with the plurality of entities is stored in an entity database; determining, at the server computer, one or more entities relevant to the primary user based on the data related to the one or more entities; determining, by the server computer, one or more potential candidate factors based on at least the one or more relevant entities; and generating, by the server computer, explanation data based on the one or more potential candidate factors used in the personalized recommendations process. 2. The method of claim 1, further comprising:
selecting, by the server computer, a set of factors from the one or more potential candidate factors; and communicating, by the server computer, the set of selected factors to the primary user, wherein the potential candidate factors are used to determine recommendations to the primary user. 3. The method of claim 1, further comprising:
determining, at the server computer, at least one group of users relevant to the primary user based on the data related to the one or more users, each relevant group including one or more relevant users to the primary user; associating, by the server computer, the at least one relevant group of users with the one or more relevant entities; determining, by the server computer, one or more potential candidate factors based on at least the relevant group of users; and generating, by the server computer, explanation data based on the one or more potential candidate factors used in the personalized recommendations process. 4. The method of claim 1, wherein associating the at least one relevant group of users with the one or more relevant entities includes determining a relationship between a relevant user with a relevant entity, a relationship between a relevant entity with another relevant entity, or a relationship between a relevant user with another relevant user. 5. The method of claim 1, wherein data related to one or more relevant users includes account information stored in a database of users, wherein the account information includes demographic data. 6. The method of claim 1, wherein the data related to one or more relevant users includes current location information of the user. 7. The method of claim 5, wherein the pre-determined attributes includes cuisine preferences or food allergies. 8. The method of claim 1, wherein the current location information is associated with data related to one or more relevant entities, wherein the data related to one of relevant entities includes previous data from the one or more relevant users related to the one or more relevant entities. 9. The method of claim 3, wherein selecting the set of factors is based on at least one of, or combinations of, spatial, temporal, social, demographic, or user history data previously stored in a database. 10. The method of claim 9, wherein selecting the set of factors is based on at least one of, or combinations of, spatial, temporal, social, demographic, or user current data collected in real-time. 11. The method of claim 9, wherein the user history data includes at least one of, or combinations of, query history, browsing history, or purchase history of the primary user. 12. The method of claim 9, wherein selecting the set of factors is based on table driven matching, wherein the tables are stored in the database. 13. The method of claim 10, wherein selecting the set of factors is based on psychological models of behavior of a demographic of the primary user. 14. The method of claim 3, further comprising:
determining a set of media objects associated with the set of selected factors, wherein the set of media objects includes at least one of, or combinations of, textual information, pictures, video, hyperlinked text, webpages, styled text, and sounds; and communicating the set of media objects with the set of selected factors for the recommendation explanation. 15. The method of claim 3, wherein communicating the set selected factors includes multi-dimensional models, vibrations, electric shocks, pulses, or scents. 16. A server computer comprising a processor and a non-transitory computer readable medium, the non-transitory computer readable medium comprising code executable by the processor to implement a computer-implemented method for generating explanatory data from a personalized recommendations process for a primary user based at least on stored data about the primary user, the method comprising:
determining the primary user for whom explanatory data is to be generated, the primary user being one of a plurality of users over which recommendations are considered; obtaining, at a server computer, data related to a set of relevant users, wherein the set of relevant users comprises one or more users in the plurality of users that are deemed relevant to the primary user, wherein relevancy of a user in the set of relevant users to the primary user is based, at least on part, on similarities, according to stored user data, between the relevant user and the primary user; obtaining, at the server computer, entity data from a plurality of data sources, wherein the entity data is associated with an entity in a plurality of entities; storing the entity data at the server computer, wherein the entity data associated with the plurality of entities is stored in an entity database; determining, at the server computer, one or more entities relevant to the primary user based on the data related to the one or more entities; determining, by the server computer, one or more potential candidate factors based on at least the one or more relevant entities; and generating, by the server computer, explanation data based on the one or more potential candidate factors used in the personalized recommendations process. 17. The server computer of claim 16, the method further comprising:
selecting, by the server computer, a set of factors from the one or more potential candidate factors; and communicating, by the server computer, the set of selected factors to the primary user, wherein the potential candidate factors are used to determine recommendations to the primary user. 18. The server computer of claim 16, the method further comprising:
determining, at the server computer, at least one group of users relevant to the primary user based on the data related to the one or more users, each relevant group including one or more relevant users to the primary user; associating, by the server computer, the at least one relevant group of users with the one or more relevant entities; determining, by the server computer, one or more potential candidate factors based on at least the relevant group of users; and generating, by the server computer, explanation data based on the one or more potential candidate factors used in the personalized recommendations process. 19. The server computer of claim 16, wherein associating the at least one relevant group of users with the one or more relevant entities includes determining a relationship between a relevant user with a relevant entity, a relationship between a relevant entity with another relevant entity, or a relationship between a relevant user with another relevant user. 20. The server computer of claim 16, wherein data related to one or more relevant users includes account information stored in a database of users, wherein the account information includes demographic data. 21. The server computer of claim 16, wherein the data related to one or more relevant users includes current location information of the user. 22. The server computer of claim 20, wherein the pre-determined attributes includes cuisine preferences or food allergies. 23. The server computer of claim 16, wherein the current location information is associated with data related to one or more relevant entities, wherein the data related to one of relevant entities includes previous data from the one or more relevant users related to the one or more relevant entities. 24. The method of claim 18, wherein selecting the set of factors is based on at least one of, or combinations of, spatial, temporal, social, demographic, or user history data previously stored in a database. 25. The method of claim 24, wherein selecting the set of factors is based on at least one of, or combinations of, spatial, temporal, social, demographic, or user current data collected in real-time. 26. The method of claim 24, wherein the user history data includes at least one of, or combinations of, query history, browsing history, or purchase history of the primary user. 27. The method of claim 24, wherein selecting the set of factors is based on table driven matching, wherein the tables are stored in the database. 28. The method of claim 25, wherein selecting the set of factors is based on psychological models of behavior of a demographic of the primary user. 29. The method of claim 18, further comprising:
determining a set of media objects associated with the set of selected factors, wherein the set of media objects includes at least one of, or combinations of, textual information, pictures, video, hyperlinked text, webpages, styled text, and sounds; and communicating the set of media objects with the set of selected factors for the recommendation explanation. 30. The method of claim 18, wherein communicating the set selected factors includes multi-dimensional models, vibrations, electric shocks, pulses, or scents. | Embodiments of the invention relate to a computer-implemented method for generating explanatory data from a personalized recommendations process for a primary user based at least on stored data about the primary user. The method comprises a server computer obtaining data related to one or more users who are relevant to the primary user, then determining at least one group of users relevant to the primary user. The server computer also obtains data related to one or more entities, determines one or more entities relevant to the primary user, and associates the at least one relevant group of users with the one or more relevant entities. One or more potential candidate factors are generated. A set of factors are selected from the one or more potential candidate factors, wherein the potential candidate factors are used as explanatory data to determine recommendations to the primary user.1. A computer-implemented method for generating explanatory data from a personalized recommendations process for a primary user based at least on stored data about the primary user, the method comprising:
determining the primary user for whom explanatory data is to be generated, the primary user being one of a plurality of users over which recommendations are considered; obtaining data related to a set of relevant users, wherein the set of relevant users comprises one or more users in the plurality of users that are deemed relevant to the primary user, wherein relevancy of a user in the set of relevant users to the primary user is based, at least in part, on similarities, according to stored user data, between the relevant user and the primary user; obtaining, at the server computer, entity data from a plurality of data sources, wherein the entity data is associated with an entity in a plurality of entities; storing the entity data at the server computer, wherein the entity data associated with the plurality of entities is stored in an entity database; determining, at the server computer, one or more entities relevant to the primary user based on the data related to the one or more entities; determining, by the server computer, one or more potential candidate factors based on at least the one or more relevant entities; and generating, by the server computer, explanation data based on the one or more potential candidate factors used in the personalized recommendations process. 2. The method of claim 1, further comprising:
selecting, by the server computer, a set of factors from the one or more potential candidate factors; and communicating, by the server computer, the set of selected factors to the primary user, wherein the potential candidate factors are used to determine recommendations to the primary user. 3. The method of claim 1, further comprising:
determining, at the server computer, at least one group of users relevant to the primary user based on the data related to the one or more users, each relevant group including one or more relevant users to the primary user; associating, by the server computer, the at least one relevant group of users with the one or more relevant entities; determining, by the server computer, one or more potential candidate factors based on at least the relevant group of users; and generating, by the server computer, explanation data based on the one or more potential candidate factors used in the personalized recommendations process. 4. The method of claim 1, wherein associating the at least one relevant group of users with the one or more relevant entities includes determining a relationship between a relevant user with a relevant entity, a relationship between a relevant entity with another relevant entity, or a relationship between a relevant user with another relevant user. 5. The method of claim 1, wherein data related to one or more relevant users includes account information stored in a database of users, wherein the account information includes demographic data. 6. The method of claim 1, wherein the data related to one or more relevant users includes current location information of the user. 7. The method of claim 5, wherein the pre-determined attributes includes cuisine preferences or food allergies. 8. The method of claim 1, wherein the current location information is associated with data related to one or more relevant entities, wherein the data related to one of relevant entities includes previous data from the one or more relevant users related to the one or more relevant entities. 9. The method of claim 3, wherein selecting the set of factors is based on at least one of, or combinations of, spatial, temporal, social, demographic, or user history data previously stored in a database. 10. The method of claim 9, wherein selecting the set of factors is based on at least one of, or combinations of, spatial, temporal, social, demographic, or user current data collected in real-time. 11. The method of claim 9, wherein the user history data includes at least one of, or combinations of, query history, browsing history, or purchase history of the primary user. 12. The method of claim 9, wherein selecting the set of factors is based on table driven matching, wherein the tables are stored in the database. 13. The method of claim 10, wherein selecting the set of factors is based on psychological models of behavior of a demographic of the primary user. 14. The method of claim 3, further comprising:
determining a set of media objects associated with the set of selected factors, wherein the set of media objects includes at least one of, or combinations of, textual information, pictures, video, hyperlinked text, webpages, styled text, and sounds; and communicating the set of media objects with the set of selected factors for the recommendation explanation. 15. The method of claim 3, wherein communicating the set selected factors includes multi-dimensional models, vibrations, electric shocks, pulses, or scents. 16. A server computer comprising a processor and a non-transitory computer readable medium, the non-transitory computer readable medium comprising code executable by the processor to implement a computer-implemented method for generating explanatory data from a personalized recommendations process for a primary user based at least on stored data about the primary user, the method comprising:
determining the primary user for whom explanatory data is to be generated, the primary user being one of a plurality of users over which recommendations are considered; obtaining, at a server computer, data related to a set of relevant users, wherein the set of relevant users comprises one or more users in the plurality of users that are deemed relevant to the primary user, wherein relevancy of a user in the set of relevant users to the primary user is based, at least on part, on similarities, according to stored user data, between the relevant user and the primary user; obtaining, at the server computer, entity data from a plurality of data sources, wherein the entity data is associated with an entity in a plurality of entities; storing the entity data at the server computer, wherein the entity data associated with the plurality of entities is stored in an entity database; determining, at the server computer, one or more entities relevant to the primary user based on the data related to the one or more entities; determining, by the server computer, one or more potential candidate factors based on at least the one or more relevant entities; and generating, by the server computer, explanation data based on the one or more potential candidate factors used in the personalized recommendations process. 17. The server computer of claim 16, the method further comprising:
selecting, by the server computer, a set of factors from the one or more potential candidate factors; and communicating, by the server computer, the set of selected factors to the primary user, wherein the potential candidate factors are used to determine recommendations to the primary user. 18. The server computer of claim 16, the method further comprising:
determining, at the server computer, at least one group of users relevant to the primary user based on the data related to the one or more users, each relevant group including one or more relevant users to the primary user; associating, by the server computer, the at least one relevant group of users with the one or more relevant entities; determining, by the server computer, one or more potential candidate factors based on at least the relevant group of users; and generating, by the server computer, explanation data based on the one or more potential candidate factors used in the personalized recommendations process. 19. The server computer of claim 16, wherein associating the at least one relevant group of users with the one or more relevant entities includes determining a relationship between a relevant user with a relevant entity, a relationship between a relevant entity with another relevant entity, or a relationship between a relevant user with another relevant user. 20. The server computer of claim 16, wherein data related to one or more relevant users includes account information stored in a database of users, wherein the account information includes demographic data. 21. The server computer of claim 16, wherein the data related to one or more relevant users includes current location information of the user. 22. The server computer of claim 20, wherein the pre-determined attributes includes cuisine preferences or food allergies. 23. The server computer of claim 16, wherein the current location information is associated with data related to one or more relevant entities, wherein the data related to one of relevant entities includes previous data from the one or more relevant users related to the one or more relevant entities. 24. The method of claim 18, wherein selecting the set of factors is based on at least one of, or combinations of, spatial, temporal, social, demographic, or user history data previously stored in a database. 25. The method of claim 24, wherein selecting the set of factors is based on at least one of, or combinations of, spatial, temporal, social, demographic, or user current data collected in real-time. 26. The method of claim 24, wherein the user history data includes at least one of, or combinations of, query history, browsing history, or purchase history of the primary user. 27. The method of claim 24, wherein selecting the set of factors is based on table driven matching, wherein the tables are stored in the database. 28. The method of claim 25, wherein selecting the set of factors is based on psychological models of behavior of a demographic of the primary user. 29. The method of claim 18, further comprising:
determining a set of media objects associated with the set of selected factors, wherein the set of media objects includes at least one of, or combinations of, textual information, pictures, video, hyperlinked text, webpages, styled text, and sounds; and communicating the set of media objects with the set of selected factors for the recommendation explanation. 30. The method of claim 18, wherein communicating the set selected factors includes multi-dimensional models, vibrations, electric shocks, pulses, or scents. | 2,100 |
6,405 | 6,405 | 16,288,217 | 2,126 | Example aspects of the present disclosure are directed to systems and methods for learning classification models which satisfy constraints such as, for example, constraints that can be expressed as a predicted positive rate or negative rate on a subset of the training dataset. In particular, through the use of quantile estimators, the systems and methods of the present disclosure can transform a constrained optimization problem into an unconstrained optimization problem that is solved more efficiently and generally than the constrained optimization problem. As one example, the unconstrained optimization problem can include optimizing an objective function where a decision threshold of the classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile. | 1. A computer-implemented method for training a machine-learned classification model to satisfy a constraint, the method comprising:
obtaining, by one or more computing devices, data descriptive of the machine-learned classification model, wherein the machine-learned classification model is configured to produce a classification score for an input and to classify the input by comparing the classification score to a decision threshold; and training, by the one or more computing devices, the machine-learned classification model based at least in part on a training dataset; wherein training, by the one or more computing devices, the machine-learned classification model comprises optimizing, by the one or more computing devices, an unconstrained objective function in which the decision threshold of the machine-learned classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile, wherein the desired quantile is based at least in part on a rate value associated with the constraint. 2. The computer-implemented method of claim 1, wherein the desired quantile is equal to one minus the rate value. 3. The computer-implemented method of claim 1, wherein the estimator of the quantile function comprises a kernel quantile estimator of the quantile function. 4. The computer-implemented method of claim 1, wherein the estimator of the quantile function comprises an interval quantile estimator of the quantile function. 5. The computer-implemented method of claim 1, wherein the estimator of the quantile function comprises an L-estimator of the quantile function or a point estimator of the quantile function. 6. The computer-implemented method of claim 1, wherein the constraint comprises a desired relationship between a predicted positive rate of the machine-learned classification model on the subset of the training dataset and the rate value. 7. The computer-implemented method of claim 1, wherein optimizing, by the one or more computing devices, the unconstrained objective function in which the decision threshold of the machine-learned classification model is expressed as the estimator of the quantile function comprises minimizing, by the one or more computing devices, an unconstrained surrogate loss function in which the decision threshold of the machine-learned classification model is expressed as the estimator of the quantile function. 8. The computer-implemented method of claim 1, wherein the constraint comprises a precision at a fixed recall constraint and optimizing, by the one or more computing devices, the unconstrained objective function comprises minimizing, by the one or more computing devices, a sum over all negative training examples in the training dataset of a logistic loss or a hinge loss of the classification score produced for such training example by the machine-learned classification model minus the estimator of the quantile function on the classification scores of the machine-learned classification model for all positive training examples of the training dataset at the desired quantile. 9. The computer-implemented method of claim 1, wherein the constraint comprises a precision at a fixed predicted positive rate constraint and optimizing, by the one or more computing devices, the unconstrained objective function comprises minimizing, by the one or more computing devices, a sum over all negative training examples in the training dataset of a logistic loss or a hinge loss of the classification score produced for such training example by the machine-learned classification model minus the estimator of the quantile function on the classification scores of the machine-learned classification model for all training examples of the training dataset at the desired quantile. 10. The computer-implemented method of claim 1, wherein the constraint comprises a precision at a fixed predicted positive rate constraint and optimizing, by the one or more computing devices, the unconstrained objective function comprises minimizing, by the one or more computing devices, a sum over all positive training examples in the training dataset of a logistic loss or a hinge loss of an inverse of the classification score produced for such training example by the machine-learned classification model plus the estimator of the quantile function on the classification scores of the machine-learned classification model for all training examples of the training dataset at the desired quantile. 11. The computer-implemented method of claim 1, wherein the subset of the training dataset comprises an entirety of the training dataset. 12. The computer-implemented method of claim 1, wherein the subset of the training dataset comprises a portion of the training dataset that exhibits a particular feature value. 13. The computer-implemented method of claim 1, wherein optimizing, by the one or more computing devices, the unconstrained objective function in which the decision threshold of the machine-learned classification model is expressed as the estimator of the quantile function comprises, for each of a plurality of iterations:
determining, by the one or more computing devices, a gradient of the unconstrained objective function in which the decision threshold of the machine-learned classification model is expressed as the estimator of the quantile function; and updating, by the one or more computing devices, one or more of a plurality of learnable parameters of the machine-learned classification model based at least in part on the determined gradient. 14. The computer-implemented method of claim 1, further comprising:
performing, by the one or more computing devices, the method of claim 1 for each of a plurality of labels such that the machine-learned classification model is trained to be a machine-learned multi-label classification model. 15. A computing system, comprising:
one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors cause the computing system to perform operations, the operations comprising:
obtaining data descriptive of the machine-learned classification model, wherein the machine-learned classification model is configured to produce a classification score for an input and to classify the input by comparing the classification score to a decision threshold; and
for each of a plurality of iterations:
obtaining a first minibatch of training data from a training dataset;
obtaining a second minibatch of training data from a specified subset of the training dataset;
determining a gradient of a loss function that describes a classification performance of the machine-learned classification model on the first minibatch of training data, wherein the loss function expresses the decision threshold of the machine-learned classification model as an estimator of a quantile function on the classification scores of the machine-learned classification model for the second minibatch of training data at a desired quantile, wherein the desired quantile is based at least in part on a rate value; and
updating one or more of a plurality of learnable parameters of the machine-learned classification model based at least in part on the gradient of the loss function. 16. The computing system of claim 15, wherein the desired quantile is equal to one minus the rate value. 17. The computing system of claim 15, wherein the estimator of the quantile function comprises a kernel quantile estimator of the quantile function. 18. The computing system of claim 15, wherein the estimator of the quantile function comprises an L-estimator of the quantile function. 19. The computing system of claim 15, wherein the constraint comprises a desired relationship between a predicted positive rate of the machine-learned on the specified subset of the training dataset and the rate value. 20. One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising:
obtaining data descriptive of a machine-learned classification model, wherein the machine-learned classification model is configured to produce a classification score for an input and to classify the input by comparing the classification score to a decision threshold; and training the machine-learned classification model based at least in part on a training dataset; wherein the operation of training the machine-learned classification model comprises optimizing an objective function in which the decision threshold of the machine-learned classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile, wherein the desired quantile is based at least in part on a rate value associated with a constraint. | Example aspects of the present disclosure are directed to systems and methods for learning classification models which satisfy constraints such as, for example, constraints that can be expressed as a predicted positive rate or negative rate on a subset of the training dataset. In particular, through the use of quantile estimators, the systems and methods of the present disclosure can transform a constrained optimization problem into an unconstrained optimization problem that is solved more efficiently and generally than the constrained optimization problem. As one example, the unconstrained optimization problem can include optimizing an objective function where a decision threshold of the classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile.1. A computer-implemented method for training a machine-learned classification model to satisfy a constraint, the method comprising:
obtaining, by one or more computing devices, data descriptive of the machine-learned classification model, wherein the machine-learned classification model is configured to produce a classification score for an input and to classify the input by comparing the classification score to a decision threshold; and training, by the one or more computing devices, the machine-learned classification model based at least in part on a training dataset; wherein training, by the one or more computing devices, the machine-learned classification model comprises optimizing, by the one or more computing devices, an unconstrained objective function in which the decision threshold of the machine-learned classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile, wherein the desired quantile is based at least in part on a rate value associated with the constraint. 2. The computer-implemented method of claim 1, wherein the desired quantile is equal to one minus the rate value. 3. The computer-implemented method of claim 1, wherein the estimator of the quantile function comprises a kernel quantile estimator of the quantile function. 4. The computer-implemented method of claim 1, wherein the estimator of the quantile function comprises an interval quantile estimator of the quantile function. 5. The computer-implemented method of claim 1, wherein the estimator of the quantile function comprises an L-estimator of the quantile function or a point estimator of the quantile function. 6. The computer-implemented method of claim 1, wherein the constraint comprises a desired relationship between a predicted positive rate of the machine-learned classification model on the subset of the training dataset and the rate value. 7. The computer-implemented method of claim 1, wherein optimizing, by the one or more computing devices, the unconstrained objective function in which the decision threshold of the machine-learned classification model is expressed as the estimator of the quantile function comprises minimizing, by the one or more computing devices, an unconstrained surrogate loss function in which the decision threshold of the machine-learned classification model is expressed as the estimator of the quantile function. 8. The computer-implemented method of claim 1, wherein the constraint comprises a precision at a fixed recall constraint and optimizing, by the one or more computing devices, the unconstrained objective function comprises minimizing, by the one or more computing devices, a sum over all negative training examples in the training dataset of a logistic loss or a hinge loss of the classification score produced for such training example by the machine-learned classification model minus the estimator of the quantile function on the classification scores of the machine-learned classification model for all positive training examples of the training dataset at the desired quantile. 9. The computer-implemented method of claim 1, wherein the constraint comprises a precision at a fixed predicted positive rate constraint and optimizing, by the one or more computing devices, the unconstrained objective function comprises minimizing, by the one or more computing devices, a sum over all negative training examples in the training dataset of a logistic loss or a hinge loss of the classification score produced for such training example by the machine-learned classification model minus the estimator of the quantile function on the classification scores of the machine-learned classification model for all training examples of the training dataset at the desired quantile. 10. The computer-implemented method of claim 1, wherein the constraint comprises a precision at a fixed predicted positive rate constraint and optimizing, by the one or more computing devices, the unconstrained objective function comprises minimizing, by the one or more computing devices, a sum over all positive training examples in the training dataset of a logistic loss or a hinge loss of an inverse of the classification score produced for such training example by the machine-learned classification model plus the estimator of the quantile function on the classification scores of the machine-learned classification model for all training examples of the training dataset at the desired quantile. 11. The computer-implemented method of claim 1, wherein the subset of the training dataset comprises an entirety of the training dataset. 12. The computer-implemented method of claim 1, wherein the subset of the training dataset comprises a portion of the training dataset that exhibits a particular feature value. 13. The computer-implemented method of claim 1, wherein optimizing, by the one or more computing devices, the unconstrained objective function in which the decision threshold of the machine-learned classification model is expressed as the estimator of the quantile function comprises, for each of a plurality of iterations:
determining, by the one or more computing devices, a gradient of the unconstrained objective function in which the decision threshold of the machine-learned classification model is expressed as the estimator of the quantile function; and updating, by the one or more computing devices, one or more of a plurality of learnable parameters of the machine-learned classification model based at least in part on the determined gradient. 14. The computer-implemented method of claim 1, further comprising:
performing, by the one or more computing devices, the method of claim 1 for each of a plurality of labels such that the machine-learned classification model is trained to be a machine-learned multi-label classification model. 15. A computing system, comprising:
one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors cause the computing system to perform operations, the operations comprising:
obtaining data descriptive of the machine-learned classification model, wherein the machine-learned classification model is configured to produce a classification score for an input and to classify the input by comparing the classification score to a decision threshold; and
for each of a plurality of iterations:
obtaining a first minibatch of training data from a training dataset;
obtaining a second minibatch of training data from a specified subset of the training dataset;
determining a gradient of a loss function that describes a classification performance of the machine-learned classification model on the first minibatch of training data, wherein the loss function expresses the decision threshold of the machine-learned classification model as an estimator of a quantile function on the classification scores of the machine-learned classification model for the second minibatch of training data at a desired quantile, wherein the desired quantile is based at least in part on a rate value; and
updating one or more of a plurality of learnable parameters of the machine-learned classification model based at least in part on the gradient of the loss function. 16. The computing system of claim 15, wherein the desired quantile is equal to one minus the rate value. 17. The computing system of claim 15, wherein the estimator of the quantile function comprises a kernel quantile estimator of the quantile function. 18. The computing system of claim 15, wherein the estimator of the quantile function comprises an L-estimator of the quantile function. 19. The computing system of claim 15, wherein the constraint comprises a desired relationship between a predicted positive rate of the machine-learned on the specified subset of the training dataset and the rate value. 20. One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising:
obtaining data descriptive of a machine-learned classification model, wherein the machine-learned classification model is configured to produce a classification score for an input and to classify the input by comparing the classification score to a decision threshold; and training the machine-learned classification model based at least in part on a training dataset; wherein the operation of training the machine-learned classification model comprises optimizing an objective function in which the decision threshold of the machine-learned classification model is expressed as an estimator of a quantile function on the classification scores of the machine-learned classification model for a subset of the training dataset at a desired quantile, wherein the desired quantile is based at least in part on a rate value associated with a constraint. | 2,100 |
6,406 | 6,406 | 14,251,833 | 2,143 | Methods, systems, and software are described herein for providing a user interface that, in one example, indicates the current point in progress for various items that are available for selection. Upon selection of one of the items of content and/or services that present the items of content, the item of content may be accessed (e.g., for display, recording, or other type of consumption) at the point in progress or at another location within the item of content that may correspond to or otherwise depend upon the point in progress. | 1. A method, comprising:
generating, by one or more computing devices, a user interface comprising a plurality of representations each corresponding to one of a plurality of items of content and each comprising a screen shot of the respective item of content related to a point in progress of the respective item of content; and in response to a user selection of one of the representations, causing one of the items of content to be accessed. 2. The method of claim 1, wherein, for each of the representations, the screen shot is visually modified in accordance with the point in progress of the respective item of content. 3. The method of claim 1, wherein, for each of the representations, the screen shot is visually modified to comprise a shaded region having a size that depends upon the point in progress of the respective item of content. 4. The method of claim 1, wherein, for each of the representations, the screen shot is visually modified to comprise a first portion of the screen shot displayed using a first color palette and a second portion of the screen shot using a different second color palette, wherein a size of the first portion of the screen shot depends upon the point in progress of the respective item of content. 5. The method of claim 1, further comprising dynamically updating at least one of the representations while the user interface is displayed as the point in progress for the at least one of the representations moves. 6. The method of claim 1, further comprising:
determining that at least one of the items of content was previously consumed; and visually differentiating the representation for the at least one of the items of content that was previously consumed from one or more others of the representations in the user interface. 7. The method of claim 6, wherein said visually differentiating comprises using a black-and-white palette for the representation for the at least one of the items of content that was previously consumed, and using at least one color palette for the one or more others of the representations in the user interface. 8. The method of claim 1, further comprising:
mapping at least some of the plurality of representations to different buttons on a user remote control; receiving an indication that a user pressed one of the buttons; and identifying a corresponding one of the representations mapped to the one of the buttons as a selected one of the plurality of representations. 9. The method of claim 1, wherein the one of the items of content is a scheduled item of content. 10. The method of claim 1 wherein the one of the items of content is currently being transmitted, and wherein the causing the one of the items of content to be accessed comprises tuning to the one of the items of content currently being transmitted. 11. The method of claim 1, wherein the one of the items of content is currently being transmitted, the method further comprising determining the point of progress for the one of the items of content as a location within the one of the items of content at which the one of the items of content is currently being transmitted. 12. The method of claim 1, further comprising determining, for the one of the items of content, the point of progress for the one of the items of content as a location within the one of the items of content at which the one of the items of content is currently scheduled to be presented. 13. A method, comprising:
generating, by one or more computing devices, a user interface comprising a plurality of representations each corresponding to one of a plurality of items of content, each of the representations comprising a screen shot of the respective item of content; determining that a first one of the items of content has been previously accessed; and causing the user interface to show the screen shot for the first one of the items of content to be visually differentiated from at least one other of the screen shots. 14. The method of claim 13, wherein the causing comprises causing the screen shot for the first one of the items to be displayed in black and white, wherein the screen shots of the at least one other of the screen shots is displayed in color. 15. The method of claim 13, further comprising, in response to a user selection of one of the representations, causing one of the items of content to be accessed. 16. The method of claim 13, further comprising:
mapping at least some of the plurality of representations to different buttons on a user device; receiving an indication that a user pressed one of the buttons; and identifying a corresponding one of the representations mapped to the one of the buttons as a selected one of the plurality of representations. 17. A method, comprising:
generating, by one or more computing devices, a user interface comprising a plurality of representations each corresponding to one of a plurality of media items; determining a layout of buttons on a user device; and mapping the representations to the buttons such that a layout of the representations as presented in the user interface depends upon the layout of the buttons. 18. The method of claim 17, wherein at least one of the plurality of media items comprises an item of content. 19. The method of claim 17, further comprising causing the user interface to include indications of the mapping. 20. The method of claim 17, further comprising:
in response to a user selection of one of the buttons, causing information about one of the media items mapped to the one of the buttons to be displayed; and in response to a subsequent user selection of the one of the buttons while the information is displayed, causing the one of the media items to be accessed. | Methods, systems, and software are described herein for providing a user interface that, in one example, indicates the current point in progress for various items that are available for selection. Upon selection of one of the items of content and/or services that present the items of content, the item of content may be accessed (e.g., for display, recording, or other type of consumption) at the point in progress or at another location within the item of content that may correspond to or otherwise depend upon the point in progress.1. A method, comprising:
generating, by one or more computing devices, a user interface comprising a plurality of representations each corresponding to one of a plurality of items of content and each comprising a screen shot of the respective item of content related to a point in progress of the respective item of content; and in response to a user selection of one of the representations, causing one of the items of content to be accessed. 2. The method of claim 1, wherein, for each of the representations, the screen shot is visually modified in accordance with the point in progress of the respective item of content. 3. The method of claim 1, wherein, for each of the representations, the screen shot is visually modified to comprise a shaded region having a size that depends upon the point in progress of the respective item of content. 4. The method of claim 1, wherein, for each of the representations, the screen shot is visually modified to comprise a first portion of the screen shot displayed using a first color palette and a second portion of the screen shot using a different second color palette, wherein a size of the first portion of the screen shot depends upon the point in progress of the respective item of content. 5. The method of claim 1, further comprising dynamically updating at least one of the representations while the user interface is displayed as the point in progress for the at least one of the representations moves. 6. The method of claim 1, further comprising:
determining that at least one of the items of content was previously consumed; and visually differentiating the representation for the at least one of the items of content that was previously consumed from one or more others of the representations in the user interface. 7. The method of claim 6, wherein said visually differentiating comprises using a black-and-white palette for the representation for the at least one of the items of content that was previously consumed, and using at least one color palette for the one or more others of the representations in the user interface. 8. The method of claim 1, further comprising:
mapping at least some of the plurality of representations to different buttons on a user remote control; receiving an indication that a user pressed one of the buttons; and identifying a corresponding one of the representations mapped to the one of the buttons as a selected one of the plurality of representations. 9. The method of claim 1, wherein the one of the items of content is a scheduled item of content. 10. The method of claim 1 wherein the one of the items of content is currently being transmitted, and wherein the causing the one of the items of content to be accessed comprises tuning to the one of the items of content currently being transmitted. 11. The method of claim 1, wherein the one of the items of content is currently being transmitted, the method further comprising determining the point of progress for the one of the items of content as a location within the one of the items of content at which the one of the items of content is currently being transmitted. 12. The method of claim 1, further comprising determining, for the one of the items of content, the point of progress for the one of the items of content as a location within the one of the items of content at which the one of the items of content is currently scheduled to be presented. 13. A method, comprising:
generating, by one or more computing devices, a user interface comprising a plurality of representations each corresponding to one of a plurality of items of content, each of the representations comprising a screen shot of the respective item of content; determining that a first one of the items of content has been previously accessed; and causing the user interface to show the screen shot for the first one of the items of content to be visually differentiated from at least one other of the screen shots. 14. The method of claim 13, wherein the causing comprises causing the screen shot for the first one of the items to be displayed in black and white, wherein the screen shots of the at least one other of the screen shots is displayed in color. 15. The method of claim 13, further comprising, in response to a user selection of one of the representations, causing one of the items of content to be accessed. 16. The method of claim 13, further comprising:
mapping at least some of the plurality of representations to different buttons on a user device; receiving an indication that a user pressed one of the buttons; and identifying a corresponding one of the representations mapped to the one of the buttons as a selected one of the plurality of representations. 17. A method, comprising:
generating, by one or more computing devices, a user interface comprising a plurality of representations each corresponding to one of a plurality of media items; determining a layout of buttons on a user device; and mapping the representations to the buttons such that a layout of the representations as presented in the user interface depends upon the layout of the buttons. 18. The method of claim 17, wherein at least one of the plurality of media items comprises an item of content. 19. The method of claim 17, further comprising causing the user interface to include indications of the mapping. 20. The method of claim 17, further comprising:
in response to a user selection of one of the buttons, causing information about one of the media items mapped to the one of the buttons to be displayed; and in response to a subsequent user selection of the one of the buttons while the information is displayed, causing the one of the media items to be accessed. | 2,100 |
6,407 | 6,407 | 15,826,703 | 2,176 | Various examples for providing for the secure view of content on devices are disclosed. In one example, a content modification service, such as an email modification service, can access a document from a server destined for a client device and generate a modified form of the document by adjusting a visual setting associated with a display of the content that affects an ability to view the content from an area beyond a predetermined range. The content modification service or a client application can selectively provide the document or the modified version of the document in a display of the client device based on a setting of the client device, a device profile, or compliance rules specified by an administrator of a management service. | 1. A system, comprising:
at least one computing device; and program instructions executable in the at least one computing device that, when executed by the at least one computing device, cause the at least one computing device to:
generate a modified form of a document comprising content destined for a client device by adjusting at least one visual setting associated with a display of the content, wherein the at least one visual setting is adjusted to make the content visible to a user of a client device within a predetermined range from a display device of the client device while impacting an ability to view the content shown on the display device from an area beyond the predetermined range; and
selectively provide at least one of the document without modification or the modified form of the document for display on the display device of the client device, the document or the modified version of the document selectively provided based at least in part on a mode of operation of the client device. 2. The system of claim 1, wherein:
the content comprises at least one image; and the modified form of the document is generated by processing the at least one image to adjust the at least one visual setting. 3. The system of claim 2, wherein:
the content comprises text; and the at least one visual setting adjusted comprises at least one of: a background color of the text, a font color of the text, a font size of the text, and a font style of the text. 4. The system of claim 3, further comprising program instructions executable in the at least one computing device that, when executed by the at least one computing device, cause the at least one computing device to generate an image of the text to replace a plain-text form of the text in the modified form of the document, wherein the image is generated based at least in part on the at least one visual setting. 5. The system of claim 3, wherein the at least one visual setting is adjusted to have the font color of the text be within a color threshold of the background color of the text. 6. The system of claim 5, wherein the color threshold is determined as a function of a size of a display of the client device using:
Hex
(
Color
Background
)
-
Hex
(
Color
Text
)
∝
1
Size
Text
. 7. The system of claim 3, wherein the modified form of the document is generated by adjusting hypertext markup language (HTML) code associated with at least one of: the background color of the text, the font color of the text, the font size of the text, and the font style of the text. 8. A non-transitory computer-readable medium embodying program instructions executable in a client device that, when executed by the client device, cause the client device to:
receive a document from an email server, the document comprising content; generate a modified form of a document comprising content by adjusting at least one visual setting associated with a display of the content, wherein the at least one visual setting is adjusted to make the content visible to a user of a client device within a predetermined range from a display device of the client device while impacting an ability to view the content shown on the display device from an area beyond the predetermined range; and selectively provide at least one of the document without modification or the modified form of the document in a display device of the client device, the document or the modified version of the document selectively provided based at least in part on a mode of operation of the client device. 9. The non-transitory computer-readable medium of claim 8, wherein:
the content comprises at least one image; and the modified form of the document is generated by processing the at least one image to adjust the at least one visual setting. 10. The non-transitory computer-readable medium of claim 9, wherein:
the content comprises text; and the at least one visual setting adjusted comprises at least one of: a background color of the text, a font color of the text, a font size of the text, and a font style of the text. 11. The non-transitory computer-readable medium of claim 10, further comprising program instructions executable in the client device that, when executed by the client device, cause the at least one computing device to generate an image of the text to replace a plaint-text form of the text in the modified form of the document, wherein the image is generated based at least in part on the at least one visual setting. 12. The non-transitory computer-readable medium of claim 10, wherein the at least one visual setting is adjusted to have the font color of the text be within a color threshold of the background color of the text. 13. The non-transitory computer-readable medium of claim 12, wherein the color threshold is determined as a function of a size of a display of the client device using:
Hex
(
Color
Background
)
-
Hex
(
Color
Text
)
∝
1
Size
Text
. 14. The non-transitory computer-readable medium of claim 10, wherein the modified form of the document is generated by adjusting hypertext markup language (HTML) code associated with at least one of: the background color of the text, the font color of the text, the font size of the text, and the font style of the text. 15. A computer-implemented method, comprising:
accessing an email document from an email server destined for a client device, the email document comprising email content; generating a modified form of the email document by adjusting at least one visual setting associated with a display of the email content, wherein the at least one visual setting is adjusted to make the email content visible to a user of a client device within a predetermined range from a display device of the client device while impacting an ability to view the email content shown on the display device from an area beyond the predetermined range; and selectively providing at least one of the email document or the modified form of the email document in a display device of the client device, the email document or the modified version of the email document selectively provided based at least in part on a mode of operation of the client device. 16. The computer-implemented method of claim 15, wherein:
the email content comprises at least one image; and the modified form of the email document is generated by processing the at least one image to adjust the at least one visual setting. 17. The computer-implemented method of claim 16, wherein:
the email content comprises text; and the at least one visual setting adjusted comprises at least one of: a background color of the text, a font color of the text, a font size of the text, and a font style of the text. 18. The computer-implemented method of claim 17, further comprising program instructions executable in the client device that, when executed by the client device, cause the at least one computing device to generate an image of the text to replace a plain-text form of the text in the modified form of the document, wherein the image is generated based at least in part on the at least one visual setting. 19. The computer-implemented method of claim 17, wherein the at least one visual setting is adjusted to have the font color of the text be within a color threshold of the background color of the text. 20. The computer-implemented method of claim 17, wherein the modified form of the email document is generated by adjusting hypertext markup language (HTML) code associated with at least one of: the background color of the text, the font color of the text, the font size of the text, and the font style of the text. | Various examples for providing for the secure view of content on devices are disclosed. In one example, a content modification service, such as an email modification service, can access a document from a server destined for a client device and generate a modified form of the document by adjusting a visual setting associated with a display of the content that affects an ability to view the content from an area beyond a predetermined range. The content modification service or a client application can selectively provide the document or the modified version of the document in a display of the client device based on a setting of the client device, a device profile, or compliance rules specified by an administrator of a management service.1. A system, comprising:
at least one computing device; and program instructions executable in the at least one computing device that, when executed by the at least one computing device, cause the at least one computing device to:
generate a modified form of a document comprising content destined for a client device by adjusting at least one visual setting associated with a display of the content, wherein the at least one visual setting is adjusted to make the content visible to a user of a client device within a predetermined range from a display device of the client device while impacting an ability to view the content shown on the display device from an area beyond the predetermined range; and
selectively provide at least one of the document without modification or the modified form of the document for display on the display device of the client device, the document or the modified version of the document selectively provided based at least in part on a mode of operation of the client device. 2. The system of claim 1, wherein:
the content comprises at least one image; and the modified form of the document is generated by processing the at least one image to adjust the at least one visual setting. 3. The system of claim 2, wherein:
the content comprises text; and the at least one visual setting adjusted comprises at least one of: a background color of the text, a font color of the text, a font size of the text, and a font style of the text. 4. The system of claim 3, further comprising program instructions executable in the at least one computing device that, when executed by the at least one computing device, cause the at least one computing device to generate an image of the text to replace a plain-text form of the text in the modified form of the document, wherein the image is generated based at least in part on the at least one visual setting. 5. The system of claim 3, wherein the at least one visual setting is adjusted to have the font color of the text be within a color threshold of the background color of the text. 6. The system of claim 5, wherein the color threshold is determined as a function of a size of a display of the client device using:
Hex
(
Color
Background
)
-
Hex
(
Color
Text
)
∝
1
Size
Text
. 7. The system of claim 3, wherein the modified form of the document is generated by adjusting hypertext markup language (HTML) code associated with at least one of: the background color of the text, the font color of the text, the font size of the text, and the font style of the text. 8. A non-transitory computer-readable medium embodying program instructions executable in a client device that, when executed by the client device, cause the client device to:
receive a document from an email server, the document comprising content; generate a modified form of a document comprising content by adjusting at least one visual setting associated with a display of the content, wherein the at least one visual setting is adjusted to make the content visible to a user of a client device within a predetermined range from a display device of the client device while impacting an ability to view the content shown on the display device from an area beyond the predetermined range; and selectively provide at least one of the document without modification or the modified form of the document in a display device of the client device, the document or the modified version of the document selectively provided based at least in part on a mode of operation of the client device. 9. The non-transitory computer-readable medium of claim 8, wherein:
the content comprises at least one image; and the modified form of the document is generated by processing the at least one image to adjust the at least one visual setting. 10. The non-transitory computer-readable medium of claim 9, wherein:
the content comprises text; and the at least one visual setting adjusted comprises at least one of: a background color of the text, a font color of the text, a font size of the text, and a font style of the text. 11. The non-transitory computer-readable medium of claim 10, further comprising program instructions executable in the client device that, when executed by the client device, cause the at least one computing device to generate an image of the text to replace a plaint-text form of the text in the modified form of the document, wherein the image is generated based at least in part on the at least one visual setting. 12. The non-transitory computer-readable medium of claim 10, wherein the at least one visual setting is adjusted to have the font color of the text be within a color threshold of the background color of the text. 13. The non-transitory computer-readable medium of claim 12, wherein the color threshold is determined as a function of a size of a display of the client device using:
Hex
(
Color
Background
)
-
Hex
(
Color
Text
)
∝
1
Size
Text
. 14. The non-transitory computer-readable medium of claim 10, wherein the modified form of the document is generated by adjusting hypertext markup language (HTML) code associated with at least one of: the background color of the text, the font color of the text, the font size of the text, and the font style of the text. 15. A computer-implemented method, comprising:
accessing an email document from an email server destined for a client device, the email document comprising email content; generating a modified form of the email document by adjusting at least one visual setting associated with a display of the email content, wherein the at least one visual setting is adjusted to make the email content visible to a user of a client device within a predetermined range from a display device of the client device while impacting an ability to view the email content shown on the display device from an area beyond the predetermined range; and selectively providing at least one of the email document or the modified form of the email document in a display device of the client device, the email document or the modified version of the email document selectively provided based at least in part on a mode of operation of the client device. 16. The computer-implemented method of claim 15, wherein:
the email content comprises at least one image; and the modified form of the email document is generated by processing the at least one image to adjust the at least one visual setting. 17. The computer-implemented method of claim 16, wherein:
the email content comprises text; and the at least one visual setting adjusted comprises at least one of: a background color of the text, a font color of the text, a font size of the text, and a font style of the text. 18. The computer-implemented method of claim 17, further comprising program instructions executable in the client device that, when executed by the client device, cause the at least one computing device to generate an image of the text to replace a plain-text form of the text in the modified form of the document, wherein the image is generated based at least in part on the at least one visual setting. 19. The computer-implemented method of claim 17, wherein the at least one visual setting is adjusted to have the font color of the text be within a color threshold of the background color of the text. 20. The computer-implemented method of claim 17, wherein the modified form of the email document is generated by adjusting hypertext markup language (HTML) code associated with at least one of: the background color of the text, the font color of the text, the font size of the text, and the font style of the text. | 2,100 |
6,408 | 6,408 | 16,255,109 | 2,129 | A method for learning parameters of a CNN using a 1×K convolution operation or a K×1 convolution operation is provided to be used for hardware optimization which satisfies KPI. The method includes steps of: a learning device (a) instructing a reshaping layer to two-dimensionally concatenate features in each group comprised of corresponding K channels of a training image or its processed feature map, to thereby generate a reshaped feature map, and instructing a subsequent convolutional layer to apply the 1×K or the K×1 convolution operation to the reshaped feature map, to thereby generate an adjusted feature map; and (b) instructing an output layer to refer to features on the adjusted feature map or its processed feature map, and instructing a loss layer to calculate losses by referring to an output from the output layer and its corresponding GT. | 1. A method for learning parameters of a CNN using a 1×K convolution operation or a K×1 convolution operation, comprising steps of:
(a) instructing, by a learning device when at least one training image is acquired, wherein a processed feature map of the training image has a width (W), a height (H), and a depth (L) comprised of a plurality of channels, wherein each channel of the plurality of channels has a plurality of features, and wherein each feature of the plurality of features corresponds to each pixel of the processed feature map of the training image, a reshaping layer to two-dimensionally concatenate each of features of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels, to thereby generate a reshaped feature map, wherein each pixel of each channel in the reshaped feature map corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and instructing a subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map, to thereby generate an adjusted feature map whose volume is adjusted; and
(b) instructing, by the learning device, an output layer to generate at least one output by referring to features on at least one of the adjusted feature map and a processed feature map of the adjusted feature map, and instructing, by the learning device, a loss layer to calculate one or more losses by referring to the output and a corresponding at least one ground truth of the output, to thereby learn at least part of parameters of the subsequent convolutional layer by backpropagating the losses;
further comprising, at the step of (a), instructing, by the learning device, when the depth (L) of the processed feature map of the training image is not a multiple of K, the reshaping layer to:
add at least one dummy channel to the plurality of channels of the processed feature map of the training image such that the depth (L) including the plurality of channels and the at least one dummy channel is a multiple of K, and
concatenate said each of features in said each group comprised of said each corresponding K channels among said all plurality of channels, including the at least one dummy channel, of the processed feature map of the training image,
wherein the processed feature map is reshaped by uniquely rearranging values of pixels, located in K channels, on a single channel, and the values of the pixels are maintained before and after being reshaped. 2. (canceled) 3. The method of claim 1, further comprising, at the step of (a), instructing, by the learning device, the reshaping layer to generate the reshaped feature map having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels. 4. The method of claim 3, wherein the number of kernels of the subsequent convolutional layer is M, and further comprising, at the step of (a), instructing, by the learning device, the subsequent convolutional layer to apply a 1×K convolution operation to the reshaped feature map, to thereby generate the adjusted feature map having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 5. The method of claim 1, further comprising, at the step of (a), instructing, by the learning device, the reshaping layer to generate the reshaped feature map having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels. 6. The method of claim 5, wherein the number of kernels of the subsequent convolutional layer is M, and further comprising, at the step of (a), instructing, by the learning device, the subsequent convolutional layer to apply a K×1 convolution operation to the reshaped feature map, to thereby generate the adjusted feature map having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 7. The method of claim 1, further comprising, at the step of (a),
instructing, by the learning device, the reshaping layer to at least one of: (i) generate the reshaped feature map having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels, and (ii) generate the reshaped feature map having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels, and
at least one of:
instructing, by the learning device when a size of a final part of the reshaped feature map on a
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W and a height of H·K, the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W and the height of H·K, and
instructing, by the learning device when the size of the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W·K and a height of H, the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W·K and the height of H. 8. A method for testing a CNN using a 1×K convolution operation or a K×1 convolution operation, comprising steps of:
(a) on condition that a learning device (i) has, upon acquiring at least one training image, wherein a processed feature map of the training image has a width (W), a height (H), and a depth (L) comprised of a plurality of channels, wherein each channel of the plurality of channels has a plurality of features for training, and wherein each feature of the plurality of features for training corresponds to each pixel of the processed feature map of the training image, instructed a reshaping layer to two-dimensionally concatenate each of features for training of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels of the processed feature map of the training image, to thereby generate a reshaped feature map for training, wherein each pixel of each channel in the reshaped feature map for training corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and has instructed a subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map for training, to thereby generate an adjusted feature map for training whose volume is adjusted, and (ii) has instructed an output layer to generate at least one output for training by referring to features on at least one of the adjusted feature map for training and a processed feature map of the adjusted feature map for training, and has instructed a loss layer to calculate one or more losses by referring to the output for training and a corresponding at least one ground truth of the output for training, to thereby learn at least part of parameters of the subsequent convolutional layer by backpropagating the losses; instructing, by a testing device when at least one test image is acquired, wherein at least one of the test image and a processed feature map of the test image has the width (W), the height (H), and the depth (L) comprised of the plurality of channels, wherein each channel of the plurality of channels has a plurality of features for testing, and wherein each feature of the plurality of features for testing corresponds to each pixel of at least one of the test image and the processed feature map of the test image, the reshaping layer to two-dimensionally concatenate each of features for testing of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels of at least one of the test image and the processed feature map of the test image, to thereby generate a reshaped feature map for testing, wherein each pixel of each channel in the reshaped feature map for testing corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and instructing the subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map for testing, to thereby generate an adjusted feature map for testing whose volume is adjusted; and
(b) the testing device instructing the output layer to generate at least one output for testing by referring to features on the adjusted feature map for testing or its processed feature map;
further comprising, at the step of (a), instructing, by the testing device, when the depth (L) of the processed feature map of the test image is not a multiple of K, the reshaping layer to:
add at least one dummy channel to the plurality of channels of the processed feature map of the test image such that the depth (L) including the plurality of channels and the at least one dummy channel is a multiple of K, and
concatenate said each of features in said each group comprised of said each corresponding K channels among said all plurality of channels, including the at least one dummy channel, of the processed feature map of the test image
wherein the processed feature map is reshaped by uniquely rearranging values of pixels, located in K channels, on a single channel, and the values of the pixels are maintained before and after being reshaped. 9. (canceled) 10. The method of claim 8, further comprising, at the step of (a), instructing, by the testing device, the reshaping layer to generate the reshaped feature map for testing having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels. 11. The method of claim 10, wherein the number of kernels of the subsequent convolutional layer is M, and further comprising, at the step of (a), instructing, by the testing device, the subsequent convolutional layer to apply a 1×K convolution operation to the reshaped feature map for testing, to thereby generate the adjusted feature map for testing having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 12. The method of claim 8, further comprising, at the step of (a),
instructing, by the testing device, the reshaping layer to generate the reshaped feature map for testing having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels. 13. The method of claim 12, wherein the number of kernels of the subsequent convolutional layer is M, and further comprising, at the step of (a), instructing, by the testing device, the subsequent convolutional layer to apply a K×1 convolution operation to the reshaped feature map for testing, to thereby generate the adjusted feature map for testing having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 14. The method of claim 8, further comprising, at the step of (a),
instructing, by the testing device, the reshaping layer to at least one of: (i) generate the reshaped feature map for testing having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels, and (ii) generate the reshaped feature map for testing having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels, and
at least one of:
instructing, by the testing device when a size of a final part of the reshaped feature map for testing on a
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W and a height of H·K, the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W and the height of H·K, and
instructing, by the testing device when the size of the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W·K and a height of H, the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W·K and the height of H. 15. A learning device for learning parameters of a CNN using a 1×K convolution operation or a K×1 convolution operation, comprising:
at least one memory that stores instructions; and
at least one processor configured to execute the instructions to: perform processes of (I) instructing, when at least one training image is acquired by the learning device, wherein a processed feature map of the training image has a width (W), a height (H), and a depth (L) comprised of a plurality of channels, wherein each channel of the plurality of channels has a plurality of features, and wherein each feature of the plurality of features corresponds to each pixel of the processed feature map of the training image, a reshaping layer to two-dimensionally concatenate each of features of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels, to thereby generate a reshaped feature map, wherein each pixel of each channel in the reshaped feature map corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and instructing a subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map, to thereby generate an adjusted feature map whose volume is adjusted, and (II) instructing an output layer to generate at least one output by referring to features on at least one of the adjusted feature map and a processed feature map of the adjusted feature map, and instructing a loss layer to calculate one or more losses by referring to the output and a corresponding at least one ground truth of the output, to thereby learn at least part of parameters of the subsequent convolutional layer by backpropagating the losses;
wherein, at the process of (I), the processor, when the depth (L) of the pre-processed feature map of the training image is not a multiple of K, instructs the reshaping layer to:
add at least one dummy channel to the plurality of channels the processed feature map of the training image such that the depth (L) including the plurality of channels and the at least one dummy channel is a multiple of K, and
concatenate said each of features in said each group comprised of said each corresponding K channels among said all plurality of channels, including the at least one dummy channel, of the pre-processed feature map of the training image;
wherein the processed feature map is reshaped by uniquely rearranging values of pixels, located in K channels, on a single channel, and the values of the pixels are maintained before and after being reshaped. 16. (canceled) 17. The learning device of claim 15, wherein at the process of (I), the processor instructs the reshaping layer to generate the reshaped feature map having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels. 18. The learning device of claim 17, wherein the number of kernels of the subsequent convolutional layer is M, at the process of (I), the processor instructs the subsequent convolutional layer to apply a 1×K convolution operation to the reshaped feature map, to thereby generate the adjusted feature map having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 19. The learning device of claim 15, wherein at the process of (I), the processor instructs the reshaping layer to generate the reshaped feature map having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels. 20. The learning device of claim 19, wherein the number of kernels of the subsequent convolutional layer is M, at the process of (I), the processor instructs the subsequent convolutional layer to apply a K×1 convolution operation to the reshaped feature map, to thereby generate the adjusted feature map having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 21. The learning device of claim 15, wherein at the process of (I),
the processor instructs the reshaping layer to at least one of: (i) generate the reshaped feature map having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels, and (ii) generate the reshaped feature map having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels, and
at least one of:
wherein, when a size of a final part of the reshaped feature map on a
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W and a height of H·K, the processor instructs the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W and the height of H·K, and
wherein, when the size of the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W·K and a height of H, the processor instructs the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W·K and the height of H. 22. A testing device for testing a CNN using a 1×K convolution operation or a K×1 convolution operation, comprising:
at least one memory that stores instructions; and
at least one processor, on condition that a learning device (i) has, upon acquiring at least one training image, wherein a processed feature map of the training image has a width (W), a height (H), and a depth (L) comprised of a plurality of channels, wherein each channel of the plurality of channels has a plurality of features for training, and wherein each feature of the plurality of features for training corresponds to each pixel of the processed feature map of the training image, instructed a reshaping layer to two-dimensionally concatenate each of features for training of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels of the processed feature map of the training image, to thereby generate a reshaped feature map for training, wherein each pixel of each channel in the reshaped feature map for training corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and has instructed a subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map for training, to thereby generate an adjusted feature map for training whose volume is adjusted, and (ii) has instructed an output layer to generate at least one output for training by referring to features on at least one of the adjusted feature map for training and a processed feature map of the adjusted feature map for training, and has instructed a loss layer to calculate one or more losses by referring to the output for training and a corresponding at least one ground truth of the output for training, to thereby learn at least part of parameters of the subsequent convolutional layer by backpropagating the losses; configured to execute the instructions to: perform processes of (I) instructing, when at least one test image is acquired, wherein at least one of the test image and a processed feature map of the test image has the width (W), the height (H), and the depth (L) comprised of the plurality of channels, wherein each channel of the plurality of channels has a plurality of features for testing, and wherein each feature of the plurality of features for testing corresponds to each pixel of at least one of the test image and the processed feature map of the test image, the reshaping layer to two-dimensionally concatenate each of features for testing of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels of at least one of the test image and the processed feature map of the test image, to thereby generate a reshaped feature map for testing, wherein each pixel of each channel in the reshaped feature map for testing corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and instructing the subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map for testing, to thereby generate an adjusted feature map for testing whose volume is adjusted, and (II) instructing the output layer to generate at least one output for testing by referring to features on the adjusted feature map for testing or its processed feature map;
wherein, at the process of (I), the processor, when the depth (L) of the processed feature map of the test image is not a multiple of K, instructs the reshaping layer to:
add at least one dummy channel to the plurality of channels of the processed feature map of the test image such that the depth (L) including the plurality of channels and the at least one dummy channel is a multiple of K, and
concatenate said each of features in said each group comprised of said each corresponding K channels among said all plurality of channels, including the at least one dummy channel, of the processed feature map of the test image
wherein the processed feature map is reshaped by uniquely rearranging values of pixels, located in K channels, on a single channel, and the values of the pixels are maintained before and after being reshaped. 23. (canceled) 24. The testing device of claim 22, wherein at the process of (I), the processor instructs the reshaping layer to generate the reshaped feature map for testing having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels. 25. The testing device of claim 24, wherein the number of kernels of the subsequent convolutional layer is M, at the process of (I), the processor instructs the subsequent convolutional layer to apply a 1×K convolution operation to the reshaped feature map for testing, to thereby generate the adjusted feature map for testing having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 26. The testing device of claim 22, wherein at the process of (I), the processor instructs the reshaping layer to generate the reshaped feature man for testing having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels. 27. The testing device of claim 26, wherein the number of kernels of the subsequent convolutional layer is M, at the process of (I), the processor instructs the subsequent convolutional layer to apply a K×1 convolution operation to the reshaped feature map for testing, to thereby generate the adjusted feature map for testing having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 28. The testing device of claim 22, wherein at the process of (I),
the processor instructs the reshaping layer to at least one of: (i) generate the reshaped feature map for testing having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels, and (ii) generate the reshaped feature map for testing having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels, and
at least one of:
wherein, when a size of a final part of the reshaped feature map for testing on a
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W and a height of H·K, the processor instructs the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W and the height of H·K, and
wherein, when the size of the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W·K and a height of H, the processor instructs the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W·K and the height of H. | A method for learning parameters of a CNN using a 1×K convolution operation or a K×1 convolution operation is provided to be used for hardware optimization which satisfies KPI. The method includes steps of: a learning device (a) instructing a reshaping layer to two-dimensionally concatenate features in each group comprised of corresponding K channels of a training image or its processed feature map, to thereby generate a reshaped feature map, and instructing a subsequent convolutional layer to apply the 1×K or the K×1 convolution operation to the reshaped feature map, to thereby generate an adjusted feature map; and (b) instructing an output layer to refer to features on the adjusted feature map or its processed feature map, and instructing a loss layer to calculate losses by referring to an output from the output layer and its corresponding GT.1. A method for learning parameters of a CNN using a 1×K convolution operation or a K×1 convolution operation, comprising steps of:
(a) instructing, by a learning device when at least one training image is acquired, wherein a processed feature map of the training image has a width (W), a height (H), and a depth (L) comprised of a plurality of channels, wherein each channel of the plurality of channels has a plurality of features, and wherein each feature of the plurality of features corresponds to each pixel of the processed feature map of the training image, a reshaping layer to two-dimensionally concatenate each of features of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels, to thereby generate a reshaped feature map, wherein each pixel of each channel in the reshaped feature map corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and instructing a subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map, to thereby generate an adjusted feature map whose volume is adjusted; and
(b) instructing, by the learning device, an output layer to generate at least one output by referring to features on at least one of the adjusted feature map and a processed feature map of the adjusted feature map, and instructing, by the learning device, a loss layer to calculate one or more losses by referring to the output and a corresponding at least one ground truth of the output, to thereby learn at least part of parameters of the subsequent convolutional layer by backpropagating the losses;
further comprising, at the step of (a), instructing, by the learning device, when the depth (L) of the processed feature map of the training image is not a multiple of K, the reshaping layer to:
add at least one dummy channel to the plurality of channels of the processed feature map of the training image such that the depth (L) including the plurality of channels and the at least one dummy channel is a multiple of K, and
concatenate said each of features in said each group comprised of said each corresponding K channels among said all plurality of channels, including the at least one dummy channel, of the processed feature map of the training image,
wherein the processed feature map is reshaped by uniquely rearranging values of pixels, located in K channels, on a single channel, and the values of the pixels are maintained before and after being reshaped. 2. (canceled) 3. The method of claim 1, further comprising, at the step of (a), instructing, by the learning device, the reshaping layer to generate the reshaped feature map having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels. 4. The method of claim 3, wherein the number of kernels of the subsequent convolutional layer is M, and further comprising, at the step of (a), instructing, by the learning device, the subsequent convolutional layer to apply a 1×K convolution operation to the reshaped feature map, to thereby generate the adjusted feature map having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 5. The method of claim 1, further comprising, at the step of (a), instructing, by the learning device, the reshaping layer to generate the reshaped feature map having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels. 6. The method of claim 5, wherein the number of kernels of the subsequent convolutional layer is M, and further comprising, at the step of (a), instructing, by the learning device, the subsequent convolutional layer to apply a K×1 convolution operation to the reshaped feature map, to thereby generate the adjusted feature map having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 7. The method of claim 1, further comprising, at the step of (a),
instructing, by the learning device, the reshaping layer to at least one of: (i) generate the reshaped feature map having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels, and (ii) generate the reshaped feature map having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels, and
at least one of:
instructing, by the learning device when a size of a final part of the reshaped feature map on a
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W and a height of H·K, the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W and the height of H·K, and
instructing, by the learning device when the size of the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W·K and a height of H, the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W·K and the height of H. 8. A method for testing a CNN using a 1×K convolution operation or a K×1 convolution operation, comprising steps of:
(a) on condition that a learning device (i) has, upon acquiring at least one training image, wherein a processed feature map of the training image has a width (W), a height (H), and a depth (L) comprised of a plurality of channels, wherein each channel of the plurality of channels has a plurality of features for training, and wherein each feature of the plurality of features for training corresponds to each pixel of the processed feature map of the training image, instructed a reshaping layer to two-dimensionally concatenate each of features for training of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels of the processed feature map of the training image, to thereby generate a reshaped feature map for training, wherein each pixel of each channel in the reshaped feature map for training corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and has instructed a subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map for training, to thereby generate an adjusted feature map for training whose volume is adjusted, and (ii) has instructed an output layer to generate at least one output for training by referring to features on at least one of the adjusted feature map for training and a processed feature map of the adjusted feature map for training, and has instructed a loss layer to calculate one or more losses by referring to the output for training and a corresponding at least one ground truth of the output for training, to thereby learn at least part of parameters of the subsequent convolutional layer by backpropagating the losses; instructing, by a testing device when at least one test image is acquired, wherein at least one of the test image and a processed feature map of the test image has the width (W), the height (H), and the depth (L) comprised of the plurality of channels, wherein each channel of the plurality of channels has a plurality of features for testing, and wherein each feature of the plurality of features for testing corresponds to each pixel of at least one of the test image and the processed feature map of the test image, the reshaping layer to two-dimensionally concatenate each of features for testing of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels of at least one of the test image and the processed feature map of the test image, to thereby generate a reshaped feature map for testing, wherein each pixel of each channel in the reshaped feature map for testing corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and instructing the subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map for testing, to thereby generate an adjusted feature map for testing whose volume is adjusted; and
(b) the testing device instructing the output layer to generate at least one output for testing by referring to features on the adjusted feature map for testing or its processed feature map;
further comprising, at the step of (a), instructing, by the testing device, when the depth (L) of the processed feature map of the test image is not a multiple of K, the reshaping layer to:
add at least one dummy channel to the plurality of channels of the processed feature map of the test image such that the depth (L) including the plurality of channels and the at least one dummy channel is a multiple of K, and
concatenate said each of features in said each group comprised of said each corresponding K channels among said all plurality of channels, including the at least one dummy channel, of the processed feature map of the test image
wherein the processed feature map is reshaped by uniquely rearranging values of pixels, located in K channels, on a single channel, and the values of the pixels are maintained before and after being reshaped. 9. (canceled) 10. The method of claim 8, further comprising, at the step of (a), instructing, by the testing device, the reshaping layer to generate the reshaped feature map for testing having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels. 11. The method of claim 10, wherein the number of kernels of the subsequent convolutional layer is M, and further comprising, at the step of (a), instructing, by the testing device, the subsequent convolutional layer to apply a 1×K convolution operation to the reshaped feature map for testing, to thereby generate the adjusted feature map for testing having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 12. The method of claim 8, further comprising, at the step of (a),
instructing, by the testing device, the reshaping layer to generate the reshaped feature map for testing having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels. 13. The method of claim 12, wherein the number of kernels of the subsequent convolutional layer is M, and further comprising, at the step of (a), instructing, by the testing device, the subsequent convolutional layer to apply a K×1 convolution operation to the reshaped feature map for testing, to thereby generate the adjusted feature map for testing having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 14. The method of claim 8, further comprising, at the step of (a),
instructing, by the testing device, the reshaping layer to at least one of: (i) generate the reshaped feature map for testing having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels, and (ii) generate the reshaped feature map for testing having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels, and
at least one of:
instructing, by the testing device when a size of a final part of the reshaped feature map for testing on a
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W and a height of H·K, the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W and the height of H·K, and
instructing, by the testing device when the size of the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W·K and a height of H, the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W·K and the height of H. 15. A learning device for learning parameters of a CNN using a 1×K convolution operation or a K×1 convolution operation, comprising:
at least one memory that stores instructions; and
at least one processor configured to execute the instructions to: perform processes of (I) instructing, when at least one training image is acquired by the learning device, wherein a processed feature map of the training image has a width (W), a height (H), and a depth (L) comprised of a plurality of channels, wherein each channel of the plurality of channels has a plurality of features, and wherein each feature of the plurality of features corresponds to each pixel of the processed feature map of the training image, a reshaping layer to two-dimensionally concatenate each of features of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels, to thereby generate a reshaped feature map, wherein each pixel of each channel in the reshaped feature map corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and instructing a subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map, to thereby generate an adjusted feature map whose volume is adjusted, and (II) instructing an output layer to generate at least one output by referring to features on at least one of the adjusted feature map and a processed feature map of the adjusted feature map, and instructing a loss layer to calculate one or more losses by referring to the output and a corresponding at least one ground truth of the output, to thereby learn at least part of parameters of the subsequent convolutional layer by backpropagating the losses;
wherein, at the process of (I), the processor, when the depth (L) of the pre-processed feature map of the training image is not a multiple of K, instructs the reshaping layer to:
add at least one dummy channel to the plurality of channels the processed feature map of the training image such that the depth (L) including the plurality of channels and the at least one dummy channel is a multiple of K, and
concatenate said each of features in said each group comprised of said each corresponding K channels among said all plurality of channels, including the at least one dummy channel, of the pre-processed feature map of the training image;
wherein the processed feature map is reshaped by uniquely rearranging values of pixels, located in K channels, on a single channel, and the values of the pixels are maintained before and after being reshaped. 16. (canceled) 17. The learning device of claim 15, wherein at the process of (I), the processor instructs the reshaping layer to generate the reshaped feature map having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels. 18. The learning device of claim 17, wherein the number of kernels of the subsequent convolutional layer is M, at the process of (I), the processor instructs the subsequent convolutional layer to apply a 1×K convolution operation to the reshaped feature map, to thereby generate the adjusted feature map having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 19. The learning device of claim 15, wherein at the process of (I), the processor instructs the reshaping layer to generate the reshaped feature map having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels. 20. The learning device of claim 19, wherein the number of kernels of the subsequent convolutional layer is M, at the process of (I), the processor instructs the subsequent convolutional layer to apply a K×1 convolution operation to the reshaped feature map, to thereby generate the adjusted feature map having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 21. The learning device of claim 15, wherein at the process of (I),
the processor instructs the reshaping layer to at least one of: (i) generate the reshaped feature map having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels, and (ii) generate the reshaped feature map having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels, and
at least one of:
wherein, when a size of a final part of the reshaped feature map on a
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W and a height of H·K, the processor instructs the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W and the height of H·K, and
wherein, when the size of the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W·K and a height of H, the processor instructs the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W·K and the height of H. 22. A testing device for testing a CNN using a 1×K convolution operation or a K×1 convolution operation, comprising:
at least one memory that stores instructions; and
at least one processor, on condition that a learning device (i) has, upon acquiring at least one training image, wherein a processed feature map of the training image has a width (W), a height (H), and a depth (L) comprised of a plurality of channels, wherein each channel of the plurality of channels has a plurality of features for training, and wherein each feature of the plurality of features for training corresponds to each pixel of the processed feature map of the training image, instructed a reshaping layer to two-dimensionally concatenate each of features for training of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels of the processed feature map of the training image, to thereby generate a reshaped feature map for training, wherein each pixel of each channel in the reshaped feature map for training corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and has instructed a subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map for training, to thereby generate an adjusted feature map for training whose volume is adjusted, and (ii) has instructed an output layer to generate at least one output for training by referring to features on at least one of the adjusted feature map for training and a processed feature map of the adjusted feature map for training, and has instructed a loss layer to calculate one or more losses by referring to the output for training and a corresponding at least one ground truth of the output for training, to thereby learn at least part of parameters of the subsequent convolutional layer by backpropagating the losses; configured to execute the instructions to: perform processes of (I) instructing, when at least one test image is acquired, wherein at least one of the test image and a processed feature map of the test image has the width (W), the height (H), and the depth (L) comprised of the plurality of channels, wherein each channel of the plurality of channels has a plurality of features for testing, and wherein each feature of the plurality of features for testing corresponds to each pixel of at least one of the test image and the processed feature map of the test image, the reshaping layer to two-dimensionally concatenate each of features for testing of K different channels corresponding to said each pixel in each group comprised of each corresponding K different channels among the plurality of channels of at least one of the test image and the processed feature map of the test image, to thereby generate a reshaped feature map for testing, wherein each pixel of each channel in the reshaped feature map for testing corresponds to each of the two-dimensionally concatenated features in said each group comprised of said each corresponding K different channels, and instructing the subsequent convolutional layer to apply the 1×K convolution operation or the K×1 convolution operation to the reshaped feature map for testing, to thereby generate an adjusted feature map for testing whose volume is adjusted, and (II) instructing the output layer to generate at least one output for testing by referring to features on the adjusted feature map for testing or its processed feature map;
wherein, at the process of (I), the processor, when the depth (L) of the processed feature map of the test image is not a multiple of K, instructs the reshaping layer to:
add at least one dummy channel to the plurality of channels of the processed feature map of the test image such that the depth (L) including the plurality of channels and the at least one dummy channel is a multiple of K, and
concatenate said each of features in said each group comprised of said each corresponding K channels among said all plurality of channels, including the at least one dummy channel, of the processed feature map of the test image
wherein the processed feature map is reshaped by uniquely rearranging values of pixels, located in K channels, on a single channel, and the values of the pixels are maintained before and after being reshaped. 23. (canceled) 24. The testing device of claim 22, wherein at the process of (I), the processor instructs the reshaping layer to generate the reshaped feature map for testing having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels. 25. The testing device of claim 24, wherein the number of kernels of the subsequent convolutional layer is M, at the process of (I), the processor instructs the subsequent convolutional layer to apply a 1×K convolution operation to the reshaped feature map for testing, to thereby generate the adjusted feature map for testing having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 26. The testing device of claim 22, wherein at the process of (I), the processor instructs the reshaping layer to generate the reshaped feature man for testing having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels. 27. The testing device of claim 26, wherein the number of kernels of the subsequent convolutional layer is M, at the process of (I), the processor instructs the subsequent convolutional layer to apply a K×1 convolution operation to the reshaped feature map for testing, to thereby generate the adjusted feature map for testing having a volume of W·H·M, resulting from a width of W, a height of H, and a depth of M channels. 28. The testing device of claim 22, wherein at the process of (I),
the processor instructs the reshaping layer to at least one of: (i) generate the reshaped feature map for testing having a width of W, a height of H·K, and a depth of
CEIL
(
L
K
)
channels, and (ii) generate the reshaped feature map for testing having a width of W·K, a height of H, and a depth of
CEIL
(
L
K
)
channels, and
at least one of:
wherein, when a size of a final part of the reshaped feature map for testing on a
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W and a height of H·K, the processor instructs the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W and the height of H·K, and
wherein, when the size of the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel is different from a size of a width of W·K and a height of H, the processor instructs the reshaping layer to add at least one row and column of zero padding such that the final part of the reshaped feature map for testing on the
{
CEIL
(
L
K
)
}
-
th
channel has the width of W·K and the height of H. | 2,100 |
6,409 | 6,409 | 15,693,530 | 2,176 | Systems and methods consistent with various disclosed embodiments provide for managing email attachments. In one embodiment, a system is disclosed for managing email attachments. The system may include a memory storing software instructions and one or more processors configured to execute the software instructions to perform one or more operations. The operations may include providing an interface for converting an original attachment to an email. The operations may also include converting the original attachment to a modified attachment based on input received through the interface. The operations may further include substituting the original attachment to the email with the modified attachment, and providing information to send the email with the modified attachment. | 1-20. (canceled) 21. A computer-based system for managing email attachments, comprising:
a memory storing software instructions; and one or more processors configured to execute the software instructions to perform operations including:
providing an interface for converting an original attachment of an email,
changing an attribute and converting the original attachment to a modified attachment based on input received through the interface, wherein changing the attribute comprises designating access rights to the original attachment, and
providing information to send the email with the modified attachment to a recipient. 22. The computer-based system of claim 21, wherein the designated access rights limit the recipient to opening the modified attachment within a virtual memory that is erased after review of the modified attachment is complete. 23. The computer-based system of claim 22, wherein opening the modified attachment within the virtual memory limits the recipient from saving or printing the modified attachment. 24. The computer-based system of claim 21, wherein the designated access rights limit the recipient to read-only access to the modified attachment. 25. The computer-based system of claim 21, wherein the designated access rights permit the recipient to edit the modified attachment. 26. The computer-based system of claim 21, wherein the one or more processors are further configured to provide information to send the email with the modified attachment to both the recipient and a second recipient, and wherein the designated access rights comprise a first access right for the recipient and a different access right for the second recipient. 27. The computer-based system of claim 21, wherein changing the attribute further comprises binding the original attachment with a second attachment. 28. The computer-based system of claim 21, wherein changing the attribute further comprises incorporating the original attachment into a zip file. 29. The computer-based system of claim 27, wherein changing the attribute further comprises incorporating the original attachment and the second attachment into a zip file. 30. The computer-based system of claim 21, wherein the one or more processors are further configured to open, within the interface, the original attachment or the modified attachment for editing. 31. A computer-based system for managing email attachments, comprising:
a memory storing software instructions; and one or more processors configured to execute the software instructions to perform operations including:
providing an interface for converting an original attachment of an email,
changing an attribute and converting the original attachment to a modified attachment based on input received through the interface, wherein changing the attribute comprises binding the original attachment with a second attachment, and
providing information to send the email with the modified attachment to a recipient. 32. The computer-based system of claim 31, wherein changing the attribute further comprises converting the original attachment from one file type to a different file type. 33. The computer-based system of claim 31, wherein changing the attribute further comprises incorporating the bound original attachment into a zip file. 34. The computer-based system of claim 31, wherein the one or more processors are further configured to open, within the interface, the original attachment or the modified attachment for editing. 35. A computer-based system for managing email attachments, comprising:
a memory storing software instructions; and one or more processors configured to execute the software instructions to perform operations including:
providing an interface for converting an original attachment of an email,
changing an attribute and converting the original attachment to a modified attachment based on input received through the interface, wherein changing the attribute comprises incorporating the bound original attachment into a zip file, and
providing information to send the email with the modified attachment to a recipient. 36. A computer-based method for managing email attachments, comprising:
receiving an email containing an original attachment; providing an interface for converting an original attachment of an email; changing an attribute and converting the original attachment to a modified attachment based on input received through the interface, wherein changing the attribute comprises:
removing metadata from the original attachment,
converting the original attachment from one file type to a different file type, and
binding the original attachment with a second attachment; and
providing information to send the email with the modified attachment to a recipient. 37. The computer-based method of claim 36, wherein the method further comprises converting the original attachment from one file type to a different file type based on a default attachment policy. 38. The computer-based method of claim 37, wherein the method further comprises removing metadata from the original attachment based on a default attachment policy. 39. The computer-based method of claim 36, wherein the method further comprises:
removing the original attachment from the email; storing the original attachment in the virtual memory; storing the modified attachment in the virtual memory; and deleting one or more of the original attachment or the modified attachment from the virtual memory after the email is sent to the recipient. 40. The computer-based system of claim 36, wherein the method further comprises opening, within the interface, the original attachment or the modified attachment for editing. | Systems and methods consistent with various disclosed embodiments provide for managing email attachments. In one embodiment, a system is disclosed for managing email attachments. The system may include a memory storing software instructions and one or more processors configured to execute the software instructions to perform one or more operations. The operations may include providing an interface for converting an original attachment to an email. The operations may also include converting the original attachment to a modified attachment based on input received through the interface. The operations may further include substituting the original attachment to the email with the modified attachment, and providing information to send the email with the modified attachment.1-20. (canceled) 21. A computer-based system for managing email attachments, comprising:
a memory storing software instructions; and one or more processors configured to execute the software instructions to perform operations including:
providing an interface for converting an original attachment of an email,
changing an attribute and converting the original attachment to a modified attachment based on input received through the interface, wherein changing the attribute comprises designating access rights to the original attachment, and
providing information to send the email with the modified attachment to a recipient. 22. The computer-based system of claim 21, wherein the designated access rights limit the recipient to opening the modified attachment within a virtual memory that is erased after review of the modified attachment is complete. 23. The computer-based system of claim 22, wherein opening the modified attachment within the virtual memory limits the recipient from saving or printing the modified attachment. 24. The computer-based system of claim 21, wherein the designated access rights limit the recipient to read-only access to the modified attachment. 25. The computer-based system of claim 21, wherein the designated access rights permit the recipient to edit the modified attachment. 26. The computer-based system of claim 21, wherein the one or more processors are further configured to provide information to send the email with the modified attachment to both the recipient and a second recipient, and wherein the designated access rights comprise a first access right for the recipient and a different access right for the second recipient. 27. The computer-based system of claim 21, wherein changing the attribute further comprises binding the original attachment with a second attachment. 28. The computer-based system of claim 21, wherein changing the attribute further comprises incorporating the original attachment into a zip file. 29. The computer-based system of claim 27, wherein changing the attribute further comprises incorporating the original attachment and the second attachment into a zip file. 30. The computer-based system of claim 21, wherein the one or more processors are further configured to open, within the interface, the original attachment or the modified attachment for editing. 31. A computer-based system for managing email attachments, comprising:
a memory storing software instructions; and one or more processors configured to execute the software instructions to perform operations including:
providing an interface for converting an original attachment of an email,
changing an attribute and converting the original attachment to a modified attachment based on input received through the interface, wherein changing the attribute comprises binding the original attachment with a second attachment, and
providing information to send the email with the modified attachment to a recipient. 32. The computer-based system of claim 31, wherein changing the attribute further comprises converting the original attachment from one file type to a different file type. 33. The computer-based system of claim 31, wherein changing the attribute further comprises incorporating the bound original attachment into a zip file. 34. The computer-based system of claim 31, wherein the one or more processors are further configured to open, within the interface, the original attachment or the modified attachment for editing. 35. A computer-based system for managing email attachments, comprising:
a memory storing software instructions; and one or more processors configured to execute the software instructions to perform operations including:
providing an interface for converting an original attachment of an email,
changing an attribute and converting the original attachment to a modified attachment based on input received through the interface, wherein changing the attribute comprises incorporating the bound original attachment into a zip file, and
providing information to send the email with the modified attachment to a recipient. 36. A computer-based method for managing email attachments, comprising:
receiving an email containing an original attachment; providing an interface for converting an original attachment of an email; changing an attribute and converting the original attachment to a modified attachment based on input received through the interface, wherein changing the attribute comprises:
removing metadata from the original attachment,
converting the original attachment from one file type to a different file type, and
binding the original attachment with a second attachment; and
providing information to send the email with the modified attachment to a recipient. 37. The computer-based method of claim 36, wherein the method further comprises converting the original attachment from one file type to a different file type based on a default attachment policy. 38. The computer-based method of claim 37, wherein the method further comprises removing metadata from the original attachment based on a default attachment policy. 39. The computer-based method of claim 36, wherein the method further comprises:
removing the original attachment from the email; storing the original attachment in the virtual memory; storing the modified attachment in the virtual memory; and deleting one or more of the original attachment or the modified attachment from the virtual memory after the email is sent to the recipient. 40. The computer-based system of claim 36, wherein the method further comprises opening, within the interface, the original attachment or the modified attachment for editing. | 2,100 |
6,410 | 6,410 | 16,155,364 | 2,191 | Various embodiments comprise systems, methods, architectures, mechanisms or apparatus configured for automatically generating a testing script. | 1. A computer-implemented method of automatically generating a testing script, the method comprising:
iteratively generating, via a graphical user interface (GUI), a plurality of GUI screens including user selectable visual objects, each GUI screen being contextually related to a temporally adjacent GUI screen, each user selectable visual object being associated with one or more keyword events; for each indication of a visual object selection or a keyword entry received via the GUI, identifying one or more functions associated with the selected visual object or keyword entry; for each of the identified one or more functions, generating a respective testing script portion; and in response to an indication of a final keyword event selection, assembling the generated testing script portions in to a complete testing script. 2. The computer implemented method of claim 1, wherein the user selectable visual objects presented by the GUI are visually adapted in response to one or more indications of keyword event selection. 3. The computer implemented method of claim 1, wherein in response to an indication of a keyword event selection requiring additional information, the user selectable visual objects presented by the GUI are adapted to enable user input of the additional information. 4. The computer implemented method of claim 1, wherein the keyword events are associated with one or more of the following event groups: Web based UI interactions, Windows based UI interactions, Webservice XML operations, Unix based interactions, Windows based DOS file operations, Database SQL operations and Java String Manipulation operations. 5. The computer implemented method of claim 2, wherein said assembling the generated testing script portions in to a complete testing script comprises:
determining a testing environment within which the test script portions are to be utilized; and generating the test script portions in accordance with the determined testing environment. 6. The computer implemented method of claim 1, further comprising: executing the complete testing script at a system or device under test. 7. The computer implemented method of claim 1, further comprising: executing the complete testing script at a Jenkins server operative to control a system or device under test. 8. The computer implemented method of claim 1, further comprising: executing the complete testing script via a testing environment operative to control a system or device under test. 9. The computer implemented method of claim 1, further comprising adding the generated test script to one or more previously generated test scripts to provide thereby a plurality of tests scripts configured to perform a series of tests of a system or device under test. 10. The method of claim 6, wherein the system or device under test comprises one or more of an application server, a database server, a web server, a UNIX server, a fileserver, a service provider device or a user device. 11. A non-transitory computer readable medium storing instructions which, when executed by a computing device, cause the computing device to perform a method of automatically generating a testing script, the method comprising:
iteratively generating, via a graphical user interface (GUI), a plurality of GUI screens including user selectable visual objects, each GUI screen being contextually related to a temporally adjacent GUI screen, each user selectable visual object being associated with one or more keyword events; for each indication of a visual object selection or a keyword entry received via the GUI, identifying one or more functions associated with the selected visual object or keyword entry; for each of the identified one or more functions, generating a respective testing script portion; and in response to an indication of a final keyword event selection, assembling the generated testing script portions in to a complete testing script. 12. A test script generation engine, comprising a processor configured for:
iteratively generating, via a graphical user interface (GUI), a plurality of GUI screens including user selectable visual objects, each GUI screen being contextually related to a temporally adjacent GUI screen, each user selectable visual object being associated with one or more keyword events; for each indication of a visual object selection or a keyword entry received via the GUI, identifying one or more functions associated with the selected visual object or keyword entry; for each of the identified one or more functions, generating a respective testing script portion; and in response to an indication of a final keyword event selection, assembling the generated testing script portions in to a complete testing script. | Various embodiments comprise systems, methods, architectures, mechanisms or apparatus configured for automatically generating a testing script.1. A computer-implemented method of automatically generating a testing script, the method comprising:
iteratively generating, via a graphical user interface (GUI), a plurality of GUI screens including user selectable visual objects, each GUI screen being contextually related to a temporally adjacent GUI screen, each user selectable visual object being associated with one or more keyword events; for each indication of a visual object selection or a keyword entry received via the GUI, identifying one or more functions associated with the selected visual object or keyword entry; for each of the identified one or more functions, generating a respective testing script portion; and in response to an indication of a final keyword event selection, assembling the generated testing script portions in to a complete testing script. 2. The computer implemented method of claim 1, wherein the user selectable visual objects presented by the GUI are visually adapted in response to one or more indications of keyword event selection. 3. The computer implemented method of claim 1, wherein in response to an indication of a keyword event selection requiring additional information, the user selectable visual objects presented by the GUI are adapted to enable user input of the additional information. 4. The computer implemented method of claim 1, wherein the keyword events are associated with one or more of the following event groups: Web based UI interactions, Windows based UI interactions, Webservice XML operations, Unix based interactions, Windows based DOS file operations, Database SQL operations and Java String Manipulation operations. 5. The computer implemented method of claim 2, wherein said assembling the generated testing script portions in to a complete testing script comprises:
determining a testing environment within which the test script portions are to be utilized; and generating the test script portions in accordance with the determined testing environment. 6. The computer implemented method of claim 1, further comprising: executing the complete testing script at a system or device under test. 7. The computer implemented method of claim 1, further comprising: executing the complete testing script at a Jenkins server operative to control a system or device under test. 8. The computer implemented method of claim 1, further comprising: executing the complete testing script via a testing environment operative to control a system or device under test. 9. The computer implemented method of claim 1, further comprising adding the generated test script to one or more previously generated test scripts to provide thereby a plurality of tests scripts configured to perform a series of tests of a system or device under test. 10. The method of claim 6, wherein the system or device under test comprises one or more of an application server, a database server, a web server, a UNIX server, a fileserver, a service provider device or a user device. 11. A non-transitory computer readable medium storing instructions which, when executed by a computing device, cause the computing device to perform a method of automatically generating a testing script, the method comprising:
iteratively generating, via a graphical user interface (GUI), a plurality of GUI screens including user selectable visual objects, each GUI screen being contextually related to a temporally adjacent GUI screen, each user selectable visual object being associated with one or more keyword events; for each indication of a visual object selection or a keyword entry received via the GUI, identifying one or more functions associated with the selected visual object or keyword entry; for each of the identified one or more functions, generating a respective testing script portion; and in response to an indication of a final keyword event selection, assembling the generated testing script portions in to a complete testing script. 12. A test script generation engine, comprising a processor configured for:
iteratively generating, via a graphical user interface (GUI), a plurality of GUI screens including user selectable visual objects, each GUI screen being contextually related to a temporally adjacent GUI screen, each user selectable visual object being associated with one or more keyword events; for each indication of a visual object selection or a keyword entry received via the GUI, identifying one or more functions associated with the selected visual object or keyword entry; for each of the identified one or more functions, generating a respective testing script portion; and in response to an indication of a final keyword event selection, assembling the generated testing script portions in to a complete testing script. | 2,100 |
6,411 | 6,411 | 14,913,349 | 2,171 | User interface navigation on a personal electronics device based on movements of a crown is disclosed. The device can select an appropriate level of information arranged along a z-axis for display based on crown movement. The navigation can be based on an angular velocity of the crown. | 1. A computer-implemented method comprising:
displaying a first plurality of icons on a touch-sensitive display of a wearable electronic device; and receiving input based on a movement of a physical crown of the wearable electronic device. 2-67. (canceled) | User interface navigation on a personal electronics device based on movements of a crown is disclosed. The device can select an appropriate level of information arranged along a z-axis for display based on crown movement. The navigation can be based on an angular velocity of the crown.1. A computer-implemented method comprising:
displaying a first plurality of icons on a touch-sensitive display of a wearable electronic device; and receiving input based on a movement of a physical crown of the wearable electronic device. 2-67. (canceled) | 2,100 |
6,412 | 6,412 | 15,370,714 | 2,153 | Techniques for targeted mentions for user correlation to a search term are described. According to various implementations, users interact over a network-based service to engage in conversations (e.g., text, voice, video, and so forth), exchange content, collaborate on projects, and so forth. In the course of these interactions, users that author content and/or interact with content can discuss certain subject matter and can tag (“targeted mention”) certain users as being knowledgeable in the subject matter. The targeted mention is then discoverable during a search process to surface a correlation between a particular user and the particular subject matter. | 1. A system comprising:
one or more processors; and one or more computer-readable storage media storing computer-executable instructions that, responsive to execution by the one or more processors, cause the system to perform operations including:
receiving a search query that includes a trigger phrase and a search term;
searching content, based on the trigger phrase, for targeted mentions of users, the targeted mentions corresponding to users that are linked to a search tag and correlated with the search term, the search tag being different than the search term;
identifying within the content a set of users with targeted mentions correlated with the search term;
ranking the set of users based on respective correlation scores for the users, the correlation scores based at least in part on a number of targeted mentions for individual users of the set of users; and
outputting, based on said ranking, a ranked set of users that are correlated with the search term. 2. The system as described in claim 1, wherein the content includes content posted to a network-based interactivity service. 3. The system as described in claim 1, wherein the content includes content posted to a network-based interactivity service, and wherein the search query is received via user input to the interactivity service. 4. The system as described in claim 1, wherein the content includes content posted to a network-based interactivity service, and wherein the search tag comprises a symbol appended to user identifiers for the users within body portions of the content. 5. The system as described in claim 1, wherein said searching comprises searching for the search tag appended to user identifiers for different users, the user identifiers being included in instances of the content that include the search term. 6. The system as described in claim 1, further comprising a search module that is configured to parse the search query to distinguish between the trigger phrase and the search term, and to recognize the trigger phrase as a request to initiate said searching. 7. The system as described in claim 1, wherein said identifying comprises identifying occurrences of the targeted mentions and the search term together in instances of the content. 8. The system as described in claim 1, wherein said ranking comprises determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, adding the weighted values to determine a targeted mentions score, and determining a correlation score for the particular user based at least in part on the targeted mentions score. 9. The system as described in claim 1, wherein said ranking comprises:
determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, a particular weighted value of a particular targeted mention being based on a number of targeted mentions of different users that are included in an instance of the content for the particular targeted mention; and adding the weighted values to determine a targeted mentions score, and determining a correlation score for the particular user based at least in part on the targeted mentions score. 10. The system as described in claim 1, wherein said ranking comprises:
determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, a particular weighted value of a particular targeted mention being reduced from a standard weighting value based on a number of targeted mentions of different users that are included in an instance of the content for the particular targeted mention exceeding a threshold number of targeted mentions; and adding the weighted values to determine a targeted mentions score, and determining a correlation score for the particular user based at least in part on the targeted mentions score. 11. The system as described in claim 1, wherein said ranking comprises:
determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, and adding the weighted values to determine a targeted mentions score for the particular user; determining an auxiliary score for the particular user based on criteria other than targeted mentions of the particular user, the criteria pertaining to correlation of the particular user with the search term; and using the targeted mentions score and the auxiliary score to determine a correlation score for the particular user. 12. A method comprising:
searching content, by a computing device, for targeted mentions of users, the targeted mentions corresponding to users that are linked to a search tag and correlated with a search term, the search tag being different than the search term; ranking a set of users based on respective correlation scores for the users, the correlation scores based at least in part on a number of targeted mentions for individual users of the set of users; and outputting, based on said ranking, a ranked set of users that are correlated with the search term. 13. The method as described in claim 12, wherein said searching is based on a search query that includes a trigger phrase and the search term, the trigger phrase causing said searching to be initiated. 14. The method as described in claim 12, wherein said ranking occurs responsive to identifying occurrences of the targeted mentions and the search term together in instances of the content. 15. The method as described in claim 12, wherein said ranking comprises determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, adding the weighted values to determine a targeted mentions score, and determining a correlation score for the particular user based at least in part on the targeted mentions score. 16. The method as described in claim 12, wherein said ranking comprises:
determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, and adding the weighted values to determine a targeted mentions score for the particular user; determining an auxiliary score for the particular user based on criteria other than targeted mentions of the particular user, the criteria pertaining to correlation of the particular user with the search term; and using the targeted mentions score and the auxiliary score to determine a correlation score for the particular user. 17. A method comprising:
searching content, by a computing device, for targeted mentions of users, the targeted mentions corresponding to users that are linked to a search tag and correlated with a search term, the search tag being different than the search term; identifying within the content a set of users with targeted mentions correlated with the search term; calculating, by the computing device, a correlation score for each user of the set of users, said calculating being based at least in part on a number of targeted mentions for each user of the set of users; ranking the set of users based on respective correlation scores for each user; and outputting, based on said ranking, a ranked set of users that are correlated with the search term. 18. The method as described in claim 17, wherein said calculating a correlation score for a particular user comprises:
determining weighted values for targeted mentions of the particular user based on a number of targeted mentions of different users in instances of content in which the targeted mentions of the particular user are located; and adding the weighted values to obtain at least a portion of the correlation score. 19. The method as described in claim 17, wherein said calculating a correlation score for a particular user comprises:
determining weighted values for targeted mentions of the particular user based on a number of targeted mentions of different users in instances of content in which the targeted mentions of the particular user are located, a particular weighted value of a particular targeted mention being reduced from a standard weighting value based on a number of targeted mentions of different users that are included in an instance of the content for the particular targeted mention exceeding a threshold number of targeted mentions; and adding the weighted values to obtain at least a portion of the correlation score. 20. The method as described in claim 17, wherein said calculating a correlation score for a particular user comprises:
determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, and adding the weighted values to determine a targeted mentions score for the particular user; determining an auxiliary score for the particular user based on criteria other than targeted mentions of the particular user, the criteria pertaining to correlation of the particular user with the search term; and using the targeted mentions score and the auxiliary score to determine a correlation score for the particular user. | Techniques for targeted mentions for user correlation to a search term are described. According to various implementations, users interact over a network-based service to engage in conversations (e.g., text, voice, video, and so forth), exchange content, collaborate on projects, and so forth. In the course of these interactions, users that author content and/or interact with content can discuss certain subject matter and can tag (“targeted mention”) certain users as being knowledgeable in the subject matter. The targeted mention is then discoverable during a search process to surface a correlation between a particular user and the particular subject matter.1. A system comprising:
one or more processors; and one or more computer-readable storage media storing computer-executable instructions that, responsive to execution by the one or more processors, cause the system to perform operations including:
receiving a search query that includes a trigger phrase and a search term;
searching content, based on the trigger phrase, for targeted mentions of users, the targeted mentions corresponding to users that are linked to a search tag and correlated with the search term, the search tag being different than the search term;
identifying within the content a set of users with targeted mentions correlated with the search term;
ranking the set of users based on respective correlation scores for the users, the correlation scores based at least in part on a number of targeted mentions for individual users of the set of users; and
outputting, based on said ranking, a ranked set of users that are correlated with the search term. 2. The system as described in claim 1, wherein the content includes content posted to a network-based interactivity service. 3. The system as described in claim 1, wherein the content includes content posted to a network-based interactivity service, and wherein the search query is received via user input to the interactivity service. 4. The system as described in claim 1, wherein the content includes content posted to a network-based interactivity service, and wherein the search tag comprises a symbol appended to user identifiers for the users within body portions of the content. 5. The system as described in claim 1, wherein said searching comprises searching for the search tag appended to user identifiers for different users, the user identifiers being included in instances of the content that include the search term. 6. The system as described in claim 1, further comprising a search module that is configured to parse the search query to distinguish between the trigger phrase and the search term, and to recognize the trigger phrase as a request to initiate said searching. 7. The system as described in claim 1, wherein said identifying comprises identifying occurrences of the targeted mentions and the search term together in instances of the content. 8. The system as described in claim 1, wherein said ranking comprises determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, adding the weighted values to determine a targeted mentions score, and determining a correlation score for the particular user based at least in part on the targeted mentions score. 9. The system as described in claim 1, wherein said ranking comprises:
determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, a particular weighted value of a particular targeted mention being based on a number of targeted mentions of different users that are included in an instance of the content for the particular targeted mention; and adding the weighted values to determine a targeted mentions score, and determining a correlation score for the particular user based at least in part on the targeted mentions score. 10. The system as described in claim 1, wherein said ranking comprises:
determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, a particular weighted value of a particular targeted mention being reduced from a standard weighting value based on a number of targeted mentions of different users that are included in an instance of the content for the particular targeted mention exceeding a threshold number of targeted mentions; and adding the weighted values to determine a targeted mentions score, and determining a correlation score for the particular user based at least in part on the targeted mentions score. 11. The system as described in claim 1, wherein said ranking comprises:
determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, and adding the weighted values to determine a targeted mentions score for the particular user; determining an auxiliary score for the particular user based on criteria other than targeted mentions of the particular user, the criteria pertaining to correlation of the particular user with the search term; and using the targeted mentions score and the auxiliary score to determine a correlation score for the particular user. 12. A method comprising:
searching content, by a computing device, for targeted mentions of users, the targeted mentions corresponding to users that are linked to a search tag and correlated with a search term, the search tag being different than the search term; ranking a set of users based on respective correlation scores for the users, the correlation scores based at least in part on a number of targeted mentions for individual users of the set of users; and outputting, based on said ranking, a ranked set of users that are correlated with the search term. 13. The method as described in claim 12, wherein said searching is based on a search query that includes a trigger phrase and the search term, the trigger phrase causing said searching to be initiated. 14. The method as described in claim 12, wherein said ranking occurs responsive to identifying occurrences of the targeted mentions and the search term together in instances of the content. 15. The method as described in claim 12, wherein said ranking comprises determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, adding the weighted values to determine a targeted mentions score, and determining a correlation score for the particular user based at least in part on the targeted mentions score. 16. The method as described in claim 12, wherein said ranking comprises:
determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, and adding the weighted values to determine a targeted mentions score for the particular user; determining an auxiliary score for the particular user based on criteria other than targeted mentions of the particular user, the criteria pertaining to correlation of the particular user with the search term; and using the targeted mentions score and the auxiliary score to determine a correlation score for the particular user. 17. A method comprising:
searching content, by a computing device, for targeted mentions of users, the targeted mentions corresponding to users that are linked to a search tag and correlated with a search term, the search tag being different than the search term; identifying within the content a set of users with targeted mentions correlated with the search term; calculating, by the computing device, a correlation score for each user of the set of users, said calculating being based at least in part on a number of targeted mentions for each user of the set of users; ranking the set of users based on respective correlation scores for each user; and outputting, based on said ranking, a ranked set of users that are correlated with the search term. 18. The method as described in claim 17, wherein said calculating a correlation score for a particular user comprises:
determining weighted values for targeted mentions of the particular user based on a number of targeted mentions of different users in instances of content in which the targeted mentions of the particular user are located; and adding the weighted values to obtain at least a portion of the correlation score. 19. The method as described in claim 17, wherein said calculating a correlation score for a particular user comprises:
determining weighted values for targeted mentions of the particular user based on a number of targeted mentions of different users in instances of content in which the targeted mentions of the particular user are located, a particular weighted value of a particular targeted mention being reduced from a standard weighting value based on a number of targeted mentions of different users that are included in an instance of the content for the particular targeted mention exceeding a threshold number of targeted mentions; and adding the weighted values to obtain at least a portion of the correlation score. 20. The method as described in claim 17, wherein said calculating a correlation score for a particular user comprises:
determining, for a particular user of the set of users, weighted values for targeted mentions of the particular user, and adding the weighted values to determine a targeted mentions score for the particular user; determining an auxiliary score for the particular user based on criteria other than targeted mentions of the particular user, the criteria pertaining to correlation of the particular user with the search term; and using the targeted mentions score and the auxiliary score to determine a correlation score for the particular user. | 2,100 |
6,413 | 6,413 | 16,371,082 | 2,139 | Powering random access memory (RAM) modules with non-volatile memory components may include providing, by a power supply, a first output voltage to one or more RAM modules, each RAM module of the one or more RAM modules comprising a volatile memory component and a non-volatile memory component; providing, by the power supply, a second output voltage to one or more system components distinct from the one or more RAM modules; detecting a power event; sending, by the power supply, in response to detecting the power event, a signal to the one or more RAM modules to initiate a save operation, wherein the save operation comprises storing, for each of the one or more RAM modules, data from the volatile memory component to the non-volatile memory component; and ceasing, by the power supply, the second output voltage while maintaining the first output voltage to facilitate the save operation. | 1. A method of powering random access memory (RAM) modules with non-volatile memory components, the method comprising:
providing, by a power supply, a first output voltage to one or more RAM modules, each RAM module of the one or more RAM modules comprising a volatile memory component and a non-volatile memory component; providing, by the power supply, a second output voltage to one or more system components distinct from the one or more RAM modules; detecting a power event; sending, by the power supply, in response to detecting the power event, a signal to the one or more RAM modules to initiate a save operation, wherein the save operation comprises storing, for each of the one or more RAM modules, data from the volatile memory component to the non-volatile memory component; and ceasing, by the power supply, the second output voltage while continuing to provide, by the power supply the first output voltage directly to the RAM modules to facilitate the save operation. 2. The method of claim 1, wherein ceasing the second output voltage while maintaining the first output voltage comprises ceasing the second output voltage after a predefined time period. 3. The method of claim 2, wherein the predefined time period is relative to a time at which the power event was detected or a time at which the signal was sent. 4. The method of claim 1, further comprising sending the signal to the one or more system components. 5. The method of claim 1, wherein the power event comprises a loss of input power to the power supply and/or the input power falling below a threshold. 6. The method of claim 1, further comprising:
determining that the power event has not ended within a ride-through time of the power supply, wherein sending the signal to the one or more RAM modules comprises sending the signal in response to the power event not ending within the ride-though time. 7. The method of claim 1, wherein the signal comprises an Emergency Power Off Warning signal. 8. A power supply for powering random access memory (RAM) modules with non-volatile memory components, the power supply configured to perform steps comprising:
providing a first output voltage to one or more RAM modules, each RAM module of the one or more RAM modules comprising a volatile memory component and a non-volatile memory component; providing a second output voltage to one or more system components distinct from the one or more RAM modules; detecting a power event; sending, in response to detecting the power event, a signal to the one or more RAM modules to initiate a save operation, wherein the save operation comprises storing, for each of the one or more RAM modules, data from the volatile memory component to the non-volatile memory component; and ceasing, by the power supply, the second output voltage while continuing to provide, by the power supply the first output voltage directly to the RAM modules to facilitate the save operation. 9. The power supply of claim 8, wherein ceasing the second output voltage while maintaining the first output voltage comprises ceasing the second output voltage after a predefined time period. 10. The power supply of claim 9, wherein the predefined time period is relative to a time at which the power event was detected or a time at which the signal was sent. 11. The power supply of claim 8, wherein the steps further comprise sending the signal to the one or more system components. 12. The power supply of claim 8, wherein the power event comprises one or more of a loss of input power to the power supply or the input power falling below a threshold. 13. The power supply of claim 8, wherein the steps further comprise:
determining that the power event has not ended within a ride-through time of the power supply, wherein sending the signal to the one or more RAM modules comprises sending the signal in response to the power event not ending within the ride-though time. 14. The power supply of claim 8, wherein the signal comprises an Emergency Power Off Warning signal. 15. An apparatus comprising:
a power supply for powering random access memory (RAM) modules with non-volatile memory components, the power supply configured to carry out the steps of:
providing a first output voltage to one or more RAM modules, each RAM module of the one or more RAM modules comprising a volatile memory component and a non-volatile memory component;
providing a second output voltage to one or more system components distinct from the one or more RAM modules;
detecting a power event;
sending, in response to detecting the power event, a signal to the one or more RAM modules to initiate a save operation, wherein the save operation comprises storing, for each of the one or more RAM modules, data from the volatile memory component to the non-volatile memory component; and
ceasing, by the power supply, the second output voltage while continuing to provide, by the power supply the first output voltage directly to the RAM modules to facilitate the save operation. 16. The apparatus of claim 15, wherein ceasing the second output voltage while maintaining the first output voltage comprises ceasing the second output voltage after a predefined time period. 17. The apparatus of claim 16, wherein the predefined time period is relative to a time at which the power event was detected or a time at which the signal was sent. 18. The apparatus of claim 15, wherein the steps further comprise sending the signal to the one or more system components. 19. The apparatus of claim 15, wherein the power event comprises a loss of input power to the power supply and/or the input power falling below a threshold. 20. The apparatus of claim 15, wherein the steps further comprise:
determining that the power event has not ended within a ride-through time of the power supply, wherein sending the signal to the one or more RAM modules comprises sending the signal in response to the power event not ending within the ride-though time. | Powering random access memory (RAM) modules with non-volatile memory components may include providing, by a power supply, a first output voltage to one or more RAM modules, each RAM module of the one or more RAM modules comprising a volatile memory component and a non-volatile memory component; providing, by the power supply, a second output voltage to one or more system components distinct from the one or more RAM modules; detecting a power event; sending, by the power supply, in response to detecting the power event, a signal to the one or more RAM modules to initiate a save operation, wherein the save operation comprises storing, for each of the one or more RAM modules, data from the volatile memory component to the non-volatile memory component; and ceasing, by the power supply, the second output voltage while maintaining the first output voltage to facilitate the save operation.1. A method of powering random access memory (RAM) modules with non-volatile memory components, the method comprising:
providing, by a power supply, a first output voltage to one or more RAM modules, each RAM module of the one or more RAM modules comprising a volatile memory component and a non-volatile memory component; providing, by the power supply, a second output voltage to one or more system components distinct from the one or more RAM modules; detecting a power event; sending, by the power supply, in response to detecting the power event, a signal to the one or more RAM modules to initiate a save operation, wherein the save operation comprises storing, for each of the one or more RAM modules, data from the volatile memory component to the non-volatile memory component; and ceasing, by the power supply, the second output voltage while continuing to provide, by the power supply the first output voltage directly to the RAM modules to facilitate the save operation. 2. The method of claim 1, wherein ceasing the second output voltage while maintaining the first output voltage comprises ceasing the second output voltage after a predefined time period. 3. The method of claim 2, wherein the predefined time period is relative to a time at which the power event was detected or a time at which the signal was sent. 4. The method of claim 1, further comprising sending the signal to the one or more system components. 5. The method of claim 1, wherein the power event comprises a loss of input power to the power supply and/or the input power falling below a threshold. 6. The method of claim 1, further comprising:
determining that the power event has not ended within a ride-through time of the power supply, wherein sending the signal to the one or more RAM modules comprises sending the signal in response to the power event not ending within the ride-though time. 7. The method of claim 1, wherein the signal comprises an Emergency Power Off Warning signal. 8. A power supply for powering random access memory (RAM) modules with non-volatile memory components, the power supply configured to perform steps comprising:
providing a first output voltage to one or more RAM modules, each RAM module of the one or more RAM modules comprising a volatile memory component and a non-volatile memory component; providing a second output voltage to one or more system components distinct from the one or more RAM modules; detecting a power event; sending, in response to detecting the power event, a signal to the one or more RAM modules to initiate a save operation, wherein the save operation comprises storing, for each of the one or more RAM modules, data from the volatile memory component to the non-volatile memory component; and ceasing, by the power supply, the second output voltage while continuing to provide, by the power supply the first output voltage directly to the RAM modules to facilitate the save operation. 9. The power supply of claim 8, wherein ceasing the second output voltage while maintaining the first output voltage comprises ceasing the second output voltage after a predefined time period. 10. The power supply of claim 9, wherein the predefined time period is relative to a time at which the power event was detected or a time at which the signal was sent. 11. The power supply of claim 8, wherein the steps further comprise sending the signal to the one or more system components. 12. The power supply of claim 8, wherein the power event comprises one or more of a loss of input power to the power supply or the input power falling below a threshold. 13. The power supply of claim 8, wherein the steps further comprise:
determining that the power event has not ended within a ride-through time of the power supply, wherein sending the signal to the one or more RAM modules comprises sending the signal in response to the power event not ending within the ride-though time. 14. The power supply of claim 8, wherein the signal comprises an Emergency Power Off Warning signal. 15. An apparatus comprising:
a power supply for powering random access memory (RAM) modules with non-volatile memory components, the power supply configured to carry out the steps of:
providing a first output voltage to one or more RAM modules, each RAM module of the one or more RAM modules comprising a volatile memory component and a non-volatile memory component;
providing a second output voltage to one or more system components distinct from the one or more RAM modules;
detecting a power event;
sending, in response to detecting the power event, a signal to the one or more RAM modules to initiate a save operation, wherein the save operation comprises storing, for each of the one or more RAM modules, data from the volatile memory component to the non-volatile memory component; and
ceasing, by the power supply, the second output voltage while continuing to provide, by the power supply the first output voltage directly to the RAM modules to facilitate the save operation. 16. The apparatus of claim 15, wherein ceasing the second output voltage while maintaining the first output voltage comprises ceasing the second output voltage after a predefined time period. 17. The apparatus of claim 16, wherein the predefined time period is relative to a time at which the power event was detected or a time at which the signal was sent. 18. The apparatus of claim 15, wherein the steps further comprise sending the signal to the one or more system components. 19. The apparatus of claim 15, wherein the power event comprises a loss of input power to the power supply and/or the input power falling below a threshold. 20. The apparatus of claim 15, wherein the steps further comprise:
determining that the power event has not ended within a ride-through time of the power supply, wherein sending the signal to the one or more RAM modules comprises sending the signal in response to the power event not ending within the ride-though time. | 2,100 |
6,414 | 6,414 | 16,256,162 | 2,184 | This application relates to methods and apparatus for transfer of data between a host device (400) and a peripheral device (300) via a USB Type-C connector (100; 304) of the host device. A data controller is described that has a path controller (309, 310; 706) for establishing signal paths between circuitry of the host device and contacts (101) of said USB Type-C connector. The path controller is operable in at least first and second modes. In the first mode the path controller establishes separate signal paths to each of at least first, second, third and fourth contacts (A6, A7, B6, B7) of the USB Type-C connector and a plurality of the established signal paths are for transfer of analogue audio data. In the second mode the path controller establishes a pair of signal paths to only a subset of said first to fourth contacts to provide a differential digital data path. | 1. A data controller for controlling transfer of data between a host device and a peripheral device via a USB Type-C connector of the host device, the data controller comprising:
a path controller for establishing signal paths between circuitry of the host device and contacts of said USB Type-C connector, wherein the path controller is operable in at least first and second modes, wherein:
in the first mode the path controller establishes separate signal paths to each of at least first, second, third and fourth contacts of the USB Type-C connector, wherein a plurality of said signal paths are for transfer of analogue audio data; and
in the second mode the path controller establishes a pair of signal paths to only a subset of said first to fourth contacts to provide a differential digital data path. 2. A data controller as claimed in claim 1 wherein the USB Type-C connector comprises first and second rows of contacts and said first and second contacts are the central two contacts of the first row and said third and fourth contacts are the central two contacts of the second row. 3. A data controller as claim in claim 1 wherein, in the first mode the path controller additionally establishes a separate analogue signal path to one of at least a fifth or sixth contact of the USB Type-C controller. 4. A data controller as claimed in claim 1 wherein, when entering the first mode, the data controller is configured to determine a connection configuration for the peripheral device, wherein the connection configuration indicates whether each of the first to fourth contacts of the USB Type-C connector is connected to a microphone or a loudspeaker of the peripheral device, and wherein the path controller establishes the signal paths in the first mode based on the determined connection configuration. 5. A data controller as claimed in claim 4 further comprising a discovery module configured to monitor the electrical properties of at least one contact of the USB Type-C connector to determine a type of connection for that contact. 6. A data controller as claimed in claim 5 wherein the discovery module is configured, when entering the first mode, to monitor the electrical properties of at least one of said first to fourth contacts of the USB Type-C connector to determine whether the respective contact is connected to a microphone or a loudspeaker of the peripheral device. 7. A data controller as claimed in claim 5 wherein the discovery module is configured to determine which of at least two predetermined additional contacts of the USB Type-C connector is a ground contact connected to a ground return for the peripheral device and the path controller is further operable to establish a ground path to said determined ground contact. 8. A data controller as claimed in claim 5 wherein the data controller is configured to determine the connection configuration based on the type of connection determined for at least one contact of the USB Type-C connector and a plurality of predetermined possible connection configurations. 9. A data controller as claimed in in claim 1 wherein the data controller comprises a switch array connected to the USB Type-C controller, wherein the path controller controls the switch array to provide at least some of said signal paths in the first mode. 10. A data controller as claimed in claim 9 wherein said switch array comprises a first set of switches, the first set of switches being operable, in the first mode:
in a first switch state to connect the first and second contacts to first and second signal paths respectively and to connect the third and fourth contacts to third and fourth signal paths respectively; and
in a second switch state to connect the first and second contacts to the third and fourth signal paths respectively and to connect the third and fourth contacts to the first and second signal paths respectively. 11. A data controller as claimed in claim 1 wherein the path controller is operable in the first mode to enable or disable one or more audio components of a host device to establish said separate signal paths. 12. A data controller as claimed in claim 1 wherein at least one of said signal paths in the first mode is a loudspeaker signal path for transfer of analogue audio signals from an amplifier of the host device to drive a loudspeaker of the peripheral device. 13. A data controller as claimed in claim 1 wherein at least one of said signal paths in the first mode is a microphone signal path for transfer of audio signals received from a microphone of the peripheral device to audio processing circuitry in the host device. 14. A data controller as claimed in claim 13 wherein at least one microphone signal path is an analogue signal path for transfer of analogue audio signals received from a microphone of the peripheral device, a digital signal path for transfer of digital audio signals received from at least one digital microphone of the peripheral device, or a signal path for audio signals received from a noise cancellation microphone and said audio processing circuitry comprises noise cancellation circuitry. 15. A data controller as claimed in claim 1 wherein the path controller is further operable in a third mode to establish just two analogue signal paths to said first to fourth contact for transfer of left and right analogue stereo audio data to loudspeakers of a peripheral device. 16. An electronic device comprising:
a USB Type-C connector; and a data controller as claimed in claim 1. 17. An electronic device as claimed in claim 16 further comprising an audio codec wherein the path controller is configured, in the first mode, to establish said signals paths between the USB Type-C connector and the audio codec. 18. An electronic device as claimed in claim 16 further comprising a USB controller wherein the path controller is configured, in the second mode, to establish said signals paths between the USB Type-C connector and the USB controller. 19. An electronic device as claimed in claim 16 wherein the electronic device is at least one of: a portable device; a battery powered device; a communications device; a computing device; a mobile telephone; a laptop, notebook or tablet computer; a personal media player; a gaming device; and a wearable device. 20. An audio controller for controlling transfer of audio data between a host device and a peripheral device via a USB Type-C connector of the host device, the audio controller comprising:
a path control module for establishing audio signal paths between audio circuitry of the host device and contacts of said USB Type-C connector, wherein the path control module is operable in a first mode to establish separate audio signal paths to each of at least four contacts of the USB Type-C connector, wherein a plurality of said audio signal paths are for transfer of analogue audio data. 21. An audio controller as claimed in claim 20 wherein, in the first mode, the path controller is operable to establish an audio signal path to five contacts of the USB Type-C connector. 22. An audio controller as claimed in claim 20 wherein, in the first mode, the path controller is further operable to establish a ground path to a contact of the USB Type-C connector to provide a ground return for the peripheral device. 23. An audio controller as claimed in claim 22 comprising a ground discovery module configured to determine which of at least two predetermined contacts of the USB Type-C connector is connected to a ground return for the peripheral device, wherein the path control module is responsive to the ground discovery module to establish appropriate signal paths. 24. An audio controller as claimed in claim 20 comprising a transducer discovery module configured to determine whether each of a predetermined set of contacts of the USB Type-C connector is connected to a microphone or a loudspeaker of the peripheral device, wherein the path control module is responsive to the ground discovery module to establish appropriate signal paths. 25. An apparatus comprising:
a first connector having at least a first set of eight contacts positioned so as exhibit rotational symmetry in a plane about a first axis such that the first connector may, in use, mate in either of two possible orientations with a connector of a peripheral device having a corresponding set of eight contacts; and a controller for controlling the configuration of signal paths to at least the four contacts of said first set which are closest to said first axis based on a determination of loads connected to at least some of said first set of contacts when mated with a connector or a peripheral device. 26. An apparatus as claimed in claim 25 wherein said controller determines the loads connected to at least some of said first set of contacts by monitoring electrical properties detected at said contacts when mated with a connector or a peripheral device. 27. An apparatus as claimed in claim 25 when said controller determines the load connected to at least one of the four contacts of said first set which are closest to said first axis. | This application relates to methods and apparatus for transfer of data between a host device (400) and a peripheral device (300) via a USB Type-C connector (100; 304) of the host device. A data controller is described that has a path controller (309, 310; 706) for establishing signal paths between circuitry of the host device and contacts (101) of said USB Type-C connector. The path controller is operable in at least first and second modes. In the first mode the path controller establishes separate signal paths to each of at least first, second, third and fourth contacts (A6, A7, B6, B7) of the USB Type-C connector and a plurality of the established signal paths are for transfer of analogue audio data. In the second mode the path controller establishes a pair of signal paths to only a subset of said first to fourth contacts to provide a differential digital data path.1. A data controller for controlling transfer of data between a host device and a peripheral device via a USB Type-C connector of the host device, the data controller comprising:
a path controller for establishing signal paths between circuitry of the host device and contacts of said USB Type-C connector, wherein the path controller is operable in at least first and second modes, wherein:
in the first mode the path controller establishes separate signal paths to each of at least first, second, third and fourth contacts of the USB Type-C connector, wherein a plurality of said signal paths are for transfer of analogue audio data; and
in the second mode the path controller establishes a pair of signal paths to only a subset of said first to fourth contacts to provide a differential digital data path. 2. A data controller as claimed in claim 1 wherein the USB Type-C connector comprises first and second rows of contacts and said first and second contacts are the central two contacts of the first row and said third and fourth contacts are the central two contacts of the second row. 3. A data controller as claim in claim 1 wherein, in the first mode the path controller additionally establishes a separate analogue signal path to one of at least a fifth or sixth contact of the USB Type-C controller. 4. A data controller as claimed in claim 1 wherein, when entering the first mode, the data controller is configured to determine a connection configuration for the peripheral device, wherein the connection configuration indicates whether each of the first to fourth contacts of the USB Type-C connector is connected to a microphone or a loudspeaker of the peripheral device, and wherein the path controller establishes the signal paths in the first mode based on the determined connection configuration. 5. A data controller as claimed in claim 4 further comprising a discovery module configured to monitor the electrical properties of at least one contact of the USB Type-C connector to determine a type of connection for that contact. 6. A data controller as claimed in claim 5 wherein the discovery module is configured, when entering the first mode, to monitor the electrical properties of at least one of said first to fourth contacts of the USB Type-C connector to determine whether the respective contact is connected to a microphone or a loudspeaker of the peripheral device. 7. A data controller as claimed in claim 5 wherein the discovery module is configured to determine which of at least two predetermined additional contacts of the USB Type-C connector is a ground contact connected to a ground return for the peripheral device and the path controller is further operable to establish a ground path to said determined ground contact. 8. A data controller as claimed in claim 5 wherein the data controller is configured to determine the connection configuration based on the type of connection determined for at least one contact of the USB Type-C connector and a plurality of predetermined possible connection configurations. 9. A data controller as claimed in in claim 1 wherein the data controller comprises a switch array connected to the USB Type-C controller, wherein the path controller controls the switch array to provide at least some of said signal paths in the first mode. 10. A data controller as claimed in claim 9 wherein said switch array comprises a first set of switches, the first set of switches being operable, in the first mode:
in a first switch state to connect the first and second contacts to first and second signal paths respectively and to connect the third and fourth contacts to third and fourth signal paths respectively; and
in a second switch state to connect the first and second contacts to the third and fourth signal paths respectively and to connect the third and fourth contacts to the first and second signal paths respectively. 11. A data controller as claimed in claim 1 wherein the path controller is operable in the first mode to enable or disable one or more audio components of a host device to establish said separate signal paths. 12. A data controller as claimed in claim 1 wherein at least one of said signal paths in the first mode is a loudspeaker signal path for transfer of analogue audio signals from an amplifier of the host device to drive a loudspeaker of the peripheral device. 13. A data controller as claimed in claim 1 wherein at least one of said signal paths in the first mode is a microphone signal path for transfer of audio signals received from a microphone of the peripheral device to audio processing circuitry in the host device. 14. A data controller as claimed in claim 13 wherein at least one microphone signal path is an analogue signal path for transfer of analogue audio signals received from a microphone of the peripheral device, a digital signal path for transfer of digital audio signals received from at least one digital microphone of the peripheral device, or a signal path for audio signals received from a noise cancellation microphone and said audio processing circuitry comprises noise cancellation circuitry. 15. A data controller as claimed in claim 1 wherein the path controller is further operable in a third mode to establish just two analogue signal paths to said first to fourth contact for transfer of left and right analogue stereo audio data to loudspeakers of a peripheral device. 16. An electronic device comprising:
a USB Type-C connector; and a data controller as claimed in claim 1. 17. An electronic device as claimed in claim 16 further comprising an audio codec wherein the path controller is configured, in the first mode, to establish said signals paths between the USB Type-C connector and the audio codec. 18. An electronic device as claimed in claim 16 further comprising a USB controller wherein the path controller is configured, in the second mode, to establish said signals paths between the USB Type-C connector and the USB controller. 19. An electronic device as claimed in claim 16 wherein the electronic device is at least one of: a portable device; a battery powered device; a communications device; a computing device; a mobile telephone; a laptop, notebook or tablet computer; a personal media player; a gaming device; and a wearable device. 20. An audio controller for controlling transfer of audio data between a host device and a peripheral device via a USB Type-C connector of the host device, the audio controller comprising:
a path control module for establishing audio signal paths between audio circuitry of the host device and contacts of said USB Type-C connector, wherein the path control module is operable in a first mode to establish separate audio signal paths to each of at least four contacts of the USB Type-C connector, wherein a plurality of said audio signal paths are for transfer of analogue audio data. 21. An audio controller as claimed in claim 20 wherein, in the first mode, the path controller is operable to establish an audio signal path to five contacts of the USB Type-C connector. 22. An audio controller as claimed in claim 20 wherein, in the first mode, the path controller is further operable to establish a ground path to a contact of the USB Type-C connector to provide a ground return for the peripheral device. 23. An audio controller as claimed in claim 22 comprising a ground discovery module configured to determine which of at least two predetermined contacts of the USB Type-C connector is connected to a ground return for the peripheral device, wherein the path control module is responsive to the ground discovery module to establish appropriate signal paths. 24. An audio controller as claimed in claim 20 comprising a transducer discovery module configured to determine whether each of a predetermined set of contacts of the USB Type-C connector is connected to a microphone or a loudspeaker of the peripheral device, wherein the path control module is responsive to the ground discovery module to establish appropriate signal paths. 25. An apparatus comprising:
a first connector having at least a first set of eight contacts positioned so as exhibit rotational symmetry in a plane about a first axis such that the first connector may, in use, mate in either of two possible orientations with a connector of a peripheral device having a corresponding set of eight contacts; and a controller for controlling the configuration of signal paths to at least the four contacts of said first set which are closest to said first axis based on a determination of loads connected to at least some of said first set of contacts when mated with a connector or a peripheral device. 26. An apparatus as claimed in claim 25 wherein said controller determines the loads connected to at least some of said first set of contacts by monitoring electrical properties detected at said contacts when mated with a connector or a peripheral device. 27. An apparatus as claimed in claim 25 when said controller determines the load connected to at least one of the four contacts of said first set which are closest to said first axis. | 2,100 |
6,415 | 6,415 | 15,658,353 | 2,161 | Methods and systems for publishing a playlist are disclosed. A user generates or selects a playlist, which is then provided (e.g., uploaded) for publishing. A playlist identifying at least one of one or more tracks and one or more albums is received. The playlist may then be published such that the playlist is viewable by one or more individuals. A user may then purchase one or more tracks/albums identified in the playlist via an online store. | 1. A method comprising:
receiving, by an online media service from a first client device associated with a user, a first playlist identifying media items associated with the first playlist; receiving, by the online media service from the first client device, a first request to publish the first playlist at the online media service; in response to the first request to publish the first playlist at the online media service:
identifying, by the online media service, one or more respective media items at the online media service corresponding to at least one of the media items associated with the first playlist;
generating, by the online media service, a second playlist at the online media service based on the one or more respective media items corresponding to the at least one of the media items associated with the first playlist; and
publishing, by the online media service, the second playlist at the online media service for access from one or more second client devices; and
receiving, by the online media service, from at least one of the one or more second client devices, a second request to access the second playlist. 2. The method of claim 1, wherein the media items associated with the first playlist comprise local media items selected for the first playlist from the first client device. 3. The method of claim 1, wherein identifying the one or more respective media items at the online media service corresponding to the at least one of the media items associated with the first playlist comprises:
matching the media items associated with the first playlist against a collection of media items available at the online media service; and based on the matching, determining, for each media item from the media items associated with the first playlist, whether a respective media item corresponding to the media item is available at the online media service. 4. The method of claim 3, wherein determining, for each media item from the media items associated with the first playlist, whether the respective media item corresponding to the media item is available at the online media service comprises determining, for at least one media item from the media items, that the respective media item corresponding to the at least one media item is not available at the online media service. 5. The method of claim 4, further comprising:
excluding the at least one media item from the media items and the respective media item corresponding to the at least one media item from the second playlist. 6. The method of claim 5, wherein the excluding comprises:
filtering the at least one media item from the media items associated with the first playlist, to yield a filtered set of media items; and generating the second playlist based on the filtered set of media items. 7. The method of claim 6, wherein the filtered set of media items comprises the at least one of the media items associated with the first playlist comprises, and wherein the second playlist comprises the one or more respective media items and the one or more respective media items correspond to the filtered set of media items. 8. The method of claim 6, wherein the first request to publish the first playlist comprises an instruction to share the first playlist with other users. 9. The method of claim 1, further comprising:
receiving a third request to modify the second playlist; modifying the second playlist to yield a third playlist; and publishing the third playlist at the online media service for access from one or more second client devices. 10. The method of claim 9, wherein modifying the second playlist comprises at least one of removing a first media item from the second playlist or adding a second media item to the second playlist. 11. A system comprising:
one or more processors; and at least one computer-readable storage medium having stored therein instructions which, when executed by the one or more processors, cause the system to:
receive, from a first client device associated with a user, a first playlist identifying media items associated with the first playlist;
receive, from the first client device, a first request to publish the first playlist;
in response to the first request to publish the first playlist, identify one or more respective media items available at the system that correspond to at least one of the media items associated with the first playlist;
generate a second playlist based on the one or more respective media items corresponding to the at least one of the media items associated with the first playlist;
publish the second playlist for access from one or more second client devices; and
receive, from at least one of the one or more second client devices, a second request to access the second playlist. 12. The system of claim 11, wherein the media items associated with the first playlist comprise local media items selected for the first playlist from the first client device. 13. The system of claim 11, wherein identifying the one or more respective media items that correspond to the at least one of the media items associated with the first playlist comprises:
matching the media items associated with the first playlist against a collection of media items available at the system; and based on the matching, determining, for each media item from the media items associated with the first playlist, whether a respective media item corresponding to the media item is available at the system. 14. The system of claim 13, wherein determining, for each media item from the media items associated with the first playlist, whether the respective media item corresponding to the media item is available at the online media service comprises:
determining, for at least one media item from the media items, that the respective media item corresponding to the at least one media item is not available at the system. 15. The system of claim 14, the at least one computer-readable storage medium storing additional instructions which, when executed by the one or more processors, cause the system to:
filter the at least one media item from the media items to yield a filtered set of media items, wherein the second playlist comprises the respective media item corresponding to each of the filtered set of media items and excludes a media item corresponding to the at least one media item filtered from the media items. 16. The system of claim 15, wherein the first request to publish the first playlist comprises an instruction to share the first playlist with other users, and wherein the at least one computer-readable storage medium stores additional instructions which, when executed by the one or more processors, cause the system to:
receive a third request to modify the second playlist; modify the second playlist to yield a third playlist; and publish the third playlist for access from one or more second client devices. 17. A non-transitory computer-readable storage medium comprising:
instructions stored thereon which, when executed by one or more processors, cause an online media service to:
receive, from a first client device associated with a user, a first playlist identifying media items associated with the first playlist;
receive, from the first client device, a first request to publish the first playlist at the online media service;
in response to the first request to publish the first playlist, identify one or more respective media items at the online media service that correspond to at least one of the media items associated with the first playlist;
generate a second playlist based on the one or more respective media items corresponding to the at least one of the media items associated with the first playlist;
publish the second playlist at the online media service for access from one or more second client devices; and
receive, from at least one of the one or more second client devices, a second request to access the second playlist. 18. The non-transitory computer-readable storage medium of claim 17, wherein the media items associated with the first playlist comprise local media items selected for the first playlist from the first client device, and wherein identifying the one or more respective media items that correspond to the at least one of the media items associated with the first playlist comprises:
matching the media items associated with the first playlist against a collection of media items available at the online media service; and based on the matching, determining, for each media item from the media items associated with the first playlist, whether a respective media item corresponding to the media item is available at the system. 19. The non-transitory computer-readable storage medium of claim 17, wherein determining whether the respective media item corresponding to the media item is available at the online media service comprises determining, for at least one media item from the media items, that the respective media item corresponding to the at least one media item is not available at the online media service, and wherein generating the second playlist comprises filtering the at least one media item from the media items to yield a filtered set of media items and excluding the at least one media item from the second playlist. 20. The non-transitory computer-readable storage medium of claim 17, storing additional instructions which, when executed by the one or more processors, cause the online media service to:
receive a third request to modify the second playlist; modify the second playlist to yield a third playlist; and publish the third playlist for access from one or more second client devices. 21. The method of claim 1, wherein the second playlist identifies a plurality of respective media items at the online media service, the plurality of respective media items corresponding to the media items identified by the first playlist. 22. The system of claim 11, wherein the second playlist identifies a plurality of respective media items at the online media service, the plurality of respective media items corresponding to the media items identified by the first playlist. 23. The non-transitory computer-readable storage medium of claim 17, wherein the second playlist identifies a plurality of respective media items at the online media service, the plurality of respective media items corresponding to the media items identified by the first playlist. | Methods and systems for publishing a playlist are disclosed. A user generates or selects a playlist, which is then provided (e.g., uploaded) for publishing. A playlist identifying at least one of one or more tracks and one or more albums is received. The playlist may then be published such that the playlist is viewable by one or more individuals. A user may then purchase one or more tracks/albums identified in the playlist via an online store.1. A method comprising:
receiving, by an online media service from a first client device associated with a user, a first playlist identifying media items associated with the first playlist; receiving, by the online media service from the first client device, a first request to publish the first playlist at the online media service; in response to the first request to publish the first playlist at the online media service:
identifying, by the online media service, one or more respective media items at the online media service corresponding to at least one of the media items associated with the first playlist;
generating, by the online media service, a second playlist at the online media service based on the one or more respective media items corresponding to the at least one of the media items associated with the first playlist; and
publishing, by the online media service, the second playlist at the online media service for access from one or more second client devices; and
receiving, by the online media service, from at least one of the one or more second client devices, a second request to access the second playlist. 2. The method of claim 1, wherein the media items associated with the first playlist comprise local media items selected for the first playlist from the first client device. 3. The method of claim 1, wherein identifying the one or more respective media items at the online media service corresponding to the at least one of the media items associated with the first playlist comprises:
matching the media items associated with the first playlist against a collection of media items available at the online media service; and based on the matching, determining, for each media item from the media items associated with the first playlist, whether a respective media item corresponding to the media item is available at the online media service. 4. The method of claim 3, wherein determining, for each media item from the media items associated with the first playlist, whether the respective media item corresponding to the media item is available at the online media service comprises determining, for at least one media item from the media items, that the respective media item corresponding to the at least one media item is not available at the online media service. 5. The method of claim 4, further comprising:
excluding the at least one media item from the media items and the respective media item corresponding to the at least one media item from the second playlist. 6. The method of claim 5, wherein the excluding comprises:
filtering the at least one media item from the media items associated with the first playlist, to yield a filtered set of media items; and generating the second playlist based on the filtered set of media items. 7. The method of claim 6, wherein the filtered set of media items comprises the at least one of the media items associated with the first playlist comprises, and wherein the second playlist comprises the one or more respective media items and the one or more respective media items correspond to the filtered set of media items. 8. The method of claim 6, wherein the first request to publish the first playlist comprises an instruction to share the first playlist with other users. 9. The method of claim 1, further comprising:
receiving a third request to modify the second playlist; modifying the second playlist to yield a third playlist; and publishing the third playlist at the online media service for access from one or more second client devices. 10. The method of claim 9, wherein modifying the second playlist comprises at least one of removing a first media item from the second playlist or adding a second media item to the second playlist. 11. A system comprising:
one or more processors; and at least one computer-readable storage medium having stored therein instructions which, when executed by the one or more processors, cause the system to:
receive, from a first client device associated with a user, a first playlist identifying media items associated with the first playlist;
receive, from the first client device, a first request to publish the first playlist;
in response to the first request to publish the first playlist, identify one or more respective media items available at the system that correspond to at least one of the media items associated with the first playlist;
generate a second playlist based on the one or more respective media items corresponding to the at least one of the media items associated with the first playlist;
publish the second playlist for access from one or more second client devices; and
receive, from at least one of the one or more second client devices, a second request to access the second playlist. 12. The system of claim 11, wherein the media items associated with the first playlist comprise local media items selected for the first playlist from the first client device. 13. The system of claim 11, wherein identifying the one or more respective media items that correspond to the at least one of the media items associated with the first playlist comprises:
matching the media items associated with the first playlist against a collection of media items available at the system; and based on the matching, determining, for each media item from the media items associated with the first playlist, whether a respective media item corresponding to the media item is available at the system. 14. The system of claim 13, wherein determining, for each media item from the media items associated with the first playlist, whether the respective media item corresponding to the media item is available at the online media service comprises:
determining, for at least one media item from the media items, that the respective media item corresponding to the at least one media item is not available at the system. 15. The system of claim 14, the at least one computer-readable storage medium storing additional instructions which, when executed by the one or more processors, cause the system to:
filter the at least one media item from the media items to yield a filtered set of media items, wherein the second playlist comprises the respective media item corresponding to each of the filtered set of media items and excludes a media item corresponding to the at least one media item filtered from the media items. 16. The system of claim 15, wherein the first request to publish the first playlist comprises an instruction to share the first playlist with other users, and wherein the at least one computer-readable storage medium stores additional instructions which, when executed by the one or more processors, cause the system to:
receive a third request to modify the second playlist; modify the second playlist to yield a third playlist; and publish the third playlist for access from one or more second client devices. 17. A non-transitory computer-readable storage medium comprising:
instructions stored thereon which, when executed by one or more processors, cause an online media service to:
receive, from a first client device associated with a user, a first playlist identifying media items associated with the first playlist;
receive, from the first client device, a first request to publish the first playlist at the online media service;
in response to the first request to publish the first playlist, identify one or more respective media items at the online media service that correspond to at least one of the media items associated with the first playlist;
generate a second playlist based on the one or more respective media items corresponding to the at least one of the media items associated with the first playlist;
publish the second playlist at the online media service for access from one or more second client devices; and
receive, from at least one of the one or more second client devices, a second request to access the second playlist. 18. The non-transitory computer-readable storage medium of claim 17, wherein the media items associated with the first playlist comprise local media items selected for the first playlist from the first client device, and wherein identifying the one or more respective media items that correspond to the at least one of the media items associated with the first playlist comprises:
matching the media items associated with the first playlist against a collection of media items available at the online media service; and based on the matching, determining, for each media item from the media items associated with the first playlist, whether a respective media item corresponding to the media item is available at the system. 19. The non-transitory computer-readable storage medium of claim 17, wherein determining whether the respective media item corresponding to the media item is available at the online media service comprises determining, for at least one media item from the media items, that the respective media item corresponding to the at least one media item is not available at the online media service, and wherein generating the second playlist comprises filtering the at least one media item from the media items to yield a filtered set of media items and excluding the at least one media item from the second playlist. 20. The non-transitory computer-readable storage medium of claim 17, storing additional instructions which, when executed by the one or more processors, cause the online media service to:
receive a third request to modify the second playlist; modify the second playlist to yield a third playlist; and publish the third playlist for access from one or more second client devices. 21. The method of claim 1, wherein the second playlist identifies a plurality of respective media items at the online media service, the plurality of respective media items corresponding to the media items identified by the first playlist. 22. The system of claim 11, wherein the second playlist identifies a plurality of respective media items at the online media service, the plurality of respective media items corresponding to the media items identified by the first playlist. 23. The non-transitory computer-readable storage medium of claim 17, wherein the second playlist identifies a plurality of respective media items at the online media service, the plurality of respective media items corresponding to the media items identified by the first playlist. | 2,100 |
6,416 | 6,416 | 14,674,833 | 2,178 | A device configured to retrieve a markup language file; identify, based on the markup language file, an item to be used on a web page, address information for retrieving an object for the item, and an object type corresponding to the object; retrieve an evaluation result for the object; identify the object as an object to be used based on the evaluation result; identify address information of the object; and retrieve and execute the object based on the address information. | 1. A device comprising:
circuitry configured to
retrieve a markup language file;
identify, based on the markup language file, an item to be used on a web page, address information for retrieving an object for the item, and an object type corresponding to the object;
retrieve an evaluation result for the object;
identify the object as an object to be used based on the evaluation result;
identify address information of the object; and
retrieve and execute the object based on the address information. 2. The device of claim 1, wherein
the evaluation result is determined based on information indicating a software configuration of a device on which the web page is displayed. 3. The device of claim 1, wherein
the evaluation result is determined based on information indicating a hardware configuration of a device on which the web page is displayed. 4. The device of claim 1, wherein
the evaluation result is determined based on information indicating a communication environment of a device on which the web page is displayed. 5. The device of claim 1, wherein
the evaluation result is determined based on information indicating a device type of a device on which the web page is displayed. 6. The device of claim 1, wherein
the circuitry is configured to identify, based on the markup language file, a plurality of objects corresponding to the item to be used on the web page. 7. The device of claim 6, wherein the circuitry is configured to:
retrieve an evaluation result for each of the plurality of objects; and identify the object having a highest evaluation result as the object to be used. 8. A non-transitory computer-readable storage medium including a program, which when executed by a device, causes the device to:
retrieve a markup language file; identify, based on the markup language file, an item to be used on a web page, address information for retrieving a an object for the item, and an object type corresponding to the object; retrieve an evaluation result for the object; identify the object to be used based on the evaluation result; and retrieve and execute the object based on the address information. 9. The non-transitory computer-readable storage medium of claim 8, wherein
the evaluation result is determined based on information indicating a software configuration of a device on which the web page is displayed. 10. The non-transitory computer-readable storage medium of claim 8, wherein
the evaluation result is determined based on information indicating a hardware configuration of a device on which the web page is displayed. 11. The non-transitory computer-readable storage medium of claim 8, wherein
the evaluation result is determined based on information indicating a communication environment of a device on which the web page is displayed. 12. The non-transitory computer-readable storage medium of claim 8, wherein
the evaluation result is determined based in information indicating a device type of a device on which the web page is displayed. 13. The non-transitory computer-readable storage medium of claim 8, wherein the program further causes the device to:
identify, based on the markup language file, a plurality of objects corresponding to the item to be used on the web page. 14. The non-transitory computer-readable storage medium of claim 13, wherein the program further causes the device to:
retrieve an evaluation result for each of the plurality of objects; and identify the object having a highest evaluation result as the object to be used. 15. A method comprising:
retrieving a markup language file; identifying, based on the markup language file, an item to be used on a web page, address information for retrieving an object for the item, and an object type corresponding to the object; retrieving an evaluation result for the object; identifying the object as an object to be used based on the evaluation result; identifying address information of the object; and retrieving and executing the object based on the address information. 16. The method of claim 15, wherein
the evaluation result is determined based on information indicating a software configuration of a device on which the web page is displayed. 17. The method of claim 15, wherein
the evaluation result is determined based on information indicating a hardware configuration of a device on which the web page is displayed. 18. The method of claim 15, wherein
the evaluation result is determined based on information indicating a communication environment of a device on which the web page is displayed. 19. The method of claim 15, wherein
the evaluation result is determined based on information indicating a device type of a device on which the web page is displayed. 20. The method of claim 15, further comprising:
identifying, based on the markup language file, a plurality of objects corresponding to the item to be used on the web page; retrieving an evaluation result for each of the plurality of objects; and identifying the object having a highest evaluation result as the object to be used. | A device configured to retrieve a markup language file; identify, based on the markup language file, an item to be used on a web page, address information for retrieving an object for the item, and an object type corresponding to the object; retrieve an evaluation result for the object; identify the object as an object to be used based on the evaluation result; identify address information of the object; and retrieve and execute the object based on the address information.1. A device comprising:
circuitry configured to
retrieve a markup language file;
identify, based on the markup language file, an item to be used on a web page, address information for retrieving an object for the item, and an object type corresponding to the object;
retrieve an evaluation result for the object;
identify the object as an object to be used based on the evaluation result;
identify address information of the object; and
retrieve and execute the object based on the address information. 2. The device of claim 1, wherein
the evaluation result is determined based on information indicating a software configuration of a device on which the web page is displayed. 3. The device of claim 1, wherein
the evaluation result is determined based on information indicating a hardware configuration of a device on which the web page is displayed. 4. The device of claim 1, wherein
the evaluation result is determined based on information indicating a communication environment of a device on which the web page is displayed. 5. The device of claim 1, wherein
the evaluation result is determined based on information indicating a device type of a device on which the web page is displayed. 6. The device of claim 1, wherein
the circuitry is configured to identify, based on the markup language file, a plurality of objects corresponding to the item to be used on the web page. 7. The device of claim 6, wherein the circuitry is configured to:
retrieve an evaluation result for each of the plurality of objects; and identify the object having a highest evaluation result as the object to be used. 8. A non-transitory computer-readable storage medium including a program, which when executed by a device, causes the device to:
retrieve a markup language file; identify, based on the markup language file, an item to be used on a web page, address information for retrieving a an object for the item, and an object type corresponding to the object; retrieve an evaluation result for the object; identify the object to be used based on the evaluation result; and retrieve and execute the object based on the address information. 9. The non-transitory computer-readable storage medium of claim 8, wherein
the evaluation result is determined based on information indicating a software configuration of a device on which the web page is displayed. 10. The non-transitory computer-readable storage medium of claim 8, wherein
the evaluation result is determined based on information indicating a hardware configuration of a device on which the web page is displayed. 11. The non-transitory computer-readable storage medium of claim 8, wherein
the evaluation result is determined based on information indicating a communication environment of a device on which the web page is displayed. 12. The non-transitory computer-readable storage medium of claim 8, wherein
the evaluation result is determined based in information indicating a device type of a device on which the web page is displayed. 13. The non-transitory computer-readable storage medium of claim 8, wherein the program further causes the device to:
identify, based on the markup language file, a plurality of objects corresponding to the item to be used on the web page. 14. The non-transitory computer-readable storage medium of claim 13, wherein the program further causes the device to:
retrieve an evaluation result for each of the plurality of objects; and identify the object having a highest evaluation result as the object to be used. 15. A method comprising:
retrieving a markup language file; identifying, based on the markup language file, an item to be used on a web page, address information for retrieving an object for the item, and an object type corresponding to the object; retrieving an evaluation result for the object; identifying the object as an object to be used based on the evaluation result; identifying address information of the object; and retrieving and executing the object based on the address information. 16. The method of claim 15, wherein
the evaluation result is determined based on information indicating a software configuration of a device on which the web page is displayed. 17. The method of claim 15, wherein
the evaluation result is determined based on information indicating a hardware configuration of a device on which the web page is displayed. 18. The method of claim 15, wherein
the evaluation result is determined based on information indicating a communication environment of a device on which the web page is displayed. 19. The method of claim 15, wherein
the evaluation result is determined based on information indicating a device type of a device on which the web page is displayed. 20. The method of claim 15, further comprising:
identifying, based on the markup language file, a plurality of objects corresponding to the item to be used on the web page; retrieving an evaluation result for each of the plurality of objects; and identifying the object having a highest evaluation result as the object to be used. | 2,100 |
6,417 | 6,417 | 16,252,595 | 2,194 | In one embodiment of the present invention, a position-based dynamics (PBD) framework provides realistic modeling and simulation for elastic rods. In particular, the twisting and bending physics of elastic rods is incorporated into the PBD framework. In operation, an elastic rod model generator represents the center line of an elastic rod as a polyline of points connected via edges. For each edge, the elastic rod model generator adds a ghost point to define the orientation of a material frame that encodes the twist of the edge. Subsequently, a PBD simulator solves for positions of both points and ghost points that, together, represent the evolving position and torsion of the elastic rod. Advantageously, the ghost points enable more realistic animation of deformable objects (e.g., curly hair) than conventional PBD frameworks. Further, unlike force based methods, elastic rod simulation in the PBD framework performs acceptably in environments where speed is critical. | 1. A computer-implemented method for simulating an elastic rod in a graphics application, the method comprising:
generating a polyline that represents a center line associated with the elastic rod as a series of edges and points; for each edge of the polyline, associating a ghost point with the edge, wherein the ghost point has coordinates that define an orientation of a material frame that encodes a torsion associated with the edge; and computing new positions of the points and the ghost points after a time interval. 2. The computer-implemented method of claim 1, wherein, for a first edge of the polyline, associating the ghost point comprises:
defining a first perpendicular bisector of the first edge, wherein the first perpendicular bisector represents the first edge in a rest state; computing a second perpendicular bisector of the first edge at an angle to the first perpendicular bisector, wherein the angle corresponds to the torsion of the first edge; and setting the coordinates of the ghost point to specify the angle. 3. The computer-implemented method of claim 2, wherein, for a first edge of the polyline, adding the ghost point further comprises setting the distance of the ghost point from the first edge based on the strength of a bending resistance associated with a portion of the elastic rod corresponding to the first edge. 4. The computer-implemented method of claim 2, wherein establishing the second perpendicular bisector comprises computing a cross-product to determine a bisector. 5. The computer-implemented method of claim 1, wherein computing comprises:
determining a first material frame based on a first ghost point and a second material frame based on a second ghost point, wherein both the first material frame and the second material frames track the motion of the center line over time; and determining a bending and twisting energy from a rate-of-change of the position of the center line between the first material frame and the second material frame. 6. The computer-implemented method of claim 5, wherein determining the bending and twisting energy comprises:
calculating a Darboux vector by performing an interpolation between the first material frame and the second material frame; and applying the Darboux vector to determine the bending and twisting energy without computing any trigonometric functions. 7. The computer-implemented method of claim 1, wherein computing the new positions comprises applying one or more constraints in a bi-directional interleaving order. 8. The computer-implemented method of claim 7, wherein the one or more constraints include a coupling between a first point and one of a frame, a triangle, and a rigid body. 9. The computer-implemented method of claim 8, further comprising generating a three-dimensional model for three-dimensional printing based on the new positions of the points of the polyline. 10. One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:
generating a polyline that represents a center line associated with an elastic rod; for each edge of the polyline, associating a ghost point with the edge, wherein the ghost point has coordinates that define an orientation of a material frame that encodes a torsion associated with the edge; and computing new positions of the ghost points and at least one point of the polyline after a time interval. 11. The one or more non-transitory computer-readable storage media of claim 10, wherein, for a first edge of the polyline, associating the ghost point comprises:
defining a first perpendicular bisector of the first edge, wherein the first perpendicular bisector represents the first edge in a rest state; computing a second perpendicular bisector of the first edge at an angle to the first perpendicular bisector, wherein the angle corresponds to the torsion of the first edge; and setting the coordinates of the ghost point to specify the angle. 12. The one or more non-transitory computer-readable storage media of claim 11, wherein, for a first edge of the polyline, adding the ghost point further comprises setting the distance of the ghost point from the first edge based on the strength of a bending resistance associated with a portion of the elastic rod corresponding to the first edge. 13. The one or more non-transitory computer-readable storage media of claim 11, wherein establishing the second perpendicular bisector comprises computing a cross-product to determine a bisector. 14. The one or more non-transitory computer-readable storage media of claim 10, wherein computing comprises:
determining a first material frame based on a first ghost point and a second material frame based on a second ghost point, wherein both the first material frame and the second material frames track the motion of the center line over time; and determining a bending and twisting energy from a rate-of-change of the position of the center line between the first material frame and the second material frame. 15. The one or more non-transitory computer-readable storage media of claim 14, wherein determining the bending and twisting energy comprises:
calculating a Darboux vector by performing an interpolation between the first material frame and the second material frame; and applying the Darboux vector to determine the bending and twisting energy without computing any trigonometric functions. 16. The one or more non-transitory computer-readable storage media of claim 10, wherein computing the new positions comprises applying one or more constraints in a bi-directional interleaving order. 17. The one or more non-transitory computer-readable storage media of claim 16, wherein the one or more constraints include a coupling between a first point and one of a frame, a triangle, and a rigid body. 18. The one or more non-transitory computer-readable storage media of claim 17, further comprising generating a three-dimensional model for three-dimensional printing based on the new position of the at least one point of the polyline. 19. A system, comprising:
one or more memories storing instructions; one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to:
generate a polyline that represents a center line associated with an elastic rod;
for each edge of the polyline connecting at least two of the points of the polyline, associate a ghost point with the edge, wherein the ghost point has coordinates that define an orientation of a material frame that encodes a torsion associated with the edge; and
compute new positions of the ghost points after a time interval; and
a display device coupled to the one or more processors and configured to display the elastic rod. | In one embodiment of the present invention, a position-based dynamics (PBD) framework provides realistic modeling and simulation for elastic rods. In particular, the twisting and bending physics of elastic rods is incorporated into the PBD framework. In operation, an elastic rod model generator represents the center line of an elastic rod as a polyline of points connected via edges. For each edge, the elastic rod model generator adds a ghost point to define the orientation of a material frame that encodes the twist of the edge. Subsequently, a PBD simulator solves for positions of both points and ghost points that, together, represent the evolving position and torsion of the elastic rod. Advantageously, the ghost points enable more realistic animation of deformable objects (e.g., curly hair) than conventional PBD frameworks. Further, unlike force based methods, elastic rod simulation in the PBD framework performs acceptably in environments where speed is critical.1. A computer-implemented method for simulating an elastic rod in a graphics application, the method comprising:
generating a polyline that represents a center line associated with the elastic rod as a series of edges and points; for each edge of the polyline, associating a ghost point with the edge, wherein the ghost point has coordinates that define an orientation of a material frame that encodes a torsion associated with the edge; and computing new positions of the points and the ghost points after a time interval. 2. The computer-implemented method of claim 1, wherein, for a first edge of the polyline, associating the ghost point comprises:
defining a first perpendicular bisector of the first edge, wherein the first perpendicular bisector represents the first edge in a rest state; computing a second perpendicular bisector of the first edge at an angle to the first perpendicular bisector, wherein the angle corresponds to the torsion of the first edge; and setting the coordinates of the ghost point to specify the angle. 3. The computer-implemented method of claim 2, wherein, for a first edge of the polyline, adding the ghost point further comprises setting the distance of the ghost point from the first edge based on the strength of a bending resistance associated with a portion of the elastic rod corresponding to the first edge. 4. The computer-implemented method of claim 2, wherein establishing the second perpendicular bisector comprises computing a cross-product to determine a bisector. 5. The computer-implemented method of claim 1, wherein computing comprises:
determining a first material frame based on a first ghost point and a second material frame based on a second ghost point, wherein both the first material frame and the second material frames track the motion of the center line over time; and determining a bending and twisting energy from a rate-of-change of the position of the center line between the first material frame and the second material frame. 6. The computer-implemented method of claim 5, wherein determining the bending and twisting energy comprises:
calculating a Darboux vector by performing an interpolation between the first material frame and the second material frame; and applying the Darboux vector to determine the bending and twisting energy without computing any trigonometric functions. 7. The computer-implemented method of claim 1, wherein computing the new positions comprises applying one or more constraints in a bi-directional interleaving order. 8. The computer-implemented method of claim 7, wherein the one or more constraints include a coupling between a first point and one of a frame, a triangle, and a rigid body. 9. The computer-implemented method of claim 8, further comprising generating a three-dimensional model for three-dimensional printing based on the new positions of the points of the polyline. 10. One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:
generating a polyline that represents a center line associated with an elastic rod; for each edge of the polyline, associating a ghost point with the edge, wherein the ghost point has coordinates that define an orientation of a material frame that encodes a torsion associated with the edge; and computing new positions of the ghost points and at least one point of the polyline after a time interval. 11. The one or more non-transitory computer-readable storage media of claim 10, wherein, for a first edge of the polyline, associating the ghost point comprises:
defining a first perpendicular bisector of the first edge, wherein the first perpendicular bisector represents the first edge in a rest state; computing a second perpendicular bisector of the first edge at an angle to the first perpendicular bisector, wherein the angle corresponds to the torsion of the first edge; and setting the coordinates of the ghost point to specify the angle. 12. The one or more non-transitory computer-readable storage media of claim 11, wherein, for a first edge of the polyline, adding the ghost point further comprises setting the distance of the ghost point from the first edge based on the strength of a bending resistance associated with a portion of the elastic rod corresponding to the first edge. 13. The one or more non-transitory computer-readable storage media of claim 11, wherein establishing the second perpendicular bisector comprises computing a cross-product to determine a bisector. 14. The one or more non-transitory computer-readable storage media of claim 10, wherein computing comprises:
determining a first material frame based on a first ghost point and a second material frame based on a second ghost point, wherein both the first material frame and the second material frames track the motion of the center line over time; and determining a bending and twisting energy from a rate-of-change of the position of the center line between the first material frame and the second material frame. 15. The one or more non-transitory computer-readable storage media of claim 14, wherein determining the bending and twisting energy comprises:
calculating a Darboux vector by performing an interpolation between the first material frame and the second material frame; and applying the Darboux vector to determine the bending and twisting energy without computing any trigonometric functions. 16. The one or more non-transitory computer-readable storage media of claim 10, wherein computing the new positions comprises applying one or more constraints in a bi-directional interleaving order. 17. The one or more non-transitory computer-readable storage media of claim 16, wherein the one or more constraints include a coupling between a first point and one of a frame, a triangle, and a rigid body. 18. The one or more non-transitory computer-readable storage media of claim 17, further comprising generating a three-dimensional model for three-dimensional printing based on the new position of the at least one point of the polyline. 19. A system, comprising:
one or more memories storing instructions; one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to:
generate a polyline that represents a center line associated with an elastic rod;
for each edge of the polyline connecting at least two of the points of the polyline, associate a ghost point with the edge, wherein the ghost point has coordinates that define an orientation of a material frame that encodes a torsion associated with the edge; and
compute new positions of the ghost points after a time interval; and
a display device coupled to the one or more processors and configured to display the elastic rod. | 2,100 |
6,418 | 6,418 | 14,014,276 | 2,119 | Embodiments of the present invention include control methods employed in multiphase distributed energy storage systems that are located behind utility meters typically located at, but not limited to, medium and large commercial and industrial locations. Current solutions for these types of electric load locations entail multiple discrete energy storage systems, where if any piece of an energy storage system is damaged, the ability of the complete power control strategy at the entire electric load location is at risk of becoming inoperable. Some embodiments of the invention include hardware and methods for dynamically reconfiguring networks of distributed energy storage systems that are able to provide automatic site layout discovery using a formed auto-discovering network formed at an electric load location. | 1. A system for managing power delivery, comprising:
a first storage device that is in electric communication with an electric power line, wherein the first storage device comprises a first energy source; a second storage device that is in electric communication with the electric power line, wherein the second storage device comprises a second energy source; a power monitor controller that is in communication with the first storage device and the second storage device, and is configured to receive information regarding the configuration of the first and second storage devices; and a first system controller that is configured to use the received information to control a transfer of energy between the electric power line and the first energy source. 2. The system of claim 1, wherein the first storage device further comprises a first bidirectional power converter, and the second storage device further comprises a second bidirectional power converter. 3. The system of claim 1, wherein the first and second energy sources each comprises one or more batteries. 4. The system of claim 1, further comprising:
a power monitor comprising the power monitor controller and a first device that is configured to measure the power transferred through the electric power line. 5. The system of claim 1, further comprising:
an optimization engine configured to receive one or more external inputs and create a set of operating parameters based on the one or more external inputs, wherein the first system controller is further configured to receive the created operating parameters and use the operating parameters to control an amount of energy flowing through a first phase of the electric power line below a threshold value. 6. The system of claim 5, wherein the set of operating parameters are derived from simulations of energy use at the electric load location, and the simulations are performed by the optimization engine. 7. The system of claim 5, wherein the one or more external inputs are selected from a group consisting of weather information, sunrise and sunset information, power usage cost information, utility's billing period, geographic location, local solar production, local incident light, customer type, electric load location building specifications, grid operator data and time data. 8. The system of claim 5, wherein the first system controller comprises a set-point controller configured to vary the threshold value. 9. The system of claim 1, further comprising:
a power monitor comprising the power monitor controller and a first device that is configured to measure the power transferred through the electric power line; an optimization engine configured to receive one or more external inputs and create one or more operating control curves based on the one or more external inputs, wherein the first system controller is further configured to
receive the one or more operating control curves,
compare the one or more operating control curves to information received from the power monitor and the first energy source, and
control a transfer of energy from or to the electric power line from the first energy source based on the comparison. 10. A system for managing power delivery, comprising:
a first storage device that is in electric communication with the multiphase electric power line, wherein the first storage device comprises a first energy source and a first system controller; a second storage device that is in electric communication with the multiphase electric power line, wherein the second storage device comprises a second energy source and a second system controller; and a power monitor controller that is in communication with the first storage device and the second storage device, and is configured to receive information regarding the configuration of the first and the second storage devices, wherein the first system controller is configured to control a transfer of energy between the multiphase electric power line and the first storage device, and the amount of energy transferred is a least partially based on the received information. 11. The system of claim 10, wherein the first and second energy sources each comprises one or more batteries. 12. The system of claim 10, further comprising:
a power monitor comprising the power monitor controller and a first device that is configured to measure the power transferred through the multiphase electric power line. 13. The system of claim 10, further comprising:
an optimization engine configured to receive one or more external inputs and create a set of operating parameters based on the one or more external inputs, wherein the first system controller is further configured to receive the created operating parameters and use the operating parameters to control an amount of energy flowing through a first phase of the electric power line below a threshold value, and wherein the second system controller is configured to receive the created operating parameters and use the operating parameters to control an amount of energy flowing through a second phase of the electric power line below a threshold value. 14. The system of claim 13, wherein the one or more external inputs are selected from a group consisting of weather information, sunrise and sunset information, power usage cost information, utility's billing period, geographic location, local solar production, local incident light, customer type, electric load location building specifications, grid operator data and time data. 15. The system of claim 10, wherein the first system controller comprises a set-point controller configured to vary the threshold value. 16. A method of managing power at an electric load location, comprising:
transferring telemetry data from a first storage device to a power monitor that is coupled to an electric power line after the first storage device is coupled to the electric power line, wherein the first storage device comprises a first energy source and a first system controller; storing the telemetry data in memory; and controlling a transfer of power between the first storage device and the electric power line using information derived from the telemetry data. 17. The method of claim 16, wherein the telemetry data transferred from the first storage device includes information selected from a group consisting of serial number, battery health and phase(s) to which the first storage device is coupled. 18. The method of claim 16, further comprising:
transferring telemetry data from a second storage device to the power monitor after the second storage device is coupled to the electric power line, wherein the first storage device comprises a first energy source and a first system controller; and controlling a transfer of power between the second storage device and the electric power line using information derived from the telemetry data received from the first storage device or the second storage device. 19. The method of claim 18, wherein the telemetry data transferred from the first or second storage devices includes information selected from a group consisting of serial number, battery health and phase(s) to which the first or the second storage device is coupled. 20. The method of claim 16, wherein the first energy source comprises a battery that is coupled to the electric power line through a bidirectional converter. | Embodiments of the present invention include control methods employed in multiphase distributed energy storage systems that are located behind utility meters typically located at, but not limited to, medium and large commercial and industrial locations. Current solutions for these types of electric load locations entail multiple discrete energy storage systems, where if any piece of an energy storage system is damaged, the ability of the complete power control strategy at the entire electric load location is at risk of becoming inoperable. Some embodiments of the invention include hardware and methods for dynamically reconfiguring networks of distributed energy storage systems that are able to provide automatic site layout discovery using a formed auto-discovering network formed at an electric load location.1. A system for managing power delivery, comprising:
a first storage device that is in electric communication with an electric power line, wherein the first storage device comprises a first energy source; a second storage device that is in electric communication with the electric power line, wherein the second storage device comprises a second energy source; a power monitor controller that is in communication with the first storage device and the second storage device, and is configured to receive information regarding the configuration of the first and second storage devices; and a first system controller that is configured to use the received information to control a transfer of energy between the electric power line and the first energy source. 2. The system of claim 1, wherein the first storage device further comprises a first bidirectional power converter, and the second storage device further comprises a second bidirectional power converter. 3. The system of claim 1, wherein the first and second energy sources each comprises one or more batteries. 4. The system of claim 1, further comprising:
a power monitor comprising the power monitor controller and a first device that is configured to measure the power transferred through the electric power line. 5. The system of claim 1, further comprising:
an optimization engine configured to receive one or more external inputs and create a set of operating parameters based on the one or more external inputs, wherein the first system controller is further configured to receive the created operating parameters and use the operating parameters to control an amount of energy flowing through a first phase of the electric power line below a threshold value. 6. The system of claim 5, wherein the set of operating parameters are derived from simulations of energy use at the electric load location, and the simulations are performed by the optimization engine. 7. The system of claim 5, wherein the one or more external inputs are selected from a group consisting of weather information, sunrise and sunset information, power usage cost information, utility's billing period, geographic location, local solar production, local incident light, customer type, electric load location building specifications, grid operator data and time data. 8. The system of claim 5, wherein the first system controller comprises a set-point controller configured to vary the threshold value. 9. The system of claim 1, further comprising:
a power monitor comprising the power monitor controller and a first device that is configured to measure the power transferred through the electric power line; an optimization engine configured to receive one or more external inputs and create one or more operating control curves based on the one or more external inputs, wherein the first system controller is further configured to
receive the one or more operating control curves,
compare the one or more operating control curves to information received from the power monitor and the first energy source, and
control a transfer of energy from or to the electric power line from the first energy source based on the comparison. 10. A system for managing power delivery, comprising:
a first storage device that is in electric communication with the multiphase electric power line, wherein the first storage device comprises a first energy source and a first system controller; a second storage device that is in electric communication with the multiphase electric power line, wherein the second storage device comprises a second energy source and a second system controller; and a power monitor controller that is in communication with the first storage device and the second storage device, and is configured to receive information regarding the configuration of the first and the second storage devices, wherein the first system controller is configured to control a transfer of energy between the multiphase electric power line and the first storage device, and the amount of energy transferred is a least partially based on the received information. 11. The system of claim 10, wherein the first and second energy sources each comprises one or more batteries. 12. The system of claim 10, further comprising:
a power monitor comprising the power monitor controller and a first device that is configured to measure the power transferred through the multiphase electric power line. 13. The system of claim 10, further comprising:
an optimization engine configured to receive one or more external inputs and create a set of operating parameters based on the one or more external inputs, wherein the first system controller is further configured to receive the created operating parameters and use the operating parameters to control an amount of energy flowing through a first phase of the electric power line below a threshold value, and wherein the second system controller is configured to receive the created operating parameters and use the operating parameters to control an amount of energy flowing through a second phase of the electric power line below a threshold value. 14. The system of claim 13, wherein the one or more external inputs are selected from a group consisting of weather information, sunrise and sunset information, power usage cost information, utility's billing period, geographic location, local solar production, local incident light, customer type, electric load location building specifications, grid operator data and time data. 15. The system of claim 10, wherein the first system controller comprises a set-point controller configured to vary the threshold value. 16. A method of managing power at an electric load location, comprising:
transferring telemetry data from a first storage device to a power monitor that is coupled to an electric power line after the first storage device is coupled to the electric power line, wherein the first storage device comprises a first energy source and a first system controller; storing the telemetry data in memory; and controlling a transfer of power between the first storage device and the electric power line using information derived from the telemetry data. 17. The method of claim 16, wherein the telemetry data transferred from the first storage device includes information selected from a group consisting of serial number, battery health and phase(s) to which the first storage device is coupled. 18. The method of claim 16, further comprising:
transferring telemetry data from a second storage device to the power monitor after the second storage device is coupled to the electric power line, wherein the first storage device comprises a first energy source and a first system controller; and controlling a transfer of power between the second storage device and the electric power line using information derived from the telemetry data received from the first storage device or the second storage device. 19. The method of claim 18, wherein the telemetry data transferred from the first or second storage devices includes information selected from a group consisting of serial number, battery health and phase(s) to which the first or the second storage device is coupled. 20. The method of claim 16, wherein the first energy source comprises a battery that is coupled to the electric power line through a bidirectional converter. | 2,100 |
6,419 | 6,419 | 14,484,346 | 2,156 | A mobile device receives media files, and automatically tags each of the media files with tags, comprising data or meta-data, based on: 1) other mobile users proximate to the mobile device, 2) a current location of the mobile device, 3) facial recognition analysis applied to each of the first media files, 4) subject or content recognition analysis applied to each of the first media files, 5) a current date, and/or 6) a usage history associated with each of the first media files. The mobile device stores the tagged media files, and receives a user request to search the media files. The mobile device searches the tags, responsive to the user request, to generate a filtered set of media files, and presents the filtered set of media files to a user of the mobile device. | 1. A method, comprising:
receiving, at a mobile device, first media files; automatically tagging, at the mobile device, each of the first media files with tags, comprising data or meta-data, based on:
1) other mobile users proximate to the mobile device,
2) a current location of the mobile device,
3) facial recognition analysis applied to each of the first media files,
4) subject or content recognition analysis applied to each of the first media files,
5) a current date, and/or
6) a usage history associated with each of the first media files;
storing the tagged first media files; receiving a user request to search the first media files; searching, responsive to the user request, the tags, comprising the data or meta-data, to generate a filtered set of media files; and presenting the filtered set of media files to a user of the mobile device. 2. The method of claim 1, wherein searching the tags is based on:
1) other identified mobile users that are proximate to the mobile device, 2) a geographic location, 3) a determination of an identity of an individual in a vicinity of the mobile device using a facial recognition media analysis technique, 4) a determination of a subject or content related to a vicinity of the mobile device using a subject or recognition media analysis technique, 5) a usage history associated with one or more of the first media files, and/or 6) a current or previous date. 3. The method of claim 2, wherein searching the tags further comprises:
comparing the other identified mobile devices that are proximate to the mobile device, the geographic location, the determined identity of the individual, the determined subject or content, the usage history, or the current or previous date with the tags comprises the data or meta-data; and generating the filtered set of media files based on the comparison. 4. The method of claim 1, where each of the first media files comprises an image file, an audio file, or a video file. 5. The method of claim 1, wherein receiving the first media files comprises:
receiving input from a camera of the mobile device to create each of the first media files. 6. The method of claim 1, wherein receiving the first media files comprises:
receiving input from a camera and a microphone of the mobile device to create each of the first media files. 7. The method of claim 1, wherein storing the tagged first media files comprises:
storing the tagged first media files in the mobile device, in a device external to the mobile device, or in cloud storage. 8. A mobile device, comprising:
a user interface; a communication interface; a camera; a memory; a processing unit configured to:
cause a media filtering application to be downloaded via the communication interface from a remote network device over a network;
receive, from the camera, media files at the mobile device;
execute the media filtering application to:
automatically tag each of the media files with data or meta-data related to:
1) other mobile users proximate to the mobile device,
2) a current location of the mobile device,
3) facial recognition analysis applied to each of the media files,
4) subject or content recognition analysis applied to each of the media files,
5) a current date, and/or
6) a usage history of each of the media files; and
store the tagged media files in the memory;
receive, via the user interface, a user request to search the media files;
further execute the media filtering application to:
search, responsive to the user request, the tags, comprising the data or meta-data, to generate a filtered set of media files; and
display, via the user interface, the filtered set of media files. 9. The mobile device of claim 7, wherein searching the tags is based on:
1) other identified mobile users that are proximate to the mobile device, 2) a geographic location, 3) a determination of an identity of an individual in a vicinity of the mobile device using a facial recognition media analysis technique, 4) a determination of a subject or content related to a vicinity of the mobile device using a subject or recognition media analysis technique, 5) a usage history associated with one or more of the first media files, and/or 6) a current or previous date. 10. The mobile device of claim 9, wherein, when searching the tags, the processing units is further configured to:
compare the other identified mobile devices that are proximate to the mobile device, the geographic location, the determined identity of the individual, the determined subject or content, the usage history, and the current or previous date with the tags comprises the data or meta-data; and generate the filtered set of media files based on the comparison. 11. The mobile device of claim 7, where each of the first media files comprises an image file, an audio file, or a video file. 12. The mobile device of claim 7, wherein, when receiving the first media files, the processing unit is configured to:
receive input from a camera of the mobile device to create each of the first media files. 13. The mobile device of claim 7, wherein, when receiving the first media files, the processing unit is configured to:
receive input from a camera and a microphone of the mobile device to create each of the first media files. 14. The mobile device of claim 7, wherein, when storing the tagged first media files, the processing unit is configured to:
store the tagged first media files in the memory of the mobile device, in a device external to the mobile device, or in cloud storage. 15. A non-transitory computer-readable medium containing instructions executable by at least one processor, the computer-readable medium comprising:
one or more instructions for receiving, at a mobile device, first media files; one or more instructions for automatically tagging each of the first media files with tags, comprising data or meta-data, based on:
1) other mobile users proximate to the mobile device,
2) a current location of the mobile device,
3) facial recognition analysis applied to each of the first media files,
4) subject or content recognition analysis applied to each of the first media files,
5) a current date, and/or
6) a usage history associated with each of the first media files;
one or more instructions for storing the tagged first media files; one or more instructions for receiving a user request to search the first media files; one or more instructions for searching, responsive to the user request, the tags, comprising the data or meta-data, to generate a filtered set of media files; and one or more instructions for presenting the filtered set of media files to a user of the mobile device. 16. The non-transitory computer-readable medium of claim 14, wherein searching the tags is based on:
1) other identified mobile users that are proximate to the mobile device, 2) a geographic location, 3) a determination of an identity of an individual in a vicinity of the mobile device using a facial recognition media analysis technique, 4) a determination of a subject or content related to a vicinity of the mobile device using a subject or recognition media analysis technique, 5) a usage history associated with one or more of the first media files, and/or 6) a current or previous date. 17. The non-transitory computer-readable medium of claim 16, wherein the one or more instructions for searching the tags further comprise:
one or more instructions for comparing the other identified mobile users that are proximate to the mobile device, the geographic location, the determined identity of the individual, the determined subject or content, the usage history, and the current or previous date with the tags comprises the data or meta-data; and one or more instructions for generating the filtered set of media files based on the comparison. 18. The non-transitory computer-readable medium of claim 15, wherein each of the first media files comprises an image file, an audio file, or a video file. 19. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions for receiving the first media files comprises:
one or more instructions for receiving input from a camera of the mobile device to create each of the first media files, or one or more instructions for receiving input from a camera and a microphone of the mobile device to create each of the first media files. 20. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions for storing the tagged first media files comprises:
one or more instructions for storing the tagged first media files in the mobile device, in a device external to the mobile device, or in cloud storage. | A mobile device receives media files, and automatically tags each of the media files with tags, comprising data or meta-data, based on: 1) other mobile users proximate to the mobile device, 2) a current location of the mobile device, 3) facial recognition analysis applied to each of the first media files, 4) subject or content recognition analysis applied to each of the first media files, 5) a current date, and/or 6) a usage history associated with each of the first media files. The mobile device stores the tagged media files, and receives a user request to search the media files. The mobile device searches the tags, responsive to the user request, to generate a filtered set of media files, and presents the filtered set of media files to a user of the mobile device.1. A method, comprising:
receiving, at a mobile device, first media files; automatically tagging, at the mobile device, each of the first media files with tags, comprising data or meta-data, based on:
1) other mobile users proximate to the mobile device,
2) a current location of the mobile device,
3) facial recognition analysis applied to each of the first media files,
4) subject or content recognition analysis applied to each of the first media files,
5) a current date, and/or
6) a usage history associated with each of the first media files;
storing the tagged first media files; receiving a user request to search the first media files; searching, responsive to the user request, the tags, comprising the data or meta-data, to generate a filtered set of media files; and presenting the filtered set of media files to a user of the mobile device. 2. The method of claim 1, wherein searching the tags is based on:
1) other identified mobile users that are proximate to the mobile device, 2) a geographic location, 3) a determination of an identity of an individual in a vicinity of the mobile device using a facial recognition media analysis technique, 4) a determination of a subject or content related to a vicinity of the mobile device using a subject or recognition media analysis technique, 5) a usage history associated with one or more of the first media files, and/or 6) a current or previous date. 3. The method of claim 2, wherein searching the tags further comprises:
comparing the other identified mobile devices that are proximate to the mobile device, the geographic location, the determined identity of the individual, the determined subject or content, the usage history, or the current or previous date with the tags comprises the data or meta-data; and generating the filtered set of media files based on the comparison. 4. The method of claim 1, where each of the first media files comprises an image file, an audio file, or a video file. 5. The method of claim 1, wherein receiving the first media files comprises:
receiving input from a camera of the mobile device to create each of the first media files. 6. The method of claim 1, wherein receiving the first media files comprises:
receiving input from a camera and a microphone of the mobile device to create each of the first media files. 7. The method of claim 1, wherein storing the tagged first media files comprises:
storing the tagged first media files in the mobile device, in a device external to the mobile device, or in cloud storage. 8. A mobile device, comprising:
a user interface; a communication interface; a camera; a memory; a processing unit configured to:
cause a media filtering application to be downloaded via the communication interface from a remote network device over a network;
receive, from the camera, media files at the mobile device;
execute the media filtering application to:
automatically tag each of the media files with data or meta-data related to:
1) other mobile users proximate to the mobile device,
2) a current location of the mobile device,
3) facial recognition analysis applied to each of the media files,
4) subject or content recognition analysis applied to each of the media files,
5) a current date, and/or
6) a usage history of each of the media files; and
store the tagged media files in the memory;
receive, via the user interface, a user request to search the media files;
further execute the media filtering application to:
search, responsive to the user request, the tags, comprising the data or meta-data, to generate a filtered set of media files; and
display, via the user interface, the filtered set of media files. 9. The mobile device of claim 7, wherein searching the tags is based on:
1) other identified mobile users that are proximate to the mobile device, 2) a geographic location, 3) a determination of an identity of an individual in a vicinity of the mobile device using a facial recognition media analysis technique, 4) a determination of a subject or content related to a vicinity of the mobile device using a subject or recognition media analysis technique, 5) a usage history associated with one or more of the first media files, and/or 6) a current or previous date. 10. The mobile device of claim 9, wherein, when searching the tags, the processing units is further configured to:
compare the other identified mobile devices that are proximate to the mobile device, the geographic location, the determined identity of the individual, the determined subject or content, the usage history, and the current or previous date with the tags comprises the data or meta-data; and generate the filtered set of media files based on the comparison. 11. The mobile device of claim 7, where each of the first media files comprises an image file, an audio file, or a video file. 12. The mobile device of claim 7, wherein, when receiving the first media files, the processing unit is configured to:
receive input from a camera of the mobile device to create each of the first media files. 13. The mobile device of claim 7, wherein, when receiving the first media files, the processing unit is configured to:
receive input from a camera and a microphone of the mobile device to create each of the first media files. 14. The mobile device of claim 7, wherein, when storing the tagged first media files, the processing unit is configured to:
store the tagged first media files in the memory of the mobile device, in a device external to the mobile device, or in cloud storage. 15. A non-transitory computer-readable medium containing instructions executable by at least one processor, the computer-readable medium comprising:
one or more instructions for receiving, at a mobile device, first media files; one or more instructions for automatically tagging each of the first media files with tags, comprising data or meta-data, based on:
1) other mobile users proximate to the mobile device,
2) a current location of the mobile device,
3) facial recognition analysis applied to each of the first media files,
4) subject or content recognition analysis applied to each of the first media files,
5) a current date, and/or
6) a usage history associated with each of the first media files;
one or more instructions for storing the tagged first media files; one or more instructions for receiving a user request to search the first media files; one or more instructions for searching, responsive to the user request, the tags, comprising the data or meta-data, to generate a filtered set of media files; and one or more instructions for presenting the filtered set of media files to a user of the mobile device. 16. The non-transitory computer-readable medium of claim 14, wherein searching the tags is based on:
1) other identified mobile users that are proximate to the mobile device, 2) a geographic location, 3) a determination of an identity of an individual in a vicinity of the mobile device using a facial recognition media analysis technique, 4) a determination of a subject or content related to a vicinity of the mobile device using a subject or recognition media analysis technique, 5) a usage history associated with one or more of the first media files, and/or 6) a current or previous date. 17. The non-transitory computer-readable medium of claim 16, wherein the one or more instructions for searching the tags further comprise:
one or more instructions for comparing the other identified mobile users that are proximate to the mobile device, the geographic location, the determined identity of the individual, the determined subject or content, the usage history, and the current or previous date with the tags comprises the data or meta-data; and one or more instructions for generating the filtered set of media files based on the comparison. 18. The non-transitory computer-readable medium of claim 15, wherein each of the first media files comprises an image file, an audio file, or a video file. 19. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions for receiving the first media files comprises:
one or more instructions for receiving input from a camera of the mobile device to create each of the first media files, or one or more instructions for receiving input from a camera and a microphone of the mobile device to create each of the first media files. 20. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions for storing the tagged first media files comprises:
one or more instructions for storing the tagged first media files in the mobile device, in a device external to the mobile device, or in cloud storage. | 2,100 |
6,420 | 6,420 | 15,711,381 | 2,178 | A slave relay station is adapted to serve and/or host pages comprising a simplified graphic user interface (GUI) encoded in a widely recognized format such as, for example, HTML or WML. The GUI embodies activatable links corresponding to control functions for configured appliances. A wireless phone or other device with network access and the capability to process and present such pages, for example via a Web browser, may then be utilized to effect control of such appliances by simply navigating to the network address of the slave relay station, obtaining an appropriate GUI page, and interacting with the links. | 1. (canceled) 2. A method for using a relay device to communicate commands to a target device, wherein the relay device has a memory in which is stored a first tag file comprising both a first definition of a controllable device and a first listing of one or more commands to be transmitted to the controllable device corresponding to the first definition, a second tag file different then the first tag file comprising both a second definition of a controllable device and a second listing of one or more commands to be transmitted to the controllable device corresponding to the second definition, a first code data for use in commanding functional operations of a first controllable appliance, and a second code data for use in commanding functional operations of a second controllable appliance, and wherein the method comprises:
providing to an input device via a first wireless communications link a graphical user interface page for display in a display associated with the input device wherein the graphical user interface page has a first activatable link that is associated with the first tag file and a second activatable link that is associated with the second tag file;
receiving by the relay device from the input device via a second wireless communications link a communication containing data that functions to indicate that one of the first activatable link and the second activatable link of the graphical user interface page was activated;
using by the relay device a one of the first definition of a controllable device and the second definition of a controllable device as stored in the one of the first tag file and the second tag file that is associated with the data that functions to indicate that one of the first activatable link and the second activatable link of the graphical user interface page was activated to select a one of the first code data and the second code data for use in commanding functional operations of the target appliance; and
transmitting a command communication from the relay device to the target device via a third communications link, where the command communication comprises one or more commands selected from the selected one of the first code data and the second code data, wherein the command communication uses a protocol defined within the selected one of the first code data and the second code data, and wherein the one or more commands are selected from the selected one of the first code data and the second code data via use of one of the first listing of one or more commands and the second listing of one or more commands as stored in the one of the first tag file and the second tag file that is associated with the data that functions to indicate that one of the first activatable link and the second activatable link of the graphical user interface page was activated. 3. The method as recited in claim 2, wherein data that functions to indicate that one of the first activatable link and the second activatable link of the graphical user interface page was activated comprises a file name of the one of the first tag file and the second tag file. 4. The method as recited in claim 2, wherein the first definition of a controllable device is the same as the second definition of the controllable device and the first listing of commands is different than the second listing of commands. 5. The method as recited in claim 2, wherein the communication received by the relay device comprises an HTTP protocol request message which includes a file name of the one of the first tag file and the second tag file. 6. The method as recited in claim 2, wherein the graphical user interface page is provided to the input device by the relay station and the first wireless communications link is the same as the second wireless communications link. 7. The method as recited in claim 6, wherein the first wireless communications link is a radio frequency protocol communications link. 8. The method as recited in claim 6, wherein the first wireless communications link is an infrared protocol communications link. 9. The method as recited in claim 2, wherein the graphical user interface page is provided to the input device by a network server device and the first wireless communications link is different than the second wireless communications link. 10. The method as recited in claim 9, wherein at least one of the first wireless communications link and the second wireless communications link is a radio frequency protocol communications link. 11. The method as recited in claim 9, wherein the second wireless communications link is an infrared communications protocol link. 12. The method as recited in claim 2, wherein the first tag file and the second tag file include instructions that are executable by the relay device. 13. The method as recited in claim 2, comprising using data indicative of capabilities of the input device to configure the graphical user interface page into a form that is appropriate for the capabilities of the input device. 14. The method as recited in claim 13, wherein the data indicative of the capabilities of the input device comprises data indicative of a make and model of the input device. 15. The method as recited in claim 13, wherein the data indicative of the capabilities of the input device comprises data indicative of an identity and revision of software used in connection with the display associated with the input device. 16. The method as recited in claim 2, wherein the graphical user interface page is selected from a plurality of preformatted graphical user interface pages as a function of capabilities of the personal communication device. 17. The method as recited in claim 2, wherein the first tag file and the second tag file are each XML encoded. 18. The method as recited in claim 2, wherein the graphical user interface page comprises a hypertext mark-up language file. 19. The method as recited in claim 2, wherein the graphical user interface page comprises data for displaying television network logos in the display of the input device. 20. The method as recited in claim 2, wherein the third communications link comprises a wireless communications link. 21. The method as recited in claim 2, wherein the third communications link comprises an infrared protocol communications link. | A slave relay station is adapted to serve and/or host pages comprising a simplified graphic user interface (GUI) encoded in a widely recognized format such as, for example, HTML or WML. The GUI embodies activatable links corresponding to control functions for configured appliances. A wireless phone or other device with network access and the capability to process and present such pages, for example via a Web browser, may then be utilized to effect control of such appliances by simply navigating to the network address of the slave relay station, obtaining an appropriate GUI page, and interacting with the links.1. (canceled) 2. A method for using a relay device to communicate commands to a target device, wherein the relay device has a memory in which is stored a first tag file comprising both a first definition of a controllable device and a first listing of one or more commands to be transmitted to the controllable device corresponding to the first definition, a second tag file different then the first tag file comprising both a second definition of a controllable device and a second listing of one or more commands to be transmitted to the controllable device corresponding to the second definition, a first code data for use in commanding functional operations of a first controllable appliance, and a second code data for use in commanding functional operations of a second controllable appliance, and wherein the method comprises:
providing to an input device via a first wireless communications link a graphical user interface page for display in a display associated with the input device wherein the graphical user interface page has a first activatable link that is associated with the first tag file and a second activatable link that is associated with the second tag file;
receiving by the relay device from the input device via a second wireless communications link a communication containing data that functions to indicate that one of the first activatable link and the second activatable link of the graphical user interface page was activated;
using by the relay device a one of the first definition of a controllable device and the second definition of a controllable device as stored in the one of the first tag file and the second tag file that is associated with the data that functions to indicate that one of the first activatable link and the second activatable link of the graphical user interface page was activated to select a one of the first code data and the second code data for use in commanding functional operations of the target appliance; and
transmitting a command communication from the relay device to the target device via a third communications link, where the command communication comprises one or more commands selected from the selected one of the first code data and the second code data, wherein the command communication uses a protocol defined within the selected one of the first code data and the second code data, and wherein the one or more commands are selected from the selected one of the first code data and the second code data via use of one of the first listing of one or more commands and the second listing of one or more commands as stored in the one of the first tag file and the second tag file that is associated with the data that functions to indicate that one of the first activatable link and the second activatable link of the graphical user interface page was activated. 3. The method as recited in claim 2, wherein data that functions to indicate that one of the first activatable link and the second activatable link of the graphical user interface page was activated comprises a file name of the one of the first tag file and the second tag file. 4. The method as recited in claim 2, wherein the first definition of a controllable device is the same as the second definition of the controllable device and the first listing of commands is different than the second listing of commands. 5. The method as recited in claim 2, wherein the communication received by the relay device comprises an HTTP protocol request message which includes a file name of the one of the first tag file and the second tag file. 6. The method as recited in claim 2, wherein the graphical user interface page is provided to the input device by the relay station and the first wireless communications link is the same as the second wireless communications link. 7. The method as recited in claim 6, wherein the first wireless communications link is a radio frequency protocol communications link. 8. The method as recited in claim 6, wherein the first wireless communications link is an infrared protocol communications link. 9. The method as recited in claim 2, wherein the graphical user interface page is provided to the input device by a network server device and the first wireless communications link is different than the second wireless communications link. 10. The method as recited in claim 9, wherein at least one of the first wireless communications link and the second wireless communications link is a radio frequency protocol communications link. 11. The method as recited in claim 9, wherein the second wireless communications link is an infrared communications protocol link. 12. The method as recited in claim 2, wherein the first tag file and the second tag file include instructions that are executable by the relay device. 13. The method as recited in claim 2, comprising using data indicative of capabilities of the input device to configure the graphical user interface page into a form that is appropriate for the capabilities of the input device. 14. The method as recited in claim 13, wherein the data indicative of the capabilities of the input device comprises data indicative of a make and model of the input device. 15. The method as recited in claim 13, wherein the data indicative of the capabilities of the input device comprises data indicative of an identity and revision of software used in connection with the display associated with the input device. 16. The method as recited in claim 2, wherein the graphical user interface page is selected from a plurality of preformatted graphical user interface pages as a function of capabilities of the personal communication device. 17. The method as recited in claim 2, wherein the first tag file and the second tag file are each XML encoded. 18. The method as recited in claim 2, wherein the graphical user interface page comprises a hypertext mark-up language file. 19. The method as recited in claim 2, wherein the graphical user interface page comprises data for displaying television network logos in the display of the input device. 20. The method as recited in claim 2, wherein the third communications link comprises a wireless communications link. 21. The method as recited in claim 2, wherein the third communications link comprises an infrared protocol communications link. | 2,100 |
6,421 | 6,421 | 15,221,291 | 2,177 | Techniques to provide for reducing the time for rendering a web page by efficiently and optimally aggregating various resources transmitted to the browser during the page load (e.g., loading of the web page). Some example implementations include, upon receipt of a request for a web page, identifying a plurality of page features for displaying the web page, where each of the page features being associated with a respective manifest of dependencies on resources. Example implementations may also include aggregating dependencies of said plurality of page features to generate an optimized set of dependencies for the web page, resolving dependencies in accordance with the optimized set of dependencies, and render the web page by rendering the plurality of page features using the resolved dependencies. | 1. A system comprising:
a network interface; and a processor configured to:
receive, from an end user device via the network interface, a request for displaying a page on the end user device;
identify a plurality of page features for displaying the page, each of the page features being associated with one or more dependencies on resources;
aggregate dependencies of said plurality of page features to generate an optimized set of dependencies for the page;
resolve dependencies in accordance with the optimized set of dependencies;
render the page by rendering the plurality of page features using the resolved dependencies; and
provide the rendered page for display to the end user device. 2. The system of claim 1, wherein the providing includes transmitting the rendered page and a file of aggregated said dependencies or resources associated with said dependencies to the end user device. 3. The system of claim 2, wherein said identified page features include at least two page features requiring a particular resource, and wherein the said file transmitted with the rendered page includes only one instance of the particular resource. 4. The system of claim 1, wherein the aggregating comprises at least one of compacting, minification and de-duplication of said dependencies. 5. The system of claim 4, wherein the aggregating comprises the de-duplication of said dependencies. 6. The system of claim 5, wherein the de-duplication includes both internally packaged scripts and externally referenced dependencies. 7. The system of claim 6, wherein said de-duplication generates a single file for substantially all said dependencies, and wherein the single file is then compacted and minified before use in rendering the web page. 8. The system of claim 1, wherein the page is a web page. 9. The system of claim 1, wherein the aggregating comprises combining a plurality of scripts corresponding to respective page features into a single optimized script. 10. The system of claim 1, wherein each page feature of the plurality of page features is included in a page feature bundle comprising the page feature and associations with one or more of a template, a script, a manifest, a style, or external file. 11. The system of claim 1, wherein the optimized set of dependencies includes a unified set of normalized, page-specific, dependencies, and wherein the of normalized dependencies include at least one of JavaScript, CSS, images, and rich media. 12. The system of claim 1, wherein each of one or more page features of the plurality of features specifies a content type and one or more attributes associated with rendering content of the content type. 13. The system of claim 1, wherein a page feature of the plurality of page features, during rendering of the page feature and before rendering of the web page, generates a dependency, and wherein the generated dependency is included in a manifest before the web page is rendered. 14. A method comprising:
receiving a request for displaying a web page on an end user device; identifying a plurality of page features for displaying the web page, each of the page features being associated with a respective manifest of dependencies on resources; aggregating dependencies of said plurality of page features to generate an optimized set of dependencies for the web page; resolving dependencies in accordance with the optimized set of dependencies; rendering the web page by rendering the plurality of page features using the resolved dependencies; and providing the rendered page for display. 15. The method of claim 14, wherein the providing includes transmitting the rendered page and a file of aggregated said dependencies or resources associated with said dependencies to the end user device. 16. A non-transitory computer readable storage medium having instructions stored thereon, the instructions when executed by a processor of a computer, causes the computer to perform operations comprising:
receiving a request for displaying a web page on an end user device; identifying a plurality of page features for displaying the web page, each of the page features being associated with a respective manifest of dependencies on resources; aggregating dependencies of said plurality of page features to generate an optimized set of dependencies for the web page; resolving dependencies in accordance with the optimized set of dependencies; rendering the web page by rendering the plurality of page features using the resolved dependencies; and providing the rendered page for display. | Techniques to provide for reducing the time for rendering a web page by efficiently and optimally aggregating various resources transmitted to the browser during the page load (e.g., loading of the web page). Some example implementations include, upon receipt of a request for a web page, identifying a plurality of page features for displaying the web page, where each of the page features being associated with a respective manifest of dependencies on resources. Example implementations may also include aggregating dependencies of said plurality of page features to generate an optimized set of dependencies for the web page, resolving dependencies in accordance with the optimized set of dependencies, and render the web page by rendering the plurality of page features using the resolved dependencies.1. A system comprising:
a network interface; and a processor configured to:
receive, from an end user device via the network interface, a request for displaying a page on the end user device;
identify a plurality of page features for displaying the page, each of the page features being associated with one or more dependencies on resources;
aggregate dependencies of said plurality of page features to generate an optimized set of dependencies for the page;
resolve dependencies in accordance with the optimized set of dependencies;
render the page by rendering the plurality of page features using the resolved dependencies; and
provide the rendered page for display to the end user device. 2. The system of claim 1, wherein the providing includes transmitting the rendered page and a file of aggregated said dependencies or resources associated with said dependencies to the end user device. 3. The system of claim 2, wherein said identified page features include at least two page features requiring a particular resource, and wherein the said file transmitted with the rendered page includes only one instance of the particular resource. 4. The system of claim 1, wherein the aggregating comprises at least one of compacting, minification and de-duplication of said dependencies. 5. The system of claim 4, wherein the aggregating comprises the de-duplication of said dependencies. 6. The system of claim 5, wherein the de-duplication includes both internally packaged scripts and externally referenced dependencies. 7. The system of claim 6, wherein said de-duplication generates a single file for substantially all said dependencies, and wherein the single file is then compacted and minified before use in rendering the web page. 8. The system of claim 1, wherein the page is a web page. 9. The system of claim 1, wherein the aggregating comprises combining a plurality of scripts corresponding to respective page features into a single optimized script. 10. The system of claim 1, wherein each page feature of the plurality of page features is included in a page feature bundle comprising the page feature and associations with one or more of a template, a script, a manifest, a style, or external file. 11. The system of claim 1, wherein the optimized set of dependencies includes a unified set of normalized, page-specific, dependencies, and wherein the of normalized dependencies include at least one of JavaScript, CSS, images, and rich media. 12. The system of claim 1, wherein each of one or more page features of the plurality of features specifies a content type and one or more attributes associated with rendering content of the content type. 13. The system of claim 1, wherein a page feature of the plurality of page features, during rendering of the page feature and before rendering of the web page, generates a dependency, and wherein the generated dependency is included in a manifest before the web page is rendered. 14. A method comprising:
receiving a request for displaying a web page on an end user device; identifying a plurality of page features for displaying the web page, each of the page features being associated with a respective manifest of dependencies on resources; aggregating dependencies of said plurality of page features to generate an optimized set of dependencies for the web page; resolving dependencies in accordance with the optimized set of dependencies; rendering the web page by rendering the plurality of page features using the resolved dependencies; and providing the rendered page for display. 15. The method of claim 14, wherein the providing includes transmitting the rendered page and a file of aggregated said dependencies or resources associated with said dependencies to the end user device. 16. A non-transitory computer readable storage medium having instructions stored thereon, the instructions when executed by a processor of a computer, causes the computer to perform operations comprising:
receiving a request for displaying a web page on an end user device; identifying a plurality of page features for displaying the web page, each of the page features being associated with a respective manifest of dependencies on resources; aggregating dependencies of said plurality of page features to generate an optimized set of dependencies for the web page; resolving dependencies in accordance with the optimized set of dependencies; rendering the web page by rendering the plurality of page features using the resolved dependencies; and providing the rendered page for display. | 2,100 |
6,422 | 6,422 | 15,398,650 | 2,177 | The present embodiments relate generally to systems and methods for generating a multimedia product. The embodiments may include: identifying a plurality of ultrasound media items from a fetal ultrasound scan, where each of the plurality of ultrasound media items have different respective attributes; and applying a theme to the plurality of ultrasound media items to generate the multimedia product. The theme may include an effect to be applied to at least one ultrasound media item of the plurality of ultrasound media items, and the applying may include adapting one of: the effect, and the attribute of the at least one ultrasound media item, to the other. | 1.-20. (canceled) 21. A system for generating a multimedia product comprising:
a viewing computing device; and a server, storing:
a plurality of ultrasound media items having associated metadata, the plurality of ultrasound media items being generated from a fetal ultrasound scan;
a mapping of metadata values to anatomical features of a fetus;
a mapping of the anatomical features of a fetus to options for one or more multimedia effects;
underlying data associated with the options for the one or more multimedia effects; and
a multimedia product generator comprising software instructions executable by at least one processor at the server or at the viewing computing device, wherein when the instructions are executed by the at least one processor, the at least one processor is configured to:
read metadata associated with at least one ultrasound media item of the plurality of ultrasound media items;
based on the mapping of metadata values to anatomical features of a fetus, determine an anatomical feature of a fetus, corresponding to the read metadata, that is viewable in the at least one ultrasound media item;
based on the mapping of the anatomical features of a fetus to options for the one or more multimedia effects, identify an option for a multimedia effect of the one or more multimedia effects; and
apply the multimedia effect, with the identified option, to the at least one ultrasound media item, to generate one or more frames of the multimedia product;
wherein the generated one or more frames of the multimedia product show the at least one ultrasound media item with the applied multimedia effect, and the applied multimedia effect has the option that matches the anatomical feature of a fetus determined to be viewable in the at least one ultrasound media item. 22. The system of claim 21, wherein the multimedia product generator is provided as a script that is transmittable to the viewing computing device for execution, and wherein when the script is executed at the viewing computing device, the viewing computing device generates the multimedia product. 23. The system of claim 22, wherein during display of the generated multimedia product, the multimedia product retrieves, from the server, the underlying data associated with the identified option of the applied multimedia effect. 24. The system of claim 22, wherein the script is written in a scripting language that can access a Web Graphics Library (Web GL) Application Programming Interface (API). 25. The system of claim 21, wherein the underlying data comprises one of: a bitmap and a sprite. 26. The system of claim 21, wherein the metadata comprises measurements, and the mapping of metadata values to anatomical features of a fetus maps types of measurements to the anatomical features of a fetus. 27. The system of claim 26, wherein the mapping of metadata values to anatomical features of a fetus maps multiple types of measurements to a single anatomical feature of a fetus. 28. The system of claim 21, wherein the metadata comprises annotations, and the mapping of metadata values to anatomical features of a fetus maps text in the annotations to the anatomical features of a fetus. 29. The system of claim 21, wherein the anatomical feature of a fetus determined to be viewable in the at least one ultrasound media item is selected from a group consisting of: heart, arm, leg, face, head, brain, spine, kidney, liver, sexual organ, digits, belly, feet and hand. 30. The system of claim 21, wherein the mapping of the anatomical features of a fetus to options for the multimedia effect maps a single anatomical feature of a fetus to multiple options for the multimedia effect. 31. The method of claim 21, wherein the multimedia effect is selected from the group consisting of: audio, animation, text, images, frames and borders. 32. The method of claim 21, wherein the multimedia product generator further configures the at least one processor to:
display a user interface for displaying the plurality of ultrasound media items, the user interface providing a user-selectable option for generating the multimedia product; and receive input that selects the user-selectable option for generating the multimedia product. 33. A method of generating a multimedia product, comprising:
identifying a plurality of ultrasound media items from a fetal ultrasound scan; reading metadata associated with at least one ultrasound media item of the plurality of ultrasound media items; based on a mapping of metadata values to anatomical features of a fetus, determining an anatomical feature of a fetus, corresponding to the read metadata, that is viewable in the at least one ultrasound media item; based on a mapping of the anatomical features of a fetus to options for a multimedia effect, identifying an option for the multimedia effect; and applying the multimedia effect, with the identified option, to the at least one ultrasound media item, to generate one or more frames of the multimedia product; wherein the generated one or more frames of the multimedia product show the at least one ultrasound media item with the applied multimedia effect, and the applied multimedia effect has the option that matches the anatomical feature of a fetus determined to be viewable in the at least one ultrasound media item. 34. The method of claim 33, wherein the metadata comprises measurements, and the mapping of metadata values to anatomical features of a fetus maps types of measurements to the anatomical features of a fetus. 35. The method of claim 34, wherein the mapping of metadata values to anatomical features of a fetus maps multiple types of measurements to a single anatomical feature of a fetus. 36. The method of claim 33, wherein the metadata comprises annotations, and the mapping of metadata values to anatomical features of a fetus maps text in the annotations to the anatomical features of a fetus. 37. The method of claim 33, wherein the anatomical feature of a fetus determined to be viewable in the at least one ultrasound media item is selected from a group consisting of: heart, arm, leg, face, head, brain, spine, kidney, liver, sexual organ, digits, belly, feet and hand. 38. The method of claim 33, wherein the mapping of the anatomical features of a fetus to options for the multimedia effect maps a single anatomical feature of a fetus to multiple options for the multimedia effect. 39. The method of claim 33, wherein the multimedia effect is selected from the group consisting of: audio, animation, text, images, frames and borders. 40. The method of claim 33, wherein prior to the identifying the plurality of ultrasound media items, the method further comprises:
displaying a user interface for displaying the plurality of ultrasound media items, the user interface providing a user-selectable option for generating the multimedia product; and receiving input that selects the user-selectable option for generating the multimedia product. | The present embodiments relate generally to systems and methods for generating a multimedia product. The embodiments may include: identifying a plurality of ultrasound media items from a fetal ultrasound scan, where each of the plurality of ultrasound media items have different respective attributes; and applying a theme to the plurality of ultrasound media items to generate the multimedia product. The theme may include an effect to be applied to at least one ultrasound media item of the plurality of ultrasound media items, and the applying may include adapting one of: the effect, and the attribute of the at least one ultrasound media item, to the other.1.-20. (canceled) 21. A system for generating a multimedia product comprising:
a viewing computing device; and a server, storing:
a plurality of ultrasound media items having associated metadata, the plurality of ultrasound media items being generated from a fetal ultrasound scan;
a mapping of metadata values to anatomical features of a fetus;
a mapping of the anatomical features of a fetus to options for one or more multimedia effects;
underlying data associated with the options for the one or more multimedia effects; and
a multimedia product generator comprising software instructions executable by at least one processor at the server or at the viewing computing device, wherein when the instructions are executed by the at least one processor, the at least one processor is configured to:
read metadata associated with at least one ultrasound media item of the plurality of ultrasound media items;
based on the mapping of metadata values to anatomical features of a fetus, determine an anatomical feature of a fetus, corresponding to the read metadata, that is viewable in the at least one ultrasound media item;
based on the mapping of the anatomical features of a fetus to options for the one or more multimedia effects, identify an option for a multimedia effect of the one or more multimedia effects; and
apply the multimedia effect, with the identified option, to the at least one ultrasound media item, to generate one or more frames of the multimedia product;
wherein the generated one or more frames of the multimedia product show the at least one ultrasound media item with the applied multimedia effect, and the applied multimedia effect has the option that matches the anatomical feature of a fetus determined to be viewable in the at least one ultrasound media item. 22. The system of claim 21, wherein the multimedia product generator is provided as a script that is transmittable to the viewing computing device for execution, and wherein when the script is executed at the viewing computing device, the viewing computing device generates the multimedia product. 23. The system of claim 22, wherein during display of the generated multimedia product, the multimedia product retrieves, from the server, the underlying data associated with the identified option of the applied multimedia effect. 24. The system of claim 22, wherein the script is written in a scripting language that can access a Web Graphics Library (Web GL) Application Programming Interface (API). 25. The system of claim 21, wherein the underlying data comprises one of: a bitmap and a sprite. 26. The system of claim 21, wherein the metadata comprises measurements, and the mapping of metadata values to anatomical features of a fetus maps types of measurements to the anatomical features of a fetus. 27. The system of claim 26, wherein the mapping of metadata values to anatomical features of a fetus maps multiple types of measurements to a single anatomical feature of a fetus. 28. The system of claim 21, wherein the metadata comprises annotations, and the mapping of metadata values to anatomical features of a fetus maps text in the annotations to the anatomical features of a fetus. 29. The system of claim 21, wherein the anatomical feature of a fetus determined to be viewable in the at least one ultrasound media item is selected from a group consisting of: heart, arm, leg, face, head, brain, spine, kidney, liver, sexual organ, digits, belly, feet and hand. 30. The system of claim 21, wherein the mapping of the anatomical features of a fetus to options for the multimedia effect maps a single anatomical feature of a fetus to multiple options for the multimedia effect. 31. The method of claim 21, wherein the multimedia effect is selected from the group consisting of: audio, animation, text, images, frames and borders. 32. The method of claim 21, wherein the multimedia product generator further configures the at least one processor to:
display a user interface for displaying the plurality of ultrasound media items, the user interface providing a user-selectable option for generating the multimedia product; and receive input that selects the user-selectable option for generating the multimedia product. 33. A method of generating a multimedia product, comprising:
identifying a plurality of ultrasound media items from a fetal ultrasound scan; reading metadata associated with at least one ultrasound media item of the plurality of ultrasound media items; based on a mapping of metadata values to anatomical features of a fetus, determining an anatomical feature of a fetus, corresponding to the read metadata, that is viewable in the at least one ultrasound media item; based on a mapping of the anatomical features of a fetus to options for a multimedia effect, identifying an option for the multimedia effect; and applying the multimedia effect, with the identified option, to the at least one ultrasound media item, to generate one or more frames of the multimedia product; wherein the generated one or more frames of the multimedia product show the at least one ultrasound media item with the applied multimedia effect, and the applied multimedia effect has the option that matches the anatomical feature of a fetus determined to be viewable in the at least one ultrasound media item. 34. The method of claim 33, wherein the metadata comprises measurements, and the mapping of metadata values to anatomical features of a fetus maps types of measurements to the anatomical features of a fetus. 35. The method of claim 34, wherein the mapping of metadata values to anatomical features of a fetus maps multiple types of measurements to a single anatomical feature of a fetus. 36. The method of claim 33, wherein the metadata comprises annotations, and the mapping of metadata values to anatomical features of a fetus maps text in the annotations to the anatomical features of a fetus. 37. The method of claim 33, wherein the anatomical feature of a fetus determined to be viewable in the at least one ultrasound media item is selected from a group consisting of: heart, arm, leg, face, head, brain, spine, kidney, liver, sexual organ, digits, belly, feet and hand. 38. The method of claim 33, wherein the mapping of the anatomical features of a fetus to options for the multimedia effect maps a single anatomical feature of a fetus to multiple options for the multimedia effect. 39. The method of claim 33, wherein the multimedia effect is selected from the group consisting of: audio, animation, text, images, frames and borders. 40. The method of claim 33, wherein prior to the identifying the plurality of ultrasound media items, the method further comprises:
displaying a user interface for displaying the plurality of ultrasound media items, the user interface providing a user-selectable option for generating the multimedia product; and receiving input that selects the user-selectable option for generating the multimedia product. | 2,100 |
6,423 | 6,423 | 15,188,391 | 2,117 | Historical sleep metrics are accessed. Historical sensor data is accessed. Incidences of low quality sleep experienced by the user are identified. Particular environmental conditions that affected the user during the incidences of low quality sleep are identified. A corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions is created. The behavior of the environmental control system is modified such that the environmental control system reduces the particular environmental conditions when the user sleeps in the bed. | 1. A method for automated control of a user's environment when the user is sleeping, the method comprising:
accessing historical sleep metrics that represent a sleep quality of the user while the user was sleeping in a bed configured to control behavior of an environmental control system, the bed comprising at least one processor and computer memory; accessing historical sensor data that represent sensor readings that measure environmental conditions affecting the user while the user was sleeping in the bed; identifying, in the historical sleep metrics, incidences of low quality sleep experienced by the user; identifying, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep; generating a corrective plan that specifies a change to the environmental control system to reduce the particular environmental conditions; and modifying, by the bed, behavior of the environmental control system according to the corrective plan such that the environmental control system reduces the particular environmental conditions when the user sleeps in the bed. 2. The method of claim 1, wherein:
the bed comprises the environmental control system; the environmental control system comprises a plurality of sensor each configured to measure at least one environmental condition; the environmental control system comprises a plurality of peripheral controllers each configured to control at least one automated device configured to influence environmental factors that affect the user; and the corrective plan comprises instructions that are operable by the environmental control system and that specify behavior of one or more automated devices such that the automated devices reduce the particular environmental conditions. 3. The method of claim 1, the method further comprising:
detecting user presence in the bed; detecting that the particular environmental condition has at least a threshold probability of occurring; and engaging the environmental control system to reduce the particular environmental condition. 4. The method of claim 1, wherein accessing historical sleep metrics that represent the sleep quality of a user while the user was sleeping in a bed comprises:
collecting, by the environmental control system, sensor readings of the environment while the user is sleeping in the bed; calculating, based on the sensor readings, the sleep metrics for each of a plurality of nights sleep by the user; storing, to a computer readable memory, the sleep metrics; and accessing, from the computer readable memory, the sleep metrics. 5. The method of claim 4, wherein the computer readable memory is communicably coupled and geographically separate from the environmental control system. 6. The method of claim 1, wherein:
identifying, in the historical sleep metrics, incidences of low quality sleep experienced by the user comprises identifying periods of user snoring; identifying, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep comprises identifying a configurations of a foundation of the bed; and generating a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions comprises generating instructions to change the configurations of the foundation of the bed so that a head portion of the bed is elevated. 7. The method of claim 1, wherein:
identifying, in the historical sleep metrics, incidences of low quality sleep experienced by the user comprises identifying periods of user restlessness; identifying, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep comprises identifying a lighting intensity and a lighting color; and generating a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions comprises generating instructions to lighting controlled by the environmental control system. 8. The method of claim 1, wherein the corrective plan further specifies human-readable instructions addressed to the user and comprising recommendations to the user to change their sleeping environment; and
wherein the method further comprises displaying the human-readable instructions to the user on an output device. 9. A system for automated control of a user's environment when the user is sleeping, the system comprising:
a bed having a mattress; a data processing system configured to:
access historical sleep metrics that represent a sleep quality of a user while the user was sleeping in a bed;
access historical sensor data that represent sensor readings that measure environmental conditions affecting the user while the user was sleeping in the bed;
identify, in the historical sleep metrics, incidences of low quality sleep experienced by the user;
identify, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep;
generate a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions; and
modify behavior of the environmental control system according to the corrective plan such that the environmental control system reduces the particular environmental conditions when the user sleeps in the bed. 10. The system of claim 9, wherein:
the data processing system comprises the environmental control system; the environmental control system comprises a plurality of sensor each configured to measure at least one environmental condition; the environmental control system comprises a plurality of peripheral controllers each configured to control at least one automated device configured to influence environmental factors that affect the user; and the corrective plan comprises instructions that are operable by the environmental control system and that specify behavior of one or more automated devices such that the automated devices reduce the particular environmental conditions. 11. The system of claim 9, wherein the data processing system is further configured to:
detect user presence in the bed; detect that the particular environmental condition has at least a threshold probability of occurring; and engage the environmental control system to reduce the particular environmental condition. 12. The system of claim 9, wherein accessing historical sleep metrics that represent the sleep quality of a user while the user was sleeping in a bed comprises:
collecting, by the environmental control system, sensor readings of the environment while the user is sleeping in the bed; calculating, based on the sensor readings, the sleep metrics for each of a plurality of nights sleep by the user; storing, to a computer readable memory, the sleep metrics; and accessing, from the computer readable memory, the sleep metrics. 13. The system of claim 12, wherein the computer readable memory is communicably coupled and geographically separate from the environmental control system. 14. The system of claim 9, wherein:
identifying, in the historical sleep metrics, incidences of low quality sleep experienced by the user comprises identifying periods of user snoring; identifying, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep comprises identifying a configurations of a foundation of the bed; and generating a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions comprises generating instructions to change the configurations of the foundation of the bed so that a head portion of the bed is elevated. 15. The system of claim 9, wherein:
identifying, in the historical sleep metrics, incidences of low quality sleep experienced by the user comprises identifying periods of user restlessness; identifying, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep comprises identifying a lighting intensity and a lighting color; and generating a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions comprises generating instructions to lighting controlled by the environmental control system. 16. The system of claim 9, wherein the corrective plan further specifies human-readable instructions addressed to the user and comprising recommendations to the user to change their sleeping environment; and
wherein the data processing system is further configured to display the human-readable instructions to the user. 17. A system for automated control of a user's environment when the user is sleeping, the system comprising:
a bed having a mattress; means for measuring at least one environmental condition; a data processing system configured to:
access historical sleep metrics that represent a sleep quality of a user while the user was sleeping in a bed;
access historical sensor data that represent sensor readings that measure environmental conditions affecting the user while the user was sleeping in the bed;
identify, in the historical sleep metrics, incidences of low quality sleep experienced by the user;
identify, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep;
generate a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions; and
modify behavior of the environmental control system according to the corrective plan such that the environmental control system reduces the particular environmental conditions when the user sleeps in the bed. | Historical sleep metrics are accessed. Historical sensor data is accessed. Incidences of low quality sleep experienced by the user are identified. Particular environmental conditions that affected the user during the incidences of low quality sleep are identified. A corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions is created. The behavior of the environmental control system is modified such that the environmental control system reduces the particular environmental conditions when the user sleeps in the bed.1. A method for automated control of a user's environment when the user is sleeping, the method comprising:
accessing historical sleep metrics that represent a sleep quality of the user while the user was sleeping in a bed configured to control behavior of an environmental control system, the bed comprising at least one processor and computer memory; accessing historical sensor data that represent sensor readings that measure environmental conditions affecting the user while the user was sleeping in the bed; identifying, in the historical sleep metrics, incidences of low quality sleep experienced by the user; identifying, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep; generating a corrective plan that specifies a change to the environmental control system to reduce the particular environmental conditions; and modifying, by the bed, behavior of the environmental control system according to the corrective plan such that the environmental control system reduces the particular environmental conditions when the user sleeps in the bed. 2. The method of claim 1, wherein:
the bed comprises the environmental control system; the environmental control system comprises a plurality of sensor each configured to measure at least one environmental condition; the environmental control system comprises a plurality of peripheral controllers each configured to control at least one automated device configured to influence environmental factors that affect the user; and the corrective plan comprises instructions that are operable by the environmental control system and that specify behavior of one or more automated devices such that the automated devices reduce the particular environmental conditions. 3. The method of claim 1, the method further comprising:
detecting user presence in the bed; detecting that the particular environmental condition has at least a threshold probability of occurring; and engaging the environmental control system to reduce the particular environmental condition. 4. The method of claim 1, wherein accessing historical sleep metrics that represent the sleep quality of a user while the user was sleeping in a bed comprises:
collecting, by the environmental control system, sensor readings of the environment while the user is sleeping in the bed; calculating, based on the sensor readings, the sleep metrics for each of a plurality of nights sleep by the user; storing, to a computer readable memory, the sleep metrics; and accessing, from the computer readable memory, the sleep metrics. 5. The method of claim 4, wherein the computer readable memory is communicably coupled and geographically separate from the environmental control system. 6. The method of claim 1, wherein:
identifying, in the historical sleep metrics, incidences of low quality sleep experienced by the user comprises identifying periods of user snoring; identifying, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep comprises identifying a configurations of a foundation of the bed; and generating a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions comprises generating instructions to change the configurations of the foundation of the bed so that a head portion of the bed is elevated. 7. The method of claim 1, wherein:
identifying, in the historical sleep metrics, incidences of low quality sleep experienced by the user comprises identifying periods of user restlessness; identifying, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep comprises identifying a lighting intensity and a lighting color; and generating a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions comprises generating instructions to lighting controlled by the environmental control system. 8. The method of claim 1, wherein the corrective plan further specifies human-readable instructions addressed to the user and comprising recommendations to the user to change their sleeping environment; and
wherein the method further comprises displaying the human-readable instructions to the user on an output device. 9. A system for automated control of a user's environment when the user is sleeping, the system comprising:
a bed having a mattress; a data processing system configured to:
access historical sleep metrics that represent a sleep quality of a user while the user was sleeping in a bed;
access historical sensor data that represent sensor readings that measure environmental conditions affecting the user while the user was sleeping in the bed;
identify, in the historical sleep metrics, incidences of low quality sleep experienced by the user;
identify, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep;
generate a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions; and
modify behavior of the environmental control system according to the corrective plan such that the environmental control system reduces the particular environmental conditions when the user sleeps in the bed. 10. The system of claim 9, wherein:
the data processing system comprises the environmental control system; the environmental control system comprises a plurality of sensor each configured to measure at least one environmental condition; the environmental control system comprises a plurality of peripheral controllers each configured to control at least one automated device configured to influence environmental factors that affect the user; and the corrective plan comprises instructions that are operable by the environmental control system and that specify behavior of one or more automated devices such that the automated devices reduce the particular environmental conditions. 11. The system of claim 9, wherein the data processing system is further configured to:
detect user presence in the bed; detect that the particular environmental condition has at least a threshold probability of occurring; and engage the environmental control system to reduce the particular environmental condition. 12. The system of claim 9, wherein accessing historical sleep metrics that represent the sleep quality of a user while the user was sleeping in a bed comprises:
collecting, by the environmental control system, sensor readings of the environment while the user is sleeping in the bed; calculating, based on the sensor readings, the sleep metrics for each of a plurality of nights sleep by the user; storing, to a computer readable memory, the sleep metrics; and accessing, from the computer readable memory, the sleep metrics. 13. The system of claim 12, wherein the computer readable memory is communicably coupled and geographically separate from the environmental control system. 14. The system of claim 9, wherein:
identifying, in the historical sleep metrics, incidences of low quality sleep experienced by the user comprises identifying periods of user snoring; identifying, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep comprises identifying a configurations of a foundation of the bed; and generating a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions comprises generating instructions to change the configurations of the foundation of the bed so that a head portion of the bed is elevated. 15. The system of claim 9, wherein:
identifying, in the historical sleep metrics, incidences of low quality sleep experienced by the user comprises identifying periods of user restlessness; identifying, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep comprises identifying a lighting intensity and a lighting color; and generating a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions comprises generating instructions to lighting controlled by the environmental control system. 16. The system of claim 9, wherein the corrective plan further specifies human-readable instructions addressed to the user and comprising recommendations to the user to change their sleeping environment; and
wherein the data processing system is further configured to display the human-readable instructions to the user. 17. A system for automated control of a user's environment when the user is sleeping, the system comprising:
a bed having a mattress; means for measuring at least one environmental condition; a data processing system configured to:
access historical sleep metrics that represent a sleep quality of a user while the user was sleeping in a bed;
access historical sensor data that represent sensor readings that measure environmental conditions affecting the user while the user was sleeping in the bed;
identify, in the historical sleep metrics, incidences of low quality sleep experienced by the user;
identify, in the historical sensor data, particular environmental conditions that affected the user during the incidences of low quality sleep;
generate a corrective plan that specifies a change to an environmental control system to reduce the particular environmental conditions; and
modify behavior of the environmental control system according to the corrective plan such that the environmental control system reduces the particular environmental conditions when the user sleeps in the bed. | 2,100 |
6,424 | 6,424 | 15,725,259 | 2,132 | In various embodiments, a write state application generates a snapshot that includes one or more data values associated with a source dataset. In operation, the write state application performs one or more compression operations on the source dataset to generate a first compressed record. The write state application then serializes the first compressed record and a second compressed record to generate a first compressed record list. Finally, the write state application generates the snapshot based on the first compressed record list. When the data values are accessed from the first snapshot, the size of the snapshot is maintained. Advantageously, because the size of the snapshot is smaller than the size of the source dataset, some consumers that are unable to store the entire source dataset in random access memory (RAM) are able to store the entire snapshot in RAM. | 1. A computer-implemented method, comprising:
performing one or more compression operations on a first source dataset having a first size to generate a first compressed record; serializing the first compressed record and a second compressed record to generate a first compressed record list; and generating a first snapshot having a second size based on the first compressed record list, wherein the second size is less than the first size, wherein, when one or more data values included in the first snapshot and associated with the first source dataset are accessed from the first snapshot, the first size of the first snapshot is maintained. 2. The computer-implemented method of claim 1, wherein the first compressed record list comprises a fixed-length and bit-aligned sequence of bits. 3. The computer-implemented method of claim 1, wherein generating the first snapshot comprises serializing the first compressed record list and at least a second compressed record list. 4. The computer-implemented method of claim 1, wherein the one or more compression operations comprise a deduplication operation, an encoding operation, a packing operation, or an overhead elimination operation. 5. The computer-implemented method of claim 1, further comprising:
determining that the source dataset has been modified to generate a second source dataset; generating a second snapshot based on the second source dataset; and generating a delta file based on at least one difference between the first source dataset and the second source dataset, wherein the delta file includes one or more instructions for generating the second snapshot from the first snapshot. 6. The computer-implemented method of claim 1, wherein an application programming interface provides a first method for accessing the one or more data values included in the first snapshot. 7. The computer-implemented method of claim 6, wherein the application programming interface further includes a second method for generating an index associated with the first compressed record list. 8. The computer-implemented method of claim 1, wherein the first compressed record is associated with a first schema included in a data model. 9. A non-transitory computer-readable storage medium including instructions that, when executed by a processor, cause the processor to perform the steps of:
performing one or more compression operations on a first source dataset having a first size based on a data model to generate a first compressed record; generating a first compressed record list based on the first compressed record and a second compressed record; and generating a first snapshot having a second size based on the first compressed record list, wherein the second size is less than the first size, wherein, when one or more data values included in the first snapshot and associated with the first source dataset are accessed from the first snapshot, the first size of the first snapshot is maintained. 10. The non-transitory computer-readable storage medium of claim 9, wherein the first compressed record list comprises a fixed-length and bit-aligned sequence of bits. 11. The non-transitory computer-readable storage medium of claim 9, wherein generating the first snapshot comprises serializing the first compressed record list and at least a second compressed record list. 12. The non-transitory computer-readable storage medium of claim 9, wherein the one or more compression operations comprise a deduplication operation, an encoding operation, a packing operation, or an overhead elimination operation. 13. The non-transitory computer-readable storage medium of claim 9, further comprising:
determining that the source dataset has been modified to generate a second source dataset; generating a second snapshot based on the second source dataset; and generating a delta file based on at least one difference between the first source dataset and the second source dataset, wherein the delta file includes one or more instructions for generating the second snapshot from the first snapshot. 14. The non-transitory computer-readable storage medium of claim 13, further comprising:
prior to determining that the source dataset has been modified, setting a latest version associated with a notification system equal to a first version associated with the first snapshot; and after generating the second snapshot, setting the latest version equal to a second version associated with the second snapshot. 15. The non-transitory computer-readable storage medium of claim 14, further comprising, prior to setting the latest version equal to the second version:
applying the delta file to the first snapshot to generate a third dataset; and determining that the third dataset matches the second snapshot. 16. The non-transitory computer-readable storage medium of claim 9, further comprising:
determining that the source dataset has been modified to generate a second source dataset; generating a second snapshot based on the second source dataset; and generating a reverse delta file based on a difference between the first source dataset and the second source dataset, wherein the reverse delta file includes one or more instructions for generating the first snapshot from the second snapshot. 17. A system comprising:
a memory storing a write state application; and a processor coupled to the memory, wherein when executed by the processor, the write state application causes the processor to:
perform one or more compression operations on a first source dataset having a first size to generate a first compressed record,
performing one or more serialization operations to generate a first snapshot having a second size based on the first compressed record and at least a second compressed record, wherein the second size is less than the first size, and
wherein, when a data value included in the first snapshot and associated with the first source dataset is accessed from the first snapshot, the first size of the first snapshot is maintained. The system of claim 17, wherein the first compressed record comprises a fixed-length and bit-aligned sequence of bits. 19. The system of claim 17, wherein the one or more compression operations comprise a deduplication operation, an encoding operation, a packing operation, or an overhead elimination operation. 20. The system of claim 17, wherein the write state application further causes the processor to:
determine that the source dataset has been modified to generate a second source dataset; generate a second snapshot based on the second source dataset; and generate a delta file based on at least one difference between the first source dataset and the second source dataset, wherein the delta file includes one or more instructions for generating the second snapshot from the first snapshot. | In various embodiments, a write state application generates a snapshot that includes one or more data values associated with a source dataset. In operation, the write state application performs one or more compression operations on the source dataset to generate a first compressed record. The write state application then serializes the first compressed record and a second compressed record to generate a first compressed record list. Finally, the write state application generates the snapshot based on the first compressed record list. When the data values are accessed from the first snapshot, the size of the snapshot is maintained. Advantageously, because the size of the snapshot is smaller than the size of the source dataset, some consumers that are unable to store the entire source dataset in random access memory (RAM) are able to store the entire snapshot in RAM.1. A computer-implemented method, comprising:
performing one or more compression operations on a first source dataset having a first size to generate a first compressed record; serializing the first compressed record and a second compressed record to generate a first compressed record list; and generating a first snapshot having a second size based on the first compressed record list, wherein the second size is less than the first size, wherein, when one or more data values included in the first snapshot and associated with the first source dataset are accessed from the first snapshot, the first size of the first snapshot is maintained. 2. The computer-implemented method of claim 1, wherein the first compressed record list comprises a fixed-length and bit-aligned sequence of bits. 3. The computer-implemented method of claim 1, wherein generating the first snapshot comprises serializing the first compressed record list and at least a second compressed record list. 4. The computer-implemented method of claim 1, wherein the one or more compression operations comprise a deduplication operation, an encoding operation, a packing operation, or an overhead elimination operation. 5. The computer-implemented method of claim 1, further comprising:
determining that the source dataset has been modified to generate a second source dataset; generating a second snapshot based on the second source dataset; and generating a delta file based on at least one difference between the first source dataset and the second source dataset, wherein the delta file includes one or more instructions for generating the second snapshot from the first snapshot. 6. The computer-implemented method of claim 1, wherein an application programming interface provides a first method for accessing the one or more data values included in the first snapshot. 7. The computer-implemented method of claim 6, wherein the application programming interface further includes a second method for generating an index associated with the first compressed record list. 8. The computer-implemented method of claim 1, wherein the first compressed record is associated with a first schema included in a data model. 9. A non-transitory computer-readable storage medium including instructions that, when executed by a processor, cause the processor to perform the steps of:
performing one or more compression operations on a first source dataset having a first size based on a data model to generate a first compressed record; generating a first compressed record list based on the first compressed record and a second compressed record; and generating a first snapshot having a second size based on the first compressed record list, wherein the second size is less than the first size, wherein, when one or more data values included in the first snapshot and associated with the first source dataset are accessed from the first snapshot, the first size of the first snapshot is maintained. 10. The non-transitory computer-readable storage medium of claim 9, wherein the first compressed record list comprises a fixed-length and bit-aligned sequence of bits. 11. The non-transitory computer-readable storage medium of claim 9, wherein generating the first snapshot comprises serializing the first compressed record list and at least a second compressed record list. 12. The non-transitory computer-readable storage medium of claim 9, wherein the one or more compression operations comprise a deduplication operation, an encoding operation, a packing operation, or an overhead elimination operation. 13. The non-transitory computer-readable storage medium of claim 9, further comprising:
determining that the source dataset has been modified to generate a second source dataset; generating a second snapshot based on the second source dataset; and generating a delta file based on at least one difference between the first source dataset and the second source dataset, wherein the delta file includes one or more instructions for generating the second snapshot from the first snapshot. 14. The non-transitory computer-readable storage medium of claim 13, further comprising:
prior to determining that the source dataset has been modified, setting a latest version associated with a notification system equal to a first version associated with the first snapshot; and after generating the second snapshot, setting the latest version equal to a second version associated with the second snapshot. 15. The non-transitory computer-readable storage medium of claim 14, further comprising, prior to setting the latest version equal to the second version:
applying the delta file to the first snapshot to generate a third dataset; and determining that the third dataset matches the second snapshot. 16. The non-transitory computer-readable storage medium of claim 9, further comprising:
determining that the source dataset has been modified to generate a second source dataset; generating a second snapshot based on the second source dataset; and generating a reverse delta file based on a difference between the first source dataset and the second source dataset, wherein the reverse delta file includes one or more instructions for generating the first snapshot from the second snapshot. 17. A system comprising:
a memory storing a write state application; and a processor coupled to the memory, wherein when executed by the processor, the write state application causes the processor to:
perform one or more compression operations on a first source dataset having a first size to generate a first compressed record,
performing one or more serialization operations to generate a first snapshot having a second size based on the first compressed record and at least a second compressed record, wherein the second size is less than the first size, and
wherein, when a data value included in the first snapshot and associated with the first source dataset is accessed from the first snapshot, the first size of the first snapshot is maintained. The system of claim 17, wherein the first compressed record comprises a fixed-length and bit-aligned sequence of bits. 19. The system of claim 17, wherein the one or more compression operations comprise a deduplication operation, an encoding operation, a packing operation, or an overhead elimination operation. 20. The system of claim 17, wherein the write state application further causes the processor to:
determine that the source dataset has been modified to generate a second source dataset; generate a second snapshot based on the second source dataset; and generate a delta file based on at least one difference between the first source dataset and the second source dataset, wherein the delta file includes one or more instructions for generating the second snapshot from the first snapshot. | 2,100 |
6,425 | 6,425 | 14,098,118 | 2,154 | An embodiment provides a method, including: collecting, using a user device, user object event data; collecting, using a user device, contextual data related to the user object event data; creating, using at least one processor, an association between the contextual data and the user object event data; forming, using a processor having access to a stored group of associations between contextual data and user object event data, a user profile based on the group of associations; and storing, in a memory, the user profile. Other aspects are described and claimed. | 1. A method, comprising:
collecting, using a user device, user object event data; collecting, using a user device, contextual data related to the user object event data; creating, using at least one processor, an association between the contextual data and the user object event data; forming, using a processor having access to a stored group of associations between contextual data and user object event data, a user profile based on the group of associations; and storing, in a memory, the user profile. 2. The method of claim 1, wherein the user profile includes a pattern of user behavior with respect to user objects. 3. The method of claim 1, wherein the stored group of associations between contextual data and user object event data comprises associations gathered from a group of associated users. 4. The method of claim 3, wherein the group of associated users comprise users having similar patterns of behavior. 5. The method of claim 3, wherein the group of associated users comprises users having a cloud account association. 6. The method of claim 1, wherein the user profile comprises a description of user behavior that is refined in response to obtaining one or more new associations. 7. The method of claim 6, wherein the one or more new associations are derived from user interaction with a user object. 8. The method of claim 6, wherein the one or more new associations are derived from a group of users. 9. The method of claim 8, wherein the group of users is identified using a similarity metric. 10. The method of claim 9, wherein the similarity metric is a cloud account association. 11. An information handling device, comprising:
a processor; and a memory device that stores instructions executable by the processor to: collect user object event data; collect contextual data related to the user object event data; create an association between the contextual data and the user object event data; form, via access to a stored group of associations between contextual data and user object event data, a user profile based on the group of associations; and store, in a memory, the user profile. 12. The information handling device of claim 11, wherein the user profile includes a pattern of user behavior with respect to user objects. 13. The information handling device of claim 11, wherein the stored group of associations between contextual data and user object event data comprises associations gathered from a group of associated users. 14. The information handling device of claim 13, wherein the group of associated users comprise users having similar patterns of behavior. 15. The information handling device of claim 13, wherein the group of associated users comprises users having a cloud account association. 16. The information handling device of claim 11, wherein the user profile comprises a description of user behavior that is refined in response to obtaining one or more new associations. 17. The information handling device of claim 16, wherein the one or more new associations are derived from user interaction with a user object. 18. The information handling device of claim 16, wherein the one or more new associations are derived from a group of users. 19. The information handling device of claim 18, wherein the group of users is identified using a similarity metric. 20. A product, comprising:
a storage device having code stored therewith, the code comprising: code that collects, using a user device, user object event data; code that collects, using a user device, contextual data related to the user object event data; code that creates, using at least one processor, an association between the contextual data and the user object event data; code that forms, using a processor having access to a stored group of associations between contextual data and user object event data, a user profile based on the group of associations; and code that stores, in a memory, the user profile. | An embodiment provides a method, including: collecting, using a user device, user object event data; collecting, using a user device, contextual data related to the user object event data; creating, using at least one processor, an association between the contextual data and the user object event data; forming, using a processor having access to a stored group of associations between contextual data and user object event data, a user profile based on the group of associations; and storing, in a memory, the user profile. Other aspects are described and claimed.1. A method, comprising:
collecting, using a user device, user object event data; collecting, using a user device, contextual data related to the user object event data; creating, using at least one processor, an association between the contextual data and the user object event data; forming, using a processor having access to a stored group of associations between contextual data and user object event data, a user profile based on the group of associations; and storing, in a memory, the user profile. 2. The method of claim 1, wherein the user profile includes a pattern of user behavior with respect to user objects. 3. The method of claim 1, wherein the stored group of associations between contextual data and user object event data comprises associations gathered from a group of associated users. 4. The method of claim 3, wherein the group of associated users comprise users having similar patterns of behavior. 5. The method of claim 3, wherein the group of associated users comprises users having a cloud account association. 6. The method of claim 1, wherein the user profile comprises a description of user behavior that is refined in response to obtaining one or more new associations. 7. The method of claim 6, wherein the one or more new associations are derived from user interaction with a user object. 8. The method of claim 6, wherein the one or more new associations are derived from a group of users. 9. The method of claim 8, wherein the group of users is identified using a similarity metric. 10. The method of claim 9, wherein the similarity metric is a cloud account association. 11. An information handling device, comprising:
a processor; and a memory device that stores instructions executable by the processor to: collect user object event data; collect contextual data related to the user object event data; create an association between the contextual data and the user object event data; form, via access to a stored group of associations between contextual data and user object event data, a user profile based on the group of associations; and store, in a memory, the user profile. 12. The information handling device of claim 11, wherein the user profile includes a pattern of user behavior with respect to user objects. 13. The information handling device of claim 11, wherein the stored group of associations between contextual data and user object event data comprises associations gathered from a group of associated users. 14. The information handling device of claim 13, wherein the group of associated users comprise users having similar patterns of behavior. 15. The information handling device of claim 13, wherein the group of associated users comprises users having a cloud account association. 16. The information handling device of claim 11, wherein the user profile comprises a description of user behavior that is refined in response to obtaining one or more new associations. 17. The information handling device of claim 16, wherein the one or more new associations are derived from user interaction with a user object. 18. The information handling device of claim 16, wherein the one or more new associations are derived from a group of users. 19. The information handling device of claim 18, wherein the group of users is identified using a similarity metric. 20. A product, comprising:
a storage device having code stored therewith, the code comprising: code that collects, using a user device, user object event data; code that collects, using a user device, contextual data related to the user object event data; code that creates, using at least one processor, an association between the contextual data and the user object event data; code that forms, using a processor having access to a stored group of associations between contextual data and user object event data, a user profile based on the group of associations; and code that stores, in a memory, the user profile. | 2,100 |
6,426 | 6,426 | 15,610,806 | 2,133 | A data storage device can be configured with a data map that has one or more custom map attributes. A non-volatile memory of the data storage device may store data organized into a data map by a mapping module. The data map consisting of at least a data address translation and a custom attribute pertaining to an operational parameter of the data map with the custom attribute generated and maintained by the mapping module. | 1. An apparatus comprising a data storage device having a non-volatile memory storing data organized into a data map by a mapping module, the data map comprising a data address translation and a custom attribute pertaining to an operational parameter of the data map, the custom attribute generated and maintained by the mapping module. 2. The apparatus of claim 1, wherein the data address translation is from a logical block address to a physical block address in the non-volatile memory. 3. The apparatus of claim 1, wherein the custom attribute has a size of a single bit. 4. The apparatus of claim 1, wherein the custom attribute has a size of multiple bytes. 5. The apparatus of claim 1, wherein the data map comprises a plurality of data pages, each data page comprising a data string having a logical block address, physical block address, offset value, and status value. 6. The apparatus of claim 5, wherein the offset value and status value each identify data stored in the non-volatile memory. 7. The apparatus of claim 1, wherein the non-volatile memory is NAND flash. 8. The apparatus of claim 1, wherein the custom attribute has a smaller size than a logical block address of the data map. 9. The apparatus of claim 1, wherein the custom attribute identifies multiple different operational parameters of the data map. 10. A data storage device comprising a non-volatile memory storing data organized into a first data map and a second data map by a mapping module, the second data map comprising a first custom attribute pertaining to one or more operational parameters of the first data map, the first custom attribute generated and maintained by the mapping module. 11. The data storage device of claim 10, wherein the first data map describes each individual blocks of data resident in, the non-volatile memory. 12. The data storage device of claim 11, wherein the second data map identifies the location of each portion of the first data map. 13. The data storage device of claim 10, wherein the first and second data maps are stored in different types of memory. 14. The data storage device of claim 10, wherein the second data map comprises a second custom attribute pertaining to at least one operational parameter of the second data map. 15. The data storage device of claim 14, wherein the first and second custom attributes are different. 16. The data storage device of claim 14, wherein the first and second custom attributes are a common type of operational parameter and are different values. 17. A method comprising:
organizing a data storage device having a non-volatile memory storing data into a data map by a mapping module; generating a custom attribute with the mapping module, the custom attribute pertaining to an operational parameter of the data map; and maintaining the custom attribute with the mapping module in response to changing conditions in the data storage device. 18. The method of claim 17, wherein the mapping module identifies an unexpected event occurring in real-time and adjusts the custom attribute to maintain data storage device performance throughout the unexpected event. 19. The method of claim 17, wherein the mapping module predicts an event occurring and proactively adjusts the custom attribute to maintain data storage device performance throughout the predicted event. 20. The method of claim 17, wherein the mapping module predicts multiple different events and discards at least one predicted event in response to the accuracy of the predicted event being below an accuracy threshold. | A data storage device can be configured with a data map that has one or more custom map attributes. A non-volatile memory of the data storage device may store data organized into a data map by a mapping module. The data map consisting of at least a data address translation and a custom attribute pertaining to an operational parameter of the data map with the custom attribute generated and maintained by the mapping module.1. An apparatus comprising a data storage device having a non-volatile memory storing data organized into a data map by a mapping module, the data map comprising a data address translation and a custom attribute pertaining to an operational parameter of the data map, the custom attribute generated and maintained by the mapping module. 2. The apparatus of claim 1, wherein the data address translation is from a logical block address to a physical block address in the non-volatile memory. 3. The apparatus of claim 1, wherein the custom attribute has a size of a single bit. 4. The apparatus of claim 1, wherein the custom attribute has a size of multiple bytes. 5. The apparatus of claim 1, wherein the data map comprises a plurality of data pages, each data page comprising a data string having a logical block address, physical block address, offset value, and status value. 6. The apparatus of claim 5, wherein the offset value and status value each identify data stored in the non-volatile memory. 7. The apparatus of claim 1, wherein the non-volatile memory is NAND flash. 8. The apparatus of claim 1, wherein the custom attribute has a smaller size than a logical block address of the data map. 9. The apparatus of claim 1, wherein the custom attribute identifies multiple different operational parameters of the data map. 10. A data storage device comprising a non-volatile memory storing data organized into a first data map and a second data map by a mapping module, the second data map comprising a first custom attribute pertaining to one or more operational parameters of the first data map, the first custom attribute generated and maintained by the mapping module. 11. The data storage device of claim 10, wherein the first data map describes each individual blocks of data resident in, the non-volatile memory. 12. The data storage device of claim 11, wherein the second data map identifies the location of each portion of the first data map. 13. The data storage device of claim 10, wherein the first and second data maps are stored in different types of memory. 14. The data storage device of claim 10, wherein the second data map comprises a second custom attribute pertaining to at least one operational parameter of the second data map. 15. The data storage device of claim 14, wherein the first and second custom attributes are different. 16. The data storage device of claim 14, wherein the first and second custom attributes are a common type of operational parameter and are different values. 17. A method comprising:
organizing a data storage device having a non-volatile memory storing data into a data map by a mapping module; generating a custom attribute with the mapping module, the custom attribute pertaining to an operational parameter of the data map; and maintaining the custom attribute with the mapping module in response to changing conditions in the data storage device. 18. The method of claim 17, wherein the mapping module identifies an unexpected event occurring in real-time and adjusts the custom attribute to maintain data storage device performance throughout the unexpected event. 19. The method of claim 17, wherein the mapping module predicts an event occurring and proactively adjusts the custom attribute to maintain data storage device performance throughout the predicted event. 20. The method of claim 17, wherein the mapping module predicts multiple different events and discards at least one predicted event in response to the accuracy of the predicted event being below an accuracy threshold. | 2,100 |
6,427 | 6,427 | 15,723,883 | 2,161 | Methods and systems are provided for enhanced data loss prevention. The enhanced data loss prevention may be applied to data that is structured and/or semi-structured data, with the data comprising a plurality of records and a plurality of categories, with each record comprising a plurality of fields, each of which being associated with a different one of the plurality of categories. The enhanced data loss prevention may comprise selecting a subset of records of the data, with the selected subset comprising at least two records having between then one or more records not included in the subset. The fields of the selected subset may be scanned for sensitive information, and a likelihood to contain the sensitive information may be computed for each category based on the scanning. A subset of categories may be selected based on the computed likelihoods, and the sensitive information in the selected subset may be searched. | 1. A method comprising:
applying data loss prevention to data that comprises a plurality of records and a plurality of categories, with each record comprising a plurality of fields and each of the plurality of fields corresponding to a different one of the plurality of categories, the applying of data loss prevention comprising:
selecting a subset of records from the plurality of records of the data, wherein the selected subset of records comprises at least two records having between them at least one record not included in the selected subset of records;
scanning fields of the selected subset of records for sensitive information, wherein the fields of the selected subset of records comprise the plurality of fields of each record of the selected subset of records;
computing based on result of the scanning, for each category a likelihood to contain the sensitive information;
selecting a subset of categories based on the computed likelihoods of the categories to contain the sensitive information; and
searching the sensitive information in the selected subset of categories. 2. The method of claim 1, comprising determining a number of records of the selected subset of records based on a number of records and/or on a number of unique records in the data. 3. The method of claim 1, comprising determining a number of records of the selected subset of records based on a parameter of an infrastructure utilized in storing the data and/or applying the data loss prevention. 4. The method of claim 3, wherein the parameter comprises a speed of a processor applying the data loss prevention or a bandwidth of a communication link between the processor and a storage element where the data is stored. 5. The method of claim 1, wherein the likelihood to contain the sensitive information comprises more than two possible values of likelihood. 6. The method of claim 1, wherein the scanning of the fields of the selected subset of records for sensitive information comprises detecting a plurality of keywords and/or at least one rule in the fields of the selected subset of records. 7. The method of claim 6, wherein:
each of the plurality of keywords and/or rules is associated with a likelihood to contain the sensitive information; and the likelihood to contain the sensitive information in a category is computed based on the likelihood associated with the keywords and/or rules in the category. 8. The method of claim 1, wherein the sensitive information comprises at least two types of sensitive information. 9. The method of claim 8, comprising scanning each of the at least two types of sensitive information in the selected subset of records based on detecting a plurality of keywords and/or at one least one rule; wherein:
at least some of the keywords and/or the at least one rule are associated with different likelihoods to the at least two types of sensitive information; and the likelihood of a category to contain one of the at least two types of sensitive information in a field is based on the likelihood of the keywords and/or the at least one rule associated with this type of sensitive information which are found in this category. 10. The method of claim 8, wherein each keyword and/or rule associated with more of the at least two types of sensitive information is searched only once for detecting potential occurrences of all types of sensitive information related to the searched keyword and/or rule. 11. A system comprising:
a storage circuit operable to store data, wherein the data comprises a plurality of records and a plurality of categories, with each record comprising a plurality of fields and each of the plurality of fields corresponding to a different one of the plurality of categories; and a processor configured to apply data loss prevention to the data, the processor being operable to:
select a subset of records from the plurality of records of the data, wherein the selected subset of records comprises at least two records having between them at least one record not included in the selected subset of records;
scan fields of the selected subset of records for sensitive information, wherein the fields of the selected subset of records comprise the plurality of fields of each record of the selected subset of records;
compute based on result of the scanning, for each category a likelihood to contain the sensitive information;
select a subset of categories based on the computed likelihoods of the categories to contain the sensitive information; and
search the sensitive information in the selected subset of categories. 12. The system of claim 11, wherein the processor is operable to determine a number of records of the selected subset of records based on a number of records and/or on a number of unique records in the data. 13. The system of claim 11, wherein the processor is operable to determine a number of records of the selected subset of records based on a parameter of the system. 14. The system of claim 13, wherein the parameter comprises a speed of the processor and/or a bandwidth of a communication link between the storage circuit and the processor. 15. The system of claim 11, wherein the likelihood to contain the sensitive information comprises more than two possible values of likelihood. 16. The system of claim 11, wherein the scanning of the fields of the selected subset of records for sensitive information comprises detecting a plurality of keywords and/or at least one rule in the fields of the selected subset of records. 17. The system of claim 16, wherein:
each of the plurality of keywords and/or rules is associated with a likelihood to contain the sensitive information; and the likelihood to contain the sensitive information in a category is computed based on the likelihood associated with the keywords and/or rules in the category. 18. The system of claim 11, wherein the sensitive information comprises at least two types of sensitive information. 19. The system of claim 18, wherein the processor is operable to scan each of the at least two types of sensitive information in the selected subset of records based on detecting a plurality of keywords and/or at one least one rule; wherein:
at least some of the keywords and/or the at least one rule are associated with different likelihoods to the at least two types of sensitive information; and the likelihood of a category to contain one of the at least two types of sensitive information in a field is based on the likelihood of the keywords and/or the at least one rule associated with this type of sensitive information which are found in this category. 20. The system of claim 18, wherein each keyword and/or rule associated with more of the at least two types of sensitive information is searched only once for detecting potential occurrences of all types of sensitive information related to the searched keyword and/or rule. | Methods and systems are provided for enhanced data loss prevention. The enhanced data loss prevention may be applied to data that is structured and/or semi-structured data, with the data comprising a plurality of records and a plurality of categories, with each record comprising a plurality of fields, each of which being associated with a different one of the plurality of categories. The enhanced data loss prevention may comprise selecting a subset of records of the data, with the selected subset comprising at least two records having between then one or more records not included in the subset. The fields of the selected subset may be scanned for sensitive information, and a likelihood to contain the sensitive information may be computed for each category based on the scanning. A subset of categories may be selected based on the computed likelihoods, and the sensitive information in the selected subset may be searched.1. A method comprising:
applying data loss prevention to data that comprises a plurality of records and a plurality of categories, with each record comprising a plurality of fields and each of the plurality of fields corresponding to a different one of the plurality of categories, the applying of data loss prevention comprising:
selecting a subset of records from the plurality of records of the data, wherein the selected subset of records comprises at least two records having between them at least one record not included in the selected subset of records;
scanning fields of the selected subset of records for sensitive information, wherein the fields of the selected subset of records comprise the plurality of fields of each record of the selected subset of records;
computing based on result of the scanning, for each category a likelihood to contain the sensitive information;
selecting a subset of categories based on the computed likelihoods of the categories to contain the sensitive information; and
searching the sensitive information in the selected subset of categories. 2. The method of claim 1, comprising determining a number of records of the selected subset of records based on a number of records and/or on a number of unique records in the data. 3. The method of claim 1, comprising determining a number of records of the selected subset of records based on a parameter of an infrastructure utilized in storing the data and/or applying the data loss prevention. 4. The method of claim 3, wherein the parameter comprises a speed of a processor applying the data loss prevention or a bandwidth of a communication link between the processor and a storage element where the data is stored. 5. The method of claim 1, wherein the likelihood to contain the sensitive information comprises more than two possible values of likelihood. 6. The method of claim 1, wherein the scanning of the fields of the selected subset of records for sensitive information comprises detecting a plurality of keywords and/or at least one rule in the fields of the selected subset of records. 7. The method of claim 6, wherein:
each of the plurality of keywords and/or rules is associated with a likelihood to contain the sensitive information; and the likelihood to contain the sensitive information in a category is computed based on the likelihood associated with the keywords and/or rules in the category. 8. The method of claim 1, wherein the sensitive information comprises at least two types of sensitive information. 9. The method of claim 8, comprising scanning each of the at least two types of sensitive information in the selected subset of records based on detecting a plurality of keywords and/or at one least one rule; wherein:
at least some of the keywords and/or the at least one rule are associated with different likelihoods to the at least two types of sensitive information; and the likelihood of a category to contain one of the at least two types of sensitive information in a field is based on the likelihood of the keywords and/or the at least one rule associated with this type of sensitive information which are found in this category. 10. The method of claim 8, wherein each keyword and/or rule associated with more of the at least two types of sensitive information is searched only once for detecting potential occurrences of all types of sensitive information related to the searched keyword and/or rule. 11. A system comprising:
a storage circuit operable to store data, wherein the data comprises a plurality of records and a plurality of categories, with each record comprising a plurality of fields and each of the plurality of fields corresponding to a different one of the plurality of categories; and a processor configured to apply data loss prevention to the data, the processor being operable to:
select a subset of records from the plurality of records of the data, wherein the selected subset of records comprises at least two records having between them at least one record not included in the selected subset of records;
scan fields of the selected subset of records for sensitive information, wherein the fields of the selected subset of records comprise the plurality of fields of each record of the selected subset of records;
compute based on result of the scanning, for each category a likelihood to contain the sensitive information;
select a subset of categories based on the computed likelihoods of the categories to contain the sensitive information; and
search the sensitive information in the selected subset of categories. 12. The system of claim 11, wherein the processor is operable to determine a number of records of the selected subset of records based on a number of records and/or on a number of unique records in the data. 13. The system of claim 11, wherein the processor is operable to determine a number of records of the selected subset of records based on a parameter of the system. 14. The system of claim 13, wherein the parameter comprises a speed of the processor and/or a bandwidth of a communication link between the storage circuit and the processor. 15. The system of claim 11, wherein the likelihood to contain the sensitive information comprises more than two possible values of likelihood. 16. The system of claim 11, wherein the scanning of the fields of the selected subset of records for sensitive information comprises detecting a plurality of keywords and/or at least one rule in the fields of the selected subset of records. 17. The system of claim 16, wherein:
each of the plurality of keywords and/or rules is associated with a likelihood to contain the sensitive information; and the likelihood to contain the sensitive information in a category is computed based on the likelihood associated with the keywords and/or rules in the category. 18. The system of claim 11, wherein the sensitive information comprises at least two types of sensitive information. 19. The system of claim 18, wherein the processor is operable to scan each of the at least two types of sensitive information in the selected subset of records based on detecting a plurality of keywords and/or at one least one rule; wherein:
at least some of the keywords and/or the at least one rule are associated with different likelihoods to the at least two types of sensitive information; and the likelihood of a category to contain one of the at least two types of sensitive information in a field is based on the likelihood of the keywords and/or the at least one rule associated with this type of sensitive information which are found in this category. 20. The system of claim 18, wherein each keyword and/or rule associated with more of the at least two types of sensitive information is searched only once for detecting potential occurrences of all types of sensitive information related to the searched keyword and/or rule. | 2,100 |
6,428 | 6,428 | 13,797,317 | 2,139 | A RAID controller attached to a storage network can detect the presence of multiple pathways to the same physical storage device. A path collection module can dynamically maintain all valid pathways to all attached storage devices. A path selection module can automatically and dynamically balance and rebalance desired paths to each storage device so as to simultaneously optimize data flow and provide continuity of I/O service throughout the attached storage network. | 1. A redundant array of independent disks (RAID) controller associated with a plurality of storage devices attached to said RAID controller, comprising:
an initiator I/O module for receiving data storage commands; a plurality of target ports for communicating with said plurality of storage devices; a network discovery module configured to identify said storage devices; a storage device interface communicating with one or more of said storage devices; a RAID I/O module directing said data storage commands to said storage device interface; a path collection module maintaining a set of active target paths available to said storage devices; a path selection module operable to select desired target paths from one or more of said active target paths, wherein said path selection module is configured to automatically configure the desired target paths to individual storage devices as a function of characteristic parameters of said target paths; and wherein said RAID controller generates one or more storage device I/O requests to said storage devices using one of the desired target paths determined by said path selection module. 2. The RAID controller of claim 1, wherein said characteristic parameter is the number of available active target paths for each unique storage device. 3. The RAID controller of claim 1, wherein the characteristic parameter is the number of active target paths per target port. 4. The RAID controller of claim 1, wherein the characteristic parameter is the number of active target paths per endpoint network device. 5. A redundant array of independent disks (RAID) controller associated with a plurality of storage devices attached to said RAID controller, comprising:
an initiator I/O module for receiving data storage commands; a plurality of target ports for communicating with said plurality of storage devices, said plurality of storage devices comprising a RAID group; a network discovery module configured to identify said storage devices; a storage device interface communicating with one or more of said storage devices; a RAID I/O module directing said data storage commands to said storage device interface; a path collection module maintaining a set of active target paths to said RAID group; a RAID configuration module for maintaining characteristic parameters of said storage devices that comprise said RAID group; a path selection module operable to select desired target paths from one or more of said active target paths, wherein said path selection module is configured to automatically configure the desired target path to one of said storage devices in said RAID group based on said characteristic parameters of said storage devices; wherein said RAID controller generates one or more storage device I/O requests to said storage devices using one of the desired target paths determined by said path selection module. 6. The RAID controller of claim 5, wherein said characteristic parameter is the number of available active target paths for each storage device. 7. The RAID controller of claim 5, wherein said characteristic parameter is the number of active target paths per target port. 8. The RAID controller of claim 5, wherein said characteristic parameter is the number of active paths per endpoint network device. 9. A redundant array of independent disks (RAID) controller associated with a plurality of storage devices attached to said RAID controller, comprising:
an initiator I/O module for receiving data storage commands; a plurality of target ports for communicating with said plurality of storage devices, said plurality of storage devices comprising a RAID group; a network discovery module configured to identify said storage devices; a storage device interface communicating with one or more of said storage devices; a RAID I/O module directing said data storage commands to said storage device interface; a path collection module maintaining a set of active target paths to said RAID group; a RAID configuration module for maintaining characteristic parameters of said storage devices that comprise said RAID group; a path selection module operable to select desired target paths from one or more of said active target paths, wherein said path selection module is configured to dynamically reconfigure desired target paths to said storage devices as a function of a characteristic triggering event; and wherein said RAID controller generates one or more storage device I/O requests to said storage devices using one of the desired target paths determined by said path selection module. 10. The RAID controller of claim 9, wherein said characteristic triggering event is a newly created RAID group. 11. The RAID controller of claim 9, wherein said characteristic triggering event is a deleted RAID group. 12. The RAID controller of claim 9, wherein said characteristic triggering event is a RAID group configuration change, wherein the configuration change comprises one of expansion of the RAID group, migration of the RAID group, drive failures within said RAID group, and replacement of an individual storage device of said RAID group. 13. The RAID controller of claim 9, wherein said characteristic triggering event is one of a loss of a previously discovered path and reconnection of a previously discovered path to a storage device of said RAID group. 14. The RAID controller of claim 9, wherein said characteristic triggering event is a newly discovered path to a storage device of said RAID group. 15. A redundant array of independent disks (RAID) controller associated with a plurality of storage devices attached to said RAID controller, comprising:
an initiator I/O module for receiving data storage commands; a plurality of target ports for communicating with said plurality of storage devices, said plurality of storage devices comprising a collection of one or more RAID groups; a network discovery module configured to identify said storage devices; a storage device interface communicating with one or more of said storage devices; a RAID I/O module directing said data storage commands to said storage device interface; a path collection module maintaining a set of active target paths to said collection of RAID groups; a RAID configuration module for maintaining characteristic parameters of said storage devices that comprise said collection of RAID groups; a path selection module operable to select desired target paths from one or more of said active target paths, wherein said path selection module is configured to automatically configure desired target paths to said storage devices as a function of the collection of RAID groups and associated member paths; wherein said RAID controller generates one or more storage device I/O requests to said storage devices using one of the desired target paths determined by said path selection module. | A RAID controller attached to a storage network can detect the presence of multiple pathways to the same physical storage device. A path collection module can dynamically maintain all valid pathways to all attached storage devices. A path selection module can automatically and dynamically balance and rebalance desired paths to each storage device so as to simultaneously optimize data flow and provide continuity of I/O service throughout the attached storage network.1. A redundant array of independent disks (RAID) controller associated with a plurality of storage devices attached to said RAID controller, comprising:
an initiator I/O module for receiving data storage commands; a plurality of target ports for communicating with said plurality of storage devices; a network discovery module configured to identify said storage devices; a storage device interface communicating with one or more of said storage devices; a RAID I/O module directing said data storage commands to said storage device interface; a path collection module maintaining a set of active target paths available to said storage devices; a path selection module operable to select desired target paths from one or more of said active target paths, wherein said path selection module is configured to automatically configure the desired target paths to individual storage devices as a function of characteristic parameters of said target paths; and wherein said RAID controller generates one or more storage device I/O requests to said storage devices using one of the desired target paths determined by said path selection module. 2. The RAID controller of claim 1, wherein said characteristic parameter is the number of available active target paths for each unique storage device. 3. The RAID controller of claim 1, wherein the characteristic parameter is the number of active target paths per target port. 4. The RAID controller of claim 1, wherein the characteristic parameter is the number of active target paths per endpoint network device. 5. A redundant array of independent disks (RAID) controller associated with a plurality of storage devices attached to said RAID controller, comprising:
an initiator I/O module for receiving data storage commands; a plurality of target ports for communicating with said plurality of storage devices, said plurality of storage devices comprising a RAID group; a network discovery module configured to identify said storage devices; a storage device interface communicating with one or more of said storage devices; a RAID I/O module directing said data storage commands to said storage device interface; a path collection module maintaining a set of active target paths to said RAID group; a RAID configuration module for maintaining characteristic parameters of said storage devices that comprise said RAID group; a path selection module operable to select desired target paths from one or more of said active target paths, wherein said path selection module is configured to automatically configure the desired target path to one of said storage devices in said RAID group based on said characteristic parameters of said storage devices; wherein said RAID controller generates one or more storage device I/O requests to said storage devices using one of the desired target paths determined by said path selection module. 6. The RAID controller of claim 5, wherein said characteristic parameter is the number of available active target paths for each storage device. 7. The RAID controller of claim 5, wherein said characteristic parameter is the number of active target paths per target port. 8. The RAID controller of claim 5, wherein said characteristic parameter is the number of active paths per endpoint network device. 9. A redundant array of independent disks (RAID) controller associated with a plurality of storage devices attached to said RAID controller, comprising:
an initiator I/O module for receiving data storage commands; a plurality of target ports for communicating with said plurality of storage devices, said plurality of storage devices comprising a RAID group; a network discovery module configured to identify said storage devices; a storage device interface communicating with one or more of said storage devices; a RAID I/O module directing said data storage commands to said storage device interface; a path collection module maintaining a set of active target paths to said RAID group; a RAID configuration module for maintaining characteristic parameters of said storage devices that comprise said RAID group; a path selection module operable to select desired target paths from one or more of said active target paths, wherein said path selection module is configured to dynamically reconfigure desired target paths to said storage devices as a function of a characteristic triggering event; and wherein said RAID controller generates one or more storage device I/O requests to said storage devices using one of the desired target paths determined by said path selection module. 10. The RAID controller of claim 9, wherein said characteristic triggering event is a newly created RAID group. 11. The RAID controller of claim 9, wherein said characteristic triggering event is a deleted RAID group. 12. The RAID controller of claim 9, wherein said characteristic triggering event is a RAID group configuration change, wherein the configuration change comprises one of expansion of the RAID group, migration of the RAID group, drive failures within said RAID group, and replacement of an individual storage device of said RAID group. 13. The RAID controller of claim 9, wherein said characteristic triggering event is one of a loss of a previously discovered path and reconnection of a previously discovered path to a storage device of said RAID group. 14. The RAID controller of claim 9, wherein said characteristic triggering event is a newly discovered path to a storage device of said RAID group. 15. A redundant array of independent disks (RAID) controller associated with a plurality of storage devices attached to said RAID controller, comprising:
an initiator I/O module for receiving data storage commands; a plurality of target ports for communicating with said plurality of storage devices, said plurality of storage devices comprising a collection of one or more RAID groups; a network discovery module configured to identify said storage devices; a storage device interface communicating with one or more of said storage devices; a RAID I/O module directing said data storage commands to said storage device interface; a path collection module maintaining a set of active target paths to said collection of RAID groups; a RAID configuration module for maintaining characteristic parameters of said storage devices that comprise said collection of RAID groups; a path selection module operable to select desired target paths from one or more of said active target paths, wherein said path selection module is configured to automatically configure desired target paths to said storage devices as a function of the collection of RAID groups and associated member paths; wherein said RAID controller generates one or more storage device I/O requests to said storage devices using one of the desired target paths determined by said path selection module. | 2,100 |
6,429 | 6,429 | 15,404,136 | 2,153 | A server system receives a plurality of comments on a post in an online service, receives feedback on respective comments of the plurality of comments from users of the online service and retrieves feedback weights for the users. The server system ranks the plurality of comments using the feedback and feedback weights and provides the plurality of comments, ordered in accordance with the ranking, for display. | 1. A method, comprising:
at a server system having one or more processors and memory storing instructions for execution by the one or more processors:
receiving a plurality of comments on a post in an online service;
receiving feedback on respective comments of the plurality of comments from users of the online service;
retrieving feedback weights for the users;
ranking the plurality of comments using the feedback and the feedback weights; and
providing the plurality of comments, ordered in accordance with the ranking, for display. 2. The method of claim 1, wherein ranking the plurality of comments comprises:
for a first comment of the plurality of comments, summing the feedback weights of the users who have provided feedback on the first comment. 3. The method of claim 1, further comprising, at the server system:
determining the feedback weights for the users; and storing the feedback weights. 4. The method of claim 3, wherein determining the feedback weights comprises:
obtaining a collection of user comments from a plurality of users on posts in the online service, wherein respective comments of the collection have been assigned respective ratings; determining a correlation between the respective comments and users who have provided feedback on the respective comments; and calculating the feedback weights based at least in part on the correlation. 5. The method of claim 4 wherein:
determining the correlation between the respective comments and users comprises training a machine-learning model using the respective comments and their assigned respective ratings; and
calculating the feedback weights comprises applying the machine-learning model to the feedback provided by the users. 6. The method of claim 1, wherein the feedback comprises indications that users like respective comments. 7. The method of claim 1, wherein ranking the plurality of comments comprises:
for a first comment of the plurality of comments, using a maximum feedback weight of the feedback weights of the users who provided feedback on the first comment. 8. The method of claim 1, wherein ranking the plurality of comments comprises:
for a first comment of the plurality of comments, using a minimum feedback weight of the feedback weights of the users who provided feedback on the first comment. 9. The method of claim 1, wherein a respective user has multiple feedback weights for different categories of subjects. 10. The method of claim 1, further comprising, at the server system, periodically updating the feedback weights for the users. 11. The method of claim 1, further comprising, at the server system, decreasing the feedback weight of a respective user in response to the respective user providing feedback that is contrary to a trend. 12. The method of claim 1, wherein:
the feedback weight of a new user is zero; and ranking the plurality of comments comprises ignoring feedback from the new user, in accordance with the zero feedback weight. 13. The method of claim 1, further comprising, at the server system, determining the feedback weight of a respective user based at least in part on timeliness of feedback from the respective user. 14. The method of claim 1, further comprising, at the server system, determining the feedback weight of a respective user based at least in part on historical data indicating whether the respective user's feedback anticipates trends. 15. The method of claim 1, further comprising, at the server system, determining the feedback weight of a respective user based at least in part on an age of an account of the respective user on the online service. 16. The method of claim 1, further comprising, at the server system, determining the feedback weight of a respective user based at least in part on a correlation between a ranking of a comment and whether feedback provided by the respective user on the comment is positive or negative. 17. The method of claim 16, wherein determining the feedback weight of the respective user comprises assigning a negative feedback weight to the respective user based at least in part on a determination that the respective user provided positive feedback on the comment and that the comment does not satisfy a ranking threshold. 18. The method of claim 16, wherein determining the feedback weight of the respective user comprises reducing the feedback weight of the respective user based at least in part on a determination that the respective user provided positive feedback on the comment and that the comment does not satisfy a ranking threshold. 19. A server system, comprising:
one or more processors; and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for:
receiving a plurality of comments on a post in an online service;
receiving feedback on respective comments of the plurality of comments from users of the online service;
retrieving feedback weights for the users;
ranking the plurality of comments using the feedback and the feedback weights; and
displaying the plurality of comments in accordance with the ranking. 20. A non-transitory computer-readable storage medium storing one or more programs for execution by one or more processors, the one or more programs including instructions for:
receiving a plurality of comments on a post in an online service; receiving feedback on respective comments of the plurality of comments from users of the online service; retrieving feedback weights for the users; ranking the plurality of comments using the feedback and the feedback weights; and displaying the plurality of comments in accordance with the ranking. | A server system receives a plurality of comments on a post in an online service, receives feedback on respective comments of the plurality of comments from users of the online service and retrieves feedback weights for the users. The server system ranks the plurality of comments using the feedback and feedback weights and provides the plurality of comments, ordered in accordance with the ranking, for display.1. A method, comprising:
at a server system having one or more processors and memory storing instructions for execution by the one or more processors:
receiving a plurality of comments on a post in an online service;
receiving feedback on respective comments of the plurality of comments from users of the online service;
retrieving feedback weights for the users;
ranking the plurality of comments using the feedback and the feedback weights; and
providing the plurality of comments, ordered in accordance with the ranking, for display. 2. The method of claim 1, wherein ranking the plurality of comments comprises:
for a first comment of the plurality of comments, summing the feedback weights of the users who have provided feedback on the first comment. 3. The method of claim 1, further comprising, at the server system:
determining the feedback weights for the users; and storing the feedback weights. 4. The method of claim 3, wherein determining the feedback weights comprises:
obtaining a collection of user comments from a plurality of users on posts in the online service, wherein respective comments of the collection have been assigned respective ratings; determining a correlation between the respective comments and users who have provided feedback on the respective comments; and calculating the feedback weights based at least in part on the correlation. 5. The method of claim 4 wherein:
determining the correlation between the respective comments and users comprises training a machine-learning model using the respective comments and their assigned respective ratings; and
calculating the feedback weights comprises applying the machine-learning model to the feedback provided by the users. 6. The method of claim 1, wherein the feedback comprises indications that users like respective comments. 7. The method of claim 1, wherein ranking the plurality of comments comprises:
for a first comment of the plurality of comments, using a maximum feedback weight of the feedback weights of the users who provided feedback on the first comment. 8. The method of claim 1, wherein ranking the plurality of comments comprises:
for a first comment of the plurality of comments, using a minimum feedback weight of the feedback weights of the users who provided feedback on the first comment. 9. The method of claim 1, wherein a respective user has multiple feedback weights for different categories of subjects. 10. The method of claim 1, further comprising, at the server system, periodically updating the feedback weights for the users. 11. The method of claim 1, further comprising, at the server system, decreasing the feedback weight of a respective user in response to the respective user providing feedback that is contrary to a trend. 12. The method of claim 1, wherein:
the feedback weight of a new user is zero; and ranking the plurality of comments comprises ignoring feedback from the new user, in accordance with the zero feedback weight. 13. The method of claim 1, further comprising, at the server system, determining the feedback weight of a respective user based at least in part on timeliness of feedback from the respective user. 14. The method of claim 1, further comprising, at the server system, determining the feedback weight of a respective user based at least in part on historical data indicating whether the respective user's feedback anticipates trends. 15. The method of claim 1, further comprising, at the server system, determining the feedback weight of a respective user based at least in part on an age of an account of the respective user on the online service. 16. The method of claim 1, further comprising, at the server system, determining the feedback weight of a respective user based at least in part on a correlation between a ranking of a comment and whether feedback provided by the respective user on the comment is positive or negative. 17. The method of claim 16, wherein determining the feedback weight of the respective user comprises assigning a negative feedback weight to the respective user based at least in part on a determination that the respective user provided positive feedback on the comment and that the comment does not satisfy a ranking threshold. 18. The method of claim 16, wherein determining the feedback weight of the respective user comprises reducing the feedback weight of the respective user based at least in part on a determination that the respective user provided positive feedback on the comment and that the comment does not satisfy a ranking threshold. 19. A server system, comprising:
one or more processors; and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for:
receiving a plurality of comments on a post in an online service;
receiving feedback on respective comments of the plurality of comments from users of the online service;
retrieving feedback weights for the users;
ranking the plurality of comments using the feedback and the feedback weights; and
displaying the plurality of comments in accordance with the ranking. 20. A non-transitory computer-readable storage medium storing one or more programs for execution by one or more processors, the one or more programs including instructions for:
receiving a plurality of comments on a post in an online service; receiving feedback on respective comments of the plurality of comments from users of the online service; retrieving feedback weights for the users; ranking the plurality of comments using the feedback and the feedback weights; and displaying the plurality of comments in accordance with the ranking. | 2,100 |
6,430 | 6,430 | 13,295,404 | 2,176 | A system and a method are provided for displaying message history while composing a message. The method includes displaying a message composition application comprising a first recipient field, a message history display area, and a message composition field; detecting an input into the first recipient field for specifying a recipient; and upon detecting that the recipient has been specified: displaying a second recipient field; navigating application focus from the first recipient field to the second recipient field; and displaying a message history associated with the recipient in the message history display area. | 1. A method of operating a mobile device comprising:
displaying a message composition application comprising a first recipient field, a message history display area, and a message composition field; detecting an input into the first recipient field for specifying a recipient; and upon detecting that the recipient has been specified:
displaying a second recipient field;
navigating application focus from the first recipient field to the second recipient field; and
displaying a message history associated with the recipient in the message history display area. 2. The method of claim 1 further comprising, when detecting application focus has moved from the second recipient field to the message composition field, continuing to display the first recipient field and the second recipient field. 3. The method of claim 1 further comprising receiving text input in the message composition field before receiving the input in the first recipient field for specifying the recipient. 4. The method of claim 1 further comprising:
receiving text in the first recipient field;
displaying a list of candidate recipients corresponding to the text in the first recipient field, the list also comprising the recipient;
detecting that a cursor has been positioned over the recipient in the list;
displaying the message history associated with the recipient in the message history display area; and
receiving a selection input specifying the recipient, wherein the selection input is the input received in the first recipient field. 5. The method of claim 4 further comprising, before receiving the selection input specifying the recipient, detecting that the cursor has been positioned over a candidate recipient, and displaying message history associated with the candidate recipient in the message history display area. 6. The method of claim 1 wherein the message history shown in the message history display area is a portion of a complete message history associated with the recipient, and a link is provided in the message composition application to navigate between different portions of the complete message history. 7. The method of claim 6 wherein the message history shown in the message history display area is the most recent portion of the complete message history, and the link, when selected, navigates to an older portion of the complete message history. 8. The method of claim 1 wherein the recipient that has been specified is a contact entry unifying multiple recipient contact addresses, and the message history displayed comprises a combination of messages exchanged with any one of the multiple recipient contact addresses. 9. The method of claim 8 wherein each of the recipient contact addresses have identical names associated with the contact entry. 10. The method of claim 1 wherein the message is a text message, which is exchanged using a short messaging service protocol or a multimedia messaging service protocol. 11. The method of claim 1 further comprising receiving an additional input specifying an additional recipient, and replacing the message history associated with the recipient with a message history associated with the additional recipient. 12. A computer readable medium comprising computer executable instructions for displaying message history while composing a message, the computer executable instructions comprising:
displaying a message composition application comprising a first recipient field, a message history display area, and a message composition field; detecting an input into the first recipient field for specifying a recipient; and upon detecting that the recipient has been specified:
displaying a second recipient field;
navigating application focus from the first recipient field to the second recipient field; and
displaying a message history associated with the recipient in the message history display area. 13. The computer readable medium of claim 12 further comprising, when detecting application focus has moved from the second recipient field to the message composition field, continuing to display the first recipient field and the second recipient field. 14. The computer readable medium of claim 12 further comprising receiving text input in the message composition field before receiving the input in the first recipient field for specifying the recipient. 15. The computer readable medium of claim 12 further comprising:
receiving text in the first recipient field;
displaying a list of candidate recipients corresponding to the text in the first recipient field, the list also comprising the recipient;
detecting that a cursor has been positioned over the recipient in the list;
displaying the message history associated with the recipient in the message history display area; and
receiving a selection input specifying the recipient, wherein the selection input is the input received in the first recipient field. 16. The computer readable medium of claim 15 further comprising, before receiving the selection input specifying the recipient, detecting that the cursor has been positioned over a candidate recipient, and displaying message history associated with the candidate recipient in the message history display area. 17. The computer readable medium of claim 12 wherein the message history shown in the message history display area is a portion of a complete message history associated with the recipient, and a link is provided in the message composition application to navigate between different portions of the complete message history. 18. The computer readable medium of claim 17 wherein the message history shown in the message history display area is the most recent portion of the complete message history, and the link, when selected, navigates to an older portion of the complete message history. 19. The computer readable medium of claim 12 wherein the recipient that has been specified is a contact entry unifying multiple recipient contact addresses, and the message history displayed comprises a combination of messages exchanged with any one of the multiple recipient contact addresses. 20. The computer readable medium of claim 19 wherein each of the recipient contact addresses have identical names associated with the contact entry. 21. The computer readable medium of claim 12 wherein the message is a text message, which is exchanged using a short messaging service protocol or a multimedia messaging service protocol. 22. The computer readable medium of claim 12 further comprising receiving an additional input specifying an additional recipient, and replacing the message history associated with the recipient with a message history associated with the additional recipient. 23. A mobile device comprising a processor, a display, and memory, the memory comprising computer executable instructions for displaying message history on the display while composing a message, the computer executable instructions comprising:
displaying a message composition application comprising a first recipient field, a message history display area, and a message composition field; detecting an input into the first recipient field for specifying a recipient; and upon detecting that the recipient has been specified:
displaying a second recipient field;
navigating application focus from the first recipient field to the second recipient field; and
displaying a message history associated with the recipient in the message history display area. | A system and a method are provided for displaying message history while composing a message. The method includes displaying a message composition application comprising a first recipient field, a message history display area, and a message composition field; detecting an input into the first recipient field for specifying a recipient; and upon detecting that the recipient has been specified: displaying a second recipient field; navigating application focus from the first recipient field to the second recipient field; and displaying a message history associated with the recipient in the message history display area.1. A method of operating a mobile device comprising:
displaying a message composition application comprising a first recipient field, a message history display area, and a message composition field; detecting an input into the first recipient field for specifying a recipient; and upon detecting that the recipient has been specified:
displaying a second recipient field;
navigating application focus from the first recipient field to the second recipient field; and
displaying a message history associated with the recipient in the message history display area. 2. The method of claim 1 further comprising, when detecting application focus has moved from the second recipient field to the message composition field, continuing to display the first recipient field and the second recipient field. 3. The method of claim 1 further comprising receiving text input in the message composition field before receiving the input in the first recipient field for specifying the recipient. 4. The method of claim 1 further comprising:
receiving text in the first recipient field;
displaying a list of candidate recipients corresponding to the text in the first recipient field, the list also comprising the recipient;
detecting that a cursor has been positioned over the recipient in the list;
displaying the message history associated with the recipient in the message history display area; and
receiving a selection input specifying the recipient, wherein the selection input is the input received in the first recipient field. 5. The method of claim 4 further comprising, before receiving the selection input specifying the recipient, detecting that the cursor has been positioned over a candidate recipient, and displaying message history associated with the candidate recipient in the message history display area. 6. The method of claim 1 wherein the message history shown in the message history display area is a portion of a complete message history associated with the recipient, and a link is provided in the message composition application to navigate between different portions of the complete message history. 7. The method of claim 6 wherein the message history shown in the message history display area is the most recent portion of the complete message history, and the link, when selected, navigates to an older portion of the complete message history. 8. The method of claim 1 wherein the recipient that has been specified is a contact entry unifying multiple recipient contact addresses, and the message history displayed comprises a combination of messages exchanged with any one of the multiple recipient contact addresses. 9. The method of claim 8 wherein each of the recipient contact addresses have identical names associated with the contact entry. 10. The method of claim 1 wherein the message is a text message, which is exchanged using a short messaging service protocol or a multimedia messaging service protocol. 11. The method of claim 1 further comprising receiving an additional input specifying an additional recipient, and replacing the message history associated with the recipient with a message history associated with the additional recipient. 12. A computer readable medium comprising computer executable instructions for displaying message history while composing a message, the computer executable instructions comprising:
displaying a message composition application comprising a first recipient field, a message history display area, and a message composition field; detecting an input into the first recipient field for specifying a recipient; and upon detecting that the recipient has been specified:
displaying a second recipient field;
navigating application focus from the first recipient field to the second recipient field; and
displaying a message history associated with the recipient in the message history display area. 13. The computer readable medium of claim 12 further comprising, when detecting application focus has moved from the second recipient field to the message composition field, continuing to display the first recipient field and the second recipient field. 14. The computer readable medium of claim 12 further comprising receiving text input in the message composition field before receiving the input in the first recipient field for specifying the recipient. 15. The computer readable medium of claim 12 further comprising:
receiving text in the first recipient field;
displaying a list of candidate recipients corresponding to the text in the first recipient field, the list also comprising the recipient;
detecting that a cursor has been positioned over the recipient in the list;
displaying the message history associated with the recipient in the message history display area; and
receiving a selection input specifying the recipient, wherein the selection input is the input received in the first recipient field. 16. The computer readable medium of claim 15 further comprising, before receiving the selection input specifying the recipient, detecting that the cursor has been positioned over a candidate recipient, and displaying message history associated with the candidate recipient in the message history display area. 17. The computer readable medium of claim 12 wherein the message history shown in the message history display area is a portion of a complete message history associated with the recipient, and a link is provided in the message composition application to navigate between different portions of the complete message history. 18. The computer readable medium of claim 17 wherein the message history shown in the message history display area is the most recent portion of the complete message history, and the link, when selected, navigates to an older portion of the complete message history. 19. The computer readable medium of claim 12 wherein the recipient that has been specified is a contact entry unifying multiple recipient contact addresses, and the message history displayed comprises a combination of messages exchanged with any one of the multiple recipient contact addresses. 20. The computer readable medium of claim 19 wherein each of the recipient contact addresses have identical names associated with the contact entry. 21. The computer readable medium of claim 12 wherein the message is a text message, which is exchanged using a short messaging service protocol or a multimedia messaging service protocol. 22. The computer readable medium of claim 12 further comprising receiving an additional input specifying an additional recipient, and replacing the message history associated with the recipient with a message history associated with the additional recipient. 23. A mobile device comprising a processor, a display, and memory, the memory comprising computer executable instructions for displaying message history on the display while composing a message, the computer executable instructions comprising:
displaying a message composition application comprising a first recipient field, a message history display area, and a message composition field; detecting an input into the first recipient field for specifying a recipient; and upon detecting that the recipient has been specified:
displaying a second recipient field;
navigating application focus from the first recipient field to the second recipient field; and
displaying a message history associated with the recipient in the message history display area. | 2,100 |
6,431 | 6,431 | 13,936,757 | 2,166 | Methods and Apparatus related to generating representations of information. The information may include menu information for merchants such as restaurants. Referring to menus, methods may include receiving potential information for a first menu, and receiving indications of associations of the information with the first menu and/or any number of additional menus. Information and/or associations may later be updated by a desired set of users. | 1-13. (canceled) 14. An apparatus comprising:
a computing device; and a non-transitory machine readable medium having stored thereon a plurality of instructions that when executed by the computing device cause the apparatus to:
present a plurality of items that may be selected for inclusion in a menu of deliverable goods for a first merchant;
receive a first selection of a subset of the plurality of items from a first user;
present a plurality of pieces of information that may be selected to include in the menu as a modifier to an item in the subset of the plurality of items;
receive a second selection of a subset of the plurality of pieces of information from a second user;
present an interface through which an edit may be made to at least one of a piece of information of the subset of the plurality of pieces of information and the item in the subset of the plurality of items;
receive the edit to at least one of the piece of information from a third user;
receive a request for the menu of deliverable goods;
in response to receiving the request, present the menu of deliverable goods based on the item, the piece of information and the edit. 15. The apparatus of claim 14, in which each respective piece of information includes an option that may be applied to the item. 16. The apparatus of claim 15, in which each option includes an addon option. 17. The apparatus of claim 15, in which each option includes a typing option. 18. The apparatus of claim 15, in which each option includes at least one of a size option, a topping option, a crust option, a flavor option, and a filling option. 19. The apparatus of claim 14, in which presenting the plurality of items includes providing an interface for a menu template. 20. The apparatus of claim 19, in which the instructions cause the apparatus to:
receive a change to the menu temple and adjust the menu and at least one other menu that may be ordered from at least one other merchant in response receiving the change. 21. The apparatus of claim 14, in which presenting the plurality of pieces of information includes providing an interface for a menu template. 22. The apparatus of claim 21, in which the instructions cause the apparatus to:
allow the second user to add at least one additional item not from the plurality of items to the menu through the interface by adding an item that is not in the menu template to the menu. 23. The apparatus of claim 14, in which the plurality of instructions cause the apparatus to receive a structure for a menu template that defines which pieces of information may be added to which items. 24. The apparatus of claim 23, in which the structure defines a position for each item and each piece of information in the menu. 25. The apparatus of claim 24, in which the structure defines a position for each selected item and each selected piece of information in the menu. 26. The apparatus of claim 14, in which the plurality of instructions cause the apparatus to receive a structure for the menu, determine the menu based on the structure, the selected subset of the plurality of items, the selected subset of the plurality of pieces of information, and the edit. 27. The apparatus of claim 14, in which the computing device includes a processor. 28. The apparatus of claim 14, in which the plurality of instructions cause the apparatus to associate the menu and a second menu of a second merchant with the edit in response to receiving the edit. 29. The apparatus of claim 14, in which the plurality of instructions cause the apparatus to determine the menu to include the edit by the third user after the edit is received and determine the menu without the edit by the third user before the edit is received. 30. The apparatus of claim 14, in which the plurality of instructions cause the apparatus to at least one of associate the edit with at least one other menu and associate the edit with only the menu. 31. The apparatus of claim 14, in which the instructions cause the apparatus to:
allow a fourth user to include one or more of the plurality of items and one or more pieces of information of the plurality of pieces of information in a second menu of items that may be delivered from a second merchant. 32. The apparatus of claim 14, in which the first user and second user are different users. 33. The apparatus of claim 14, in which the instructions cause the apparatus to:
allow the second user to add at least one additional item not from the plurality of items to the menu. 34. The apparatus of claim 14, in which the third user is different from the first and second users. 35. A method comprising:
presenting, by a computing device, a plurality of items that may be selected for inclusion in a menu of deliverable goods; receiving, by the computing device, a first selection of a subset of the plurality of items from a first user; presenting, by the computing device, a plurality of pieces of information that may be selected to include in the menu as a modifier to an item in the subset of the plurality of items; receiving, by the computing device, a second selection of a subset of the plurality of pieces of information from a second user; presenting, by the computing device, an interface through which an edit may be made to at least one of a piece of information of the subset of the plurality of pieces of information and the item in the subset of the plurality of items; receiving, by the computing device, the edit to at least one of the piece of information from a third user; receiving, by the computing device, a request for the menu of deliverable goods; in response to receiving the request, presenting, by the computing device, the menu of deliverable goods based on the item, the piece of information and the edit. | Methods and Apparatus related to generating representations of information. The information may include menu information for merchants such as restaurants. Referring to menus, methods may include receiving potential information for a first menu, and receiving indications of associations of the information with the first menu and/or any number of additional menus. Information and/or associations may later be updated by a desired set of users.1-13. (canceled) 14. An apparatus comprising:
a computing device; and a non-transitory machine readable medium having stored thereon a plurality of instructions that when executed by the computing device cause the apparatus to:
present a plurality of items that may be selected for inclusion in a menu of deliverable goods for a first merchant;
receive a first selection of a subset of the plurality of items from a first user;
present a plurality of pieces of information that may be selected to include in the menu as a modifier to an item in the subset of the plurality of items;
receive a second selection of a subset of the plurality of pieces of information from a second user;
present an interface through which an edit may be made to at least one of a piece of information of the subset of the plurality of pieces of information and the item in the subset of the plurality of items;
receive the edit to at least one of the piece of information from a third user;
receive a request for the menu of deliverable goods;
in response to receiving the request, present the menu of deliverable goods based on the item, the piece of information and the edit. 15. The apparatus of claim 14, in which each respective piece of information includes an option that may be applied to the item. 16. The apparatus of claim 15, in which each option includes an addon option. 17. The apparatus of claim 15, in which each option includes a typing option. 18. The apparatus of claim 15, in which each option includes at least one of a size option, a topping option, a crust option, a flavor option, and a filling option. 19. The apparatus of claim 14, in which presenting the plurality of items includes providing an interface for a menu template. 20. The apparatus of claim 19, in which the instructions cause the apparatus to:
receive a change to the menu temple and adjust the menu and at least one other menu that may be ordered from at least one other merchant in response receiving the change. 21. The apparatus of claim 14, in which presenting the plurality of pieces of information includes providing an interface for a menu template. 22. The apparatus of claim 21, in which the instructions cause the apparatus to:
allow the second user to add at least one additional item not from the plurality of items to the menu through the interface by adding an item that is not in the menu template to the menu. 23. The apparatus of claim 14, in which the plurality of instructions cause the apparatus to receive a structure for a menu template that defines which pieces of information may be added to which items. 24. The apparatus of claim 23, in which the structure defines a position for each item and each piece of information in the menu. 25. The apparatus of claim 24, in which the structure defines a position for each selected item and each selected piece of information in the menu. 26. The apparatus of claim 14, in which the plurality of instructions cause the apparatus to receive a structure for the menu, determine the menu based on the structure, the selected subset of the plurality of items, the selected subset of the plurality of pieces of information, and the edit. 27. The apparatus of claim 14, in which the computing device includes a processor. 28. The apparatus of claim 14, in which the plurality of instructions cause the apparatus to associate the menu and a second menu of a second merchant with the edit in response to receiving the edit. 29. The apparatus of claim 14, in which the plurality of instructions cause the apparatus to determine the menu to include the edit by the third user after the edit is received and determine the menu without the edit by the third user before the edit is received. 30. The apparatus of claim 14, in which the plurality of instructions cause the apparatus to at least one of associate the edit with at least one other menu and associate the edit with only the menu. 31. The apparatus of claim 14, in which the instructions cause the apparatus to:
allow a fourth user to include one or more of the plurality of items and one or more pieces of information of the plurality of pieces of information in a second menu of items that may be delivered from a second merchant. 32. The apparatus of claim 14, in which the first user and second user are different users. 33. The apparatus of claim 14, in which the instructions cause the apparatus to:
allow the second user to add at least one additional item not from the plurality of items to the menu. 34. The apparatus of claim 14, in which the third user is different from the first and second users. 35. A method comprising:
presenting, by a computing device, a plurality of items that may be selected for inclusion in a menu of deliverable goods; receiving, by the computing device, a first selection of a subset of the plurality of items from a first user; presenting, by the computing device, a plurality of pieces of information that may be selected to include in the menu as a modifier to an item in the subset of the plurality of items; receiving, by the computing device, a second selection of a subset of the plurality of pieces of information from a second user; presenting, by the computing device, an interface through which an edit may be made to at least one of a piece of information of the subset of the plurality of pieces of information and the item in the subset of the plurality of items; receiving, by the computing device, the edit to at least one of the piece of information from a third user; receiving, by the computing device, a request for the menu of deliverable goods; in response to receiving the request, presenting, by the computing device, the menu of deliverable goods based on the item, the piece of information and the edit. | 2,100 |
6,432 | 6,432 | 15,123,607 | 2,179 | In a method for controlling the selection of media files for playback, graphical objects are displayed, and each media file is assigned to one graphical object in each case. In addition, first probabilities, by which the media files are selected for playback, are indicated in the method via a feature of the graphical objects. An operating action by a user, by which the feature of at least one graphical object is modified, is detected. Moreover, second probabilities are assigned to the graphical objects as a function of the modification of the feature of the at least one graphical object. The media files are selected for replay as a function of the second probabilities. In addition, a device is arranged for controlling the selection of media files for replay purposes. | 1-15. (canceled) 16. A method for controlling selection of media files for playback, comprising:
displaying graphical objects, each media file being assigned to a respective graphical object; indicating first probabilities by a feature of the graphical objects, based on a selection of the media files for playback; detecting an operating action of a user, by which the feature of at least one graphical object is modified; assigning second probabilities to the graphical objects as a function of the modification of the feature of the at least one graphical object; and selecting the media files for playback as a function of the second probabilities. 17. The method according to claim 16, wherein the feature of the graphical objects is the same for all graphical objects prior to the operating action, so that each media file is selected for playback based on the same first probability, the feature of the at least one graphical object is modified by the operating action, and following the operating action, the media file to which the one graphical object is assigned has a higher/lower second probability as a function of the operating action, whereas other media files have a correspondingly lower/greater second probability. 18. The method according to claim 16, wherein the feature includes a size of the graphical objects, and the first probability of the media file assigned to the at least one graphical object is increased or decreased by enlarging or reducing the at least one graphical object. 19. The method according to claim 16, wherein the operating action includes a gesture of the user on a display area having a touch-sensitive surface. 20. The method according to claim 19, wherein the at least one graphical object is selected while other graphical objects are not selected, and the touch-sensitive surface is touched in at least two points, and the selected graphical object is enlarged or reduced in that a distance between the two points is enlarged or reduced. 21. The method according to claim 20, wherein the selected at least one graphical object has a boundary, and the at least two points lie within the boundary of the selected at least one graphical object. 22. The method according to claim 20, wherein a size of the non-selected graphical objects are reduced or enlarged in accordance with the enlargement or reduction of a size of the selected at least one graphical object. 23. The method according to claim 16, wherein the feature of the at least one graphical object correlates linearly with the second probability. 24. The method according to claim 16, wherein the media files and the first and/or second probabilities are displayed in a list, and the media file having a greatest first and/or second probability is displayed in first place on the list. 25. The method according to claim 16, wherein the media files include music titles, and the graphical objects includes representations of album covers of the music titles. 26. A device for controlling selection of media files for playback, comprising:
a display device adapted to display graphical objects, each media file being assignable to a respective graphical object; a control unit adapted to actuate the display device such that first probabilities are displayable by a feature of the graphical objects by which the media files are selectable for playback; a detection device adapted to detect an operating action of a user, the feature of at least one graphical object being modifiable by the operating action; a computing unit adapted to assign second probabilities to the media files as a function of the modification of the feature of the at least one graphical object, the media files being selectable with the aid of the control unit as a function of the second probabilities; and a playback device adapted to play back selected media files. 27. The device according to claim 26, wherein the detection device includes a touch-sensitive area on a display surface of the display device. 28. The device according to claim 26, wherein the device includes a media system adapted to store the media files. 29. The device according to claim 26, wherein the device is arranged in an interior space of a vehicle. 30. A vehicle, comprising a device as recited in claim 26. | In a method for controlling the selection of media files for playback, graphical objects are displayed, and each media file is assigned to one graphical object in each case. In addition, first probabilities, by which the media files are selected for playback, are indicated in the method via a feature of the graphical objects. An operating action by a user, by which the feature of at least one graphical object is modified, is detected. Moreover, second probabilities are assigned to the graphical objects as a function of the modification of the feature of the at least one graphical object. The media files are selected for replay as a function of the second probabilities. In addition, a device is arranged for controlling the selection of media files for replay purposes.1-15. (canceled) 16. A method for controlling selection of media files for playback, comprising:
displaying graphical objects, each media file being assigned to a respective graphical object; indicating first probabilities by a feature of the graphical objects, based on a selection of the media files for playback; detecting an operating action of a user, by which the feature of at least one graphical object is modified; assigning second probabilities to the graphical objects as a function of the modification of the feature of the at least one graphical object; and selecting the media files for playback as a function of the second probabilities. 17. The method according to claim 16, wherein the feature of the graphical objects is the same for all graphical objects prior to the operating action, so that each media file is selected for playback based on the same first probability, the feature of the at least one graphical object is modified by the operating action, and following the operating action, the media file to which the one graphical object is assigned has a higher/lower second probability as a function of the operating action, whereas other media files have a correspondingly lower/greater second probability. 18. The method according to claim 16, wherein the feature includes a size of the graphical objects, and the first probability of the media file assigned to the at least one graphical object is increased or decreased by enlarging or reducing the at least one graphical object. 19. The method according to claim 16, wherein the operating action includes a gesture of the user on a display area having a touch-sensitive surface. 20. The method according to claim 19, wherein the at least one graphical object is selected while other graphical objects are not selected, and the touch-sensitive surface is touched in at least two points, and the selected graphical object is enlarged or reduced in that a distance between the two points is enlarged or reduced. 21. The method according to claim 20, wherein the selected at least one graphical object has a boundary, and the at least two points lie within the boundary of the selected at least one graphical object. 22. The method according to claim 20, wherein a size of the non-selected graphical objects are reduced or enlarged in accordance with the enlargement or reduction of a size of the selected at least one graphical object. 23. The method according to claim 16, wherein the feature of the at least one graphical object correlates linearly with the second probability. 24. The method according to claim 16, wherein the media files and the first and/or second probabilities are displayed in a list, and the media file having a greatest first and/or second probability is displayed in first place on the list. 25. The method according to claim 16, wherein the media files include music titles, and the graphical objects includes representations of album covers of the music titles. 26. A device for controlling selection of media files for playback, comprising:
a display device adapted to display graphical objects, each media file being assignable to a respective graphical object; a control unit adapted to actuate the display device such that first probabilities are displayable by a feature of the graphical objects by which the media files are selectable for playback; a detection device adapted to detect an operating action of a user, the feature of at least one graphical object being modifiable by the operating action; a computing unit adapted to assign second probabilities to the media files as a function of the modification of the feature of the at least one graphical object, the media files being selectable with the aid of the control unit as a function of the second probabilities; and a playback device adapted to play back selected media files. 27. The device according to claim 26, wherein the detection device includes a touch-sensitive area on a display surface of the display device. 28. The device according to claim 26, wherein the device includes a media system adapted to store the media files. 29. The device according to claim 26, wherein the device is arranged in an interior space of a vehicle. 30. A vehicle, comprising a device as recited in claim 26. | 2,100 |
6,433 | 6,433 | 13,373,830 | 2,169 | Structures and protocols are presented for facilitating a search partly based on a movement status of a search-capable device or on a position of a person's limb or on an association with another search-capable device. | 1. A system comprising:
circuitry for obtaining a first parameter from a first search task initiated at a first interface device; and circuitry for causing a second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on an association between the second interface device and the first interface device and partly based on a difference between a first prior location of the second interface device and a second prior location of the second interface device. 2. (canceled) 3. The system of claim 1, further comprising:
the first interface device including the circuitry for obtaining the first parameter from the first search task initiated at the first interface device and including the circuitry for causing the second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on the association between the second interface device and the first interface device and partly based on the difference between the first prior location of the second interface device and the second prior location of the second interface device. 4. (canceled) 5. The system of claim 1, further comprising:
circuitry for determining from key press input from a user whether the user has apparently corroborated a configuration feature at the second interface device, the configuration feature being a search term that includes the first parameter from the first search task initiated at the first interface device. 6. The system of claim 1, further comprising:
circuitry for determining from auditory data whether a user has apparently corroborated a configuration feature at the second interface device, the configuration feature being a search term that includes the first parameter from the first search task initiated at the first interface device. 7. The system of claim 1, further comprising:
circuitry for determining from optical data whether a user has apparently corroborated a configuration feature at the second interface device, the configuration feature being a search term that includes the first parameter from the first search task initiated at the first interface device. 8. The system of claim 1, further comprising:
circuitry for associating a first device used by a first user with a second device used by a second user in response to an association request from the first user, the first device being the first interface device, the second device being the second interface device, the association request from the first user manifesting the association between the second interface device and the first interface device. 9. The system of claim 1, further comprising:
circuitry for associating a first device with a second device by recording an identification of the first device in a memory of the second device, the first device being the first interface device, the second device being the second interface device, a record of the identification of the first device in the memory of the second device manifesting the association between the second interface device and the first interface device. 10. The system of claim 1, further comprising:
circuitry for generating a first coordinate and a second coordinate that jointly characterize a position of a portion of an appendage of a user in relation to the second interface device. 11. The system of claim 1, further comprising:
circuitry for determining whether a user has apparently assented to a structured dialog via the second interface device. 12. The system of claim 1, further comprising:
circuitry for adjusting a rectangular image component aspect ratio in response to an indication of a symbol adjacent an edge of a portion of an image, the symbol being a component of the first parameter from the first search task initiated at the first interface device. 13. The system of claim 1, further comprising:
circuitry for responding to an indication of a symbol adjacent an edge of a region of an image by adjusting a size of the region of the image, the symbol being a component of the first parameter from the first search task initiated at the first interface device. 14. The system of claim 1, further comprising:
circuitry for comparing a local time with temporal data in a first cached document and with temporal data in a second cached document, the temporal data in the first cached document being the first parameter from the first search task initiated at the first interface device. 15. The system of claim 1, further comprising:
circuitry for initiating a search using a character obtained from a photograph, the character obtained from the photograph being a component of the first parameter from the first search task initiated at the first interface device. 16. The system of claim 1, further comprising:
circuitry for displaying a search expression that includes a character sequence obtained from a photograph, the search expression being the first parameter from the first search task initiated at the first interface device. 17. The system of claim 1, further comprising:
circuitry for expressing a search term at a first interface that includes a character sequence obtained from a second interface, the first interface device being the second interface, the second interface device being the first interface, the character sequence being a component of the first parameter from the first search task initiated at the first interface device. 18. The system of claim 1, further comprising:
circuitry for highlighting an areal portion of an image selectively in response to a position of a depiction of an appendage in the image, the areal portion of the image including the first parameter from the first search task initiated at the first interface device. 19. The system of claim 1, further comprising:
circuitry for recognizing a literal expression of a time signifying when a specific time interval begins, a result of the first search task initiated at the first interface device including an expression of the specific time interval and including the literal expression of the time signifying when the specific time interval begins. 20. The system of claim 1, further comprising:
circuitry for obtaining a boolean expression indicative of whether content includes a literal expression of a time of day, the content being a result of the first search task initiated at the first interface device. 21. The system of claim 1, further comprising:
circuitry for obtaining a boolean expression indicative of whether content includes a geographic identifier, the content being a result of the first search task initiated at the first interface device. 22. The system of claim 1, further comprising:
circuitry for ranking a first component of a search result in relation to a second component of the search result partly based on a digital expression of a time of day and partly based on whether a confirmation of a search criterion has been received, the search criterion including the first parameter from the first search task initiated at the first interface device. 23. The system of claim 1, further comprising:
circuitry for causing a first search term and a second search term to be transmitted to a mobile device after initiating a first search using the first search term and after initiating a second search using the second search term, the mobile device being the second interface device, the first search including the first search task initiated at the first interface device, the first search term being the first parameter. 24. The system of claim 1, further comprising:
circuitry for causing a search engine to use a search term conditionally in response to a user of a mobile device corroborating the search term via the mobile device, the mobile device being the second interface device, the search term including the first parameter from the first search task initiated at the first interface device. 25. The system of claim 1, further comprising:
circuitry for receiving a result of a search from an Internet search engine into a memory of a mobile device, the mobile device being the second interface device, the search including the first search task initiated at the first interface device, the result including the first parameter from the first search task initiated at the first interface device. 26. (canceled) 27. The system of claim 1, further comprising:
circuitry for triggering an anticipatory search task by transmitting a search expression to a remote search engine in response to detecting a component of the search expression in a component of a visual image without first detecting a confirmation of the search expression, the anticipatory search task being a second search task and being initiated at the second interface device, the component of the search expression being the first parameter from the first search task initiated at the first interface device. 28. The system of claim 1, further comprising:
circuitry for highlighting a first portion of an image without highlighting a remainder of the image, the first portion having a location that depends upon whether the image depicts an appendage of a user, the first portion of the image including an informational element, the informational element being the first parameter from the first search task initiated at the first interface device. 29. The system of claim 1, further comprising:
circuitry for performing an image modification upon a first region of a photographic image without performing the image modification upon a remainder of the photographic image, the first region having a location that depends upon whether the photographic image includes a semblance of an appendage, the remainder of the photographic image including an informational element, the informational element being the first parameter from the first search task initiated at the first interface device. 30. The system of claim 1, further comprising:
circuitry for responding to a detection of a depiction of an appendage by discarding a major portion of a photographic image without discarding a remainder of the photographic image, the remainder of the photographic image having a rectangular shape, the major portion of the photographic image encompassing more than 50% of an area of the depiction of the appendage and more than 50% of an area of the photographic image, the remainder of the photographic image including an informational element, the informational element being the first parameter from the first search task initiated at the first interface device. 31. The system of claim 1, further comprising:
circuitry for responding to a depiction of an appendage in a data file representative of a graphic image by discarding a first informational element in the data file without discarding a second informational element in the data file, the second informational element in the data file including the first parameter from the first search task initiated at the first interface device. 32. A method comprising:
obtaining a first parameter from a first search task initiated at a first interface device; and invoking circuitry for causing a second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on an association between the second interface device and the first interface device and partly based on a difference between a first prior location of the second interface device and a second prior location of the second interface device. 33-59. (canceled) 60. A system comprising:
means for obtaining a first parameter from a first search task initiated at a first interface device; and means for causing a second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on an association between the second interface device and the first interface device and partly based on a difference between a first prior location of the second interface device and a second prior location of the second interface device. 61-87. (canceled) 88. An article of manufacture comprising:
one or more physical media configured to bear a device-detectable implementation of a method including at least obtaining a first parameter from a first search task initiated at a first interface device; and causing a second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on an association between the second interface device and the first interface device and partly based on a difference between a first prior location of the second interface device and a second prior location of the second interface device. 89-115. (canceled) 116. An article of manufacture comprising:
one or more physical media bearing a device-detectable outcome indicating an occurrence of obtaining a first parameter from a first search task initiated at a first interface device; and causing a second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on an association between the second interface device and the first interface device and partly based on a difference between a first prior location of the second interface device and a second prior location of the second interface device. 117-143. (canceled) 144. The system of claim 1, further comprising:
circuitry for determining from at least one of optical data or auditory data whether a first user has apparently corroborated a configuration feature at the second interface device, the configuration feature being a first search term that includes the first parameter from the first search task initiated at the first interface device; circuitry for causing the first search term and a second search term to be transmitted to a mobile device after initiating a first search using the first search term and after initiating a second search using the second search term, the mobile device being the second interface device, the first search including the first search task initiated at the first interface device; circuitry for adjusting a rectangular image component aspect ratio in response to an indication of a symbol adjacent an edge of a portion of an image, the symbol being a component of the first parameter from the first search task initiated at the first interface device; circuitry for highlighting an areal portion of the image selectively in response to a position of a depiction of an appendage in the image, the areal portion of the image including the first parameter from the first search task initiated at the first interface device; circuitry for triggering an anticipatory search task by transmitting a search expression to a remote search engine in response to detecting a component of the search expression in a component of the image without first detecting a confirmation of the search expression, the anticipatory search task being a second search task and being initiated at the second interface device, the component of the search expression being the first parameter from the first search task initiated at the first interface device; circuitry for recognizing a literal expression of a time signifying when a specific time interval begins, a result of the first search task initiated at the first interface device including an expression of the specific time interval and including the literal expression of the time signifying when the specific time interval begins; circuitry for comparing a local time with temporal data in a first cached document and with temporal data in a second cached document, the temporal data in the first cached document being the first parameter from the first search task initiated at the first interface device; circuitry for obtaining a boolean expression indicative of whether content includes a literal expression of a time of day, the content being the result of the first search task initiated at the first interface device; and circuitry for ranking a first component of the result of the first search task initiated at the first interface device in relation to a second component of the result of the first search task initiated at the first interface device partly based on a digital expression of a time of day and partly based on whether a confirmation of a search criterion has been received, the search criterion including the first parameter from the first search task initiated at the first interface device. 145. The system of claim 1, further comprising:
circuitry for associating a first device used by a first user with a second device used by a second user by recording an identification of the second device in a memory of the first device in response to an association request from the first user, the first device being the first interface device, the second device being the second interface device, the association request from the first user manifesting the association between the second interface device and the first interface device; circuitry for responding to an indication of a symbol adjacent an edge of a region of a photographic image by adjusting a size of the region of the photographic image, the symbol being a component of the first parameter from the first search task initiated at the first interface device; circuitry for performing an image modification upon a first region of the photographic image without performing the image modification upon a remainder of the photographic image, the first region having a location that depends upon whether the photographic image includes a semblance of an appendage, the remainder of the photographic image including an informational element, the informational element being the first parameter from the first search task initiated at the first interface device circuitry for displaying a search expression that includes a character sequence obtained from the photographic image, the search expression being the first parameter from the first search task initiated at the first interface device; circuitry for expressing a search term at a first interface that includes a character sequence obtained from a second interface, the first interface device being the second interface, the second interface device being the first interface, the character sequence being a component of the first parameter from the first search task initiated at the first interface device; and circuitry for obtaining a boolean expression indicative of whether content includes a geographic identifier, the content being a result of the first search task initiated at the first interface device. | Structures and protocols are presented for facilitating a search partly based on a movement status of a search-capable device or on a position of a person's limb or on an association with another search-capable device.1. A system comprising:
circuitry for obtaining a first parameter from a first search task initiated at a first interface device; and circuitry for causing a second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on an association between the second interface device and the first interface device and partly based on a difference between a first prior location of the second interface device and a second prior location of the second interface device. 2. (canceled) 3. The system of claim 1, further comprising:
the first interface device including the circuitry for obtaining the first parameter from the first search task initiated at the first interface device and including the circuitry for causing the second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on the association between the second interface device and the first interface device and partly based on the difference between the first prior location of the second interface device and the second prior location of the second interface device. 4. (canceled) 5. The system of claim 1, further comprising:
circuitry for determining from key press input from a user whether the user has apparently corroborated a configuration feature at the second interface device, the configuration feature being a search term that includes the first parameter from the first search task initiated at the first interface device. 6. The system of claim 1, further comprising:
circuitry for determining from auditory data whether a user has apparently corroborated a configuration feature at the second interface device, the configuration feature being a search term that includes the first parameter from the first search task initiated at the first interface device. 7. The system of claim 1, further comprising:
circuitry for determining from optical data whether a user has apparently corroborated a configuration feature at the second interface device, the configuration feature being a search term that includes the first parameter from the first search task initiated at the first interface device. 8. The system of claim 1, further comprising:
circuitry for associating a first device used by a first user with a second device used by a second user in response to an association request from the first user, the first device being the first interface device, the second device being the second interface device, the association request from the first user manifesting the association between the second interface device and the first interface device. 9. The system of claim 1, further comprising:
circuitry for associating a first device with a second device by recording an identification of the first device in a memory of the second device, the first device being the first interface device, the second device being the second interface device, a record of the identification of the first device in the memory of the second device manifesting the association between the second interface device and the first interface device. 10. The system of claim 1, further comprising:
circuitry for generating a first coordinate and a second coordinate that jointly characterize a position of a portion of an appendage of a user in relation to the second interface device. 11. The system of claim 1, further comprising:
circuitry for determining whether a user has apparently assented to a structured dialog via the second interface device. 12. The system of claim 1, further comprising:
circuitry for adjusting a rectangular image component aspect ratio in response to an indication of a symbol adjacent an edge of a portion of an image, the symbol being a component of the first parameter from the first search task initiated at the first interface device. 13. The system of claim 1, further comprising:
circuitry for responding to an indication of a symbol adjacent an edge of a region of an image by adjusting a size of the region of the image, the symbol being a component of the first parameter from the first search task initiated at the first interface device. 14. The system of claim 1, further comprising:
circuitry for comparing a local time with temporal data in a first cached document and with temporal data in a second cached document, the temporal data in the first cached document being the first parameter from the first search task initiated at the first interface device. 15. The system of claim 1, further comprising:
circuitry for initiating a search using a character obtained from a photograph, the character obtained from the photograph being a component of the first parameter from the first search task initiated at the first interface device. 16. The system of claim 1, further comprising:
circuitry for displaying a search expression that includes a character sequence obtained from a photograph, the search expression being the first parameter from the first search task initiated at the first interface device. 17. The system of claim 1, further comprising:
circuitry for expressing a search term at a first interface that includes a character sequence obtained from a second interface, the first interface device being the second interface, the second interface device being the first interface, the character sequence being a component of the first parameter from the first search task initiated at the first interface device. 18. The system of claim 1, further comprising:
circuitry for highlighting an areal portion of an image selectively in response to a position of a depiction of an appendage in the image, the areal portion of the image including the first parameter from the first search task initiated at the first interface device. 19. The system of claim 1, further comprising:
circuitry for recognizing a literal expression of a time signifying when a specific time interval begins, a result of the first search task initiated at the first interface device including an expression of the specific time interval and including the literal expression of the time signifying when the specific time interval begins. 20. The system of claim 1, further comprising:
circuitry for obtaining a boolean expression indicative of whether content includes a literal expression of a time of day, the content being a result of the first search task initiated at the first interface device. 21. The system of claim 1, further comprising:
circuitry for obtaining a boolean expression indicative of whether content includes a geographic identifier, the content being a result of the first search task initiated at the first interface device. 22. The system of claim 1, further comprising:
circuitry for ranking a first component of a search result in relation to a second component of the search result partly based on a digital expression of a time of day and partly based on whether a confirmation of a search criterion has been received, the search criterion including the first parameter from the first search task initiated at the first interface device. 23. The system of claim 1, further comprising:
circuitry for causing a first search term and a second search term to be transmitted to a mobile device after initiating a first search using the first search term and after initiating a second search using the second search term, the mobile device being the second interface device, the first search including the first search task initiated at the first interface device, the first search term being the first parameter. 24. The system of claim 1, further comprising:
circuitry for causing a search engine to use a search term conditionally in response to a user of a mobile device corroborating the search term via the mobile device, the mobile device being the second interface device, the search term including the first parameter from the first search task initiated at the first interface device. 25. The system of claim 1, further comprising:
circuitry for receiving a result of a search from an Internet search engine into a memory of a mobile device, the mobile device being the second interface device, the search including the first search task initiated at the first interface device, the result including the first parameter from the first search task initiated at the first interface device. 26. (canceled) 27. The system of claim 1, further comprising:
circuitry for triggering an anticipatory search task by transmitting a search expression to a remote search engine in response to detecting a component of the search expression in a component of a visual image without first detecting a confirmation of the search expression, the anticipatory search task being a second search task and being initiated at the second interface device, the component of the search expression being the first parameter from the first search task initiated at the first interface device. 28. The system of claim 1, further comprising:
circuitry for highlighting a first portion of an image without highlighting a remainder of the image, the first portion having a location that depends upon whether the image depicts an appendage of a user, the first portion of the image including an informational element, the informational element being the first parameter from the first search task initiated at the first interface device. 29. The system of claim 1, further comprising:
circuitry for performing an image modification upon a first region of a photographic image without performing the image modification upon a remainder of the photographic image, the first region having a location that depends upon whether the photographic image includes a semblance of an appendage, the remainder of the photographic image including an informational element, the informational element being the first parameter from the first search task initiated at the first interface device. 30. The system of claim 1, further comprising:
circuitry for responding to a detection of a depiction of an appendage by discarding a major portion of a photographic image without discarding a remainder of the photographic image, the remainder of the photographic image having a rectangular shape, the major portion of the photographic image encompassing more than 50% of an area of the depiction of the appendage and more than 50% of an area of the photographic image, the remainder of the photographic image including an informational element, the informational element being the first parameter from the first search task initiated at the first interface device. 31. The system of claim 1, further comprising:
circuitry for responding to a depiction of an appendage in a data file representative of a graphic image by discarding a first informational element in the data file without discarding a second informational element in the data file, the second informational element in the data file including the first parameter from the first search task initiated at the first interface device. 32. A method comprising:
obtaining a first parameter from a first search task initiated at a first interface device; and invoking circuitry for causing a second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on an association between the second interface device and the first interface device and partly based on a difference between a first prior location of the second interface device and a second prior location of the second interface device. 33-59. (canceled) 60. A system comprising:
means for obtaining a first parameter from a first search task initiated at a first interface device; and means for causing a second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on an association between the second interface device and the first interface device and partly based on a difference between a first prior location of the second interface device and a second prior location of the second interface device. 61-87. (canceled) 88. An article of manufacture comprising:
one or more physical media configured to bear a device-detectable implementation of a method including at least obtaining a first parameter from a first search task initiated at a first interface device; and causing a second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on an association between the second interface device and the first interface device and partly based on a difference between a first prior location of the second interface device and a second prior location of the second interface device. 89-115. (canceled) 116. An article of manufacture comprising:
one or more physical media bearing a device-detectable outcome indicating an occurrence of obtaining a first parameter from a first search task initiated at a first interface device; and causing a second interface device to indicate the first parameter from the first search task initiated at the first interface device partly based on an association between the second interface device and the first interface device and partly based on a difference between a first prior location of the second interface device and a second prior location of the second interface device. 117-143. (canceled) 144. The system of claim 1, further comprising:
circuitry for determining from at least one of optical data or auditory data whether a first user has apparently corroborated a configuration feature at the second interface device, the configuration feature being a first search term that includes the first parameter from the first search task initiated at the first interface device; circuitry for causing the first search term and a second search term to be transmitted to a mobile device after initiating a first search using the first search term and after initiating a second search using the second search term, the mobile device being the second interface device, the first search including the first search task initiated at the first interface device; circuitry for adjusting a rectangular image component aspect ratio in response to an indication of a symbol adjacent an edge of a portion of an image, the symbol being a component of the first parameter from the first search task initiated at the first interface device; circuitry for highlighting an areal portion of the image selectively in response to a position of a depiction of an appendage in the image, the areal portion of the image including the first parameter from the first search task initiated at the first interface device; circuitry for triggering an anticipatory search task by transmitting a search expression to a remote search engine in response to detecting a component of the search expression in a component of the image without first detecting a confirmation of the search expression, the anticipatory search task being a second search task and being initiated at the second interface device, the component of the search expression being the first parameter from the first search task initiated at the first interface device; circuitry for recognizing a literal expression of a time signifying when a specific time interval begins, a result of the first search task initiated at the first interface device including an expression of the specific time interval and including the literal expression of the time signifying when the specific time interval begins; circuitry for comparing a local time with temporal data in a first cached document and with temporal data in a second cached document, the temporal data in the first cached document being the first parameter from the first search task initiated at the first interface device; circuitry for obtaining a boolean expression indicative of whether content includes a literal expression of a time of day, the content being the result of the first search task initiated at the first interface device; and circuitry for ranking a first component of the result of the first search task initiated at the first interface device in relation to a second component of the result of the first search task initiated at the first interface device partly based on a digital expression of a time of day and partly based on whether a confirmation of a search criterion has been received, the search criterion including the first parameter from the first search task initiated at the first interface device. 145. The system of claim 1, further comprising:
circuitry for associating a first device used by a first user with a second device used by a second user by recording an identification of the second device in a memory of the first device in response to an association request from the first user, the first device being the first interface device, the second device being the second interface device, the association request from the first user manifesting the association between the second interface device and the first interface device; circuitry for responding to an indication of a symbol adjacent an edge of a region of a photographic image by adjusting a size of the region of the photographic image, the symbol being a component of the first parameter from the first search task initiated at the first interface device; circuitry for performing an image modification upon a first region of the photographic image without performing the image modification upon a remainder of the photographic image, the first region having a location that depends upon whether the photographic image includes a semblance of an appendage, the remainder of the photographic image including an informational element, the informational element being the first parameter from the first search task initiated at the first interface device circuitry for displaying a search expression that includes a character sequence obtained from the photographic image, the search expression being the first parameter from the first search task initiated at the first interface device; circuitry for expressing a search term at a first interface that includes a character sequence obtained from a second interface, the first interface device being the second interface, the second interface device being the first interface, the character sequence being a component of the first parameter from the first search task initiated at the first interface device; and circuitry for obtaining a boolean expression indicative of whether content includes a geographic identifier, the content being a result of the first search task initiated at the first interface device. | 2,100 |
6,434 | 6,434 | 14,507,993 | 2,168 | Systems and methods of providing a configurable table of rules that defines a repository/archive search priority that includes multiple repositories/archives. In this manner, repository/archives are successively searched and after a first result is returned the search is stopped. Repository/archives searched in priority order based on location in pre-configured “tiers.” This enables searches to be directed to repository/archives that are best able to handle load for different types of searches, and for different types of studies as well. A duplicate priority list enables an administrator to designate which repository/archive will appear on search results list if duplicates are found. For example, in clinical study archiving systems, the search priority enables an administrator to direct searches to repository best able to handle load for different types of searches and for different types of studies. | 1. A method for searching multiple repositories, comprising:
organizing the multiple repositories into a predetermined hierarchy that includes at least one tier; receiving a search request from a requester; searching the multiple repositories in accordance with the predetermined hierarchy; stopping the searching when a result to the search request is found; and communicating the result to the requester. 2. The method of claim 1, wherein the predetermined hierarchy defines a plurality of tiers, wherein each of the plurality of tiers includes at least one of the multiple repositories. 3. The method of claim 2, wherein at least one tier includes at least two of the multiple repositories, and for each tier, the method further comprising:
running the search at a first identified repository of the at least two of the multiple repositories; and if no result is found, running the search at the next identified repository of the at least two of the multiple repositories. 4. The method of claim 2, further comprising providing a default tier at a lowest priority. 5. The method of claim 1, further comprising providing the hierarchy in a configuration file. 6. The method of claim 5, further comprising reading the configuration file in accordance with a characteristic of the requester. 7. The method of claim 1, further comprising providing a graphical user interface to define the predetermined hierarchy. 8. The method of claim 7, further comprising generating a configuration file in accordance with inputs received by the graphical user interface. 9. The method of claim 1, wherein the search request is for medical image data. 10. The method of claim 1, further comprising:
determining if the result to the search request is one of duplicate results; and removing the duplicate results prior to communicating the result to the requester. 11. A method of configuring a hierarchy of repositories, comprising:
providing a user interface, the user interface displaying a list of repositories; displaying, in the user interface, repository tiers to which the repositories are associated as defined in a configuration file; providing an edit user interface wherein an association of a repository to a tier can be changed; and reflecting changes received in the edit user interface to the configuration file as changes are received. 12. The method of claim 11, further comprising providing an interface to set priorities within each of the repository tiers. 13. The method of claim 12, wherein the interface to set priorities is provided as a duplicate management user interface to reorder the list of repositories such that only one result is returned in response to a search request when duplicate results are found in the repositories. 14. The method of claim 11, further comprising identifying unassigned repositories within the list of repositories within a lowest row in the user interface. 15. The method of claim 11, wherein the configuration file is an eXtensible Markup Language file. 16. A method for searching multiple repositories, comprising:
receiving a search request from a requester; and determining if a configuration is enabled for searching the multiple repositories, and if the configuration is enabled:
searching the multiple repositories in accordance with a predetermined hierarchy tiers of the multiple repositories;
stopping the searching when at least one result to the search request is found within a tier of the predetermined hierarchy tiers of the multiple repositories;
removing duplicate results if more than one result to the search request is found; and
communicating the result to the requester. 17. The method of claim 16, wherein the tier includes at least two of the multiple repositories, the method further comprising:
running the search at a first identified repository of the at least two of the multiple repositories; and if no result is found, running the search at the next identified repository of the at least two of the multiple repositories. 18. The method of claim 16, if the configuration is not enabled, then the method further comprising:
searching all of the multiple repositories; and communicating all results found to the requester. 19. The method of claim 18, further comprising enabling the configuration using a Uniform Resource Locator (URL)-configurable option. 20. The method of claim 16, further comprising setting the predetermined hierarchy tiers of the multiple repositories in accordance with a prioritization of the multiple repositories. 21. The method of claim 20, further comprising defining the prioritization to manage duplicate results. | Systems and methods of providing a configurable table of rules that defines a repository/archive search priority that includes multiple repositories/archives. In this manner, repository/archives are successively searched and after a first result is returned the search is stopped. Repository/archives searched in priority order based on location in pre-configured “tiers.” This enables searches to be directed to repository/archives that are best able to handle load for different types of searches, and for different types of studies as well. A duplicate priority list enables an administrator to designate which repository/archive will appear on search results list if duplicates are found. For example, in clinical study archiving systems, the search priority enables an administrator to direct searches to repository best able to handle load for different types of searches and for different types of studies.1. A method for searching multiple repositories, comprising:
organizing the multiple repositories into a predetermined hierarchy that includes at least one tier; receiving a search request from a requester; searching the multiple repositories in accordance with the predetermined hierarchy; stopping the searching when a result to the search request is found; and communicating the result to the requester. 2. The method of claim 1, wherein the predetermined hierarchy defines a plurality of tiers, wherein each of the plurality of tiers includes at least one of the multiple repositories. 3. The method of claim 2, wherein at least one tier includes at least two of the multiple repositories, and for each tier, the method further comprising:
running the search at a first identified repository of the at least two of the multiple repositories; and if no result is found, running the search at the next identified repository of the at least two of the multiple repositories. 4. The method of claim 2, further comprising providing a default tier at a lowest priority. 5. The method of claim 1, further comprising providing the hierarchy in a configuration file. 6. The method of claim 5, further comprising reading the configuration file in accordance with a characteristic of the requester. 7. The method of claim 1, further comprising providing a graphical user interface to define the predetermined hierarchy. 8. The method of claim 7, further comprising generating a configuration file in accordance with inputs received by the graphical user interface. 9. The method of claim 1, wherein the search request is for medical image data. 10. The method of claim 1, further comprising:
determining if the result to the search request is one of duplicate results; and removing the duplicate results prior to communicating the result to the requester. 11. A method of configuring a hierarchy of repositories, comprising:
providing a user interface, the user interface displaying a list of repositories; displaying, in the user interface, repository tiers to which the repositories are associated as defined in a configuration file; providing an edit user interface wherein an association of a repository to a tier can be changed; and reflecting changes received in the edit user interface to the configuration file as changes are received. 12. The method of claim 11, further comprising providing an interface to set priorities within each of the repository tiers. 13. The method of claim 12, wherein the interface to set priorities is provided as a duplicate management user interface to reorder the list of repositories such that only one result is returned in response to a search request when duplicate results are found in the repositories. 14. The method of claim 11, further comprising identifying unassigned repositories within the list of repositories within a lowest row in the user interface. 15. The method of claim 11, wherein the configuration file is an eXtensible Markup Language file. 16. A method for searching multiple repositories, comprising:
receiving a search request from a requester; and determining if a configuration is enabled for searching the multiple repositories, and if the configuration is enabled:
searching the multiple repositories in accordance with a predetermined hierarchy tiers of the multiple repositories;
stopping the searching when at least one result to the search request is found within a tier of the predetermined hierarchy tiers of the multiple repositories;
removing duplicate results if more than one result to the search request is found; and
communicating the result to the requester. 17. The method of claim 16, wherein the tier includes at least two of the multiple repositories, the method further comprising:
running the search at a first identified repository of the at least two of the multiple repositories; and if no result is found, running the search at the next identified repository of the at least two of the multiple repositories. 18. The method of claim 16, if the configuration is not enabled, then the method further comprising:
searching all of the multiple repositories; and communicating all results found to the requester. 19. The method of claim 18, further comprising enabling the configuration using a Uniform Resource Locator (URL)-configurable option. 20. The method of claim 16, further comprising setting the predetermined hierarchy tiers of the multiple repositories in accordance with a prioritization of the multiple repositories. 21. The method of claim 20, further comprising defining the prioritization to manage duplicate results. | 2,100 |
6,435 | 6,435 | 16,196,001 | 2,193 | Data characterizing an object for deprecation and code characterizing a modification to an application is received at a code development system. The object is for deprecation and the application is deployed on a plurality of tenants of a production database management system. A first application patch is generated according to the received code. The first patch includes computer executable instructions that during deployment to a target system configure the target system to prevent further usage by tenants of the object for deprecation. The first application patch is deployed to the plurality of tenants. A second application patch is generated. The second application patch includes computer executable instructions that during deployment to the target system configure the target system to allow deletion of the object from the target system. The second application patch is deployed to the plurality of tenants. Related apparatus, systems, techniques, and articles are also described. | 1. A method comprising:
receiving, at a code development system, data characterizing an object for deprecation and code characterizing a modification to an application, the object for deprecation and the application deployed on a plurality of tenants of a production database management system; generating, according to the received code, a first application patch including computer executable instructions that during deployment to a target system configure the target system to prevent further usage by tenants of the object for deprecation while the application is configured to continue utilization of the object, the target system including one or more tenants of the plurality of tenants, wherein the usage of the object is prevented based on at least a lifecycle status of the object; deploying the first application patch to the plurality of tenants; generating a second application patch including computer executable instructions that during deployment to the target system configure the target system to allow deletion of the object from the target system; and deploying the second application patch to the plurality of tenants. 2. The method of claim 1, further comprising:
determining whether the object for deprecation is utilized by the application. 3. The method of claim 2, further comprising:
generating a list indicating locations within the application that the object for deprecation is utilized, wherein the first application patch includes the list indicating locations within the application that the object for deprecation is utilized. 4. The method of claim 2, further comprising:
presenting, to a user, a visualization characterizing the determination. 5. The method of claim 1, further comprising:
preventing, by at least one tenant of the multitenant database system, utilization of the object. 6. The method of claim 1, further comprising:
deleting, by at least one tenant of the multitenant database system, the object from the multitenant database system. 7. The method of claim 1, wherein the object includes a data source, a field within the data source, a node, an element of the node, an association between two nodes, a table, or a web service. 8. The method of claim 1, wherein the application includes functions from a software development kit including predefined application functions. 9. The method of claim 1, wherein the code development system includes at least one processor and at least one memory. 10. A system comprising:
at least one processor; memory storing instructions which, when executed by the at least one processor, causes the at least one processor to perform operations comprising:
receiving data characterizing an object for deprecation and code characterizing a modification to an application, the object for deprecation and the application deployed on a plurality of tenants of a production database management system;
generating, according to the received code, a first application patch including computer executable instructions that during deployment to a target system configure the target system to prevent further usage by tenants of the object for deprecation while the application is configured to continue utilization of the object, the target system including one or more tenants of the plurality of tenants, wherein the usage of the object is prevented based on at least a lifecycle status of the object;
deploying the first application patch to the plurality of tenants;
generating a second application patch including computer executable instructions that during deployment to the target system configure the target system to allow deletion of the object from the target system; and
deploying the second application patch to the plurality of tenants. 11. The system of claim 10, the operations further comprising:
determining whether the object for deprecation is utilized by the application. 12. The system of claim 11, the operations further comprising:
generating a list indicating locations within the application that the object for deprecation is utilized, wherein the first application patch includes the list indicating locations within the application that the object for deprecation is utilized. 13. The system of claim 11, the operations further comprising:
presenting, to a user, a visualization characterizing the determination. 14. The system of claim 10, the operations further comprising:
preventing, by at least one tenant of the multitenant database system, utilization of the object. 15. The system of claim 10, the operations further comprising:
deleting, by at least one tenant of the multitenant database system, the object from the multitenant database system. 16. The system of claim 10, wherein the object includes a data source, a field within the data source, a node, an element of the node, an association between two nodes, a table, or a web service. 17. The system of claim 10, wherein the application includes functions from a software development kit including predefined application functions. 18. A non-transitory computer readable medium storing instructions, which when executed by at least one processor of at least one computing system, implement operations comprising:
receiving, at a code development system, data characterizing an object for deprecation and code characterizing a modification to an application, the object for deprecation and the application deployed on a plurality of tenants of a production database management system; generating, according to the received code, a first application patch including computer executable instructions that during deployment to a target system configure the target system to prevent further usage by tenants of the object for deprecation while the application is configured to continue utilization of the object, the target system including one or more tenants of the plurality of tenants, wherein the usage of the object is prevented based on at least a lifecycle status of the object; deploying the first application patch to the plurality of tenants; generating a second application patch including computer executable instructions that during deployment to the target system configure the target system to allow deletion of the object from the target system; and deploying the second application patch to the plurality of tenants. 19. The computer readable medium of claim 18, the operations further comprising:
determining whether the object for deprecation is utilized by the application. 20. The computer readable medium of claim 19, the operations further comprising:
generating a list indicating locations within the application that the object for deprecation is utilized, wherein the first application patch includes the list indicating locations within the application that the object for deprecation is utilized. | Data characterizing an object for deprecation and code characterizing a modification to an application is received at a code development system. The object is for deprecation and the application is deployed on a plurality of tenants of a production database management system. A first application patch is generated according to the received code. The first patch includes computer executable instructions that during deployment to a target system configure the target system to prevent further usage by tenants of the object for deprecation. The first application patch is deployed to the plurality of tenants. A second application patch is generated. The second application patch includes computer executable instructions that during deployment to the target system configure the target system to allow deletion of the object from the target system. The second application patch is deployed to the plurality of tenants. Related apparatus, systems, techniques, and articles are also described.1. A method comprising:
receiving, at a code development system, data characterizing an object for deprecation and code characterizing a modification to an application, the object for deprecation and the application deployed on a plurality of tenants of a production database management system; generating, according to the received code, a first application patch including computer executable instructions that during deployment to a target system configure the target system to prevent further usage by tenants of the object for deprecation while the application is configured to continue utilization of the object, the target system including one or more tenants of the plurality of tenants, wherein the usage of the object is prevented based on at least a lifecycle status of the object; deploying the first application patch to the plurality of tenants; generating a second application patch including computer executable instructions that during deployment to the target system configure the target system to allow deletion of the object from the target system; and deploying the second application patch to the plurality of tenants. 2. The method of claim 1, further comprising:
determining whether the object for deprecation is utilized by the application. 3. The method of claim 2, further comprising:
generating a list indicating locations within the application that the object for deprecation is utilized, wherein the first application patch includes the list indicating locations within the application that the object for deprecation is utilized. 4. The method of claim 2, further comprising:
presenting, to a user, a visualization characterizing the determination. 5. The method of claim 1, further comprising:
preventing, by at least one tenant of the multitenant database system, utilization of the object. 6. The method of claim 1, further comprising:
deleting, by at least one tenant of the multitenant database system, the object from the multitenant database system. 7. The method of claim 1, wherein the object includes a data source, a field within the data source, a node, an element of the node, an association between two nodes, a table, or a web service. 8. The method of claim 1, wherein the application includes functions from a software development kit including predefined application functions. 9. The method of claim 1, wherein the code development system includes at least one processor and at least one memory. 10. A system comprising:
at least one processor; memory storing instructions which, when executed by the at least one processor, causes the at least one processor to perform operations comprising:
receiving data characterizing an object for deprecation and code characterizing a modification to an application, the object for deprecation and the application deployed on a plurality of tenants of a production database management system;
generating, according to the received code, a first application patch including computer executable instructions that during deployment to a target system configure the target system to prevent further usage by tenants of the object for deprecation while the application is configured to continue utilization of the object, the target system including one or more tenants of the plurality of tenants, wherein the usage of the object is prevented based on at least a lifecycle status of the object;
deploying the first application patch to the plurality of tenants;
generating a second application patch including computer executable instructions that during deployment to the target system configure the target system to allow deletion of the object from the target system; and
deploying the second application patch to the plurality of tenants. 11. The system of claim 10, the operations further comprising:
determining whether the object for deprecation is utilized by the application. 12. The system of claim 11, the operations further comprising:
generating a list indicating locations within the application that the object for deprecation is utilized, wherein the first application patch includes the list indicating locations within the application that the object for deprecation is utilized. 13. The system of claim 11, the operations further comprising:
presenting, to a user, a visualization characterizing the determination. 14. The system of claim 10, the operations further comprising:
preventing, by at least one tenant of the multitenant database system, utilization of the object. 15. The system of claim 10, the operations further comprising:
deleting, by at least one tenant of the multitenant database system, the object from the multitenant database system. 16. The system of claim 10, wherein the object includes a data source, a field within the data source, a node, an element of the node, an association between two nodes, a table, or a web service. 17. The system of claim 10, wherein the application includes functions from a software development kit including predefined application functions. 18. A non-transitory computer readable medium storing instructions, which when executed by at least one processor of at least one computing system, implement operations comprising:
receiving, at a code development system, data characterizing an object for deprecation and code characterizing a modification to an application, the object for deprecation and the application deployed on a plurality of tenants of a production database management system; generating, according to the received code, a first application patch including computer executable instructions that during deployment to a target system configure the target system to prevent further usage by tenants of the object for deprecation while the application is configured to continue utilization of the object, the target system including one or more tenants of the plurality of tenants, wherein the usage of the object is prevented based on at least a lifecycle status of the object; deploying the first application patch to the plurality of tenants; generating a second application patch including computer executable instructions that during deployment to the target system configure the target system to allow deletion of the object from the target system; and deploying the second application patch to the plurality of tenants. 19. The computer readable medium of claim 18, the operations further comprising:
determining whether the object for deprecation is utilized by the application. 20. The computer readable medium of claim 19, the operations further comprising:
generating a list indicating locations within the application that the object for deprecation is utilized, wherein the first application patch includes the list indicating locations within the application that the object for deprecation is utilized. | 2,100 |
6,436 | 6,436 | 15,842,377 | 2,154 | User information categorization using consent-based class rules is described. Consent from a user is received regarding at least one functional area where user information is shareable is received. Based on the consent, at least one data class that is permitted to be shared is determined. A user information designation is associated with the at least one data class and class rules are applied to user information associated with the user information designation based on the association between the user information designation and the at least one data class. | 1. A computer-implemented method comprising:
receiving consent, from a user, to share user information in at least one functional area; determining, based on the consent, at least one data class that is permitted to be shared; associating a user information designation with the at least one data class; and applying, to user information associated with the user information designation, class rules based on the association between the user information designation and the at least one data class. 2. The computer-implemented method of claim 1, wherein consent is received per functional area. 3. The computer-implemented method of claim 1, wherein:
the consent received is user specific; and the class rules are applied to user information for multiple users. 4. The computer-implemented method of claim 1, wherein associating the user information designation to the at least one data class comprises:
converting the consent into a numerical representation and converting the user information designation into a numerical representation. 5. The computer-implemented method of claim 4, further comprising:
forming a feature vector from the numerical representations of the consent and the user information designation; and determining a data class to which the user information designation belongs based on the feature vector. 6. The computer-implemented method of claim 5, wherein the numerical representation of the user information designation is represented in a term frequency/inverse document frequency form. 7. The computer-implemented method of claim 5, wherein associating the user information designation to the at least one data class comprises:
determining a designation profile for the user information designation; converting the designation profile into a numerical representation; and adding the numerical representation for the designation profile to the feature vector. 8. The computer-implemented method of claim 7, wherein the designation profile comprises designation type information, length information, and null quantity information. 9. The computer-implemented method of claim 5, further comprising, during a training phase, forming a mapping between the user information designation and the at least one data class. 10. The computer-implemented method of claim 9, wherein:
forming a mapping between the user information designation and the at least one data class comprises:
forming a number of training vectors for training information designations which are associated with predetermined data classes; and
calculating association variables based on the training vectors and the predetermined data classes. 11. The computer-implemented method of claim 10, wherein associating the user information designation with the at least one data classes comprises applying the association variables to the feature vector. 12. A system comprising:
a database comprising user information for a number of users, wherein the user information is grouped by user information designations; an interface to receive an indication, per functional area, of user consent to share user information; and a management controller comprising:
a class database comprising:
a number of data classes; and
class rules indicating which data classes are permitted to be shared based on consent received; and
an associator to associate user information designations with the number of data classes in the management controller. 13. The system of claim 12, wherein the associator:
receives as input, a feature vector which is a numerical representations of the user consent indications and a user information designation; and converts the feature vector into values that map to data classes. 14. The system of claim 12, wherein:
the management controller further comprises a mapping between data classes and functional areas; and at least one data class pertains to multiple functional areas. 15. The system of claim 12, wherein:
the associator is a neural network associator to form a mapping between user information designations and data classes by:
forming training vectors for training information designations which are associated with predetermined data classes; and
calculating association variables based on the training vectors and predetermined data classes; and
the associator associates user information designations with data classes by applying the association variables the feature vector. 16. The system of claim 12, wherein the user information designations in the database are unique to the system. 17. A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
form, during a training phase, a mapping between user information designations and data classes by:
forming training vectors for training info nation designations which are associated with predetermined data classes; and
calculating association variables based on the training vectors and the predetermined data classes;
determine, based on consent received, data classes that are permitted to be shared; extract a user information designation stored in a database; determine a designation profile for the user information designation; convert the consent into a numerical representation; convert the user information designation into a numerical representation; convert the designation profile into a numerical representation; form a feature vector from the numerical representations of the consent, user information designation, and designation profile; associate the user information designation with a data class on the feature vector and association variables; and apply, to user information associated with the user information designation, class rules based on the association between the user information designation and the associated data class. 18. The computer program product of claim 17, further comprising program instructions executable by a processor to cause the processor to, when the user information designation is not similar to any data class, generate a new data class. 19. The computer program product of claim 18, wherein generating a new data class is based on user input. 20. The computer program product of claim 18, wherein generating a new data class further comprises setting class rules for the new data class. | User information categorization using consent-based class rules is described. Consent from a user is received regarding at least one functional area where user information is shareable is received. Based on the consent, at least one data class that is permitted to be shared is determined. A user information designation is associated with the at least one data class and class rules are applied to user information associated with the user information designation based on the association between the user information designation and the at least one data class.1. A computer-implemented method comprising:
receiving consent, from a user, to share user information in at least one functional area; determining, based on the consent, at least one data class that is permitted to be shared; associating a user information designation with the at least one data class; and applying, to user information associated with the user information designation, class rules based on the association between the user information designation and the at least one data class. 2. The computer-implemented method of claim 1, wherein consent is received per functional area. 3. The computer-implemented method of claim 1, wherein:
the consent received is user specific; and the class rules are applied to user information for multiple users. 4. The computer-implemented method of claim 1, wherein associating the user information designation to the at least one data class comprises:
converting the consent into a numerical representation and converting the user information designation into a numerical representation. 5. The computer-implemented method of claim 4, further comprising:
forming a feature vector from the numerical representations of the consent and the user information designation; and determining a data class to which the user information designation belongs based on the feature vector. 6. The computer-implemented method of claim 5, wherein the numerical representation of the user information designation is represented in a term frequency/inverse document frequency form. 7. The computer-implemented method of claim 5, wherein associating the user information designation to the at least one data class comprises:
determining a designation profile for the user information designation; converting the designation profile into a numerical representation; and adding the numerical representation for the designation profile to the feature vector. 8. The computer-implemented method of claim 7, wherein the designation profile comprises designation type information, length information, and null quantity information. 9. The computer-implemented method of claim 5, further comprising, during a training phase, forming a mapping between the user information designation and the at least one data class. 10. The computer-implemented method of claim 9, wherein:
forming a mapping between the user information designation and the at least one data class comprises:
forming a number of training vectors for training information designations which are associated with predetermined data classes; and
calculating association variables based on the training vectors and the predetermined data classes. 11. The computer-implemented method of claim 10, wherein associating the user information designation with the at least one data classes comprises applying the association variables to the feature vector. 12. A system comprising:
a database comprising user information for a number of users, wherein the user information is grouped by user information designations; an interface to receive an indication, per functional area, of user consent to share user information; and a management controller comprising:
a class database comprising:
a number of data classes; and
class rules indicating which data classes are permitted to be shared based on consent received; and
an associator to associate user information designations with the number of data classes in the management controller. 13. The system of claim 12, wherein the associator:
receives as input, a feature vector which is a numerical representations of the user consent indications and a user information designation; and converts the feature vector into values that map to data classes. 14. The system of claim 12, wherein:
the management controller further comprises a mapping between data classes and functional areas; and at least one data class pertains to multiple functional areas. 15. The system of claim 12, wherein:
the associator is a neural network associator to form a mapping between user information designations and data classes by:
forming training vectors for training information designations which are associated with predetermined data classes; and
calculating association variables based on the training vectors and predetermined data classes; and
the associator associates user information designations with data classes by applying the association variables the feature vector. 16. The system of claim 12, wherein the user information designations in the database are unique to the system. 17. A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
form, during a training phase, a mapping between user information designations and data classes by:
forming training vectors for training info nation designations which are associated with predetermined data classes; and
calculating association variables based on the training vectors and the predetermined data classes;
determine, based on consent received, data classes that are permitted to be shared; extract a user information designation stored in a database; determine a designation profile for the user information designation; convert the consent into a numerical representation; convert the user information designation into a numerical representation; convert the designation profile into a numerical representation; form a feature vector from the numerical representations of the consent, user information designation, and designation profile; associate the user information designation with a data class on the feature vector and association variables; and apply, to user information associated with the user information designation, class rules based on the association between the user information designation and the associated data class. 18. The computer program product of claim 17, further comprising program instructions executable by a processor to cause the processor to, when the user information designation is not similar to any data class, generate a new data class. 19. The computer program product of claim 18, wherein generating a new data class is based on user input. 20. The computer program product of claim 18, wherein generating a new data class further comprises setting class rules for the new data class. | 2,100 |
6,437 | 6,437 | 15,473,479 | 2,165 | A system for coordinating the transfer of data between a network-attachable data transfer device and a data storage system. The system may establish a communications channel with a data storage service and copy a set of data objects from the data transfer device to the data storage system. The system may receive a communication from a service indicating a transfer status of the set of data objects to the data storage system. A receipt manifest may be generated providing information based on the transfer status for one or more of the set of data objects. The receipt manifest may be transferred to the data transfer device to be transported to the client. The system may process the data transfer system for reuse by another entity. | 1. A computer-implemented method, comprising:
reading a first set of data objects from a network-attachable data transfer device connected to a service provider computer system of a service provider, the network-attachable data transfer having been physically transferred from a customer data center of a customer of the service provider to a location of the service provider; transferring, via application programming interface calls to a data storage system of the service provider, the first set of data objects to the data storage system; determining, based at least in part on responses to the application programming interface calls, that the first set of data objects have been successfully received by the data storage system; generating a manifest inventorying at least the one or more data objects successfully stored to the data storage system; and providing the manifest to the network-attachable data transfer device. 2. The computer-implemented method of claim 1, further comprising:
processing the network-attachable data transfer device for reuse at least by performing a delete operation on one or more of the first set of data objects after determining that the first set of data objects have been successfully received by the data storage system. 3. The computer-implemented method of claim 1, wherein generating the receipt manifest includes digitally signing the receipt manifest using a cryptographic key of a service that performs the computer-implemented method. 4. The computer-implemented method of claim 1, wherein the receipt manifest includes, for each of the one or more data objects successfully transferred, a data object identifier and a status indicating successful transfer of the data object to the data transfer system. 5. A system, comprising:
one or more processors; memory that stores computer-executable instructions that, if executed, cause the one or more processors to:
read a data object from a network-attachable transfer device;
transmit the data object to a data storage service;
determine, based at least in part on information received from the data storage service, a status of the data object at the data storage service; and
update a manifest based at least in part on the status, the manifest indicating a individual statuses for a plurality of data storage objects read from the network-attachable transfer device. 6. The system of claim 5, wherein the request comprises a web service application programming interface call on to the storage service. 7. The system of claim 6, wherein the information from the data storage service is a response to the web service application programming interface call. 8. The system of claim 5, wherein:
the information from the data storage service comprises integrity information for the data object; and the status is determined based at least in part on the integrity information. 9. The system of claim 8, wherein the status is further determined based at least in part on comparing the integrity information with other integrity information for the data object from another manifest different from the manifest. 10. The system of claim 5, wherein the information written to the manifest includes a data object identifier corresponding to the data object. 11. The system of claim 5, wherein the communication session is established according to a handshaking protocol established for communications between the system and the storage service. 12. The system of claim 5, wherein the system stores the manifest on the network-attachable transfer device. 13. A non-transitory computer-readable storage medium having stored thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:
read a plurality of data objects from a network-attachable transfer device; transmit the plurality of data object to a data storage service; generate a manifest that, for individual data objects in the plurality of data objects, indicates statuses for the individual data objects; and make the manifest available to an entity associated with the network-attachable transfer device. 14. The non-transitory computer-readable storage medium of claim 13, wherein the instructions further comprise instructions that, as a result of being executed by the one or more processors, cause the computer system to delete the plurality of data objects from the network-attachable transfer device. 15. The non-transitory computer-readable storage medium of claim 13, wherein the instructions that cause the computer system to make the manifest available cause the computer system to store the manifest on the network-attachable transfer device. 16. The non-transitory computer-readable storage medium of claim 13, wherein the instructions that cause the computer system to make the manifest available cause the computer system to store the manifest on another network-attachable transfer device that is to be allocated to an entity associated with the network-attachable transfer device. 17. The non-transitory computer-readable storage medium of claim 13, wherein the instructions further comprise instructions that, as a result of being executed by the one or more processors, cause the computer system to:
read a second manifest indicating the plurality of data objects being stored on the network-attachable transfer device; compare the manifest to the second manifest to determine a data object from the plurality of data objects unsuccessfully received by the data storage service; and generate a notification specifying the data object. 18. The non-transitory computer-readable storage medium of claim 17, wherein instructions further comprise instructions that, as a result of execution by the one or more processors, cause the computer system to perform a comparison integrity information of the manifest with corresponding integrity information of the second manifest; and
wherein the manifest is generated based at least in part on the comparison. 19. The non-transitory computer-readable storage medium of claim 13, wherein the instructions further comprise instructions that, as a result of being executed by the one or more processors, cause the computer system to write, to the manifest, a data object identifier for each of the one or more of the set of objects. 20. The non-transitory computer-readable storage medium of claim 13, wherein making the manifest available comprises making the manifest available via a user interface to a service that comprises the computer system. | A system for coordinating the transfer of data between a network-attachable data transfer device and a data storage system. The system may establish a communications channel with a data storage service and copy a set of data objects from the data transfer device to the data storage system. The system may receive a communication from a service indicating a transfer status of the set of data objects to the data storage system. A receipt manifest may be generated providing information based on the transfer status for one or more of the set of data objects. The receipt manifest may be transferred to the data transfer device to be transported to the client. The system may process the data transfer system for reuse by another entity.1. A computer-implemented method, comprising:
reading a first set of data objects from a network-attachable data transfer device connected to a service provider computer system of a service provider, the network-attachable data transfer having been physically transferred from a customer data center of a customer of the service provider to a location of the service provider; transferring, via application programming interface calls to a data storage system of the service provider, the first set of data objects to the data storage system; determining, based at least in part on responses to the application programming interface calls, that the first set of data objects have been successfully received by the data storage system; generating a manifest inventorying at least the one or more data objects successfully stored to the data storage system; and providing the manifest to the network-attachable data transfer device. 2. The computer-implemented method of claim 1, further comprising:
processing the network-attachable data transfer device for reuse at least by performing a delete operation on one or more of the first set of data objects after determining that the first set of data objects have been successfully received by the data storage system. 3. The computer-implemented method of claim 1, wherein generating the receipt manifest includes digitally signing the receipt manifest using a cryptographic key of a service that performs the computer-implemented method. 4. The computer-implemented method of claim 1, wherein the receipt manifest includes, for each of the one or more data objects successfully transferred, a data object identifier and a status indicating successful transfer of the data object to the data transfer system. 5. A system, comprising:
one or more processors; memory that stores computer-executable instructions that, if executed, cause the one or more processors to:
read a data object from a network-attachable transfer device;
transmit the data object to a data storage service;
determine, based at least in part on information received from the data storage service, a status of the data object at the data storage service; and
update a manifest based at least in part on the status, the manifest indicating a individual statuses for a plurality of data storage objects read from the network-attachable transfer device. 6. The system of claim 5, wherein the request comprises a web service application programming interface call on to the storage service. 7. The system of claim 6, wherein the information from the data storage service is a response to the web service application programming interface call. 8. The system of claim 5, wherein:
the information from the data storage service comprises integrity information for the data object; and the status is determined based at least in part on the integrity information. 9. The system of claim 8, wherein the status is further determined based at least in part on comparing the integrity information with other integrity information for the data object from another manifest different from the manifest. 10. The system of claim 5, wherein the information written to the manifest includes a data object identifier corresponding to the data object. 11. The system of claim 5, wherein the communication session is established according to a handshaking protocol established for communications between the system and the storage service. 12. The system of claim 5, wherein the system stores the manifest on the network-attachable transfer device. 13. A non-transitory computer-readable storage medium having stored thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:
read a plurality of data objects from a network-attachable transfer device; transmit the plurality of data object to a data storage service; generate a manifest that, for individual data objects in the plurality of data objects, indicates statuses for the individual data objects; and make the manifest available to an entity associated with the network-attachable transfer device. 14. The non-transitory computer-readable storage medium of claim 13, wherein the instructions further comprise instructions that, as a result of being executed by the one or more processors, cause the computer system to delete the plurality of data objects from the network-attachable transfer device. 15. The non-transitory computer-readable storage medium of claim 13, wherein the instructions that cause the computer system to make the manifest available cause the computer system to store the manifest on the network-attachable transfer device. 16. The non-transitory computer-readable storage medium of claim 13, wherein the instructions that cause the computer system to make the manifest available cause the computer system to store the manifest on another network-attachable transfer device that is to be allocated to an entity associated with the network-attachable transfer device. 17. The non-transitory computer-readable storage medium of claim 13, wherein the instructions further comprise instructions that, as a result of being executed by the one or more processors, cause the computer system to:
read a second manifest indicating the plurality of data objects being stored on the network-attachable transfer device; compare the manifest to the second manifest to determine a data object from the plurality of data objects unsuccessfully received by the data storage service; and generate a notification specifying the data object. 18. The non-transitory computer-readable storage medium of claim 17, wherein instructions further comprise instructions that, as a result of execution by the one or more processors, cause the computer system to perform a comparison integrity information of the manifest with corresponding integrity information of the second manifest; and
wherein the manifest is generated based at least in part on the comparison. 19. The non-transitory computer-readable storage medium of claim 13, wherein the instructions further comprise instructions that, as a result of being executed by the one or more processors, cause the computer system to write, to the manifest, a data object identifier for each of the one or more of the set of objects. 20. The non-transitory computer-readable storage medium of claim 13, wherein making the manifest available comprises making the manifest available via a user interface to a service that comprises the computer system. | 2,100 |
6,438 | 6,438 | 15,417,753 | 2,176 | Systems, methods, and other embodiments associated with self-transformation objects are described. In one embodiment, a method includes determining that a content object is to be rendered. The example method may also include evaluating attributes of a user to identify a content preference of the user. The example method may also include identifying a content transformation mapping that corresponds to the content preference. The example method may also include parsing the content object to identify a transformation script. The example method may also include executing the transformation script to parse the content object to identify elements that are tagged with a transformation tag. The example method may also include executing the transformation script to apply corresponding transformations from the content transformation mapping to the tagged elements. The example method may also include rendering the content object with the transformed elements. | 1. A non-transitory computer-readable medium storing computer-executable instructions that when executed by a processor of a computer causes the processor to:
determine that a content object is to be rendered through a display; evaluate attributes of a user to identify a content preference of the user; identify a content transformation mapping corresponding to the content preference, wherein the content transformation mapping comprises mappings between element identifiers of elements and transformations to apply to the elements; parse the content object to identify a transformation script embedded within the content object; execute the transformation script on the content object to cause the content object to transform itself by:
(i) parsing the content object to identify an element tagged with a transformation tag indicating that the element is to be transformed;
(ii) evaluating the content transformation mapping using an element identifier of the element to identify a transformation to apply to the element; and
(iii) applying the transformation to the element to create a transformed element of the content object; and
render the content object with the transformed element through the display. 2. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for parsing the content object to identify the element comprise computer-executable instructions to cause the processor to:
execute the transformation script to parse a document object model of the content object during a rendering phase to identify markup language within the document object model matching a transformation tag template. 3. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for applying the transformation comprise computer-executable instructions to cause the processor to:
execute the transformation script to apply the transformation to the element during a rendering phase, wherein the transformation script replaces a first value of an attribute of the element with a second value specified by the transformation. 4. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for applying the transformation comprise computer-executable instructions to cause the processor to:
execute the transformation script to replace a text string of the element in a first language to a second text string in a second language specified by the transformation. 5. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for identifying the content transformation mapping comprise computer-executable instructions to cause the processor to:
perform an asynchronous call to a server to retrieve a property file comprising the content transformation mapping. 6. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for applying the transformation comprise computer-executable instructions to cause the processor to:
execute the transformation script to change an attribute of the element from a first value to a second value specified by the transformation. 7. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for applying the transformation comprise computer-executable instructions to cause the processor to:
execute the transformation script to change a visibility attribute of the element from a first value to a second value specified by the transformation. 8. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for applying the transformation comprise computer-executable instructions to cause the processor to:
execute the transformation script to change the element from displaying a first image to displaying a second image specified by the transformation. 9. A computing system, comprising:
a processor connected to memory; and a transformation module stored on a non-transitory computer readable medium and configured with instructions that when executed by the processor cause the processor to:
determine that a content object is to be rendered;
evaluate attributes of a user to identify a content preference of the user;
identify a content transformation mapping corresponding to the content preference, wherein the content transformation mapping comprises mappings between element identifiers of elements and transformations to apply to the elements;
parse the content object to identify a transformation script embedded within the content object;
execute the transformation script on the content object to cause the content object to transform itself by:
(i) parsing the content object to identify an element tagged with a transformation tag indicating that the element is to be transformed;
(ii) evaluating the content transformation mapping using an element identifier of the element to identify a transformation to apply to the element; and
(iii) applying the transformation to the element to create a transformed element of the content object; and
render the content object with the transformed element. 10. The computing system of claim 9, wherein the content object comprises a website, and the instructions for parsing the content object to identify the element comprise instructions to cause the processor to:
execute the transformation script to:
parse the website during a page load event to identify transformation tags; and
apply transformations to elements tagged with the transformation tags. 11. The computing system of claim 9, wherein the element is tagged with declarative syntax defining the transformation tag. 12. The computing system of claim 9, wherein the instructions for parsing the content object to identify the element comprise instructions to cause the processor to:
execute the transformation script to parse a document object model associated with the content object to identify transformation tags used to tag elements within the content object. 13. The computing system of claim 9, wherein the instructions for applying the transformation comprise instructions to cause the processor to:
execute the transformation script to change a color property setting for the element from a first color value to a second color value specified by the transformation. 14. The computing system of claim 9, wherein the instructions cause the processor to:
determine that the content object is to be displayed to a second user; evaluate second attributes of the second user to identify a second content preference of the second user; identify a second content transformation mapping corresponding to the second content preference; evaluate the second content transformation mapping to identify a second transformation, different than the transformation, to apply to the element; and apply the second transformation to the element to create a second transformed element of the content object. 15. A computer-implemented method, the computer-implemented method involving a computing device comprising a processor, and the computer-implemented method comprising:
determining, by the processor, that a content object is to be rendered; evaluating, by the processor, attributes of a user to identify a content preference of the user; identifying, by the processor, a content transformation mapping corresponding to the content preference, wherein the content transformation mapping comprises mappings between element identifiers of elements and transformations to apply to the elements; parsing, by the processor, the content object to identify a transformation script embedded within the content object; executing, by the processor, the transformation script on the content object to cause the content object to transform itself by:
(i) parsing, by the processor, the content object to identify an element tagged with a transformation tag indicating that the element is to be transformed;
(ii) evaluating, by the processor, the content transformation mapping using an element identifier of the element to identify a transformation to apply to the element; and
(iii) applying, by the processor, the transformation to the element to create a transformed element of the content object; and
rendering, by the processor, the content object with the transformed element. 16. The computer-implemented method of claim 15, further comprising:
parsing, by the processor, multiple webpages within the content object to identify elements that are tagged with transformation tags. 17. The computer-implemented method of claim 15, further comprising:
receiving, by the processor, an update for the content object; and performing, by the processor, a single update operation to update the content object for a plurality of supported transformations. 18. The computer-implemented method of claim 15, wherein the content object comprises a website, and the parsing the content object to identify the element further comprising:
parsing, by the processor, the website during a page load event to identify transformation tags; and applying, by the processor, transformations to elements tagged with the transformation tags. 19. The computer-implemented method of claim 15, wherein the attributes of the user comprise at least one of a location of the user, a language preference specified by the user, a user profile, social network data, or demographic data of the user. 20. The computer-implemented method of claim 15, wherein the transformation comprises a language translation of text of the element. | Systems, methods, and other embodiments associated with self-transformation objects are described. In one embodiment, a method includes determining that a content object is to be rendered. The example method may also include evaluating attributes of a user to identify a content preference of the user. The example method may also include identifying a content transformation mapping that corresponds to the content preference. The example method may also include parsing the content object to identify a transformation script. The example method may also include executing the transformation script to parse the content object to identify elements that are tagged with a transformation tag. The example method may also include executing the transformation script to apply corresponding transformations from the content transformation mapping to the tagged elements. The example method may also include rendering the content object with the transformed elements.1. A non-transitory computer-readable medium storing computer-executable instructions that when executed by a processor of a computer causes the processor to:
determine that a content object is to be rendered through a display; evaluate attributes of a user to identify a content preference of the user; identify a content transformation mapping corresponding to the content preference, wherein the content transformation mapping comprises mappings between element identifiers of elements and transformations to apply to the elements; parse the content object to identify a transformation script embedded within the content object; execute the transformation script on the content object to cause the content object to transform itself by:
(i) parsing the content object to identify an element tagged with a transformation tag indicating that the element is to be transformed;
(ii) evaluating the content transformation mapping using an element identifier of the element to identify a transformation to apply to the element; and
(iii) applying the transformation to the element to create a transformed element of the content object; and
render the content object with the transformed element through the display. 2. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for parsing the content object to identify the element comprise computer-executable instructions to cause the processor to:
execute the transformation script to parse a document object model of the content object during a rendering phase to identify markup language within the document object model matching a transformation tag template. 3. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for applying the transformation comprise computer-executable instructions to cause the processor to:
execute the transformation script to apply the transformation to the element during a rendering phase, wherein the transformation script replaces a first value of an attribute of the element with a second value specified by the transformation. 4. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for applying the transformation comprise computer-executable instructions to cause the processor to:
execute the transformation script to replace a text string of the element in a first language to a second text string in a second language specified by the transformation. 5. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for identifying the content transformation mapping comprise computer-executable instructions to cause the processor to:
perform an asynchronous call to a server to retrieve a property file comprising the content transformation mapping. 6. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for applying the transformation comprise computer-executable instructions to cause the processor to:
execute the transformation script to change an attribute of the element from a first value to a second value specified by the transformation. 7. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for applying the transformation comprise computer-executable instructions to cause the processor to:
execute the transformation script to change a visibility attribute of the element from a first value to a second value specified by the transformation. 8. The non-transitory computer-readable medium of claim 1, wherein the computer-executable instructions for applying the transformation comprise computer-executable instructions to cause the processor to:
execute the transformation script to change the element from displaying a first image to displaying a second image specified by the transformation. 9. A computing system, comprising:
a processor connected to memory; and a transformation module stored on a non-transitory computer readable medium and configured with instructions that when executed by the processor cause the processor to:
determine that a content object is to be rendered;
evaluate attributes of a user to identify a content preference of the user;
identify a content transformation mapping corresponding to the content preference, wherein the content transformation mapping comprises mappings between element identifiers of elements and transformations to apply to the elements;
parse the content object to identify a transformation script embedded within the content object;
execute the transformation script on the content object to cause the content object to transform itself by:
(i) parsing the content object to identify an element tagged with a transformation tag indicating that the element is to be transformed;
(ii) evaluating the content transformation mapping using an element identifier of the element to identify a transformation to apply to the element; and
(iii) applying the transformation to the element to create a transformed element of the content object; and
render the content object with the transformed element. 10. The computing system of claim 9, wherein the content object comprises a website, and the instructions for parsing the content object to identify the element comprise instructions to cause the processor to:
execute the transformation script to:
parse the website during a page load event to identify transformation tags; and
apply transformations to elements tagged with the transformation tags. 11. The computing system of claim 9, wherein the element is tagged with declarative syntax defining the transformation tag. 12. The computing system of claim 9, wherein the instructions for parsing the content object to identify the element comprise instructions to cause the processor to:
execute the transformation script to parse a document object model associated with the content object to identify transformation tags used to tag elements within the content object. 13. The computing system of claim 9, wherein the instructions for applying the transformation comprise instructions to cause the processor to:
execute the transformation script to change a color property setting for the element from a first color value to a second color value specified by the transformation. 14. The computing system of claim 9, wherein the instructions cause the processor to:
determine that the content object is to be displayed to a second user; evaluate second attributes of the second user to identify a second content preference of the second user; identify a second content transformation mapping corresponding to the second content preference; evaluate the second content transformation mapping to identify a second transformation, different than the transformation, to apply to the element; and apply the second transformation to the element to create a second transformed element of the content object. 15. A computer-implemented method, the computer-implemented method involving a computing device comprising a processor, and the computer-implemented method comprising:
determining, by the processor, that a content object is to be rendered; evaluating, by the processor, attributes of a user to identify a content preference of the user; identifying, by the processor, a content transformation mapping corresponding to the content preference, wherein the content transformation mapping comprises mappings between element identifiers of elements and transformations to apply to the elements; parsing, by the processor, the content object to identify a transformation script embedded within the content object; executing, by the processor, the transformation script on the content object to cause the content object to transform itself by:
(i) parsing, by the processor, the content object to identify an element tagged with a transformation tag indicating that the element is to be transformed;
(ii) evaluating, by the processor, the content transformation mapping using an element identifier of the element to identify a transformation to apply to the element; and
(iii) applying, by the processor, the transformation to the element to create a transformed element of the content object; and
rendering, by the processor, the content object with the transformed element. 16. The computer-implemented method of claim 15, further comprising:
parsing, by the processor, multiple webpages within the content object to identify elements that are tagged with transformation tags. 17. The computer-implemented method of claim 15, further comprising:
receiving, by the processor, an update for the content object; and performing, by the processor, a single update operation to update the content object for a plurality of supported transformations. 18. The computer-implemented method of claim 15, wherein the content object comprises a website, and the parsing the content object to identify the element further comprising:
parsing, by the processor, the website during a page load event to identify transformation tags; and applying, by the processor, transformations to elements tagged with the transformation tags. 19. The computer-implemented method of claim 15, wherein the attributes of the user comprise at least one of a location of the user, a language preference specified by the user, a user profile, social network data, or demographic data of the user. 20. The computer-implemented method of claim 15, wherein the transformation comprises a language translation of text of the element. | 2,100 |
6,439 | 6,439 | 14,802,760 | 2,148 | A learning computer system may estimate unknown parameters and states of a stochastic or uncertain system having a probability structure. The system may include a data processing system that may include a hardware processor that has a configuration that: receives data; generates random, chaotic, fuzzy, or other numerical perturbations of the data, one or more of the states, or the probability structure; estimates observed and hidden states of the stochastic or uncertain system using the data, the generated perturbations, previous states of the stochastic or uncertain system, or estimated states of the stochastic or uncertain system; and causes perturbations or independent noise to be injected into the data, the states, or the stochastic or uncertain system so as to speed up training or learning of the probability structure and of the system parameters or the states. | 1. A learning computer system that estimates unknown parameters and states of a stochastic or uncertain system having a probability structure comprising a data processing system that includes a hardware processor that has a configuration that:
receives data; generates random, chaotic, fuzzy, or other numerical perturbations of the data, one or more of the states, or the probability structure; estimates observed and hidden states of the stochastic or uncertain system using the data, the generated perturbations, previous states of the stochastic or uncertain system, or estimated states of the stochastic or uncertain system; and causes perturbations or independent noise to be injected into the data, the states, or the stochastic or uncertain system so as to speed up training or learning of the probability structure and of the system parameters or the states. 2. The learning computer system of claim 1 wherein the data processing system has a configuration that causes the perturbations of the data, states, or probability structure to speed up training of a hidden Markov model. 3. The learning computer system of claim 2 wherein the perturbations of the data, states, or probability structure satisfy the Noisy Expectation Maximization (NEM) condition. 4. The learning computer system of claim 3 wherein the system makes HMM state or parameter estimates and the perturbations are used to improve the accuracy of the estimates. 5. The learning computer system of claim 2 wherein the data processing system has a configuration that causes the perturbations of the data to speed up training of the hidden Markov model. 6. The learning computer system of claim 5 wherein the perturbations train or update one or more mixture models in the probability structure. 7. The learning computer system of claim 6 wherein one or more of the mixture models includes a Gaussian mixture model. 8. The learning computer system of claim 2 wherein the injected perturbations or noise has a rate that decays as the training proceeds. 9. The learning computer system of claim 1 wherein the injection includes adding, multiplying, exponentiating the data, states, or probability structure with the perturbations or independent noise. 10. A non-transitory, tangible, computer-readable storage media containing a program of instructions that cause a computer system comprising a data processing system that includes a hardware processor running the program of instructions to estimate unknown parameters and states of a stochastic or uncertain system having a probability structure that:
receives data; generates random, chaotic, fuzzy, or other numerical perturbations of the data, one or more of the states, or the probability structure; estimates observed and hidden states of the stochastic or uncertain system using the data, the generated perturbations, previous states of the stochastic or uncertain system, or estimated states of the stochastic or uncertain system; and causes perturbations or independent noise to be injected into the data, the states, or the stochastic or uncertain system so as to speed up training or learning of the probability structure and of the system parameters or the states. 11. The storage media of claim 10 wherein the program of instructions when run causes the perturbations of the data, states, or probability structure to speed up training of a hidden Markov model. 12. The storage media of claim 11 wherein the perturbations of the data, states, or probability structure satisfy the Noisy Expectation Maximization (NEM) condition. 13. The storage media of claim 12 wherein the program of instructions when run makes HMM state or parameter estimates and the perturbations are used to improve the accuracy of the estimates. 14. The storage media of claim 11 wherein the program of instructions when run causes the perturbations of the data to speed up training of the hidden Markov model. 15. The storage media of claim 14 wherein the perturbations train or update one or more mixture models in the probability structure. 16. The storage media of claim 15 wherein one or more of the mixture models includes a Gaussian mixture model. 17. The storage media of claim 11 wherein the injected perturbations or noise has a rate that decays as the training proceeds. 18. The storage media of claim 10 wherein the injection includes adding, multiplying, exponentiating the data, states, or probability structure with the perturbations or independent noise. | A learning computer system may estimate unknown parameters and states of a stochastic or uncertain system having a probability structure. The system may include a data processing system that may include a hardware processor that has a configuration that: receives data; generates random, chaotic, fuzzy, or other numerical perturbations of the data, one or more of the states, or the probability structure; estimates observed and hidden states of the stochastic or uncertain system using the data, the generated perturbations, previous states of the stochastic or uncertain system, or estimated states of the stochastic or uncertain system; and causes perturbations or independent noise to be injected into the data, the states, or the stochastic or uncertain system so as to speed up training or learning of the probability structure and of the system parameters or the states.1. A learning computer system that estimates unknown parameters and states of a stochastic or uncertain system having a probability structure comprising a data processing system that includes a hardware processor that has a configuration that:
receives data; generates random, chaotic, fuzzy, or other numerical perturbations of the data, one or more of the states, or the probability structure; estimates observed and hidden states of the stochastic or uncertain system using the data, the generated perturbations, previous states of the stochastic or uncertain system, or estimated states of the stochastic or uncertain system; and causes perturbations or independent noise to be injected into the data, the states, or the stochastic or uncertain system so as to speed up training or learning of the probability structure and of the system parameters or the states. 2. The learning computer system of claim 1 wherein the data processing system has a configuration that causes the perturbations of the data, states, or probability structure to speed up training of a hidden Markov model. 3. The learning computer system of claim 2 wherein the perturbations of the data, states, or probability structure satisfy the Noisy Expectation Maximization (NEM) condition. 4. The learning computer system of claim 3 wherein the system makes HMM state or parameter estimates and the perturbations are used to improve the accuracy of the estimates. 5. The learning computer system of claim 2 wherein the data processing system has a configuration that causes the perturbations of the data to speed up training of the hidden Markov model. 6. The learning computer system of claim 5 wherein the perturbations train or update one or more mixture models in the probability structure. 7. The learning computer system of claim 6 wherein one or more of the mixture models includes a Gaussian mixture model. 8. The learning computer system of claim 2 wherein the injected perturbations or noise has a rate that decays as the training proceeds. 9. The learning computer system of claim 1 wherein the injection includes adding, multiplying, exponentiating the data, states, or probability structure with the perturbations or independent noise. 10. A non-transitory, tangible, computer-readable storage media containing a program of instructions that cause a computer system comprising a data processing system that includes a hardware processor running the program of instructions to estimate unknown parameters and states of a stochastic or uncertain system having a probability structure that:
receives data; generates random, chaotic, fuzzy, or other numerical perturbations of the data, one or more of the states, or the probability structure; estimates observed and hidden states of the stochastic or uncertain system using the data, the generated perturbations, previous states of the stochastic or uncertain system, or estimated states of the stochastic or uncertain system; and causes perturbations or independent noise to be injected into the data, the states, or the stochastic or uncertain system so as to speed up training or learning of the probability structure and of the system parameters or the states. 11. The storage media of claim 10 wherein the program of instructions when run causes the perturbations of the data, states, or probability structure to speed up training of a hidden Markov model. 12. The storage media of claim 11 wherein the perturbations of the data, states, or probability structure satisfy the Noisy Expectation Maximization (NEM) condition. 13. The storage media of claim 12 wherein the program of instructions when run makes HMM state or parameter estimates and the perturbations are used to improve the accuracy of the estimates. 14. The storage media of claim 11 wherein the program of instructions when run causes the perturbations of the data to speed up training of the hidden Markov model. 15. The storage media of claim 14 wherein the perturbations train or update one or more mixture models in the probability structure. 16. The storage media of claim 15 wherein one or more of the mixture models includes a Gaussian mixture model. 17. The storage media of claim 11 wherein the injected perturbations or noise has a rate that decays as the training proceeds. 18. The storage media of claim 10 wherein the injection includes adding, multiplying, exponentiating the data, states, or probability structure with the perturbations or independent noise. | 2,100 |
6,440 | 6,440 | 15,773,395 | 2,166 | A non-transitory computer readable medium can store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations. The operations can include establishing a connection between data stores (such as a relational data store and a graph engine), wherein the connection includes a shared memory buffer storing data in a data format according to internal structures of the graph engine. The connection between the data stores is bi-directional. The connection enables data that is stored in the shared memory to be processed by either of the graph engine and the relational database. Upon receiving a query, the graph engine or the relational database can be selected to process the data based on a query. The data can be processed by the selected one of the graph engine or the relational database. | 1. A non-transitory computer readable medium to store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations, the operations comprising:
establishing a connection between data stores, wherein the data stores comprise a relational database and a graph engine, wherein the connection comprises a shared memory buffer storing data in a data format according to internal structures of the graph engine, and wherein the connection is bi-directional; enabling data that is stored in the shared memory to be processed by either of the graph engine and the relational database; and selecting the graph engine or the relational database to process the data based on a query; and processing the data that is stored in the shared memory by the selected one of the graph engine or the relational database. 2. The non-transitory computer readable medium of claim 1, wherein the graph engine is embedded within the relational database. 3. The non-transitory computer readable medium of claim 1, wherein the processing the data that is stored in the shared memory further comprises:
receiving, at the relational database from an application, a query for data; converting the data from the relational database to a format of the graph engine using the internal data structures of the graph engine; performing a computation on the data using the graph engine, wherein the computation is based on the query; and using the relational database or the graph engine to report a result of the query to the application. 4. The non-transitory computer readable medium of claim 3, further comprising:
determining, whether to process the data with the relational database or the graph engine in response to the query. 5. The non-transitory computer readable medium of claim 1, the operations further comprising:
transforming relational data from the relational database into a graph engine format of the graph engine according to internal structures of the graph engine, wherein the transforming further comprises: ingesting the relational data from the relational database into the shared memory buffer as vertex data and edge data corresponding to the transformed data; generating location identifiers for each of the vertex data and the edge data; and accessing the vertex data and the edge data from the shared memory buffer. 6. The non-transitory computer readable medium of claim 5,
wherein accessing the vertex data and the edge data from the shared memory buffer is performed by the graph engine, and wherein the graph engine processes the accessed vertex data and edge data. 7. A system comprising:
a non-transitory memory to store machine-readable instructions; and a processing resource to access the memory and execute the machine-readable instructions, the machine-readable instructions comprising:
a connector to establish a connection between a relational data store and a graph engine, wherein the connection comprises a shared memory buffer; and
a convertor system to transform relational data from a relational data format to a graph engine format according to internal data structures of the graph engine and to store the transformed data, including vertex data and edge data, in the shared memory buffer of the connection. 8. The system of claim 7, wherein the graph engine is embedded within the relational data store to facilitate the exporting the relational data. 9. The system of claim 7, wherein the graph engine is to perform at least one of graph processing, array processing, signal processing, and video processing. 10. The system of claim 7, wherein the connector comprises an ingest convertor to ingest the relational data from the relational data store and store the vertex data and the edge data into the shared memory buffer. 11. The system of claim 7, wherein the connector comprises an export convertor to access the edge data and the vertex data from the shared memory buffer and transform the edge data and the vertex data as corresponding relational data in the relational data format to the relational data store. 12. The system of claim 11, wherein the graph engine accesses the edge data and the vertex data based on location identifiers provided to the graph engine via an application interface. 13. The system of claim 7, further comprising an application to provide a query to the relational data store relating to the relational data,
wherein the relational data store comprises a query engine to determine whether to perform processing on the relational data of the relational data store or to pass the query to the graph engine for processing by the graph engine. 14. The system of claim 13, wherein, in response to the query engine determining to pass the query to the graph engine, the convertor system transforming the relational data into the graph engine format and storing the relational data as the edge data and the vertex data in the shared memory buffer of the connection, the application providing the graph engine with location identifiers for the edge data and the vertex data to enable processing thereof by the graph engine,
wherein a result of the query is reported to the application using the relational data store or the graph engine. 15. A method comprising:
receiving, at a relational database from an application, a query for data; transforming the data from the relational database to a graph engine format according to internal data structures of a graph engine, the transformed data being stored in a shared memory of a connection that employs; providing location references to the graph engine for accessing the transformed data in the shared memory; performing a computation on the data in the graph engine format using the graph engine, wherein the computation is based on the query; and using the relational database or the graph engine to report a result of the query to the application. | A non-transitory computer readable medium can store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations. The operations can include establishing a connection between data stores (such as a relational data store and a graph engine), wherein the connection includes a shared memory buffer storing data in a data format according to internal structures of the graph engine. The connection between the data stores is bi-directional. The connection enables data that is stored in the shared memory to be processed by either of the graph engine and the relational database. Upon receiving a query, the graph engine or the relational database can be selected to process the data based on a query. The data can be processed by the selected one of the graph engine or the relational database.1. A non-transitory computer readable medium to store machine readable instructions that when accessed and executed by a processing resource cause a computing device to perform operations, the operations comprising:
establishing a connection between data stores, wherein the data stores comprise a relational database and a graph engine, wherein the connection comprises a shared memory buffer storing data in a data format according to internal structures of the graph engine, and wherein the connection is bi-directional; enabling data that is stored in the shared memory to be processed by either of the graph engine and the relational database; and selecting the graph engine or the relational database to process the data based on a query; and processing the data that is stored in the shared memory by the selected one of the graph engine or the relational database. 2. The non-transitory computer readable medium of claim 1, wherein the graph engine is embedded within the relational database. 3. The non-transitory computer readable medium of claim 1, wherein the processing the data that is stored in the shared memory further comprises:
receiving, at the relational database from an application, a query for data; converting the data from the relational database to a format of the graph engine using the internal data structures of the graph engine; performing a computation on the data using the graph engine, wherein the computation is based on the query; and using the relational database or the graph engine to report a result of the query to the application. 4. The non-transitory computer readable medium of claim 3, further comprising:
determining, whether to process the data with the relational database or the graph engine in response to the query. 5. The non-transitory computer readable medium of claim 1, the operations further comprising:
transforming relational data from the relational database into a graph engine format of the graph engine according to internal structures of the graph engine, wherein the transforming further comprises: ingesting the relational data from the relational database into the shared memory buffer as vertex data and edge data corresponding to the transformed data; generating location identifiers for each of the vertex data and the edge data; and accessing the vertex data and the edge data from the shared memory buffer. 6. The non-transitory computer readable medium of claim 5,
wherein accessing the vertex data and the edge data from the shared memory buffer is performed by the graph engine, and wherein the graph engine processes the accessed vertex data and edge data. 7. A system comprising:
a non-transitory memory to store machine-readable instructions; and a processing resource to access the memory and execute the machine-readable instructions, the machine-readable instructions comprising:
a connector to establish a connection between a relational data store and a graph engine, wherein the connection comprises a shared memory buffer; and
a convertor system to transform relational data from a relational data format to a graph engine format according to internal data structures of the graph engine and to store the transformed data, including vertex data and edge data, in the shared memory buffer of the connection. 8. The system of claim 7, wherein the graph engine is embedded within the relational data store to facilitate the exporting the relational data. 9. The system of claim 7, wherein the graph engine is to perform at least one of graph processing, array processing, signal processing, and video processing. 10. The system of claim 7, wherein the connector comprises an ingest convertor to ingest the relational data from the relational data store and store the vertex data and the edge data into the shared memory buffer. 11. The system of claim 7, wherein the connector comprises an export convertor to access the edge data and the vertex data from the shared memory buffer and transform the edge data and the vertex data as corresponding relational data in the relational data format to the relational data store. 12. The system of claim 11, wherein the graph engine accesses the edge data and the vertex data based on location identifiers provided to the graph engine via an application interface. 13. The system of claim 7, further comprising an application to provide a query to the relational data store relating to the relational data,
wherein the relational data store comprises a query engine to determine whether to perform processing on the relational data of the relational data store or to pass the query to the graph engine for processing by the graph engine. 14. The system of claim 13, wherein, in response to the query engine determining to pass the query to the graph engine, the convertor system transforming the relational data into the graph engine format and storing the relational data as the edge data and the vertex data in the shared memory buffer of the connection, the application providing the graph engine with location identifiers for the edge data and the vertex data to enable processing thereof by the graph engine,
wherein a result of the query is reported to the application using the relational data store or the graph engine. 15. A method comprising:
receiving, at a relational database from an application, a query for data; transforming the data from the relational database to a graph engine format according to internal data structures of a graph engine, the transformed data being stored in a shared memory of a connection that employs; providing location references to the graph engine for accessing the transformed data in the shared memory; performing a computation on the data in the graph engine format using the graph engine, wherein the computation is based on the query; and using the relational database or the graph engine to report a result of the query to the application. | 2,100 |
6,441 | 6,441 | 14,652,447 | 2,175 | A method for activating a mobile device in a network is disclosed wherein the network further comprises at least one display device coupled to a sensor. This method comprises:
detecting the mobile device via the sensor coupled to the display device; displaying a virtual device representative of the detected mobile device on the display device such that the position of the virtual device on the display device is linked to the position of the mobile device in the capture field of the sensor; activating the mobile device in the network when a determined action is applied by the user to the mobile device.
A display device and a system implementing the method are further disclosed. | 1-14. (canceled) 15. A method for activating a mobile device in a network, said network comprising at least one display device coupled to a sensor, said method comprising:
detecting the mobile device via said sensor coupled to said display device, displaying a virtual device representative of said detected mobile device on said display device such that the position of said virtual device on said display device is linked to the position of the mobile device in the capture field of the sensor, activating said mobile device in the network when a determined action is applied by said user to said mobile device. 16. The method according to claim 15 wherein the sensor comprises at least an image capture device. 17. The method according to claim 16 wherein a determined shape is associated with said mobile device, wherein detecting the mobile device comprises an object recognition from shapes and wherein a representation of detected the mobile device by said virtual device is based on said determined shape. 18. The method according to claim 15 wherein detecting the mobile device comprises determining the position of said mobile device in the capture field of the sensor. 19. The method according to claim 15 wherein detecting the mobile device comprises determining the orientation of said mobile device in the capture field of the sensor. 20. The method according to claim 15 wherein said determined action for the activation comprises a displacement of the mobile device adapted for displacing the virtual device to a binding area on said display device. 21. The method according to claim 15 wherein said determined action for the activation comprises the pressing of a determined area on said mobile device. 22. The method according to claim 15 wherein said determined action for the activation comprises a determined movement applied by the user to said mobile device. 23. The method according to claim 15 wherein said network further comprises at least one second device and wherein activating further comprises the entry of a command on said display device or on at least one second device. 24. The method according to claim 15 wherein detecting is initiated by a preliminary detection in a wireless communication field. 25. A display device configured to activate a mobile device in a network, said display device being coupled to a sensor configured to detect said mobile device, and said display device comprising:
an object recognition module configured to recognise said detected mobile device; a module for calculating a position of said detected mobile device in the capture field of the sensor; a module for synthesising a virtual device representative of said detected mobile device on said display device such that a position of the virtual device on said display device is linked to the position of the mobile device in the capture field of the sensor; a module for activating said mobile device in the network when a determined action is applied by said user to said mobile device. 26. The display device according to claim 25 wherein the sensor comprises at least one camera configured to capture at least one image. 27. The display device according to claim 26 further comprising a module for reproducing on said display device a binding area; and wherein the position of said virtual device on the binding area activates the activation module. 28. A system for activating a mobile device in a network, said system comprising:
a display device; a mobile device; a sensor coupled to said display device;
and wherein the system further comprises:
an object recognition module configured to recognise said detected mobile device;
a module for calculating a position of said detected mobile device in the capture field of the sensor;
a module for synthesising a virtual device representative of said detected mobile device on said display device such that a position of the virtual device on said display device is linked to said position of the mobile device in the capture field of the sensor;
a module for activating said mobile device in the network when a determined action is applied by said user to said mobile device. 29. The system according to claim 28 wherein the sensor comprises at least one camera configured to capture at least one image. 30. The system according to claim 29 further comprising a module for reproducing on said display device a binding area; and wherein the position of said virtual device on the binding area activates the activation module. 31. The system according to claim 28 wherein said determined action for the activation comprises a determined movement applied by the user to said mobile device. 32. A display device configured to activate a mobile device in a network, said display device being coupled to a sensor configured to detect said mobile device; and said display device comprising a processor configured to:
recognise said detected mobile device; calculate a position of said detected mobile device in the capture field of the sensor; synthesise a virtual device representative of said detected mobile device for display on said display device such that the position of the virtual device on said display device is linked to said position of the mobile device in the capture field of the sensor; activate said mobile device in the network when a determined action is applied by said user to said mobile device. 33. The display device according to claim 32 wherein the sensor comprises at least one camera configured to capture at least one image. 34. The display device according to claim 33 wherein the processor is further configured to reproduce on said display device a binding area; and wherein the position of said virtual device on the binding area activates the activation module. 35. The display device according to claim 33 wherein said determined action for the activation comprises a determined movement applied by the user to said mobile device. 36. The display device according to claim 27 wherein said determined action for the activation comprises a determined movement applied by the user to said mobile device. | A method for activating a mobile device in a network is disclosed wherein the network further comprises at least one display device coupled to a sensor. This method comprises:
detecting the mobile device via the sensor coupled to the display device; displaying a virtual device representative of the detected mobile device on the display device such that the position of the virtual device on the display device is linked to the position of the mobile device in the capture field of the sensor; activating the mobile device in the network when a determined action is applied by the user to the mobile device.
A display device and a system implementing the method are further disclosed.1-14. (canceled) 15. A method for activating a mobile device in a network, said network comprising at least one display device coupled to a sensor, said method comprising:
detecting the mobile device via said sensor coupled to said display device, displaying a virtual device representative of said detected mobile device on said display device such that the position of said virtual device on said display device is linked to the position of the mobile device in the capture field of the sensor, activating said mobile device in the network when a determined action is applied by said user to said mobile device. 16. The method according to claim 15 wherein the sensor comprises at least an image capture device. 17. The method according to claim 16 wherein a determined shape is associated with said mobile device, wherein detecting the mobile device comprises an object recognition from shapes and wherein a representation of detected the mobile device by said virtual device is based on said determined shape. 18. The method according to claim 15 wherein detecting the mobile device comprises determining the position of said mobile device in the capture field of the sensor. 19. The method according to claim 15 wherein detecting the mobile device comprises determining the orientation of said mobile device in the capture field of the sensor. 20. The method according to claim 15 wherein said determined action for the activation comprises a displacement of the mobile device adapted for displacing the virtual device to a binding area on said display device. 21. The method according to claim 15 wherein said determined action for the activation comprises the pressing of a determined area on said mobile device. 22. The method according to claim 15 wherein said determined action for the activation comprises a determined movement applied by the user to said mobile device. 23. The method according to claim 15 wherein said network further comprises at least one second device and wherein activating further comprises the entry of a command on said display device or on at least one second device. 24. The method according to claim 15 wherein detecting is initiated by a preliminary detection in a wireless communication field. 25. A display device configured to activate a mobile device in a network, said display device being coupled to a sensor configured to detect said mobile device, and said display device comprising:
an object recognition module configured to recognise said detected mobile device; a module for calculating a position of said detected mobile device in the capture field of the sensor; a module for synthesising a virtual device representative of said detected mobile device on said display device such that a position of the virtual device on said display device is linked to the position of the mobile device in the capture field of the sensor; a module for activating said mobile device in the network when a determined action is applied by said user to said mobile device. 26. The display device according to claim 25 wherein the sensor comprises at least one camera configured to capture at least one image. 27. The display device according to claim 26 further comprising a module for reproducing on said display device a binding area; and wherein the position of said virtual device on the binding area activates the activation module. 28. A system for activating a mobile device in a network, said system comprising:
a display device; a mobile device; a sensor coupled to said display device;
and wherein the system further comprises:
an object recognition module configured to recognise said detected mobile device;
a module for calculating a position of said detected mobile device in the capture field of the sensor;
a module for synthesising a virtual device representative of said detected mobile device on said display device such that a position of the virtual device on said display device is linked to said position of the mobile device in the capture field of the sensor;
a module for activating said mobile device in the network when a determined action is applied by said user to said mobile device. 29. The system according to claim 28 wherein the sensor comprises at least one camera configured to capture at least one image. 30. The system according to claim 29 further comprising a module for reproducing on said display device a binding area; and wherein the position of said virtual device on the binding area activates the activation module. 31. The system according to claim 28 wherein said determined action for the activation comprises a determined movement applied by the user to said mobile device. 32. A display device configured to activate a mobile device in a network, said display device being coupled to a sensor configured to detect said mobile device; and said display device comprising a processor configured to:
recognise said detected mobile device; calculate a position of said detected mobile device in the capture field of the sensor; synthesise a virtual device representative of said detected mobile device for display on said display device such that the position of the virtual device on said display device is linked to said position of the mobile device in the capture field of the sensor; activate said mobile device in the network when a determined action is applied by said user to said mobile device. 33. The display device according to claim 32 wherein the sensor comprises at least one camera configured to capture at least one image. 34. The display device according to claim 33 wherein the processor is further configured to reproduce on said display device a binding area; and wherein the position of said virtual device on the binding area activates the activation module. 35. The display device according to claim 33 wherein said determined action for the activation comprises a determined movement applied by the user to said mobile device. 36. The display device according to claim 27 wherein said determined action for the activation comprises a determined movement applied by the user to said mobile device. | 2,100 |
6,442 | 6,442 | 15,714,887 | 2,179 | The present disclosure generally relates to using avatars and image data for enhanced user interactions. In some examples, user status dependent avatars are generated and displayed with a message associated with the user status. In some examples, a device captures image information to scan an object to create a 3D model of the object. The device determines an algorithm for the 3D model based on the capture image information and provides visual feedback on additional image data that is needed for the algorithm to build the 3D model. In some examples, an application's operation on a device is restricted based on whether an authorized user is identified as using the device based on captured image data. | 1. (canceled) 2. An electronic device, comprising:
a display; one or more processors; one or more input devices; a memory; and one or more programs, wherein the one or more programs are stored in memory and configured to be executed by the one or more processors, the one or more programs including instructions for:
displaying, on the display, content in an application, wherein the content is displayed while the application is in a first configuration;
while displaying the content, capturing image data from the one or more image sensors of the electronic device;
after capturing the image data, receiving a request to navigate away from the content; and
in response to receiving a request to navigate away from the content:
in accordance with a determination that a first set of content-lock criteria have been met, preventing navigation away from the content while maintaining display of the content, wherein the first set of content-lock criteria includes a first criterion that is met when the captured image data indicates that an unauthorized user is using the device; and
in accordance with a determination that the first set of content-lock criteria have not been met, navigating away from the content in accordance with the request. 3. The electronic device of claim 2, the one or more programs further including instructions for:
in accordance with a determination that the first set of content-lock criteria is no longer met, allowing navigation away from the content. 4. The electronic device of claim 2, wherein the first set of lock-criteria includes a second criterion that is met when the captured image data indicates that an authorized user of the electronic device is not using the device. 5. The electronic device of claim 2, wherein the first set of lock-criteria includes a third criterion that is met when the captured image data indicates that the unauthorized user is present and an authorized user is not present. 6. The electronic device of claim 2, wherein the first set of lock-criteria is met when the captured image data indicates that the unauthorized user is present without regard to whether or not an authorized user is present. 7. The electronic device of claim 2, wherein the one or more programs further include instructions for:
in accordance with a determination that a second set of content-lock criteria has been met, disabling at least one function of the electronic device. 8. The electronic device of claim 7, wherein the first set of lock-criteria and the second set of lock-criteria are different. 9. The electronic device of claim 2, wherein the one or more programs further include instructions for:
in accordance with a determination that a third set of content-lock criteria has been met, switching the application to a second configuration that limits operation of the application as compared to the first configuration. 10. The electronic device of claim 2, wherein the one or more programs further include instructions for:
in accordance with the determination that a fourth set of content-lock criteria have been met, locking other functionality of the electronic device while continuing to display the content in the application. 11. The electronic device of claim 2, wherein the one or more programs further include instructions for:
in accordance with the determination that a fifth set of content-lock criteria have been met, preventing the display of a notification related to a communication received at the electronic device. 12. The electronic device of claim 11, wherein:
the fifth set of lock-criteria includes a fourth criterion that is met when the captured image data indicates that an unauthorized user is using the electronic device and the fifth set of lock-criteria is met if the fourth criterion is met; and the first set of lock-criteria includes a fifth criteria that is met when the captured image data indicates the absence of an authorized user. 13. The electronic device of claim 12, wherein the one or more programs further include instructions for:
in accordance with the fourth criterion being met, preventing navigation between applications on the electronic device; and in accordance with the fifth criterion being met, preventing navigation within the application. 14. The electronic device of claim 2, wherein the one or more programs further include instructions for:
determining whether the captured image data indicates the presence of an unauthorized user of the electronic device. 15. The electronic device of claim 2, wherein the image data includes optical data and depth data, and wherein determining whether the first set of content-lock criteria have been met is based on the optical data and the depth data. 16. The electronic device of claim 2, wherein navigating away from the content includes translating currently displayed content. 17. The electronic device of claim 2, wherein navigating away from the content includes switching between content items in an application. 18. The electronic device of claim 2, wherein navigating away from the content includes switching applications or closing the application to display the home screen. 19. The electronic device of claim 2, wherein the one or more programs further include instructions for:
receiving unlock information associated with an authorized user of the electronic device; determining whether the unlock information is authentic; and in accordance with a determination that the unlock information is authentic, enabling navigation away from the content. 20. A method comprising:
at an electronic device with a display and one or more image sensors; displaying, on the display, content in an application, wherein the content is displayed while the application is in a first configuration; while displaying the content, capturing image data from the one or more image sensors of the electronic device; after capturing the image data, receiving a request to navigate away from the content; and in response to receiving a request to navigate away from the content:
in accordance with a determination that a first set of content-lock criteria have been met, preventing navigation away from the content while maintaining display of the content, wherein the first set of content-lock criteria includes a first criterion that is met when the captured image data indicates that an unauthorized user is using the device; and
in accordance with a determination that the first set of content-lock criteria have not been met, navigating away from the content in accordance with the request. 21. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device with a display and one or more input devices, cause the device to:
display, on the display, content in an application, wherein the content is displayed while the application is in a first configuration; while displaying the content, capture image data from the one or more image sensors of the electronic device; after capturing the image data, receive a request to navigate away from the content; and in response to receiving a request to navigate away from the content:
in accordance with a determination that a first set of content-lock criteria have been met, prevent navigation away from the content while maintaining display of the content, wherein the first set of content-lock criteria includes a first criterion that is met when the captured image data indicates that an unauthorized user is using the device; and
in accordance with a determination that the first set of content-lock criteria have not been met, navigate away from the content in accordance with the request. | The present disclosure generally relates to using avatars and image data for enhanced user interactions. In some examples, user status dependent avatars are generated and displayed with a message associated with the user status. In some examples, a device captures image information to scan an object to create a 3D model of the object. The device determines an algorithm for the 3D model based on the capture image information and provides visual feedback on additional image data that is needed for the algorithm to build the 3D model. In some examples, an application's operation on a device is restricted based on whether an authorized user is identified as using the device based on captured image data.1. (canceled) 2. An electronic device, comprising:
a display; one or more processors; one or more input devices; a memory; and one or more programs, wherein the one or more programs are stored in memory and configured to be executed by the one or more processors, the one or more programs including instructions for:
displaying, on the display, content in an application, wherein the content is displayed while the application is in a first configuration;
while displaying the content, capturing image data from the one or more image sensors of the electronic device;
after capturing the image data, receiving a request to navigate away from the content; and
in response to receiving a request to navigate away from the content:
in accordance with a determination that a first set of content-lock criteria have been met, preventing navigation away from the content while maintaining display of the content, wherein the first set of content-lock criteria includes a first criterion that is met when the captured image data indicates that an unauthorized user is using the device; and
in accordance with a determination that the first set of content-lock criteria have not been met, navigating away from the content in accordance with the request. 3. The electronic device of claim 2, the one or more programs further including instructions for:
in accordance with a determination that the first set of content-lock criteria is no longer met, allowing navigation away from the content. 4. The electronic device of claim 2, wherein the first set of lock-criteria includes a second criterion that is met when the captured image data indicates that an authorized user of the electronic device is not using the device. 5. The electronic device of claim 2, wherein the first set of lock-criteria includes a third criterion that is met when the captured image data indicates that the unauthorized user is present and an authorized user is not present. 6. The electronic device of claim 2, wherein the first set of lock-criteria is met when the captured image data indicates that the unauthorized user is present without regard to whether or not an authorized user is present. 7. The electronic device of claim 2, wherein the one or more programs further include instructions for:
in accordance with a determination that a second set of content-lock criteria has been met, disabling at least one function of the electronic device. 8. The electronic device of claim 7, wherein the first set of lock-criteria and the second set of lock-criteria are different. 9. The electronic device of claim 2, wherein the one or more programs further include instructions for:
in accordance with a determination that a third set of content-lock criteria has been met, switching the application to a second configuration that limits operation of the application as compared to the first configuration. 10. The electronic device of claim 2, wherein the one or more programs further include instructions for:
in accordance with the determination that a fourth set of content-lock criteria have been met, locking other functionality of the electronic device while continuing to display the content in the application. 11. The electronic device of claim 2, wherein the one or more programs further include instructions for:
in accordance with the determination that a fifth set of content-lock criteria have been met, preventing the display of a notification related to a communication received at the electronic device. 12. The electronic device of claim 11, wherein:
the fifth set of lock-criteria includes a fourth criterion that is met when the captured image data indicates that an unauthorized user is using the electronic device and the fifth set of lock-criteria is met if the fourth criterion is met; and the first set of lock-criteria includes a fifth criteria that is met when the captured image data indicates the absence of an authorized user. 13. The electronic device of claim 12, wherein the one or more programs further include instructions for:
in accordance with the fourth criterion being met, preventing navigation between applications on the electronic device; and in accordance with the fifth criterion being met, preventing navigation within the application. 14. The electronic device of claim 2, wherein the one or more programs further include instructions for:
determining whether the captured image data indicates the presence of an unauthorized user of the electronic device. 15. The electronic device of claim 2, wherein the image data includes optical data and depth data, and wherein determining whether the first set of content-lock criteria have been met is based on the optical data and the depth data. 16. The electronic device of claim 2, wherein navigating away from the content includes translating currently displayed content. 17. The electronic device of claim 2, wherein navigating away from the content includes switching between content items in an application. 18. The electronic device of claim 2, wherein navigating away from the content includes switching applications or closing the application to display the home screen. 19. The electronic device of claim 2, wherein the one or more programs further include instructions for:
receiving unlock information associated with an authorized user of the electronic device; determining whether the unlock information is authentic; and in accordance with a determination that the unlock information is authentic, enabling navigation away from the content. 20. A method comprising:
at an electronic device with a display and one or more image sensors; displaying, on the display, content in an application, wherein the content is displayed while the application is in a first configuration; while displaying the content, capturing image data from the one or more image sensors of the electronic device; after capturing the image data, receiving a request to navigate away from the content; and in response to receiving a request to navigate away from the content:
in accordance with a determination that a first set of content-lock criteria have been met, preventing navigation away from the content while maintaining display of the content, wherein the first set of content-lock criteria includes a first criterion that is met when the captured image data indicates that an unauthorized user is using the device; and
in accordance with a determination that the first set of content-lock criteria have not been met, navigating away from the content in accordance with the request. 21. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device with a display and one or more input devices, cause the device to:
display, on the display, content in an application, wherein the content is displayed while the application is in a first configuration; while displaying the content, capture image data from the one or more image sensors of the electronic device; after capturing the image data, receive a request to navigate away from the content; and in response to receiving a request to navigate away from the content:
in accordance with a determination that a first set of content-lock criteria have been met, prevent navigation away from the content while maintaining display of the content, wherein the first set of content-lock criteria includes a first criterion that is met when the captured image data indicates that an unauthorized user is using the device; and
in accordance with a determination that the first set of content-lock criteria have not been met, navigate away from the content in accordance with the request. | 2,100 |
6,443 | 6,443 | 16,224,620 | 2,136 | A computer-implemented method, according to one embodiment, includes: receiving tracking information which corresponds to an amount that at least one supplemental data storage drive of an automated data storage library was used during a period of time. The automated data storage library in turn includes: one or more primary data storage drives, and one or more robotic accessors physically configured to access each of the one or more primary data storage drives and the at least one supplemental storage drive. Accordingly, the tracking information is used to calculate a usage fee which corresponds to the amount that the at least one supplemental data storage drive was used during the period of time. Furthermore, the usage fee is sent to a user associated with the automated data storage library. | 1. A computer-implemented method, comprising:
receiving tracking information which corresponds to an amount that at least one supplemental data storage drive of an automated data storage library was used during a period of time, wherein the automated data storage library includes: one or more primary data storage drives, and one or more robotic accessors physically configured to access each of the one or more primary data storage drives and the at least one supplemental storage drive; using the tracking information to calculate a usage fee which corresponds to the amount that the at least one supplemental data storage drive was used during the period of time; and sending the usage fee to a user associated with the automated data storage library, wherein the automated data storage library is a magnetic tape library, wherein the at least one supplemental data storage drive and each of the one or more primary data storage drives are magnetic tape drives. 2. The computer-implemented method of claim 1, wherein each of the one or more primary data storage drives has a higher level of performance than a level of performance of each of the at least one supplemental data storage drive. 3. The computer-implemented method of claim 1, wherein the tracking information includes a type of information selected from the group consisting of: a first time use of the at least one supplemental data storage drive, a number of hours that the at least one supplemental data storage drive was powered up, a number of mount operations performed by the at least one supplemental data storage drive, a number of bytes read and/or written by the at least one supplemental data storage drive, an amount of time the at least one supplemental data storage drive was used, a number of tapes that were processed by the at least one supplemental data storage drive, and a length of recording surface scanned by the at least one supplemental data storage drive. 4. The computer-implemented method of claim 3, wherein the tracking information includes: a number of mount operations performed by the at least one supplemental data storage drive and a length of recording surface scanned by the at least one supplemental data storage drive. 5. The computer-implemented method of claim 1, wherein the tracking information indicates whether the at least one supplemental data storage drive still remains in the automated data storage library, wherein the usage fee is a flat fee for the period of time, wherein the usage fee is a predetermined percentage of a manufacturer's suggested retail price for the at least one supplemental data storage drive. 6. The computer-implemented method of claim 5, comprising:
determining whether the at least one supplemental data storage drive has been utilized for a first time; in response to determining that the at least one supplemental data storage drive has been utilized for a first time, supplementing the flat usage fee with an incremental fee. 7. The computer-implemented method of claim 1, wherein each of the magnetic tape drives includes:
a magnetic head; and a drive mechanism for passing a magnetic tape over the magnetic head. 8. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable and/or executable by a processor to cause the processor to:
receive, by the processor, tracking information which indicates whether at least one supplemental data storage drive of an automated data storage library still remains in the automated data storage library after a period of time has passed, wherein the automated data storage library includes: one or more primary data storage drives, and one or more robotic accessors physically configured to access each of the one or more primary data storage drives and the at least one supplemental storage drive; in response to determining that the at least one supplemental data storage drive still remains in the automated data storage library after the period of time has passed, determine, by the processor, a flat usage fee for the period of time; and send, by the processor, the flat usage fee to a user associated with the automated data storage library. 9. The computer program product of claim 8, wherein each of the one or more primary data storage drives has a higher level of performance than a level of performance of each of the at least one supplemental data storage drive. 10. The computer program product of claim 8, wherein sending the flat usage fee to the user associated with the automated data storage library includes: posting the flat usage fee on an interface of the automated data storage library. 11. The computer program product of claim 10, wherein the flat usage fee is a predetermined percentage of a manufacturer's suggested retail price for the at least one supplemental data storage drive. 12. The computer program product of claim 8, wherein the program instructions are readable and/or executable by the processor to cause the processor to:
determine, by the processor, whether the at least one supplemental data storage drive has been utilized for a first time; in response to determining that the at least one supplemental data storage drive has been utilized for a first time, supplement, by the processor, the flat usage fee with an incremental fee. 13. The computer program product of claim 8, wherein the automated data storage library is a magnetic tape library, wherein the at least one supplemental data storage drive and each of the one or more primary data storage drives are magnetic tape drives. 14. The computer program product of claim 13, wherein each of the magnetic tape drives includes:
a magnetic head; and a drive mechanism for passing a magnetic tape over the magnetic head. 15. A storage system, comprising:
a magnetic tape library, having:
one or more primary data storage drives magnetic tape drives,
at least one supplemental magnetic tape drive,
one or more robotic accessors physically configured to access each of the one or more primary magnetic tape drives and the at least one supplemental storage drive magnetic tape drive;
a processor; and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor, the logic being configured to:
track an amount that the at least one supplemental magnetic tape drive is used during a period of time, and
outputting usage data which corresponds to the amount that the at least one supplemental magnetic tape drive is used during the period of time,
wherein tracking an amount that the at least one supplemental magnetic tape drive is used during a period of time includes tracking a length of recording surface scanned by the at least one supplemental data storage drive. 16. The storage system of claim 15, wherein each of the one or more primary data storage drives has a higher level of performance than a level of performance of each of the at least one supplemental data storage drive. 17. The storage system of claim 15, wherein tracking the amount that the at least one supplemental data storage drive is used during the period of time includes: tracking a number of mount operations performed by the at least one supplemental data storage drive. 18. The storage system of claim 15, the logic being configured to:
receive, by the processor, a usage fee which corresponds to the usage data. 19. The storage system of claim 15, the logic being configured to:
receive, by the processor, a usage fee which corresponds to the usage data; verify, by the processor, the received usage fee based on a pricing scheme received from an entity that provided the at least one supplemental magnetic tape drive; and issue, by the processor, payment of at least a portion of the received usage fee, wherein each of the one or more primary data storage drives has a higher level of performance than a level of performance of each of the at least one supplemental data storage drive, wherein tracking an amount that the at least one supplemental magnetic tape drive is used during a period of time includes tracking a number of hours that the at least one supplemental data storage drive was powered up. 20. The storage system of claim 19, wherein each of the magnetic tape drives includes:
a magnetic head; and a drive mechanism for passing a magnetic tape over the magnetic head. | A computer-implemented method, according to one embodiment, includes: receiving tracking information which corresponds to an amount that at least one supplemental data storage drive of an automated data storage library was used during a period of time. The automated data storage library in turn includes: one or more primary data storage drives, and one or more robotic accessors physically configured to access each of the one or more primary data storage drives and the at least one supplemental storage drive. Accordingly, the tracking information is used to calculate a usage fee which corresponds to the amount that the at least one supplemental data storage drive was used during the period of time. Furthermore, the usage fee is sent to a user associated with the automated data storage library.1. A computer-implemented method, comprising:
receiving tracking information which corresponds to an amount that at least one supplemental data storage drive of an automated data storage library was used during a period of time, wherein the automated data storage library includes: one or more primary data storage drives, and one or more robotic accessors physically configured to access each of the one or more primary data storage drives and the at least one supplemental storage drive; using the tracking information to calculate a usage fee which corresponds to the amount that the at least one supplemental data storage drive was used during the period of time; and sending the usage fee to a user associated with the automated data storage library, wherein the automated data storage library is a magnetic tape library, wherein the at least one supplemental data storage drive and each of the one or more primary data storage drives are magnetic tape drives. 2. The computer-implemented method of claim 1, wherein each of the one or more primary data storage drives has a higher level of performance than a level of performance of each of the at least one supplemental data storage drive. 3. The computer-implemented method of claim 1, wherein the tracking information includes a type of information selected from the group consisting of: a first time use of the at least one supplemental data storage drive, a number of hours that the at least one supplemental data storage drive was powered up, a number of mount operations performed by the at least one supplemental data storage drive, a number of bytes read and/or written by the at least one supplemental data storage drive, an amount of time the at least one supplemental data storage drive was used, a number of tapes that were processed by the at least one supplemental data storage drive, and a length of recording surface scanned by the at least one supplemental data storage drive. 4. The computer-implemented method of claim 3, wherein the tracking information includes: a number of mount operations performed by the at least one supplemental data storage drive and a length of recording surface scanned by the at least one supplemental data storage drive. 5. The computer-implemented method of claim 1, wherein the tracking information indicates whether the at least one supplemental data storage drive still remains in the automated data storage library, wherein the usage fee is a flat fee for the period of time, wherein the usage fee is a predetermined percentage of a manufacturer's suggested retail price for the at least one supplemental data storage drive. 6. The computer-implemented method of claim 5, comprising:
determining whether the at least one supplemental data storage drive has been utilized for a first time; in response to determining that the at least one supplemental data storage drive has been utilized for a first time, supplementing the flat usage fee with an incremental fee. 7. The computer-implemented method of claim 1, wherein each of the magnetic tape drives includes:
a magnetic head; and a drive mechanism for passing a magnetic tape over the magnetic head. 8. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable and/or executable by a processor to cause the processor to:
receive, by the processor, tracking information which indicates whether at least one supplemental data storage drive of an automated data storage library still remains in the automated data storage library after a period of time has passed, wherein the automated data storage library includes: one or more primary data storage drives, and one or more robotic accessors physically configured to access each of the one or more primary data storage drives and the at least one supplemental storage drive; in response to determining that the at least one supplemental data storage drive still remains in the automated data storage library after the period of time has passed, determine, by the processor, a flat usage fee for the period of time; and send, by the processor, the flat usage fee to a user associated with the automated data storage library. 9. The computer program product of claim 8, wherein each of the one or more primary data storage drives has a higher level of performance than a level of performance of each of the at least one supplemental data storage drive. 10. The computer program product of claim 8, wherein sending the flat usage fee to the user associated with the automated data storage library includes: posting the flat usage fee on an interface of the automated data storage library. 11. The computer program product of claim 10, wherein the flat usage fee is a predetermined percentage of a manufacturer's suggested retail price for the at least one supplemental data storage drive. 12. The computer program product of claim 8, wherein the program instructions are readable and/or executable by the processor to cause the processor to:
determine, by the processor, whether the at least one supplemental data storage drive has been utilized for a first time; in response to determining that the at least one supplemental data storage drive has been utilized for a first time, supplement, by the processor, the flat usage fee with an incremental fee. 13. The computer program product of claim 8, wherein the automated data storage library is a magnetic tape library, wherein the at least one supplemental data storage drive and each of the one or more primary data storage drives are magnetic tape drives. 14. The computer program product of claim 13, wherein each of the magnetic tape drives includes:
a magnetic head; and a drive mechanism for passing a magnetic tape over the magnetic head. 15. A storage system, comprising:
a magnetic tape library, having:
one or more primary data storage drives magnetic tape drives,
at least one supplemental magnetic tape drive,
one or more robotic accessors physically configured to access each of the one or more primary magnetic tape drives and the at least one supplemental storage drive magnetic tape drive;
a processor; and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor, the logic being configured to:
track an amount that the at least one supplemental magnetic tape drive is used during a period of time, and
outputting usage data which corresponds to the amount that the at least one supplemental magnetic tape drive is used during the period of time,
wherein tracking an amount that the at least one supplemental magnetic tape drive is used during a period of time includes tracking a length of recording surface scanned by the at least one supplemental data storage drive. 16. The storage system of claim 15, wherein each of the one or more primary data storage drives has a higher level of performance than a level of performance of each of the at least one supplemental data storage drive. 17. The storage system of claim 15, wherein tracking the amount that the at least one supplemental data storage drive is used during the period of time includes: tracking a number of mount operations performed by the at least one supplemental data storage drive. 18. The storage system of claim 15, the logic being configured to:
receive, by the processor, a usage fee which corresponds to the usage data. 19. The storage system of claim 15, the logic being configured to:
receive, by the processor, a usage fee which corresponds to the usage data; verify, by the processor, the received usage fee based on a pricing scheme received from an entity that provided the at least one supplemental magnetic tape drive; and issue, by the processor, payment of at least a portion of the received usage fee, wherein each of the one or more primary data storage drives has a higher level of performance than a level of performance of each of the at least one supplemental data storage drive, wherein tracking an amount that the at least one supplemental magnetic tape drive is used during a period of time includes tracking a number of hours that the at least one supplemental data storage drive was powered up. 20. The storage system of claim 19, wherein each of the magnetic tape drives includes:
a magnetic head; and a drive mechanism for passing a magnetic tape over the magnetic head. | 2,100 |
6,444 | 6,444 | 14,294,000 | 2,122 | Systems and methods are disclosed for a future event prediction. Embodiments include capturing spatiotemporal data pertaining to activities, wherein the activities include a plurality of events, and employing an augmented-hidden-conditional-random-field (a-HCRF) predictor to generate a future event prediction based on a parameter-vector input, hidden states, and the spatiotemporal data. Methods therein utilize a graph including a first node associated with random variables corresponding to a future event state, a second node associated with random variables corresponding to spatiotemporal input data, a first group of nodes, each node therein associated with random variables corresponding to a subset of the spatiotemporal input data, a second group of nodes, each node therein associated with random variables corresponding to a hidden-state; wherein the edges connect the first node with the second node, the first node with the second group of nodes, and the first group of nodes with the second group of nodes. | 1. A future event prediction method being executed by at least one processor, comprising:
capturing spatiotemporal data pertaining to activities wherein the activities include a plurality of events; and employing an augmented hidden conditional random field (a-HCRF) predictor to generate a future event prediction based on a parameter-vector input, hidden states, and the spatiotemporal data. 2. The method of claim 1, wherein employing the a-HCRF predictor further includes operating on a potential function, the potential function comprising:
a first term reflecting the compatibility between the hidden states and the spatiotemporal data; a second term reflecting the compatibility between the future event and the hidden states; a third term reflecting the compatibility between the future event and a pair of connected hidden states; and a fourth term reflecting the compatibility between the future event and the spatiotemporal data. 3. The method of claim 1, further comprising:
computing the parameter-vector input based on a first training dataset. 4. The method of claim 3, further comprising:
computing the parameter-vector input based on a second training dataset. 5. The method of claim 1, wherein:
events, from the plurality of events, occur in a continuous temporal sequence; and each event, from the plurality of events, is associated with a subset of spatiotemporal data captured within a temporal window relative to the each event's temporal position in the continuous temporal sequence. 6. The method of claim 1, wherein:
capturing spatiotemporal data further includes extracting a feature-vector from the spatiotemporal data; and employing the a-HCRF predictor further includes operating on the feature-vector. 7. The method of claim 1, wherein the activities are team-games, the plurality of events is a plurality of game-events occurring at current and past times, and the future event is a game-event occurring at a future time. 8. The method of claim 7, wherein the team-games are one of a football, a soccer, a basketball, a hockey, a tennis, a baseball, a lacrosse, a cricket, and a softball game, and the game-events are one of an ownership of a playing object and a location of the playing object. 9. The method of claim 1, wherein the future event prediction is used to control a measurement device capturing part of the spatiotemporal data pertaining to the activities. 10. The method of claim 1, wherein the future event prediction is used to insert a graphic into a video stream capturing the activities. 11. A future event prediction system, comprising:
a capturing system configured to capture spatiotemporal data pertaining to activities wherein the activities include a plurality of events; and an augmented hidden conditional random field (a-HCRF) predictor configured to generate a future event prediction based on a parameter-vector input, hidden states, and the spatiotemporal data. 12. The system of claim 11, wherein the a-HCRF predictor operates on a potential function, the potential function comprising:
a first term reflecting the compatibility between the hidden states and the spatiotemporal data; a second term reflecting the compatibility between the future event and the hidden states; a third term reflecting the compatibility between the future event and a pair of connected hidden states; and a fourth term reflecting the compatibility between the future event and the spatiotemporal data. 13. The system of claim 11, wherein the a-HCRF predictor is configured to compute the parameter-vector input based on a first training dataset. 14. The system of claim 13, wherein the a-HCRF predictor is configured to compute the parameter-vector input based on a second training dataset. 15. The system of claim 11, wherein
events, from the plurality of events, occur in a continuous temporal sequence; and each event, from the plurality of events, is associated with a subset of spatiotemporal data captured within a temporal window relative to the event's temporal position in the continuous temporal sequence. 16. The system of claim 11, wherein
the capturing system is further configured to extract a feature-vector from the spatiotemporal data; and the a-HCRF predictor is further configured to operate on the feature-vector. 17. The system of claim 11, wherein the activities are team-games, the plurality of events is a plurality of game-events occurring at current and past times, and the future event is a game-event occurring at a future time. 18. The system of claim 17, wherein the team-games are one of a football, a soccer, a basketball, a hockey, a tennis, a baseball, a lacrosse, a cricket, and a softball game, and the game-events are one of an ownership of a playing object and a location of the playing object. 19. The system of claim 11, wherein the future event prediction is used to control a measurement device capturing part of the spatiotemporal data pertaining to the activities. 20. The system of claim 11, wherein the future event prediction is used to insert a graphic into a video stream capturing the activities. 21. A future event prediction system, comprising:
a processor configured to execute a future event prediction algorithm including a graph; and a memory configured to store the future event prediction algorithm, wherein: the graph is comprised of nodes associated with random variables, the nodes connected by edges if their associated random variables are statistically dependent, the nodes including:
a first node associated with random variables corresponding to a future event state,
a second node associated with random variables corresponding to spatiotemporal input data,
a first group of nodes, each node therein associated with random variables corresponding to a subset of the spatiotemporal input data,
a second group of nodes, each node therein associated with random variables corresponding to a hidden-state; wherein:
the edges connect the first node with the second node, the first node with the second group of nodes, and the first group of nodes with the second group of nodes. 22. A non-transitory computer-readable storage medium storing a set of instructions that is executable by a processor, the set of instructions, when executed by the processor, causing the processor to perform operations comprising:
capturing spatiotemporal data pertaining to activities wherein the activities include a plurality of events; employing an augmented hidden conditional random field (a-HCRF) predictor in a training-phase to compute a parameter-vector based on a training dataset; and employing a-HCRF predictor in a testing-phase to generate a future event prediction based on the parameter-vector, hidden states, and the spatiotemporal data. | Systems and methods are disclosed for a future event prediction. Embodiments include capturing spatiotemporal data pertaining to activities, wherein the activities include a plurality of events, and employing an augmented-hidden-conditional-random-field (a-HCRF) predictor to generate a future event prediction based on a parameter-vector input, hidden states, and the spatiotemporal data. Methods therein utilize a graph including a first node associated with random variables corresponding to a future event state, a second node associated with random variables corresponding to spatiotemporal input data, a first group of nodes, each node therein associated with random variables corresponding to a subset of the spatiotemporal input data, a second group of nodes, each node therein associated with random variables corresponding to a hidden-state; wherein the edges connect the first node with the second node, the first node with the second group of nodes, and the first group of nodes with the second group of nodes.1. A future event prediction method being executed by at least one processor, comprising:
capturing spatiotemporal data pertaining to activities wherein the activities include a plurality of events; and employing an augmented hidden conditional random field (a-HCRF) predictor to generate a future event prediction based on a parameter-vector input, hidden states, and the spatiotemporal data. 2. The method of claim 1, wherein employing the a-HCRF predictor further includes operating on a potential function, the potential function comprising:
a first term reflecting the compatibility between the hidden states and the spatiotemporal data; a second term reflecting the compatibility between the future event and the hidden states; a third term reflecting the compatibility between the future event and a pair of connected hidden states; and a fourth term reflecting the compatibility between the future event and the spatiotemporal data. 3. The method of claim 1, further comprising:
computing the parameter-vector input based on a first training dataset. 4. The method of claim 3, further comprising:
computing the parameter-vector input based on a second training dataset. 5. The method of claim 1, wherein:
events, from the plurality of events, occur in a continuous temporal sequence; and each event, from the plurality of events, is associated with a subset of spatiotemporal data captured within a temporal window relative to the each event's temporal position in the continuous temporal sequence. 6. The method of claim 1, wherein:
capturing spatiotemporal data further includes extracting a feature-vector from the spatiotemporal data; and employing the a-HCRF predictor further includes operating on the feature-vector. 7. The method of claim 1, wherein the activities are team-games, the plurality of events is a plurality of game-events occurring at current and past times, and the future event is a game-event occurring at a future time. 8. The method of claim 7, wherein the team-games are one of a football, a soccer, a basketball, a hockey, a tennis, a baseball, a lacrosse, a cricket, and a softball game, and the game-events are one of an ownership of a playing object and a location of the playing object. 9. The method of claim 1, wherein the future event prediction is used to control a measurement device capturing part of the spatiotemporal data pertaining to the activities. 10. The method of claim 1, wherein the future event prediction is used to insert a graphic into a video stream capturing the activities. 11. A future event prediction system, comprising:
a capturing system configured to capture spatiotemporal data pertaining to activities wherein the activities include a plurality of events; and an augmented hidden conditional random field (a-HCRF) predictor configured to generate a future event prediction based on a parameter-vector input, hidden states, and the spatiotemporal data. 12. The system of claim 11, wherein the a-HCRF predictor operates on a potential function, the potential function comprising:
a first term reflecting the compatibility between the hidden states and the spatiotemporal data; a second term reflecting the compatibility between the future event and the hidden states; a third term reflecting the compatibility between the future event and a pair of connected hidden states; and a fourth term reflecting the compatibility between the future event and the spatiotemporal data. 13. The system of claim 11, wherein the a-HCRF predictor is configured to compute the parameter-vector input based on a first training dataset. 14. The system of claim 13, wherein the a-HCRF predictor is configured to compute the parameter-vector input based on a second training dataset. 15. The system of claim 11, wherein
events, from the plurality of events, occur in a continuous temporal sequence; and each event, from the plurality of events, is associated with a subset of spatiotemporal data captured within a temporal window relative to the event's temporal position in the continuous temporal sequence. 16. The system of claim 11, wherein
the capturing system is further configured to extract a feature-vector from the spatiotemporal data; and the a-HCRF predictor is further configured to operate on the feature-vector. 17. The system of claim 11, wherein the activities are team-games, the plurality of events is a plurality of game-events occurring at current and past times, and the future event is a game-event occurring at a future time. 18. The system of claim 17, wherein the team-games are one of a football, a soccer, a basketball, a hockey, a tennis, a baseball, a lacrosse, a cricket, and a softball game, and the game-events are one of an ownership of a playing object and a location of the playing object. 19. The system of claim 11, wherein the future event prediction is used to control a measurement device capturing part of the spatiotemporal data pertaining to the activities. 20. The system of claim 11, wherein the future event prediction is used to insert a graphic into a video stream capturing the activities. 21. A future event prediction system, comprising:
a processor configured to execute a future event prediction algorithm including a graph; and a memory configured to store the future event prediction algorithm, wherein: the graph is comprised of nodes associated with random variables, the nodes connected by edges if their associated random variables are statistically dependent, the nodes including:
a first node associated with random variables corresponding to a future event state,
a second node associated with random variables corresponding to spatiotemporal input data,
a first group of nodes, each node therein associated with random variables corresponding to a subset of the spatiotemporal input data,
a second group of nodes, each node therein associated with random variables corresponding to a hidden-state; wherein:
the edges connect the first node with the second node, the first node with the second group of nodes, and the first group of nodes with the second group of nodes. 22. A non-transitory computer-readable storage medium storing a set of instructions that is executable by a processor, the set of instructions, when executed by the processor, causing the processor to perform operations comprising:
capturing spatiotemporal data pertaining to activities wherein the activities include a plurality of events; employing an augmented hidden conditional random field (a-HCRF) predictor in a training-phase to compute a parameter-vector based on a training dataset; and employing a-HCRF predictor in a testing-phase to generate a future event prediction based on the parameter-vector, hidden states, and the spatiotemporal data. | 2,100 |
6,445 | 6,445 | 16,180,777 | 2,179 | Nested pagination for presenting a data set in a graphical user interface (GUI) including receiving a request for the data set to present in a GUI, wherein the request comprises a group name identifying a nested group of rows in the data set; selecting an anchor point from the data set using the group name, wherein the anchor point is within the group of rows identified by the group name; selecting, using the anchor point, a subset of the data set for presentation in the GUI, wherein the subset of the data set comprises the anchor point; and presenting the subset of the data set in the GUI. | 1. A method of nested pagination for presenting a data set in a graphical user interface (GUI), the method comprising:
receiving a request for the data set to present in a GUI, wherein the request comprises a group name identifying a nested group of rows in the data set, the data set comprises data stored in a cloud-based data warehouse, and the GUI comprises a web browser; selecting an anchor point from the data set using the group name, wherein the anchor point is within the group of rows identified by the group name; selecting, using the anchor point, a subset of the data set for presentation in the GUI, wherein the subset of the data set comprises the anchor point; and presenting the subset of the data set in the GUI. 2. The method of claim 1, wherein selecting, using the anchor point, the subset of the data set for presentation in the GUI comprises selecting the subset of the data set for presentation in the GUI using a row limit and an offset from the anchor point. 3. The method of claim 1,
wherein the request for the data set to present in the GUI comprises a parent group name identifying a parent group to the nested group of rows in the data set, and wherein selecting the anchor point from the data set using the group name comprises selecting a parent anchor point from within the nested group of rows. 4. The method of claim 1, wherein receiving the request for the data set to present in the GUI comprises receiving an instruction to expand the group of rows. 5. The method of claim 1, wherein receiving the request for the data set to present in the GUI comprises detecting that a user has scrolled to an edge of the subset of the data set. 6. The method of claim 1, wherein the subset of the data set comprises at least one row from outside the nested group of rows. 7. The method of claim 1, wherein the anchor point is selected without using a row number. 8. An apparatus for nested pagination for presenting a data set in a graphical user interface (GUI), the apparatus comprising a computer processor, a computer memory operatively coupled to the computer processor, the computer memory having disposed within it computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of:
receiving a request for the data set to present in a GUI, wherein the request comprises a group name identifying a nested group of rows in the data set, the data set comprises data stored in a cloud-based data warehouse, and the GUI comprises a web browser; selecting an anchor point from the data set using the group name, wherein the anchor point is within the group of rows identified by the group name; selecting, using the anchor point, a subset of the data set for presentation in the GUI, wherein the subset of the data set comprises the anchor point; and presenting the subset of the data set in the GUI. 9. The apparatus of claim 8, wherein selecting, using the anchor point, the subset of the data set for presentation in the GUI comprises selecting the subset of the data set for presentation in the GUI using a row limit and an offset from the anchor point. 10. The apparatus of claim 8,
wherein the request for the data set to present in the GUI comprises a parent group name identifying a parent group to the nested group of rows in the data set, and wherein selecting the anchor point from the data set using the group name comprises selecting a parent anchor point from within the nested group of rows. 11. The apparatus of claim 8, wherein receiving the request for the data set to present in the GUI comprises receiving an instruction to expand the group of rows. 12. The apparatus of claim 8, wherein receiving the request for the data set to present in the GUI comprises detecting that a user has scrolled to an edge of the subset of the data set. 13. The apparatus of claim 8, wherein the subset of the data set comprises at least one row from outside the nested group of rows. 14. The apparatus of claim 8, wherein the anchor point is selected without using a row number. 15. A computer program product for nested pagination for presenting a data set in a graphical user interface (GUI), the computer program product disposed upon a non-transitory computer readable medium, the computer program product comprising computer program instructions that, when executed, cause a computer to carry out the steps of:
receiving a request for the data set to present in a GUI, wherein the request comprises a group name identifying a nested group of rows in the data set, the data set comprises data stored in a cloud-based data warehouse, and the GUI comprises a web browser; selecting an anchor point from the data set using the group name, wherein the anchor point is within the group of rows identified by the group name; selecting, using the anchor point, a subset of the data set for presentation in the GUI, wherein the subset of the data set comprises the anchor point; and presenting the subset of the data set in the GUI. 16. The computer program product of claim 15, wherein selecting, using the anchor point, the subset of the data set for presentation in the GUI comprises selecting the subset of the data set for presentation in the GUI using a row limit and an offset from the anchor point. 17. The computer program product of claim 15,
wherein the request for the data set to present in the GUI comprises a parent group name identifying a parent group to the nested group of rows in the data set, and wherein selecting the anchor point from the data set using the group name comprises selecting a parent anchor point from within the nested group of rows. 18. The computer program product of claim 15, wherein receiving the request for the data set to present in the GUI comprises receiving an instruction to expand the group of rows. 19. The computer program product of claim 15, wherein receiving the request for the data set to present in the GUI comprises detecting that a user has scrolled to an edge of the subset of the data set. 20. The computer program product of claim 15, wherein the subset of the data set comprises at least one row from outside the nested group of rows. | Nested pagination for presenting a data set in a graphical user interface (GUI) including receiving a request for the data set to present in a GUI, wherein the request comprises a group name identifying a nested group of rows in the data set; selecting an anchor point from the data set using the group name, wherein the anchor point is within the group of rows identified by the group name; selecting, using the anchor point, a subset of the data set for presentation in the GUI, wherein the subset of the data set comprises the anchor point; and presenting the subset of the data set in the GUI.1. A method of nested pagination for presenting a data set in a graphical user interface (GUI), the method comprising:
receiving a request for the data set to present in a GUI, wherein the request comprises a group name identifying a nested group of rows in the data set, the data set comprises data stored in a cloud-based data warehouse, and the GUI comprises a web browser; selecting an anchor point from the data set using the group name, wherein the anchor point is within the group of rows identified by the group name; selecting, using the anchor point, a subset of the data set for presentation in the GUI, wherein the subset of the data set comprises the anchor point; and presenting the subset of the data set in the GUI. 2. The method of claim 1, wherein selecting, using the anchor point, the subset of the data set for presentation in the GUI comprises selecting the subset of the data set for presentation in the GUI using a row limit and an offset from the anchor point. 3. The method of claim 1,
wherein the request for the data set to present in the GUI comprises a parent group name identifying a parent group to the nested group of rows in the data set, and wherein selecting the anchor point from the data set using the group name comprises selecting a parent anchor point from within the nested group of rows. 4. The method of claim 1, wherein receiving the request for the data set to present in the GUI comprises receiving an instruction to expand the group of rows. 5. The method of claim 1, wherein receiving the request for the data set to present in the GUI comprises detecting that a user has scrolled to an edge of the subset of the data set. 6. The method of claim 1, wherein the subset of the data set comprises at least one row from outside the nested group of rows. 7. The method of claim 1, wherein the anchor point is selected without using a row number. 8. An apparatus for nested pagination for presenting a data set in a graphical user interface (GUI), the apparatus comprising a computer processor, a computer memory operatively coupled to the computer processor, the computer memory having disposed within it computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of:
receiving a request for the data set to present in a GUI, wherein the request comprises a group name identifying a nested group of rows in the data set, the data set comprises data stored in a cloud-based data warehouse, and the GUI comprises a web browser; selecting an anchor point from the data set using the group name, wherein the anchor point is within the group of rows identified by the group name; selecting, using the anchor point, a subset of the data set for presentation in the GUI, wherein the subset of the data set comprises the anchor point; and presenting the subset of the data set in the GUI. 9. The apparatus of claim 8, wherein selecting, using the anchor point, the subset of the data set for presentation in the GUI comprises selecting the subset of the data set for presentation in the GUI using a row limit and an offset from the anchor point. 10. The apparatus of claim 8,
wherein the request for the data set to present in the GUI comprises a parent group name identifying a parent group to the nested group of rows in the data set, and wherein selecting the anchor point from the data set using the group name comprises selecting a parent anchor point from within the nested group of rows. 11. The apparatus of claim 8, wherein receiving the request for the data set to present in the GUI comprises receiving an instruction to expand the group of rows. 12. The apparatus of claim 8, wherein receiving the request for the data set to present in the GUI comprises detecting that a user has scrolled to an edge of the subset of the data set. 13. The apparatus of claim 8, wherein the subset of the data set comprises at least one row from outside the nested group of rows. 14. The apparatus of claim 8, wherein the anchor point is selected without using a row number. 15. A computer program product for nested pagination for presenting a data set in a graphical user interface (GUI), the computer program product disposed upon a non-transitory computer readable medium, the computer program product comprising computer program instructions that, when executed, cause a computer to carry out the steps of:
receiving a request for the data set to present in a GUI, wherein the request comprises a group name identifying a nested group of rows in the data set, the data set comprises data stored in a cloud-based data warehouse, and the GUI comprises a web browser; selecting an anchor point from the data set using the group name, wherein the anchor point is within the group of rows identified by the group name; selecting, using the anchor point, a subset of the data set for presentation in the GUI, wherein the subset of the data set comprises the anchor point; and presenting the subset of the data set in the GUI. 16. The computer program product of claim 15, wherein selecting, using the anchor point, the subset of the data set for presentation in the GUI comprises selecting the subset of the data set for presentation in the GUI using a row limit and an offset from the anchor point. 17. The computer program product of claim 15,
wherein the request for the data set to present in the GUI comprises a parent group name identifying a parent group to the nested group of rows in the data set, and wherein selecting the anchor point from the data set using the group name comprises selecting a parent anchor point from within the nested group of rows. 18. The computer program product of claim 15, wherein receiving the request for the data set to present in the GUI comprises receiving an instruction to expand the group of rows. 19. The computer program product of claim 15, wherein receiving the request for the data set to present in the GUI comprises detecting that a user has scrolled to an edge of the subset of the data set. 20. The computer program product of claim 15, wherein the subset of the data set comprises at least one row from outside the nested group of rows. | 2,100 |
6,446 | 6,446 | 14,447,768 | 2,145 | Embodiments include an electronic device that has a display configured to display a graphical user interface (GUI) for a user to control aspects of the electronic device, and a touch panel superimposed on or integrated with the display. The electronic device also has circuitry that is configured to initiate a process to shift the GUI on the display upon determining that an area of a touch input exceeds a predetermined area or a continuous duration of the touch input exceeds a predetermined period of time or an applied pressure of the touch input exceeds a predetermined pressure during movement of the area of the touch input. | 1. An electronic device, comprising:
a display configured to display a graphical user interface (GUI) for a user to control aspects of the electronic device; a touch panel superimposed on or integrated with the display; and circuitry configured to
initiate a process to shift the GUI on the display upon determining that an area of a touch input exceeds a predetermined area or a continuous duration of the touch input exceeds a predetermined period of time or an applied pressure of the touch input exceeds a predetermined pressure during movement of the area of the touch input. 2. The electronic device of claim 1, wherein the movement of the area of the touch input comprises movement of a finger or a thumb of a hand grasping the electronic device towards a palm of the hand. 3. The electronic device of claim 2, wherein the movement of the area of the touch input has a horizontal component and a vertical component. 4. The electronic device of claim 3, wherein the shifted GUI returns to an original position within the display when the predetermined area value of the touch input is removed from the touch panel. 5. The electronic device of claim 1, wherein the GUI on the display is shifted towards a lower right area of the electronic device for a grasping right hand, and is shifted towards a lower left area of the electronic device for a grasping left hand. 6. The electronic device of claim 5, wherein the circuitry is configured to determine whether the touch input comprises a touch from a left-handed or a right-handed finger or thumb. 7. The electronic device of claim 6, wherein the right-handed finger or thumb initiates shifting of the GUI when the area of the touch input is moved towards a lower right area of the electronic device, and shifting of the GUI is not initiated when the area of the touch input is moved towards an upper left area of the electronic device. 8. The electronic device of claim 6, wherein the left-handed finger or thumb initiates shifting of the GUI when the area of the touch input is moved towards a lower left area of the electronic device, and shifting of the GUI is not initiated when the area of the touch input is moved towards an upper right area of the electronic device. 9. The electronic device of claim 1, wherein a predetermined value of a ratio of a longitudinal axis versus a transversal axis of the area of the touch input initiates the GUI of the display to shift a proportional distance. 10. The electronic device of claim 9, wherein the proportional distance of the shifted GUI is equal to a distance of the movement of the area of the touch input multiplied by a coefficient. 11. The electronic device of claim 10, wherein a value of the coefficient is proportional to the area of the touch input. 12. The electronic device of claim 10, wherein a moving direction of the shifted GUI is equal to a moving direction of the area of the touch input. 13. The electronic device of claim 1, wherein the GUI comprises more than one specific layer of icons. 14. The electronic device of claim 13, wherein only one specific layer of icons is shifted, and icons from other specific layers are not shifted. 15. The electronic device of claim 13, wherein each of the specific layers of icons comprise similar content-related icons. 16. The electronic device of claim 13, wherein at least one of the specific layers of icons comprises a pop-up window. 17. The electronic device of claim 1, wherein the electronic device comprises a wireless smartphone. 18. The electronic device of claim 1, wherein the electronic device comprises a wireless tablet. 19. A method of shifting a graphical user interface (GUI) of an electronic device having a touch panel superimposed on or integrated with a display, the method comprising:
setting a screen shift mode of the electronic device when a touch exceeds a predetermined area of touch or a predetermined pressure of touch or a continuous duration of time has been detected upon movement of the touch panel of the electronic device; and shifting at least a portion of the GUI in proportion to the movement of the touch panel upon setting the screen shift mode, via a processor of the electronic device. 20. A non-transitory computer readable medium having instructions stored thereon that when executed by one or more processors cause an electronic device to perform a method comprising:
setting a screen shift mode of the electronic device when a touch exceeds a predetermined area of touch or a predetermined pressure of touch or a continuous duration of time has been detected upon movement of a touch panel of the electronic device; and shifting at least a portion of a graphical user interface of the electronic device in proportion to the movement of the touch panel upon setting the screen shift mode, via a processor of the electronic device. | Embodiments include an electronic device that has a display configured to display a graphical user interface (GUI) for a user to control aspects of the electronic device, and a touch panel superimposed on or integrated with the display. The electronic device also has circuitry that is configured to initiate a process to shift the GUI on the display upon determining that an area of a touch input exceeds a predetermined area or a continuous duration of the touch input exceeds a predetermined period of time or an applied pressure of the touch input exceeds a predetermined pressure during movement of the area of the touch input.1. An electronic device, comprising:
a display configured to display a graphical user interface (GUI) for a user to control aspects of the electronic device; a touch panel superimposed on or integrated with the display; and circuitry configured to
initiate a process to shift the GUI on the display upon determining that an area of a touch input exceeds a predetermined area or a continuous duration of the touch input exceeds a predetermined period of time or an applied pressure of the touch input exceeds a predetermined pressure during movement of the area of the touch input. 2. The electronic device of claim 1, wherein the movement of the area of the touch input comprises movement of a finger or a thumb of a hand grasping the electronic device towards a palm of the hand. 3. The electronic device of claim 2, wherein the movement of the area of the touch input has a horizontal component and a vertical component. 4. The electronic device of claim 3, wherein the shifted GUI returns to an original position within the display when the predetermined area value of the touch input is removed from the touch panel. 5. The electronic device of claim 1, wherein the GUI on the display is shifted towards a lower right area of the electronic device for a grasping right hand, and is shifted towards a lower left area of the electronic device for a grasping left hand. 6. The electronic device of claim 5, wherein the circuitry is configured to determine whether the touch input comprises a touch from a left-handed or a right-handed finger or thumb. 7. The electronic device of claim 6, wherein the right-handed finger or thumb initiates shifting of the GUI when the area of the touch input is moved towards a lower right area of the electronic device, and shifting of the GUI is not initiated when the area of the touch input is moved towards an upper left area of the electronic device. 8. The electronic device of claim 6, wherein the left-handed finger or thumb initiates shifting of the GUI when the area of the touch input is moved towards a lower left area of the electronic device, and shifting of the GUI is not initiated when the area of the touch input is moved towards an upper right area of the electronic device. 9. The electronic device of claim 1, wherein a predetermined value of a ratio of a longitudinal axis versus a transversal axis of the area of the touch input initiates the GUI of the display to shift a proportional distance. 10. The electronic device of claim 9, wherein the proportional distance of the shifted GUI is equal to a distance of the movement of the area of the touch input multiplied by a coefficient. 11. The electronic device of claim 10, wherein a value of the coefficient is proportional to the area of the touch input. 12. The electronic device of claim 10, wherein a moving direction of the shifted GUI is equal to a moving direction of the area of the touch input. 13. The electronic device of claim 1, wherein the GUI comprises more than one specific layer of icons. 14. The electronic device of claim 13, wherein only one specific layer of icons is shifted, and icons from other specific layers are not shifted. 15. The electronic device of claim 13, wherein each of the specific layers of icons comprise similar content-related icons. 16. The electronic device of claim 13, wherein at least one of the specific layers of icons comprises a pop-up window. 17. The electronic device of claim 1, wherein the electronic device comprises a wireless smartphone. 18. The electronic device of claim 1, wherein the electronic device comprises a wireless tablet. 19. A method of shifting a graphical user interface (GUI) of an electronic device having a touch panel superimposed on or integrated with a display, the method comprising:
setting a screen shift mode of the electronic device when a touch exceeds a predetermined area of touch or a predetermined pressure of touch or a continuous duration of time has been detected upon movement of the touch panel of the electronic device; and shifting at least a portion of the GUI in proportion to the movement of the touch panel upon setting the screen shift mode, via a processor of the electronic device. 20. A non-transitory computer readable medium having instructions stored thereon that when executed by one or more processors cause an electronic device to perform a method comprising:
setting a screen shift mode of the electronic device when a touch exceeds a predetermined area of touch or a predetermined pressure of touch or a continuous duration of time has been detected upon movement of a touch panel of the electronic device; and shifting at least a portion of a graphical user interface of the electronic device in proportion to the movement of the touch panel upon setting the screen shift mode, via a processor of the electronic device. | 2,100 |
6,447 | 6,447 | 15,850,377 | 2,199 | Techniques are described for providing a framework for generically evaluating the performance and usability of custom entities in a development infrastructure. In an example method, a request to execute a performance and usability analysis on a particular entity within a cloud development system is received. An entity type associated with the particular entity is identified. Based on the identified entity type and from a repository of automated testing procedures, a first set of automated testing procedures corresponding to the particular entity is determined. The first set of automated testing procedures are executed on the particular entity, and a set of results associated with the execution of the first set of automated testing procedures is provided for presentation to a user interface. | 1. A method associated with a performance testing framework, the method executed by at least one processor and comprising:
receiving a request to execute a performance and usability analysis on a particular entity within a cloud development system; identifying an entity type associated with the particular entity; determining, based on the identified entity type and from a repository of automated testing procedures, a first set of automated testing procedures corresponding to the particular entity and context-specific to the identified entity type; executing the first set of automated testing procedures on the particular entity; and providing, for presentation to a user interface, a set of results associated with the execution of the first set of automated testing procedures. 2. The method of claim 1, the method further comprising:
accessing an entity definition associated with the particular entity, the entity definition containing at least one operation performed by the particular entity; and determining, based on the entity definition, a second set of automated testing procedures corresponding to at least one of the at least one operations performed by the particular entity; wherein executing the first set of automated testing procedures on the particular entity comprises executing the first and second set of automated testing procedures on the particular entity; and providing the set of results comprises providing the set of results associated with the execution of the first set of automated testing procedures and a set of results associated with the execution of the second set of automated testing procedures. 3. The method of claim 2, wherein the entity definition includes at least one of an identification of a particular function performed by the particular entity, a particular format of the particular entity, and one or more programming languages associated with the particular entity. 4. The method of claim 2, wherein determining the second set of automated testing procedures corresponding to the at least one of the at least one operations performed by the particular entity comprises determining the second set of automated testing procedures based on the identified entity type and the entity definition of the particular entity. 5. The method of claim 1, wherein identifying an entity type associated with the particular entity is based on at least one of a file name extension of the particular entity, metadata associated with the particular entity, one or more attributes or parameters associated with or related to the particular entity, and an entry associated with the particular entity included in a lookup table or index storing information related to a plurality of entities. 6. The method of claim 1, wherein providing the set of results associated with the execution of the first set of automated testing procedures includes identifying at least one issue corresponding to the set of results of the execution. 7. The method of claim 6, wherein identifying the at least one issue includes identifying a location within the particular entity associated with at least one of the at least one identified issues. 8. The method of claim 1, wherein the request to execute the performance and usability analysis on the particular entity comprises an automatically triggered request in response to a developer action associated with development of the particular entity. 9. A system comprising:
at least one processor; and a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed, cause the at least one processor to perform operations comprising:
receiving a request to execute a performance and usability analysis on a particular entity within a cloud development system;
identifying an entity type associated with the particular entity;
determining, based on the identified entity type and from a repository of automated testing procedures, a first set of automated testing procedures corresponding to the particular entity and context-specific to the identified entity type;
executing the first set of automated testing procedures on the particular entity; and
providing, for presentation to a user interface, a set of results associated with the execution of the first set of automated testing procedures. 10. The system of claim 9, the operation further comprising:
accessing an entity definition associated with the particular entity, the entity definition containing at least one operation performed by the particular entity; and determining, based on the entity definition, a second set of automated testing procedures corresponding to at least one of the at least one operations performed by the particular entity; wherein executing the first set of automated testing procedures on the particular entity comprises executing the first and second set of automated testing procedures on the particular entity; and providing the set of results comprises providing the set of results associated with the execution of the first set of automated testing procedures and a set of results associated with the execution of the second set of automated testing procedures. 11. The system of claim 10, wherein the entity definition includes at least one of an identification of a particular function performed by the particular entity, a particular format of the particular entity, and one or more programming languages associated with the particular entity. 12. The system of claim 10, wherein determining the second set of automated testing procedures corresponding to the at least one of the at least one operations performed by the particular entity comprises determining the second set of automated testing procedures based on the identified entity type and the entity definition of the particular entity. 13. The system of claim 9, wherein identifying an entity type associated with the particular entity is based on at least one of a file name extension of the particular entity, metadata associated with the particular entity, one or more attributes or parameters associated with or related to the particular entity, and an entry associated with the particular entity included in a lookup table or index storing information related to a plurality of entities. 14. The system of claim 9, wherein providing the set of results associated with the execution of the first set of automated testing procedures includes identifying at least one issue corresponding to the set of results of the execution. 15. The system of claim 14, wherein identifying the at least one issue includes identifying a location within the particular entity associated with at least one of the at least one identified issues. 16. The system of claim 9, wherein the request to execute the performance and usability analysis on the particular entity comprises an automatically triggered request in response to a developer action associated with development of the particular entity. 17. A non-transitory computer-readable medium storing instructions which, when executed, cause at least one processor to perform operations comprising:
receiving a request to execute a performance and usability analysis on a particular entity within a cloud development system; identifying an entity type associated with the particular entity; determining, based on the identified entity type and from a repository of automated testing procedures, a first set of automated testing procedures corresponding to the particular entity and context-specific to the identified entity type; executing the first set of automated testing procedures on the particular entity; and providing, for presentation to a user interface, a set of results associated with the execution of the first set of automated testing procedures. 18. The computer-readable medium of claim 17, the operation further comprising:
accessing an entity definition associated with the particular entity, the entity definition containing at least one operation performed by the particular entity; and determining, based on the entity definition, a second set of automated testing procedures corresponding to at least one of the at least one operations performed by the particular entity; wherein executing the first set of automated testing procedures on the particular entity comprises executing the first and second set of automated testing procedures on the particular entity; and providing the set of results comprises providing the set of results associated with the execution of the first set of automated testing procedures and a set of results associated with the execution of the second set of automated testing procedures. 19. The computer-readable medium of claim 18, wherein determining the second set of automated testing procedures corresponding to the at least one of the at least one operations performed by the particular entity comprises determining the second set of automated testing procedures based on the identified entity type and the entity definition of the particular entity. 20. The computer-readable medium of claim 17, wherein providing the set of results associated with the execution of the first set of automated testing procedures includes identifying at least one issue corresponding to the set of results of the execution, and wherein identifying the at least one issue includes identifying a location within the particular entity associated with at least one of the at least one identified issues. | Techniques are described for providing a framework for generically evaluating the performance and usability of custom entities in a development infrastructure. In an example method, a request to execute a performance and usability analysis on a particular entity within a cloud development system is received. An entity type associated with the particular entity is identified. Based on the identified entity type and from a repository of automated testing procedures, a first set of automated testing procedures corresponding to the particular entity is determined. The first set of automated testing procedures are executed on the particular entity, and a set of results associated with the execution of the first set of automated testing procedures is provided for presentation to a user interface.1. A method associated with a performance testing framework, the method executed by at least one processor and comprising:
receiving a request to execute a performance and usability analysis on a particular entity within a cloud development system; identifying an entity type associated with the particular entity; determining, based on the identified entity type and from a repository of automated testing procedures, a first set of automated testing procedures corresponding to the particular entity and context-specific to the identified entity type; executing the first set of automated testing procedures on the particular entity; and providing, for presentation to a user interface, a set of results associated with the execution of the first set of automated testing procedures. 2. The method of claim 1, the method further comprising:
accessing an entity definition associated with the particular entity, the entity definition containing at least one operation performed by the particular entity; and determining, based on the entity definition, a second set of automated testing procedures corresponding to at least one of the at least one operations performed by the particular entity; wherein executing the first set of automated testing procedures on the particular entity comprises executing the first and second set of automated testing procedures on the particular entity; and providing the set of results comprises providing the set of results associated with the execution of the first set of automated testing procedures and a set of results associated with the execution of the second set of automated testing procedures. 3. The method of claim 2, wherein the entity definition includes at least one of an identification of a particular function performed by the particular entity, a particular format of the particular entity, and one or more programming languages associated with the particular entity. 4. The method of claim 2, wherein determining the second set of automated testing procedures corresponding to the at least one of the at least one operations performed by the particular entity comprises determining the second set of automated testing procedures based on the identified entity type and the entity definition of the particular entity. 5. The method of claim 1, wherein identifying an entity type associated with the particular entity is based on at least one of a file name extension of the particular entity, metadata associated with the particular entity, one or more attributes or parameters associated with or related to the particular entity, and an entry associated with the particular entity included in a lookup table or index storing information related to a plurality of entities. 6. The method of claim 1, wherein providing the set of results associated with the execution of the first set of automated testing procedures includes identifying at least one issue corresponding to the set of results of the execution. 7. The method of claim 6, wherein identifying the at least one issue includes identifying a location within the particular entity associated with at least one of the at least one identified issues. 8. The method of claim 1, wherein the request to execute the performance and usability analysis on the particular entity comprises an automatically triggered request in response to a developer action associated with development of the particular entity. 9. A system comprising:
at least one processor; and a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed, cause the at least one processor to perform operations comprising:
receiving a request to execute a performance and usability analysis on a particular entity within a cloud development system;
identifying an entity type associated with the particular entity;
determining, based on the identified entity type and from a repository of automated testing procedures, a first set of automated testing procedures corresponding to the particular entity and context-specific to the identified entity type;
executing the first set of automated testing procedures on the particular entity; and
providing, for presentation to a user interface, a set of results associated with the execution of the first set of automated testing procedures. 10. The system of claim 9, the operation further comprising:
accessing an entity definition associated with the particular entity, the entity definition containing at least one operation performed by the particular entity; and determining, based on the entity definition, a second set of automated testing procedures corresponding to at least one of the at least one operations performed by the particular entity; wherein executing the first set of automated testing procedures on the particular entity comprises executing the first and second set of automated testing procedures on the particular entity; and providing the set of results comprises providing the set of results associated with the execution of the first set of automated testing procedures and a set of results associated with the execution of the second set of automated testing procedures. 11. The system of claim 10, wherein the entity definition includes at least one of an identification of a particular function performed by the particular entity, a particular format of the particular entity, and one or more programming languages associated with the particular entity. 12. The system of claim 10, wherein determining the second set of automated testing procedures corresponding to the at least one of the at least one operations performed by the particular entity comprises determining the second set of automated testing procedures based on the identified entity type and the entity definition of the particular entity. 13. The system of claim 9, wherein identifying an entity type associated with the particular entity is based on at least one of a file name extension of the particular entity, metadata associated with the particular entity, one or more attributes or parameters associated with or related to the particular entity, and an entry associated with the particular entity included in a lookup table or index storing information related to a plurality of entities. 14. The system of claim 9, wherein providing the set of results associated with the execution of the first set of automated testing procedures includes identifying at least one issue corresponding to the set of results of the execution. 15. The system of claim 14, wherein identifying the at least one issue includes identifying a location within the particular entity associated with at least one of the at least one identified issues. 16. The system of claim 9, wherein the request to execute the performance and usability analysis on the particular entity comprises an automatically triggered request in response to a developer action associated with development of the particular entity. 17. A non-transitory computer-readable medium storing instructions which, when executed, cause at least one processor to perform operations comprising:
receiving a request to execute a performance and usability analysis on a particular entity within a cloud development system; identifying an entity type associated with the particular entity; determining, based on the identified entity type and from a repository of automated testing procedures, a first set of automated testing procedures corresponding to the particular entity and context-specific to the identified entity type; executing the first set of automated testing procedures on the particular entity; and providing, for presentation to a user interface, a set of results associated with the execution of the first set of automated testing procedures. 18. The computer-readable medium of claim 17, the operation further comprising:
accessing an entity definition associated with the particular entity, the entity definition containing at least one operation performed by the particular entity; and determining, based on the entity definition, a second set of automated testing procedures corresponding to at least one of the at least one operations performed by the particular entity; wherein executing the first set of automated testing procedures on the particular entity comprises executing the first and second set of automated testing procedures on the particular entity; and providing the set of results comprises providing the set of results associated with the execution of the first set of automated testing procedures and a set of results associated with the execution of the second set of automated testing procedures. 19. The computer-readable medium of claim 18, wherein determining the second set of automated testing procedures corresponding to the at least one of the at least one operations performed by the particular entity comprises determining the second set of automated testing procedures based on the identified entity type and the entity definition of the particular entity. 20. The computer-readable medium of claim 17, wherein providing the set of results associated with the execution of the first set of automated testing procedures includes identifying at least one issue corresponding to the set of results of the execution, and wherein identifying the at least one issue includes identifying a location within the particular entity associated with at least one of the at least one identified issues. | 2,100 |
6,448 | 6,448 | 14,000,788 | 2,147 | Systems and methods for computer-aided vaccine design may comprise performing one or more molecular dynamics simulations of a protein assembly having at least one epitope, determining a fluctuation measurement for the at least one epitope using the one or more molecular dynamics simulations, and predicting the immunogenicity of the protein assembly in response to the fluctuation measurement are disclosed. | 1. A computer-aided vaccine design method comprising:
performing one or more molecular dynamics simulations of a protein assembly having at least one epitope; determining a fluctuation measurement for the at least one epitope using the one or more molecular dynamics simulations; and predicting the immunogenicity of the protein assembly in response to the fluctuation measurement. 2. The computer-aided vaccine design method of claim 1, wherein the protein assembly is a virus-like particle. 3. The computer-aided vaccine design method of claim 1, wherein the protein assembly is a pentamer. 4. The computer-aided vaccine design method of claim 1, wherein the one or more molecular dynamics simulations are performed using a deductive multiscale simulator. 5. The computer-aided vaccine design method of claim 1, wherein determining the fluctuation measurement for the at least one epitope comprises determining a distribution of backbone dihedral angles in conformational space. 6. The computer-aided vaccine design method of claim 1, wherein determining the fluctuation measurement for the at least one epitope comprises calculating a root-mean-square deviation of epitope fluctuation. 7. The computer-aided vaccine design method of claim 6, wherein calculating the root-mean-square deviation of epitope fluctuation comprises summing fluctuations for each backbone atom during the one or more molecular dynamics simulations and dividing by a total number of backbone atoms. 8. The computer-aided vaccine design method of claim 1, wherein determining the fluctuation measurement for the at least one epitope comprises calculating a correlation matrix between the at least one epitope and other structures in the protein assembly. 9. The computer-aided vaccine design method of claim 1, wherein determining the fluctuation measurement for the at least one epitope comprises analyzing a Fourier spectrum of epitope fluctuation. 10. The computer-aided vaccine design method of claim 1, wherein predicting the immunogenicity of the protein assembly in response to the fluctuation measurement further comprises predicting the immunogenicity of the protein assembly in response to at least one other molecular descriptor of the protein assembly. 11. The computer-aided vaccine design method of claim 1, further comprising synthesizing a vaccine comprising the protein assembly. 12. One or more non-transitory, computer readable media comprising a plurality of instructions which, when executed by one or more processors, cause the one or more processors to:
perform one or more molecular dynamics simulations of a protein assembly having at least one epitope; determine a fluctuation measurement for the at least one epitope using the one or more molecular dynamics simulations; and predict the immunogenicity of the protein assembly in response to the fluctuation measurement. 13. The one or more non-transitory, computer readable media of claim 12, wherein the protein assembly is a virus-like particle. 14. The one or more non-transitory, computer readable media of claim 12, wherein the protein assembly is a pentamer. 15. The one or more non-transitory, computer readable media of claim 12, wherein the plurality of instructions cause the one or more processors to perform the one or more molecular dynamics simulations using a deductive multiscale simulator. 16. The one or more non-transitory, computer readable media of claim 12, wherein the plurality of instructions cause the one or more processors to determine the fluctuation measurement for the at least one epitope, at least in part, by determining a distribution of backbone dihedral angles in conformational space. 17. The one or more non-transitory, computer readable media of claim 12, wherein the plurality of instructions cause the one or more processors to determine the fluctuation measurement for the at least one epitope, at least in part, by calculating a root-mean-square deviation of epitope fluctuation. 18. The one or more non-transitory, computer readable media of claim 17, wherein the plurality of instructions cause the one or more processors to calculate the root-mean-square deviation of epitope fluctuation by summing fluctuations for each backbone atom during the one or more molecular dynamics simulations and dividing by a total number of backbone atoms. 19. The one or more non-transitory, computer readable media of claim 12, wherein the plurality of instructions cause the one or more processors to determine the fluctuation measurement for the at least one epitope, at least in part, by calculating a correlation matrix between the at least one epitope and other structures in the protein assembly. 20. The one or more non-transitory, computer readable media of claim 12, wherein the plurality of instructions cause the one or more processors to determine the fluctuation measurement for the at least one epitope, at least in part, by analyzing a Fourier spectrum of epitope fluctuation. 21. The one or more non-transitory, computer readable media according to claim 12, wherein the plurality of instructions further cause the one or more processors to predict the immunogenicity of the protein assembly in response to at least one other molecular descriptor of the protein assembly. | Systems and methods for computer-aided vaccine design may comprise performing one or more molecular dynamics simulations of a protein assembly having at least one epitope, determining a fluctuation measurement for the at least one epitope using the one or more molecular dynamics simulations, and predicting the immunogenicity of the protein assembly in response to the fluctuation measurement are disclosed.1. A computer-aided vaccine design method comprising:
performing one or more molecular dynamics simulations of a protein assembly having at least one epitope; determining a fluctuation measurement for the at least one epitope using the one or more molecular dynamics simulations; and predicting the immunogenicity of the protein assembly in response to the fluctuation measurement. 2. The computer-aided vaccine design method of claim 1, wherein the protein assembly is a virus-like particle. 3. The computer-aided vaccine design method of claim 1, wherein the protein assembly is a pentamer. 4. The computer-aided vaccine design method of claim 1, wherein the one or more molecular dynamics simulations are performed using a deductive multiscale simulator. 5. The computer-aided vaccine design method of claim 1, wherein determining the fluctuation measurement for the at least one epitope comprises determining a distribution of backbone dihedral angles in conformational space. 6. The computer-aided vaccine design method of claim 1, wherein determining the fluctuation measurement for the at least one epitope comprises calculating a root-mean-square deviation of epitope fluctuation. 7. The computer-aided vaccine design method of claim 6, wherein calculating the root-mean-square deviation of epitope fluctuation comprises summing fluctuations for each backbone atom during the one or more molecular dynamics simulations and dividing by a total number of backbone atoms. 8. The computer-aided vaccine design method of claim 1, wherein determining the fluctuation measurement for the at least one epitope comprises calculating a correlation matrix between the at least one epitope and other structures in the protein assembly. 9. The computer-aided vaccine design method of claim 1, wherein determining the fluctuation measurement for the at least one epitope comprises analyzing a Fourier spectrum of epitope fluctuation. 10. The computer-aided vaccine design method of claim 1, wherein predicting the immunogenicity of the protein assembly in response to the fluctuation measurement further comprises predicting the immunogenicity of the protein assembly in response to at least one other molecular descriptor of the protein assembly. 11. The computer-aided vaccine design method of claim 1, further comprising synthesizing a vaccine comprising the protein assembly. 12. One or more non-transitory, computer readable media comprising a plurality of instructions which, when executed by one or more processors, cause the one or more processors to:
perform one or more molecular dynamics simulations of a protein assembly having at least one epitope; determine a fluctuation measurement for the at least one epitope using the one or more molecular dynamics simulations; and predict the immunogenicity of the protein assembly in response to the fluctuation measurement. 13. The one or more non-transitory, computer readable media of claim 12, wherein the protein assembly is a virus-like particle. 14. The one or more non-transitory, computer readable media of claim 12, wherein the protein assembly is a pentamer. 15. The one or more non-transitory, computer readable media of claim 12, wherein the plurality of instructions cause the one or more processors to perform the one or more molecular dynamics simulations using a deductive multiscale simulator. 16. The one or more non-transitory, computer readable media of claim 12, wherein the plurality of instructions cause the one or more processors to determine the fluctuation measurement for the at least one epitope, at least in part, by determining a distribution of backbone dihedral angles in conformational space. 17. The one or more non-transitory, computer readable media of claim 12, wherein the plurality of instructions cause the one or more processors to determine the fluctuation measurement for the at least one epitope, at least in part, by calculating a root-mean-square deviation of epitope fluctuation. 18. The one or more non-transitory, computer readable media of claim 17, wherein the plurality of instructions cause the one or more processors to calculate the root-mean-square deviation of epitope fluctuation by summing fluctuations for each backbone atom during the one or more molecular dynamics simulations and dividing by a total number of backbone atoms. 19. The one or more non-transitory, computer readable media of claim 12, wherein the plurality of instructions cause the one or more processors to determine the fluctuation measurement for the at least one epitope, at least in part, by calculating a correlation matrix between the at least one epitope and other structures in the protein assembly. 20. The one or more non-transitory, computer readable media of claim 12, wherein the plurality of instructions cause the one or more processors to determine the fluctuation measurement for the at least one epitope, at least in part, by analyzing a Fourier spectrum of epitope fluctuation. 21. The one or more non-transitory, computer readable media according to claim 12, wherein the plurality of instructions further cause the one or more processors to predict the immunogenicity of the protein assembly in response to at least one other molecular descriptor of the protein assembly. | 2,100 |
6,449 | 6,449 | 15,912,307 | 2,196 | Systems and methods are provided for configuring a programmable integrated circuit device. A hard processor region of the programmable integrated circuit device includes a processor that identifies one or more tasks for assigning to an offload region of the programmable integrated circuit. The processor in the hard processor region transmits an instruction to the offload region. The plurality of offload nodes in the offload region are configured to perform the one or more tasks. | 1. A method of configuring a programmable integrated circuit device, the method comprising:
identifying, by a processor in a hard processor region of the programmable integrated circuit device, one or more tasks for assigning to an offload region of the programmable integrated circuit device; transmitting, by the processor in the hard processor region, an instruction to the offload region; configuring a plurality of offload nodes in the offload region to perform the one or more tasks. | Systems and methods are provided for configuring a programmable integrated circuit device. A hard processor region of the programmable integrated circuit device includes a processor that identifies one or more tasks for assigning to an offload region of the programmable integrated circuit. The processor in the hard processor region transmits an instruction to the offload region. The plurality of offload nodes in the offload region are configured to perform the one or more tasks.1. A method of configuring a programmable integrated circuit device, the method comprising:
identifying, by a processor in a hard processor region of the programmable integrated circuit device, one or more tasks for assigning to an offload region of the programmable integrated circuit device; transmitting, by the processor in the hard processor region, an instruction to the offload region; configuring a plurality of offload nodes in the offload region to perform the one or more tasks. | 2,100 |
6,450 | 6,450 | 15,921,489 | 2,114 | Techniques are provided herein for pre-emptively reinforcing one or more buses of a computing device against the effects of signal noise that could cause a reduction in signal integrity. The techniques generally include detecting an event (or “trigger”) that would tend to indicate that a reduction in signal integrity will occur, examining a reinforcement action policy and system status to determine what reinforcement action to take, and performing the reinforcement action. | 1. A method for pre-emptively reinforcing one or more buses of a computing device against signal noise, the method comprising:
detecting a triggering event that is predictive of signal noise occurring on one or more buses of the computing device; identifying one or more buses of the computing device on which to perform a reinforcement action to reinforce against signal noise, based on a reinforcement action policy and a type of the triggering event; identifying one or more reinforcement actions to be taken based on the reinforcement action policy, the type of the triggering event, and a system status of the computing device; and performing the reinforcement action on the identified one or more buses. 2. The method of claim 1, further comprising:
reverting the reinforcement action after a determined duration. 3. The method of claim 2, wherein the determined duration is one of a duration-driven reversion or an event completion-driven reversion. 4. The method of claim 3, further comprising determining whether the duration is a duration-driven reversion or an event completion-driven reversion based on the type of the trigger. 5. The method of claim 1, wherein the triggering event comprises one or more of system management unit activity, interrupt activity, a bus status change request, communication over a bus, a workload change request, a temperature or humidity change, or a notification of an error rate change in a second computing device. 6. The method of claim 1, wherein the reinforcement action comprises one or more of increasing the voltage of a bus, reducing the clock frequency of a bus, or increasing the error correcting code level of the bus. 7. The method of claim 1, wherein the one or more identified buses are identified from a group of external buses that couple the computing device to a device external to the computing device. 8. The method of claim 1, wherein identifying the one or more reinforcement actions based on the system status comprises determining whether an aspect of system status has reached a bound and not performing a reinforcement action for an aspect of system status for which the bound has been reached. 9. The method of claim 1, wherein the reinforcement actions are prioritized. 10. A computing device for pre-emptively reinforcing one or more buses of a plurality of buses of the computing device against signal noise, the computing device comprising:
the plurality of buses, including the one or more buses; and a signal integrity controller configured to:
detect a triggering event that is predictive of signal noise occurring on the one or more buses;
identify one or more buses of the plurality of buses on which to perform a reinforcement action to reinforce against signal noise, based on a reinforcement action policy and a type of the triggering event;
identify one or more reinforcement actions to be taken based on the reinforcement action policy, the type of the triggering event, and a system status of the computing device; and
perform the reinforcement action on the identified one or more buses. 11. The computing device of claim 10, wherein the signal integrity controller is further configured to:
revert the reinforcement action after a determined duration. 12. The computing device of claim 11, wherein the determined duration is one of a duration-driven reversion or an event completion-driven reversion. 13. The computing device of claim 12, wherein the signal integrity controller is configured to determine whether the duration is a duration-driven reversion or an event completion-driven reversion based on the type of the trigger. 14. The computing device of claim 10, wherein the triggering event comprises one or more of system management unit activity, interrupt activity, a bus status change request, communication over a bus, a workload change request, a temperature or humidity change, or a notification of an error rate change in a second computing device. 15. The computing device of claim 10, wherein the reinforcement action comprises one or more of increasing the voltage of a bus, reducing the clock frequency of a bus, or increasing the error correcting code level of the bus. 16. The computing device of claim 10, wherein the one or more identified buses are identified from a group of external buses that couple the computing device to a device external to the computing device. 17. The computing device of claim 10, wherein the signal integrity controller is further configured to identify the one or more reinforcement actions based on the system status by determining whether an aspect of system status has reached a bound and not performing a reinforcement action for an aspect of system status for which the bound has been reached. 18. The computing device of claim 10, wherein the reinforcement actions are prioritized. 19. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to pre-emptively reinforce one or more buses of a computing device against signal noise, by:
detecting a triggering event that is predictive of signal noise occurring on one or more buses of the computing device; identifying one or more buses of the computing device on which to perform a reinforcement action to reinforce against signal noise, based on a reinforcement action policy and a type of the triggering event; identifying one or more reinforcement actions to be taken based on the reinforcement action policy, the type of the triggering event, and a system status of the computing device; and performing the reinforcement action on the identified one or more buses. 20. The non-transitory computer-readable medium of claim 19, wherein:
the triggering event comprises one or more of system management unit activity, interrupt activity, a bus status change request, communication over a bus, a workload change request, a temperature or humidity change, or a notification of an error rate change in a second computing device; and the reinforcement action comprises one or more of increasing the voltage of a bus, reducing the clock frequency of a bus, or increasing the error correcting code level of the bus. | Techniques are provided herein for pre-emptively reinforcing one or more buses of a computing device against the effects of signal noise that could cause a reduction in signal integrity. The techniques generally include detecting an event (or “trigger”) that would tend to indicate that a reduction in signal integrity will occur, examining a reinforcement action policy and system status to determine what reinforcement action to take, and performing the reinforcement action.1. A method for pre-emptively reinforcing one or more buses of a computing device against signal noise, the method comprising:
detecting a triggering event that is predictive of signal noise occurring on one or more buses of the computing device; identifying one or more buses of the computing device on which to perform a reinforcement action to reinforce against signal noise, based on a reinforcement action policy and a type of the triggering event; identifying one or more reinforcement actions to be taken based on the reinforcement action policy, the type of the triggering event, and a system status of the computing device; and performing the reinforcement action on the identified one or more buses. 2. The method of claim 1, further comprising:
reverting the reinforcement action after a determined duration. 3. The method of claim 2, wherein the determined duration is one of a duration-driven reversion or an event completion-driven reversion. 4. The method of claim 3, further comprising determining whether the duration is a duration-driven reversion or an event completion-driven reversion based on the type of the trigger. 5. The method of claim 1, wherein the triggering event comprises one or more of system management unit activity, interrupt activity, a bus status change request, communication over a bus, a workload change request, a temperature or humidity change, or a notification of an error rate change in a second computing device. 6. The method of claim 1, wherein the reinforcement action comprises one or more of increasing the voltage of a bus, reducing the clock frequency of a bus, or increasing the error correcting code level of the bus. 7. The method of claim 1, wherein the one or more identified buses are identified from a group of external buses that couple the computing device to a device external to the computing device. 8. The method of claim 1, wherein identifying the one or more reinforcement actions based on the system status comprises determining whether an aspect of system status has reached a bound and not performing a reinforcement action for an aspect of system status for which the bound has been reached. 9. The method of claim 1, wherein the reinforcement actions are prioritized. 10. A computing device for pre-emptively reinforcing one or more buses of a plurality of buses of the computing device against signal noise, the computing device comprising:
the plurality of buses, including the one or more buses; and a signal integrity controller configured to:
detect a triggering event that is predictive of signal noise occurring on the one or more buses;
identify one or more buses of the plurality of buses on which to perform a reinforcement action to reinforce against signal noise, based on a reinforcement action policy and a type of the triggering event;
identify one or more reinforcement actions to be taken based on the reinforcement action policy, the type of the triggering event, and a system status of the computing device; and
perform the reinforcement action on the identified one or more buses. 11. The computing device of claim 10, wherein the signal integrity controller is further configured to:
revert the reinforcement action after a determined duration. 12. The computing device of claim 11, wherein the determined duration is one of a duration-driven reversion or an event completion-driven reversion. 13. The computing device of claim 12, wherein the signal integrity controller is configured to determine whether the duration is a duration-driven reversion or an event completion-driven reversion based on the type of the trigger. 14. The computing device of claim 10, wherein the triggering event comprises one or more of system management unit activity, interrupt activity, a bus status change request, communication over a bus, a workload change request, a temperature or humidity change, or a notification of an error rate change in a second computing device. 15. The computing device of claim 10, wherein the reinforcement action comprises one or more of increasing the voltage of a bus, reducing the clock frequency of a bus, or increasing the error correcting code level of the bus. 16. The computing device of claim 10, wherein the one or more identified buses are identified from a group of external buses that couple the computing device to a device external to the computing device. 17. The computing device of claim 10, wherein the signal integrity controller is further configured to identify the one or more reinforcement actions based on the system status by determining whether an aspect of system status has reached a bound and not performing a reinforcement action for an aspect of system status for which the bound has been reached. 18. The computing device of claim 10, wherein the reinforcement actions are prioritized. 19. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to pre-emptively reinforce one or more buses of a computing device against signal noise, by:
detecting a triggering event that is predictive of signal noise occurring on one or more buses of the computing device; identifying one or more buses of the computing device on which to perform a reinforcement action to reinforce against signal noise, based on a reinforcement action policy and a type of the triggering event; identifying one or more reinforcement actions to be taken based on the reinforcement action policy, the type of the triggering event, and a system status of the computing device; and performing the reinforcement action on the identified one or more buses. 20. The non-transitory computer-readable medium of claim 19, wherein:
the triggering event comprises one or more of system management unit activity, interrupt activity, a bus status change request, communication over a bus, a workload change request, a temperature or humidity change, or a notification of an error rate change in a second computing device; and the reinforcement action comprises one or more of increasing the voltage of a bus, reducing the clock frequency of a bus, or increasing the error correcting code level of the bus. | 2,100 |
6,451 | 6,451 | 15,115,425 | 2,192 | A method comprising receiving motion information indicative of an input gesture by way of at least one motion sensor comprised by the apparatus, determining that the input gesture is a firmware update gesture, the firmware update gesture being indicative of a directive to update a firmware of the apparatus, sending a firmware download request to a separate apparatus based, at least in part, on determining that the input gesture is the firmware update gesture, receiving firmware update information from the separate apparatus based, at least in part, on the firmware download request, and updating the firmware of the apparatus based, at least in part, on the firmware update information is disclosed. | 1-20. (canceled) 21. An apparatus, comprising:
at least one processor; at least one memory including computer program code, the memory and the computer program code configured to, working with the processor, cause the apparatus to perform at least the following: receipt of motion information indicative of an input gesture by way of at least one motion sensor comprised by the apparatus, wherein the apparatus is absent an input display; determination that the input gesture is a firmware update gesture indicative of a directive to update a firmware of the apparatus; sending of a firmware download request to a separate apparatus based, at least in part, on the determination that the input gesture is the firmware update gesture; receipt of firmware update information from the separate apparatus based, at least in part, on the firmware download request; and update of the firmware of the apparatus based, at least in part, on the firmware update information. 22. The apparatus of claim 21, wherein the apparatus is absent an alphanumeric display. 23. The apparatus of claim 21, wherein the apparatus is absent a graphical display. 24. The apparatus of claim 21, wherein the apparatus comprises one or more non-alphanumeric light emitting diodes. 25. The apparatus of claim 24, wherein the memory includes computer program code configured to, working with the processor, cause the apparatus to perform actuation of a visual signal by way of the non-alphanumeric light emitting diode. 26. The apparatus of claim 21, wherein the memory includes computer program code configured to, working with the processor, cause the apparatus to perform:
determination of a version of the firmware; and determination that an updated version of the firmware is available for download, wherein the sending of the firmware download request to the separate apparatus is based, at least in part, on the determination that the updated version of the firmware is available for download, and the firmware update information comprises at least a portion of the updated version of the firmware. 27. The apparatus of claim 21, wherein the memory includes computer program code configured to, working with the processor, cause the apparatus to perform:
receipt of motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determination that the other input gesture is another firmware update gesture, the other firmware update gesture being indicative of a directive to update a firmware of the apparatus; determination of a version of the firmware; determination that an updated version of the firmware is unavailable for download; and preclusion of sending of another firmware download request to the separate apparatus based, at least in part, on the determination that the updated version of the firmware is unavailable for download. 28. The apparatus of claim 21, wherein the memory includes computer program code configured to, working with the processor, cause the apparatus to perform:
receipt of motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determination that the other input gesture is a factory reset gesture, the factory reset gesture being indicative of a directive for the apparatus to perform a factory reset; and performance of the factory reset based, at least in part, on the determination that the other input gesture is the factory reset gesture. 29. The apparatus of claim 21, wherein the sending of the firmware download request to the separate apparatus is performed by way of at least one proximity-based communication channel, and the receipt of the firmware update information from the separate apparatus is performed by way of at least one proximity-based communication channel. 30. A method comprising:
receiving motion information indicative of an input gesture by way of at least one motion sensor comprised by the apparatus, wherein the apparatus is absent an input display; determining that the input gesture is a firmware update gesture, the firmware update gesture being indicative of a directive to update a firmware of the apparatus; sending a firmware download request to a separate apparatus based, at least in part, on determining that the input gesture is the firmware update gesture; receiving firmware update information from the separate apparatus based, at least in part, on the firmware download request; and updating the firmware of the apparatus based, at least in part, on the firmware update information. 31. The method of claim 30, wherein the apparatus comprises one or more non-alphanumeric light emitting diodes. 32. The method of claim 30, further comprising performing actuation of a visual signal by way of the non-alphanumeric light emitting diode. 33. The method of claim 30, further comprising:
determining a version of the firmware; and determining that an updated version of the firmware is available for download, wherein sending the firmware download request to the separate apparatus is based, at least in part, on determining that the updated version of the firmware is available for download, and the firmware update information comprises at least a portion of the updated version of the firmware. 34. The method of claim 30, further comprising:
receiving motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determining that the other input gesture is another firmware update gesture, the other firmware update gesture being indicative of a directive to update a firmware of the apparatus; determining a version of the firmware; determining that an updated version of the firmware is unavailable for download; and precluding sending of another firmware download request to the separate apparatus based, at least in part, on determining that the updated version of the firmware is unavailable for download. 35. The method of claim 30, further comprising:
receiving motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determining that the other input gesture is a factory reset gesture, the factory reset gesture being indicative of a directive for the apparatus to perform a factory reset; and performing the factory reset based, at least in part, on determining that the other input gesture is the factory reset gesture. 36. The method of claim 30, wherein sending the firmware download request to the separate apparatus is performed by way of at least one proximity-based communication channel, and receiving the firmware update information from the separate apparatus is performed by way of at least one proximity-based communication channel. 37. At least one computer-readable medium encoded with instructions that, when executed by a processor, perform:
receipt of motion information indicative of an input gesture by way of at least one motion sensor comprised by the apparatus, wherein the apparatus is absent an input display; determination that the input gesture is a firmware update gesture, the firmware update gesture being indicative of a directive to update a firmware of the apparatus; sending of a firmware download request to a separate apparatus based, at least in part, on the determination that the input gesture is the firmware update gesture; receipt of firmware update information from the separate apparatus based, at least in part, on the firmware download request; and update of the firmware of the apparatus based, at least in part, on the firmware update information. 38. The computer-readable medium of claim 37, further encoded with instructions that, when executed by a processor, perform:
determination of a version of the firmware; and determination that an updated version of the firmware is available for download, wherein the sending of the firmware download request to the separate apparatus is based, at least in part, on the determination that the updated version of the firmware is available for download, and the firmware update information comprises at least a portion of the updated version of the firmware. 39. The computer-readable medium of claim 37, further encoded with instructions that, when executed by a processor, perform:
receipt of motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determination that the other input gesture is another firmware update gesture, the other firmware update gesture being indicative of a directive to update a firmware of the apparatus; determination of a version of the firmware; determination that an updated version of the firmware is unavailable for download; and preclusion of sending of another firmware download request to the separate apparatus based, at least in part, on the determination that the updated version of the firmware is unavailable for download. 40. The computer-readable medium of claim 37, further encoded with instructions that, when executed by a processor, perform:
receipt of motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determination that the other input gesture is a factory reset gesture, the factory reset gesture being indicative of a directive for the apparatus to perform a factory reset; and performance of the factory reset based, at least in part, on the determination that the other input gesture is the factory reset gesture. | A method comprising receiving motion information indicative of an input gesture by way of at least one motion sensor comprised by the apparatus, determining that the input gesture is a firmware update gesture, the firmware update gesture being indicative of a directive to update a firmware of the apparatus, sending a firmware download request to a separate apparatus based, at least in part, on determining that the input gesture is the firmware update gesture, receiving firmware update information from the separate apparatus based, at least in part, on the firmware download request, and updating the firmware of the apparatus based, at least in part, on the firmware update information is disclosed.1-20. (canceled) 21. An apparatus, comprising:
at least one processor; at least one memory including computer program code, the memory and the computer program code configured to, working with the processor, cause the apparatus to perform at least the following: receipt of motion information indicative of an input gesture by way of at least one motion sensor comprised by the apparatus, wherein the apparatus is absent an input display; determination that the input gesture is a firmware update gesture indicative of a directive to update a firmware of the apparatus; sending of a firmware download request to a separate apparatus based, at least in part, on the determination that the input gesture is the firmware update gesture; receipt of firmware update information from the separate apparatus based, at least in part, on the firmware download request; and update of the firmware of the apparatus based, at least in part, on the firmware update information. 22. The apparatus of claim 21, wherein the apparatus is absent an alphanumeric display. 23. The apparatus of claim 21, wherein the apparatus is absent a graphical display. 24. The apparatus of claim 21, wherein the apparatus comprises one or more non-alphanumeric light emitting diodes. 25. The apparatus of claim 24, wherein the memory includes computer program code configured to, working with the processor, cause the apparatus to perform actuation of a visual signal by way of the non-alphanumeric light emitting diode. 26. The apparatus of claim 21, wherein the memory includes computer program code configured to, working with the processor, cause the apparatus to perform:
determination of a version of the firmware; and determination that an updated version of the firmware is available for download, wherein the sending of the firmware download request to the separate apparatus is based, at least in part, on the determination that the updated version of the firmware is available for download, and the firmware update information comprises at least a portion of the updated version of the firmware. 27. The apparatus of claim 21, wherein the memory includes computer program code configured to, working with the processor, cause the apparatus to perform:
receipt of motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determination that the other input gesture is another firmware update gesture, the other firmware update gesture being indicative of a directive to update a firmware of the apparatus; determination of a version of the firmware; determination that an updated version of the firmware is unavailable for download; and preclusion of sending of another firmware download request to the separate apparatus based, at least in part, on the determination that the updated version of the firmware is unavailable for download. 28. The apparatus of claim 21, wherein the memory includes computer program code configured to, working with the processor, cause the apparatus to perform:
receipt of motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determination that the other input gesture is a factory reset gesture, the factory reset gesture being indicative of a directive for the apparatus to perform a factory reset; and performance of the factory reset based, at least in part, on the determination that the other input gesture is the factory reset gesture. 29. The apparatus of claim 21, wherein the sending of the firmware download request to the separate apparatus is performed by way of at least one proximity-based communication channel, and the receipt of the firmware update information from the separate apparatus is performed by way of at least one proximity-based communication channel. 30. A method comprising:
receiving motion information indicative of an input gesture by way of at least one motion sensor comprised by the apparatus, wherein the apparatus is absent an input display; determining that the input gesture is a firmware update gesture, the firmware update gesture being indicative of a directive to update a firmware of the apparatus; sending a firmware download request to a separate apparatus based, at least in part, on determining that the input gesture is the firmware update gesture; receiving firmware update information from the separate apparatus based, at least in part, on the firmware download request; and updating the firmware of the apparatus based, at least in part, on the firmware update information. 31. The method of claim 30, wherein the apparatus comprises one or more non-alphanumeric light emitting diodes. 32. The method of claim 30, further comprising performing actuation of a visual signal by way of the non-alphanumeric light emitting diode. 33. The method of claim 30, further comprising:
determining a version of the firmware; and determining that an updated version of the firmware is available for download, wherein sending the firmware download request to the separate apparatus is based, at least in part, on determining that the updated version of the firmware is available for download, and the firmware update information comprises at least a portion of the updated version of the firmware. 34. The method of claim 30, further comprising:
receiving motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determining that the other input gesture is another firmware update gesture, the other firmware update gesture being indicative of a directive to update a firmware of the apparatus; determining a version of the firmware; determining that an updated version of the firmware is unavailable for download; and precluding sending of another firmware download request to the separate apparatus based, at least in part, on determining that the updated version of the firmware is unavailable for download. 35. The method of claim 30, further comprising:
receiving motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determining that the other input gesture is a factory reset gesture, the factory reset gesture being indicative of a directive for the apparatus to perform a factory reset; and performing the factory reset based, at least in part, on determining that the other input gesture is the factory reset gesture. 36. The method of claim 30, wherein sending the firmware download request to the separate apparatus is performed by way of at least one proximity-based communication channel, and receiving the firmware update information from the separate apparatus is performed by way of at least one proximity-based communication channel. 37. At least one computer-readable medium encoded with instructions that, when executed by a processor, perform:
receipt of motion information indicative of an input gesture by way of at least one motion sensor comprised by the apparatus, wherein the apparatus is absent an input display; determination that the input gesture is a firmware update gesture, the firmware update gesture being indicative of a directive to update a firmware of the apparatus; sending of a firmware download request to a separate apparatus based, at least in part, on the determination that the input gesture is the firmware update gesture; receipt of firmware update information from the separate apparatus based, at least in part, on the firmware download request; and update of the firmware of the apparatus based, at least in part, on the firmware update information. 38. The computer-readable medium of claim 37, further encoded with instructions that, when executed by a processor, perform:
determination of a version of the firmware; and determination that an updated version of the firmware is available for download, wherein the sending of the firmware download request to the separate apparatus is based, at least in part, on the determination that the updated version of the firmware is available for download, and the firmware update information comprises at least a portion of the updated version of the firmware. 39. The computer-readable medium of claim 37, further encoded with instructions that, when executed by a processor, perform:
receipt of motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determination that the other input gesture is another firmware update gesture, the other firmware update gesture being indicative of a directive to update a firmware of the apparatus; determination of a version of the firmware; determination that an updated version of the firmware is unavailable for download; and preclusion of sending of another firmware download request to the separate apparatus based, at least in part, on the determination that the updated version of the firmware is unavailable for download. 40. The computer-readable medium of claim 37, further encoded with instructions that, when executed by a processor, perform:
receipt of motion information indicative of another input gesture by way of the motion sensor comprised by the apparatus; determination that the other input gesture is a factory reset gesture, the factory reset gesture being indicative of a directive for the apparatus to perform a factory reset; and performance of the factory reset based, at least in part, on the determination that the other input gesture is the factory reset gesture. | 2,100 |
6,452 | 6,452 | 15,819,288 | 2,187 | The described embodiments include an electronic device that has a hardware controller and one or more hardware subsystems. The one or more hardware subsystems support an active state, a first low power state, and a second low power state. The first low power state and second low power states are separate low power states, with the first low power state being associated with a more rapid resumption of the active state than the second low power state. The hardware controller is configured to cause the one or more hardware subsystems to transition from the first low power state to the second low power state upon detecting an idle event that indicates that a user interaction is not likely to occur and to transition from the second low power state to the first low power state upon detecting an active event that indicates that a user interaction is likely to occur. | 1. An electronic device, comprising:
a hardware controller; and one or more hardware subsystems, wherein the one or more hardware subsystems support an active state, a first low power state, and a second low power state, wherein the first low power state and the second low power state are separate low power states, wherein the first low power state is configured for a more rapid resumption of the active state than the second low power state; wherein the hardware controller is configured to cause the one or more hardware subsystems to:
transition from the first low power state to the second low power state upon detecting an idle event that indicates that a user interaction is not likely to occur; and
transition from the second low power state to the first low power state upon detecting an active event that indicates that a user interaction is likely to occur. 2. The electronic device of claim 1, wherein the idle event comprises one or more of:
a low power state inactivity timer expiring, the low power state inactivity timer having been started upon detecting a most recent user interaction; a user presence timer expiring, the user presence timer having been started when a user could no longer be detected proximal to the electronic device; and a specified time of day or night occurring. 3. The electronic device of claim 2, wherein the hardware controller is further configured to:
receive, from a source external to the electronic device, some or all of a low power state inactivity timer value, a user presence timer value, and the specified time. 4. The electronic device of claim 1, wherein the hardware controller is further configured to:
based at least in part on one or more previous user interactions, generate a prediction of whether a user interaction is likely to occur within a specified time; detect the active event when the prediction is that the user interaction is likely to occur within the specified time; and detect the idle event when the prediction is that the user interaction is not likely to occur within the specified time. 5. The electronic device of claim 1, wherein the active event comprises one or more of:
a specified time of day or night occurring; and a detection of a user proximal to the electronic device. 6. The electronic device of claim 5, wherein the hardware controller is further configured to:
receive, from a source external to the electronic device, the specified time. 7. The electronic device of claim 1, wherein the hardware controller is further configured to cause the one or more hardware subsystems to:
transition from the first low power state to the second low power state upon detecting a system event, the system event comprising one or more of:
a particular battery discharge level occurring;
a specified trend in a battery discharge rate occurring;
a threshold temperature occurring for one or more portions of the electronic device; and
a receipt of a request for transitioning from an external source. 8. The electronic device of claim 1, wherein the hardware controller is further configured to cause the one or more hardware subsystems to:
remain in the first low power state and not transition to the second low power state when operating in a given operating mode, the given operating mode comprising a wake on voice state or an audio playback state. 9. The electronic device of claim 1, wherein the hardware controller is further configured to cause the one or more hardware subsystems to:
transition from the active state to the first low power state upon an expiration of an inactivity timer that was started upon detecting a last user interaction in the active state. 10. The electronic device of claim 1, wherein the hardware controller is further configured to cause the one or more hardware subsystems to:
transition from the first low power state or the second low power state to the active state upon detecting a user interaction. 11. A method for controlling a power state of an electronic device that comprises a hardware controller and one or more hardware subsystems, the one or more hardware subsystems supporting an active state, a first low power state, and a second low power state, the first low power state and the second low power state being separate low power states, and the first low power state being configured for a more rapid resumption of the active state than the second low power state, the method comprising:
causing, by the hardware controller, the one or more hardware subsystems to:
transition from the first low power state to the second low power state upon detecting an idle event that indicates that a user interaction is not likely to occur; and
transition from the second low power state to the first low power state upon detecting an active event that indicates that a user interaction is likely to occur. 12. The method of claim 11, wherein the idle event comprises one or more of:
a low power state inactivity timer expiring, the low power state inactivity timer having been started upon detecting a most recent user interaction; a user presence timer expiring, the user presence timer having been started when a user could no longer be detected proximal to the electronic device; and a specified time of day or night occurring. 13. The method of claim 12, further comprising:
receiving, by the hardware controller, from a source external to the electronic device, some or all of a low power state inactivity timer value, a user presence timer value, and the specified time. 14. The method of claim 11, further comprising:
based at least in part on one or more previous user interactions, generating, by the hardware controller, a prediction of whether a user interaction is likely to occur within a specified time; detecting, by the hardware controller, the active event when the prediction is that the user interaction is likely to occur within the specified time; and detecting, by the hardware controller, the idle event when the prediction is that the user interaction is not likely to occur within the specified time. 15. The method of claim 11, wherein the active event comprises one or more of:
a specified time of day or night occurring; and a detection of a user proximal to the electronic device. 16. The method of claim 15, further comprising:
receiving, by the hardware controller, from a source external to the electronic device, the specified time. 17. The method of claim 11, further comprising:
causing, by the hardware controller, the one or more hardware subsystems to:
transition from the first low power state to the second low power state upon detecting a system event, the system event comprising one or more of:
a particular battery discharge level occurring;
a specified trend in a battery discharge rate occurring;
a threshold temperature occurring for one or more portions of the electronic device; and
a receipt of a request for transitioning from an external source. 18. The method of claim 11, further comprising:
causing, by the hardware controller, the one or more hardware subsystems to:
remain in the first low power state and not transition to the second low power state when operating in a given operating mode, the given operating mode comprising a wake on voice state or an audio playback state. 19. The method of claim 11, further comprising:
causing, by the hardware controller, the one or more hardware subsystems to:
transition from the active state to the first low power state upon an expiration of an inactivity timer that was started upon detecting a last user interaction in the active state. 20. The method of claim 11, further comprising:
causing, by the hardware controller, the one or more hardware subsystems to:
transition from the first low power state or the second low power state to the active state upon detecting a user interaction. | The described embodiments include an electronic device that has a hardware controller and one or more hardware subsystems. The one or more hardware subsystems support an active state, a first low power state, and a second low power state. The first low power state and second low power states are separate low power states, with the first low power state being associated with a more rapid resumption of the active state than the second low power state. The hardware controller is configured to cause the one or more hardware subsystems to transition from the first low power state to the second low power state upon detecting an idle event that indicates that a user interaction is not likely to occur and to transition from the second low power state to the first low power state upon detecting an active event that indicates that a user interaction is likely to occur.1. An electronic device, comprising:
a hardware controller; and one or more hardware subsystems, wherein the one or more hardware subsystems support an active state, a first low power state, and a second low power state, wherein the first low power state and the second low power state are separate low power states, wherein the first low power state is configured for a more rapid resumption of the active state than the second low power state; wherein the hardware controller is configured to cause the one or more hardware subsystems to:
transition from the first low power state to the second low power state upon detecting an idle event that indicates that a user interaction is not likely to occur; and
transition from the second low power state to the first low power state upon detecting an active event that indicates that a user interaction is likely to occur. 2. The electronic device of claim 1, wherein the idle event comprises one or more of:
a low power state inactivity timer expiring, the low power state inactivity timer having been started upon detecting a most recent user interaction; a user presence timer expiring, the user presence timer having been started when a user could no longer be detected proximal to the electronic device; and a specified time of day or night occurring. 3. The electronic device of claim 2, wherein the hardware controller is further configured to:
receive, from a source external to the electronic device, some or all of a low power state inactivity timer value, a user presence timer value, and the specified time. 4. The electronic device of claim 1, wherein the hardware controller is further configured to:
based at least in part on one or more previous user interactions, generate a prediction of whether a user interaction is likely to occur within a specified time; detect the active event when the prediction is that the user interaction is likely to occur within the specified time; and detect the idle event when the prediction is that the user interaction is not likely to occur within the specified time. 5. The electronic device of claim 1, wherein the active event comprises one or more of:
a specified time of day or night occurring; and a detection of a user proximal to the electronic device. 6. The electronic device of claim 5, wherein the hardware controller is further configured to:
receive, from a source external to the electronic device, the specified time. 7. The electronic device of claim 1, wherein the hardware controller is further configured to cause the one or more hardware subsystems to:
transition from the first low power state to the second low power state upon detecting a system event, the system event comprising one or more of:
a particular battery discharge level occurring;
a specified trend in a battery discharge rate occurring;
a threshold temperature occurring for one or more portions of the electronic device; and
a receipt of a request for transitioning from an external source. 8. The electronic device of claim 1, wherein the hardware controller is further configured to cause the one or more hardware subsystems to:
remain in the first low power state and not transition to the second low power state when operating in a given operating mode, the given operating mode comprising a wake on voice state or an audio playback state. 9. The electronic device of claim 1, wherein the hardware controller is further configured to cause the one or more hardware subsystems to:
transition from the active state to the first low power state upon an expiration of an inactivity timer that was started upon detecting a last user interaction in the active state. 10. The electronic device of claim 1, wherein the hardware controller is further configured to cause the one or more hardware subsystems to:
transition from the first low power state or the second low power state to the active state upon detecting a user interaction. 11. A method for controlling a power state of an electronic device that comprises a hardware controller and one or more hardware subsystems, the one or more hardware subsystems supporting an active state, a first low power state, and a second low power state, the first low power state and the second low power state being separate low power states, and the first low power state being configured for a more rapid resumption of the active state than the second low power state, the method comprising:
causing, by the hardware controller, the one or more hardware subsystems to:
transition from the first low power state to the second low power state upon detecting an idle event that indicates that a user interaction is not likely to occur; and
transition from the second low power state to the first low power state upon detecting an active event that indicates that a user interaction is likely to occur. 12. The method of claim 11, wherein the idle event comprises one or more of:
a low power state inactivity timer expiring, the low power state inactivity timer having been started upon detecting a most recent user interaction; a user presence timer expiring, the user presence timer having been started when a user could no longer be detected proximal to the electronic device; and a specified time of day or night occurring. 13. The method of claim 12, further comprising:
receiving, by the hardware controller, from a source external to the electronic device, some or all of a low power state inactivity timer value, a user presence timer value, and the specified time. 14. The method of claim 11, further comprising:
based at least in part on one or more previous user interactions, generating, by the hardware controller, a prediction of whether a user interaction is likely to occur within a specified time; detecting, by the hardware controller, the active event when the prediction is that the user interaction is likely to occur within the specified time; and detecting, by the hardware controller, the idle event when the prediction is that the user interaction is not likely to occur within the specified time. 15. The method of claim 11, wherein the active event comprises one or more of:
a specified time of day or night occurring; and a detection of a user proximal to the electronic device. 16. The method of claim 15, further comprising:
receiving, by the hardware controller, from a source external to the electronic device, the specified time. 17. The method of claim 11, further comprising:
causing, by the hardware controller, the one or more hardware subsystems to:
transition from the first low power state to the second low power state upon detecting a system event, the system event comprising one or more of:
a particular battery discharge level occurring;
a specified trend in a battery discharge rate occurring;
a threshold temperature occurring for one or more portions of the electronic device; and
a receipt of a request for transitioning from an external source. 18. The method of claim 11, further comprising:
causing, by the hardware controller, the one or more hardware subsystems to:
remain in the first low power state and not transition to the second low power state when operating in a given operating mode, the given operating mode comprising a wake on voice state or an audio playback state. 19. The method of claim 11, further comprising:
causing, by the hardware controller, the one or more hardware subsystems to:
transition from the active state to the first low power state upon an expiration of an inactivity timer that was started upon detecting a last user interaction in the active state. 20. The method of claim 11, further comprising:
causing, by the hardware controller, the one or more hardware subsystems to:
transition from the first low power state or the second low power state to the active state upon detecting a user interaction. | 2,100 |
6,453 | 6,453 | 15,515,218 | 2,173 | A hierarchical representation of entities relate to development of at least one product. A portion of a document is selected. For the selected portion of the document, an indication associated with at least one of the entities in the hierarchical representation is received. Based on the received indication, a link is created between the selected portion of the document and the at least one of the entities in the hierarchical representation. | 1. A method comprising:
presenting, by a system including a processor, for display a hierarchical representation of entities relating to development of at least one product; receiving, by the system, selection of a portion of a document; receiving, by the system for the selected portion of the document, an indication associated with at least one of the entities in the hierarchical representation; and creating, by the system based on the received indication, a link between the selected portion of the document and the at least one of the entities in the hierarchical representation. 2. The method of claim 1, further comprising:
presenting, for display, the document, wherein the received selection is of the portion of the displayed document. 3. The method of claim 2, wherein the document is presented for display in a graphical user interface window, and wherein presenting the hierarchical representation of the entities comprises presenting, for display, the hierarchical representation of the entities in the graphical user interface window. 4. The method of claim 3, wherein the graphical user interface window includes user-selectable elements associated with the respective entities of the hierarchical representation, and wherein the received indication is responsive to selection of at least one of the user-selectable elements. 5. The method of claim 1, further comprising:
detecting a change in the at least one entity linked to the selected portion of the document: and in response to detecting the change, generating an output that identifies the document or a portion of the document affected by the change. 6. The method of claim 5, further comprising causing modification of the document in response to detecting the change. 7. The method of claim 1, further comprising:
detecting a change in the selected portion of the document; and in response to detecting the change, generating an output that identifies the at least one entity affected by the detected change. 8. The method of claim 1, wherein the development of the at least one product uses a development tool, and the document is created using an authoring tool, the method further comprising:
providing a mapping layer between the development tool and the authoring tool, wherein the mapping layer performs the presenting, the receiving, and the creating. 9. The method of claim 8, wherein the mapping layer includes a plug-in code to plug into one or both of the development tool and the authoring tool. 10. The method of claim 1, further comprising delivering, by an enterprise to a customer, the at least one product along with the document. 11. An article comprising at least one non-transitory computer-readable storage medium storing instructions that upon execution cause a system to:
present, in a graphical view, a tree representing a hierarchical representation of entities associated with development of at least one product; receive selection of a portion of a document displayed in the graphical view; receive, for the selected portion of the document, a selection of at least one item in the tree, the at least one item representing at least one of the entities in the hierarchical representation; and in response to the received selection of the at least one item in the tree, create a link between the at least one entity in the hierarchical representation and the selected portion of the document. 12. The article of claim 11, wherein the instructions upon execution cause the system to further:
detect a change in the at least one entity; and in response to detecting the change, present an output highlighting the selected portion of the document. 13. The article of claim 12, wherein the output further comprises information relating to the change in the at least one entity. 14. A system comprising;
at least one processor to:
cause display of a hierarchical representation of entities relating to development of at least one product;
receive user selection of a portion of a displayed document;
receive, for the selected portion of the document, a selection of at least one of the entities in the hierarchical representation;
create, based on the received selection of the at least one entity, a link between the selected portion of the document and the at least one entity;
detect a change in the at least one entity; and
in response to the detected change, update the document. 15. The system of claim 14, further comprising a development tool to develop the at least one product, an authoring tool to create the document, and a mapping layer between the development tool and the authoring tool, the mapping layer to perform the causing of the display of the hierarchical representation, the receiving of the selection of the portion of the document, the receiving of the selection of the at least one entity, the creating of the link, the detecting of the change, and the updating of the selected portion of the document. | A hierarchical representation of entities relate to development of at least one product. A portion of a document is selected. For the selected portion of the document, an indication associated with at least one of the entities in the hierarchical representation is received. Based on the received indication, a link is created between the selected portion of the document and the at least one of the entities in the hierarchical representation.1. A method comprising:
presenting, by a system including a processor, for display a hierarchical representation of entities relating to development of at least one product; receiving, by the system, selection of a portion of a document; receiving, by the system for the selected portion of the document, an indication associated with at least one of the entities in the hierarchical representation; and creating, by the system based on the received indication, a link between the selected portion of the document and the at least one of the entities in the hierarchical representation. 2. The method of claim 1, further comprising:
presenting, for display, the document, wherein the received selection is of the portion of the displayed document. 3. The method of claim 2, wherein the document is presented for display in a graphical user interface window, and wherein presenting the hierarchical representation of the entities comprises presenting, for display, the hierarchical representation of the entities in the graphical user interface window. 4. The method of claim 3, wherein the graphical user interface window includes user-selectable elements associated with the respective entities of the hierarchical representation, and wherein the received indication is responsive to selection of at least one of the user-selectable elements. 5. The method of claim 1, further comprising:
detecting a change in the at least one entity linked to the selected portion of the document: and in response to detecting the change, generating an output that identifies the document or a portion of the document affected by the change. 6. The method of claim 5, further comprising causing modification of the document in response to detecting the change. 7. The method of claim 1, further comprising:
detecting a change in the selected portion of the document; and in response to detecting the change, generating an output that identifies the at least one entity affected by the detected change. 8. The method of claim 1, wherein the development of the at least one product uses a development tool, and the document is created using an authoring tool, the method further comprising:
providing a mapping layer between the development tool and the authoring tool, wherein the mapping layer performs the presenting, the receiving, and the creating. 9. The method of claim 8, wherein the mapping layer includes a plug-in code to plug into one or both of the development tool and the authoring tool. 10. The method of claim 1, further comprising delivering, by an enterprise to a customer, the at least one product along with the document. 11. An article comprising at least one non-transitory computer-readable storage medium storing instructions that upon execution cause a system to:
present, in a graphical view, a tree representing a hierarchical representation of entities associated with development of at least one product; receive selection of a portion of a document displayed in the graphical view; receive, for the selected portion of the document, a selection of at least one item in the tree, the at least one item representing at least one of the entities in the hierarchical representation; and in response to the received selection of the at least one item in the tree, create a link between the at least one entity in the hierarchical representation and the selected portion of the document. 12. The article of claim 11, wherein the instructions upon execution cause the system to further:
detect a change in the at least one entity; and in response to detecting the change, present an output highlighting the selected portion of the document. 13. The article of claim 12, wherein the output further comprises information relating to the change in the at least one entity. 14. A system comprising;
at least one processor to:
cause display of a hierarchical representation of entities relating to development of at least one product;
receive user selection of a portion of a displayed document;
receive, for the selected portion of the document, a selection of at least one of the entities in the hierarchical representation;
create, based on the received selection of the at least one entity, a link between the selected portion of the document and the at least one entity;
detect a change in the at least one entity; and
in response to the detected change, update the document. 15. The system of claim 14, further comprising a development tool to develop the at least one product, an authoring tool to create the document, and a mapping layer between the development tool and the authoring tool, the mapping layer to perform the causing of the display of the hierarchical representation, the receiving of the selection of the portion of the document, the receiving of the selection of the at least one entity, the creating of the link, the detecting of the change, and the updating of the selected portion of the document. | 2,100 |
6,454 | 6,454 | 13,766,749 | 2,147 | Computer-implemented techniques are disclosed for verifying circuit designs using subgraph caching. A device under test (DUT) is modeled as a graph. The graph is partitioned into one or more subgraphs and problems are generated for each subgraph. Graph and subgraph problem generation is repeated numerous times throughout the verification process. Problems and sub-problems are generated and solved. When a subgraph problem is solved, the problem's variables, values, and information can be stored in a cache. The storage can be based on entropy of variables used in the graph and subgraph problems. The subgraph problem storage cache can be searched for previously stored problems which match another problem in need of a solution. By retrieving subproblem variables, values, and information from the cache, the computational overhead of circuit design verification is reduced as problems are reused. Caching can be accomplished using an information theoretic approach. | 1. A computer-implemented method for design analysis comprising:
determining a graph for a semiconductor circuit design; partitioning the graph into subgraphs; solving a design problem for one or more subgraphs; and caching information on the one or more subgraphs into subgraph storage. 2. The method of claim 1 further comprising searching through the subgraph storage to identify a previously cached subgraph which matches a second design problem. 3. The method of claim 2 further comprising retrieving a solution, from subgraph storage, for the previously cached subgraph which matches the second design problem. 4. The method of claim 1 wherein the determining the graph is based on logic components and connections between the logic components within the semiconductor circuit design. 5. The method of claim 1 wherein the design problem includes one or more of timing, power, or area. 6. The method of claim 1 wherein the partitioning groups one or more logic components into a subgraph. 7. The method of claim 6 wherein the partitioning is based on hierarchy. 8. The method of claim 6 wherein the partitioning is accomplished after flattening of the semiconductor circuit design. 9. The method of claim 6 wherein the partitioning is based on a cut through the graph. 10. The method of claim 1 wherein the caching stores information about a subgraph. 11. The method of claim 10 wherein the information about the subgraph includes variables and values for the variables. 12. The method of claim 11 wherein the variables and the values are applied through a hierarchy within the semiconductor circuit design and wherein the information which is cached is selected based on a frequency with which the variables are used. 13. The method of claim 10 wherein the information about the subgraph includes information on one or more of timing, power, or area. 14. The method of claim 13 wherein the information on timing includes one or more of setup time, hold time, access time, or cycle time. 15. The method of claim 14 wherein the design problem includes analysis of arrival time and slack time. 16. The method of claim 13 wherein the information on power includes one or more of standby power, leakage power, dynamic power, power-down power, or static power. 17. The method of claim 13 wherein the information on area includes one or more of metallization area, diffusion area, or polysilicon area. 18. The method of claim 10 wherein the caching is based on information content about the subgraph. 19. The method of claim 18 wherein the information content is based on evaluating changes in the information content. 20. The method of claim 18 wherein the caching is based on entropy evaluation of the information content. 21. The method of claim 20 wherein the entropy evaluation includes Shannon entropy. 22. The method of claim 1 further comprising obtaining the design for which a graph is to be determined. 23. A computer-implemented method for design analysis comprising:
determining a graph for a semiconductor circuit design wherein the graph describes interaction between logic components within the semiconductor circuit design; partitioning the graph into subgraphs where the subgraphs describe portions of the semiconductor circuit design; solving a design problem for one or more subgraphs within the semiconductor circuit design; caching information on the one or more subgraphs into subgraph storage; searching through the subgraph storage to identify a previously cached subgraph which matches a second design problem; and retrieving a solution, from subgraph storage, for the previously cached subgraph which matches the second design problem wherein the solution is used to solve an overall design problem for the semiconductor circuit design. 24. A computer system for design analysis comprising:
a memory which stores instructions; one or more processors coupled to the memory wherein the one or more processors are configured to:
determining a graph for a semiconductor circuit design;
partitioning the graph into subgraphs;
solving a design problem for one or more subgraphs; and
caching information on the one or more subgraphs into subgraph storage. 25. A computer program product embodied in a non-transitory computer readable medium for design analysis comprising:
code for determining a graph for a semiconductor circuit design; code for partitioning the graph into subgraphs; code for solving a design problem for one or more subgraphs; and code for caching information on the one or more subgraphs into subgraph storage. | Computer-implemented techniques are disclosed for verifying circuit designs using subgraph caching. A device under test (DUT) is modeled as a graph. The graph is partitioned into one or more subgraphs and problems are generated for each subgraph. Graph and subgraph problem generation is repeated numerous times throughout the verification process. Problems and sub-problems are generated and solved. When a subgraph problem is solved, the problem's variables, values, and information can be stored in a cache. The storage can be based on entropy of variables used in the graph and subgraph problems. The subgraph problem storage cache can be searched for previously stored problems which match another problem in need of a solution. By retrieving subproblem variables, values, and information from the cache, the computational overhead of circuit design verification is reduced as problems are reused. Caching can be accomplished using an information theoretic approach.1. A computer-implemented method for design analysis comprising:
determining a graph for a semiconductor circuit design; partitioning the graph into subgraphs; solving a design problem for one or more subgraphs; and caching information on the one or more subgraphs into subgraph storage. 2. The method of claim 1 further comprising searching through the subgraph storage to identify a previously cached subgraph which matches a second design problem. 3. The method of claim 2 further comprising retrieving a solution, from subgraph storage, for the previously cached subgraph which matches the second design problem. 4. The method of claim 1 wherein the determining the graph is based on logic components and connections between the logic components within the semiconductor circuit design. 5. The method of claim 1 wherein the design problem includes one or more of timing, power, or area. 6. The method of claim 1 wherein the partitioning groups one or more logic components into a subgraph. 7. The method of claim 6 wherein the partitioning is based on hierarchy. 8. The method of claim 6 wherein the partitioning is accomplished after flattening of the semiconductor circuit design. 9. The method of claim 6 wherein the partitioning is based on a cut through the graph. 10. The method of claim 1 wherein the caching stores information about a subgraph. 11. The method of claim 10 wherein the information about the subgraph includes variables and values for the variables. 12. The method of claim 11 wherein the variables and the values are applied through a hierarchy within the semiconductor circuit design and wherein the information which is cached is selected based on a frequency with which the variables are used. 13. The method of claim 10 wherein the information about the subgraph includes information on one or more of timing, power, or area. 14. The method of claim 13 wherein the information on timing includes one or more of setup time, hold time, access time, or cycle time. 15. The method of claim 14 wherein the design problem includes analysis of arrival time and slack time. 16. The method of claim 13 wherein the information on power includes one or more of standby power, leakage power, dynamic power, power-down power, or static power. 17. The method of claim 13 wherein the information on area includes one or more of metallization area, diffusion area, or polysilicon area. 18. The method of claim 10 wherein the caching is based on information content about the subgraph. 19. The method of claim 18 wherein the information content is based on evaluating changes in the information content. 20. The method of claim 18 wherein the caching is based on entropy evaluation of the information content. 21. The method of claim 20 wherein the entropy evaluation includes Shannon entropy. 22. The method of claim 1 further comprising obtaining the design for which a graph is to be determined. 23. A computer-implemented method for design analysis comprising:
determining a graph for a semiconductor circuit design wherein the graph describes interaction between logic components within the semiconductor circuit design; partitioning the graph into subgraphs where the subgraphs describe portions of the semiconductor circuit design; solving a design problem for one or more subgraphs within the semiconductor circuit design; caching information on the one or more subgraphs into subgraph storage; searching through the subgraph storage to identify a previously cached subgraph which matches a second design problem; and retrieving a solution, from subgraph storage, for the previously cached subgraph which matches the second design problem wherein the solution is used to solve an overall design problem for the semiconductor circuit design. 24. A computer system for design analysis comprising:
a memory which stores instructions; one or more processors coupled to the memory wherein the one or more processors are configured to:
determining a graph for a semiconductor circuit design;
partitioning the graph into subgraphs;
solving a design problem for one or more subgraphs; and
caching information on the one or more subgraphs into subgraph storage. 25. A computer program product embodied in a non-transitory computer readable medium for design analysis comprising:
code for determining a graph for a semiconductor circuit design; code for partitioning the graph into subgraphs; code for solving a design problem for one or more subgraphs; and code for caching information on the one or more subgraphs into subgraph storage. | 2,100 |
6,455 | 6,455 | 14,507,562 | 2,154 | Examples of dynamic database query efficiency improvement are provided herein. Query portions of a received database query can be identified as candidates for replacement. The candidates for replacement can be query portions that reduce the efficiency of the query. Alternative queries can be determined that include substitute query portion(s) in place of candidate(s) for replacement. An expected performance of the alternative queries can be determined. Based at least in part on the expected performance of the alternative queries, one or more alternative queries can be selected as replacement database queries for the received database query. | 1. One or more computer-readable storage media storing instructions that, when executed by a computing device, perform a method of improving efficiency of a database query, the method comprising:
receiving a database query; identifying one or more query portions of the database query as candidates for replacement; determining a plurality of alternative queries, the respective alternative queries including at least one substitute query portion in place of at least one of the candidates for replacement; determining an expected performance of at least some of the plurality of alternative queries; and based at least in part on the expected performance of the at least some of the plurality of alternative queries, selecting one or more alternative queries of the plurality of alternative queries as replacement database queries for the received database query. 2. The computer-readable storage media of claim 1, wherein determining the expected performance comprises simulating the respective alternative queries. 3. The computer-readable storage media of claim 1, wherein determining the expected performance comprises performing some of the at least some of the plurality of alternative queries on a sample dataset. 4. The computer-readable storage media of claim 1, wherein the expected performance is a completion time for a respective alternative query, and wherein the one or more replacement database queries have faster completion times than the received database query. 5. The computer-readable storage media of claim 1, wherein the identifying one or more query portions of the database query as candidates for replacement comprises:
parsing the received database query; and comparing query portions identified through the parsing to a set of query rules, the query rules having associated substitute query portions. 6. The computer-readable storage media of claim 5, wherein at least some of the query rules relate to parallel processing or to a type of query engine used. 7. The computer-readable storage media of claim 1, wherein the received database query is a structured query language (SQL) query. 8. The computer-readable storage media of claim 7, wherein the determined plurality of alternative queries comprises at least one of: an alternative SQL query, an alternative query comprising a stored procedure, including a stored procedure using CE operators, a data modeling view query, an information model, a SQL view, or code that uses an underlying database engine application program interface (API). 9. The computer-readable storage media of claim 1, wherein the method further comprises performing one of the one or more replacement database queries. 10. A computer-implemented method of improving efficiency of a database query, the method comprising:
receiving a structured query language (SQL) query; parsing the SQL query into a plurality of query portions; forming a plurality of alternative queries, the respective alternative queries including at least one substitute query portion in place of at least one of the plurality of query portions; simulating performance of the plurality of alternative queries; based at least in part on results of the simulating, selecting a subset of the plurality of alternative queries; performing the respective alternative queries of the subset using a sample dataset; and based at least in part on the performing, selecting an alternative query of the subset as a replacement database query for the received SQL query. 11. The computer-implemented method of claim 10, wherein the alternative queries of the subset have a simulated completion time faster than a simulated completion time of the received SQL query. 12. The computer-implemented method of claim 10, wherein forming the plurality of alternative queries comprises identifying one or more query portions of the plurality of query portions as candidates for replacement based on a set of query rules. 13. The computer-implemented method of claim 10, wherein at least one of the plurality of alternative queries is configured to: perform a greater percentage of processing as parallel processing as compared to the received SQL query or decrease the number of calculation engine operators as compared to the received SQL query. 14. The computer-implemented method of claim 10, wherein the received SQL query is specified to be applied against a dataset in a database, and wherein the sample dataset is a subset of the dataset. 15. The computer-implemented method of claim 10, wherein the plurality of alternative queries comprises at least one of: an alternative SQL query, an alternative query comprising a stored procedure, including a stored procedure using CE operators, a data modeling view query, an information model, a SQL view, or code that uses an underlying database engine application program interface (API). 16. One or more server computers implementing a database query improvement system, the system comprising:
a query parser that receives a database query and parses the database query into a plurality of query portions; a query rules data store containing a plurality of query rules, the respective query rules specifying at least one or more query portions and one or more corresponding substitute query portions; an alternative query generator that determines a plurality of alternative queries based on the plurality of query portions and at least some of the plurality of query rules; and a query evaluation engine that:
determines an expected performance of at least some of the plurality of alternative queries; and
based at least in part on the expected performance, selects one or more of the at least some of the plurality of alternative queries as replacement database queries for the parsed database query. 17. The one or more server computers of claim 16, wherein the query evaluation engine comprises:
a performance simulator that simulates performance of the plurality of alternative queries and identifies a group of the plurality of alternative queries having lower simulated query completion times than other alternative queries of the plurality of alternative queries; and a performance estimator that performs the alternative queries of the group on a sample dataset. 18. The one or more server computers of claim 16, wherein the received database query is a structured query language (SQL) query, and wherein the one or more query portions comprise at least one of an operator, a statement, a function, an expression, or a clause. 19. The one or more server computers of claim 18, wherein the plurality of alternative queries comprises at least one of: an alternative SQL query, an alternative query comprising a stored procedure, including a stored procedure using CE operators, a data modeling view query, an information model, a SQL view, or code that uses an underlying database engine application program interface (API). 20. The one or more server computers of claim 16, wherein the replacement database queries have a faster performance time than the received database query. | Examples of dynamic database query efficiency improvement are provided herein. Query portions of a received database query can be identified as candidates for replacement. The candidates for replacement can be query portions that reduce the efficiency of the query. Alternative queries can be determined that include substitute query portion(s) in place of candidate(s) for replacement. An expected performance of the alternative queries can be determined. Based at least in part on the expected performance of the alternative queries, one or more alternative queries can be selected as replacement database queries for the received database query.1. One or more computer-readable storage media storing instructions that, when executed by a computing device, perform a method of improving efficiency of a database query, the method comprising:
receiving a database query; identifying one or more query portions of the database query as candidates for replacement; determining a plurality of alternative queries, the respective alternative queries including at least one substitute query portion in place of at least one of the candidates for replacement; determining an expected performance of at least some of the plurality of alternative queries; and based at least in part on the expected performance of the at least some of the plurality of alternative queries, selecting one or more alternative queries of the plurality of alternative queries as replacement database queries for the received database query. 2. The computer-readable storage media of claim 1, wherein determining the expected performance comprises simulating the respective alternative queries. 3. The computer-readable storage media of claim 1, wherein determining the expected performance comprises performing some of the at least some of the plurality of alternative queries on a sample dataset. 4. The computer-readable storage media of claim 1, wherein the expected performance is a completion time for a respective alternative query, and wherein the one or more replacement database queries have faster completion times than the received database query. 5. The computer-readable storage media of claim 1, wherein the identifying one or more query portions of the database query as candidates for replacement comprises:
parsing the received database query; and comparing query portions identified through the parsing to a set of query rules, the query rules having associated substitute query portions. 6. The computer-readable storage media of claim 5, wherein at least some of the query rules relate to parallel processing or to a type of query engine used. 7. The computer-readable storage media of claim 1, wherein the received database query is a structured query language (SQL) query. 8. The computer-readable storage media of claim 7, wherein the determined plurality of alternative queries comprises at least one of: an alternative SQL query, an alternative query comprising a stored procedure, including a stored procedure using CE operators, a data modeling view query, an information model, a SQL view, or code that uses an underlying database engine application program interface (API). 9. The computer-readable storage media of claim 1, wherein the method further comprises performing one of the one or more replacement database queries. 10. A computer-implemented method of improving efficiency of a database query, the method comprising:
receiving a structured query language (SQL) query; parsing the SQL query into a plurality of query portions; forming a plurality of alternative queries, the respective alternative queries including at least one substitute query portion in place of at least one of the plurality of query portions; simulating performance of the plurality of alternative queries; based at least in part on results of the simulating, selecting a subset of the plurality of alternative queries; performing the respective alternative queries of the subset using a sample dataset; and based at least in part on the performing, selecting an alternative query of the subset as a replacement database query for the received SQL query. 11. The computer-implemented method of claim 10, wherein the alternative queries of the subset have a simulated completion time faster than a simulated completion time of the received SQL query. 12. The computer-implemented method of claim 10, wherein forming the plurality of alternative queries comprises identifying one or more query portions of the plurality of query portions as candidates for replacement based on a set of query rules. 13. The computer-implemented method of claim 10, wherein at least one of the plurality of alternative queries is configured to: perform a greater percentage of processing as parallel processing as compared to the received SQL query or decrease the number of calculation engine operators as compared to the received SQL query. 14. The computer-implemented method of claim 10, wherein the received SQL query is specified to be applied against a dataset in a database, and wherein the sample dataset is a subset of the dataset. 15. The computer-implemented method of claim 10, wherein the plurality of alternative queries comprises at least one of: an alternative SQL query, an alternative query comprising a stored procedure, including a stored procedure using CE operators, a data modeling view query, an information model, a SQL view, or code that uses an underlying database engine application program interface (API). 16. One or more server computers implementing a database query improvement system, the system comprising:
a query parser that receives a database query and parses the database query into a plurality of query portions; a query rules data store containing a plurality of query rules, the respective query rules specifying at least one or more query portions and one or more corresponding substitute query portions; an alternative query generator that determines a plurality of alternative queries based on the plurality of query portions and at least some of the plurality of query rules; and a query evaluation engine that:
determines an expected performance of at least some of the plurality of alternative queries; and
based at least in part on the expected performance, selects one or more of the at least some of the plurality of alternative queries as replacement database queries for the parsed database query. 17. The one or more server computers of claim 16, wherein the query evaluation engine comprises:
a performance simulator that simulates performance of the plurality of alternative queries and identifies a group of the plurality of alternative queries having lower simulated query completion times than other alternative queries of the plurality of alternative queries; and a performance estimator that performs the alternative queries of the group on a sample dataset. 18. The one or more server computers of claim 16, wherein the received database query is a structured query language (SQL) query, and wherein the one or more query portions comprise at least one of an operator, a statement, a function, an expression, or a clause. 19. The one or more server computers of claim 18, wherein the plurality of alternative queries comprises at least one of: an alternative SQL query, an alternative query comprising a stored procedure, including a stored procedure using CE operators, a data modeling view query, an information model, a SQL view, or code that uses an underlying database engine application program interface (API). 20. The one or more server computers of claim 16, wherein the replacement database queries have a faster performance time than the received database query. | 2,100 |
6,456 | 6,456 | 15,791,187 | 2,144 | Systems and methods for presenting CRM data. Users may configure what to display on a custom report and how to arrange and display the content (e.g., size, color, theme), so that they can visualize the CRM data the way they want. A data visualization interface may be used to generate the custom report, and may use HTML to specify the CRM data to be displayed and their location on the custom report according to user configuration. The data visualization interface may use JavaScript for specifying the objects and fields users want to query to obtain the CRM data to fill up the custom report. An API may communicate with the JavaScript in the data visualization interface and then query data and objects in the CRM to get a result set. The API may be a unified API which may work across multiple platforms and query various types of database, e.g., iOS, Windows, and the browser for Salesforce online. | 1. A computer-implemented method for displaying data from a data storage system, the method comprising:
enabling generation of a data visualization interface for rendering a first custom report according to previously received user configuration, wherein the user configuration comprises a first type of data to be displayed at a first location on the first custom report and a second type of data to be displayed at a second location on the first custom report; wherein the first type of data and the second type of data are obtained from the data storage system; wherein the data visualization interface comprises instructions in a markup language for specifying the first type of data, the first location, the second type of data, and the second location, and instructions in a second programing language for obtaining the first type of data and the second type of data from the data storage system, and wherein the data visualization interface does not display the first type of data, or the second type of data; receiving the instructions in the second programing language from the data visualization interface at an application programming interface (“API”); sending an API call to the data storage system to obtain the first type of data and the second type of data; receiving the first type of data and the second type of data at the data visualization interface; rendering the first custom report, wherein the first custom report is based on the first type of data, the first location, the second type of data and the second location in the user configuration; displaying the first custom report on a user interface separate from the data visualization interface; receiving new data and storing the new data in a memory device; and updating the first customer report rendered by the data visualization interface with the new data from the memory device while the new data is being saved to the data storage system. 2. The method of claim 1, wherein the makeup language is HyperText Markup Language (“HTML”). 3. The method of claim 1, wherein the second programming language is JavaScript. 4. The method of claim 3, further comprising: putting the first type of data at the first location and the second type of data at the second location with the JavaScript. 5. The method of claim 1, wherein the API is a unified API which can access a first type of data storage system and a second type of data storage system. 6. The method of claim 5, wherein the first type of data storage system is based on a first customer relationship management (“CRM”) system. 7. The method of claim 6, wherein the second type of data storage system is a database based on a first operating system. 8. The method of claim 6, wherein the second type of data storage system is a database based on a second operating system. 9. The method of claim 5, wherein the first type of data storage system is an online data source. 10. The method of claim 5, wherein the first type of data storage system is an offline local data source 11. The method of claim 5, wherein the second type of data storage system is a local data source. 12. The method of claim 1, wherein the user configuration comprises: displaying the first type of data as a picture, a pie chart, bar chart, donut chart, histogram, line chart, or scatter plot. 13. The method of claim 1, wherein the user configuration comprises: displaying the first type of data in a first color. 14. The method of claim 1, wherein the data visualization interface is a webpage. 15. The method of claim 1, wherein the data visualization interface is an iFrame. 16. The method of claim 1, wherein the data visualization interface is a Webview. 17. The method of claim 3, further comprising: making calls to a data access library in the API by the JavaScript. 18. The method of claim 17, further comprising: querying objects and fields in the data storage system by the data access library, wherein a same call from the data access library can query both an online data source and a local data source. 19. The method of claim 18, wherein the API further comprises a bridge library for receiving a message from the data access library, and querying the data storage system for the data requested by the JavaScript. 20. canceled. | Systems and methods for presenting CRM data. Users may configure what to display on a custom report and how to arrange and display the content (e.g., size, color, theme), so that they can visualize the CRM data the way they want. A data visualization interface may be used to generate the custom report, and may use HTML to specify the CRM data to be displayed and their location on the custom report according to user configuration. The data visualization interface may use JavaScript for specifying the objects and fields users want to query to obtain the CRM data to fill up the custom report. An API may communicate with the JavaScript in the data visualization interface and then query data and objects in the CRM to get a result set. The API may be a unified API which may work across multiple platforms and query various types of database, e.g., iOS, Windows, and the browser for Salesforce online.1. A computer-implemented method for displaying data from a data storage system, the method comprising:
enabling generation of a data visualization interface for rendering a first custom report according to previously received user configuration, wherein the user configuration comprises a first type of data to be displayed at a first location on the first custom report and a second type of data to be displayed at a second location on the first custom report; wherein the first type of data and the second type of data are obtained from the data storage system; wherein the data visualization interface comprises instructions in a markup language for specifying the first type of data, the first location, the second type of data, and the second location, and instructions in a second programing language for obtaining the first type of data and the second type of data from the data storage system, and wherein the data visualization interface does not display the first type of data, or the second type of data; receiving the instructions in the second programing language from the data visualization interface at an application programming interface (“API”); sending an API call to the data storage system to obtain the first type of data and the second type of data; receiving the first type of data and the second type of data at the data visualization interface; rendering the first custom report, wherein the first custom report is based on the first type of data, the first location, the second type of data and the second location in the user configuration; displaying the first custom report on a user interface separate from the data visualization interface; receiving new data and storing the new data in a memory device; and updating the first customer report rendered by the data visualization interface with the new data from the memory device while the new data is being saved to the data storage system. 2. The method of claim 1, wherein the makeup language is HyperText Markup Language (“HTML”). 3. The method of claim 1, wherein the second programming language is JavaScript. 4. The method of claim 3, further comprising: putting the first type of data at the first location and the second type of data at the second location with the JavaScript. 5. The method of claim 1, wherein the API is a unified API which can access a first type of data storage system and a second type of data storage system. 6. The method of claim 5, wherein the first type of data storage system is based on a first customer relationship management (“CRM”) system. 7. The method of claim 6, wherein the second type of data storage system is a database based on a first operating system. 8. The method of claim 6, wherein the second type of data storage system is a database based on a second operating system. 9. The method of claim 5, wherein the first type of data storage system is an online data source. 10. The method of claim 5, wherein the first type of data storage system is an offline local data source 11. The method of claim 5, wherein the second type of data storage system is a local data source. 12. The method of claim 1, wherein the user configuration comprises: displaying the first type of data as a picture, a pie chart, bar chart, donut chart, histogram, line chart, or scatter plot. 13. The method of claim 1, wherein the user configuration comprises: displaying the first type of data in a first color. 14. The method of claim 1, wherein the data visualization interface is a webpage. 15. The method of claim 1, wherein the data visualization interface is an iFrame. 16. The method of claim 1, wherein the data visualization interface is a Webview. 17. The method of claim 3, further comprising: making calls to a data access library in the API by the JavaScript. 18. The method of claim 17, further comprising: querying objects and fields in the data storage system by the data access library, wherein a same call from the data access library can query both an online data source and a local data source. 19. The method of claim 18, wherein the API further comprises a bridge library for receiving a message from the data access library, and querying the data storage system for the data requested by the JavaScript. 20. canceled. | 2,100 |
6,457 | 6,457 | 15,195,680 | 2,195 | A method for migrating a virtual machine (VM) includes establishing a first connection to a first cloud computing system executing a first VM, and establishing a second connection to a second cloud computing system managed by a second cloud provider, which is different form the first cloud provider. The method further includes instantiating a second VM designated as a destination VM in the second cloud computing system, and installing a migration agent on each of the first VM and the second VM. The migration agents execute a migration process of the first VM to the second VM by (1) iteratively copying guest data from the first VM to the second VM until a switchover criteria of the migration operation is met, and (2) copying a remainder of guest data from the first VM to the second VM when the switchover criteria is met. | 1. A method for migrating a virtual machine, the method comprising:
establishing a first connection, as a first tenant, to a first cloud computing system executing a first virtual machine (VM) associated with the first tenant, wherein the first cloud computing system is managed by a first cloud provider; establishing a second connection, as the first tenant, to a second cloud computing system managed by a second cloud provider, wherein the first cloud provider and the second cloud provider are different; instantiating a second VM designated as a destination VM in the second cloud computing system; installing a guest-level migration agent on each of the first VM and the second VM, wherein the guest-level migration agents are configured to execute within a virtual machine at a guest level; and executing a migration process of the first VM in the first cloud computing system to the second VM in the second cloud computing system using the guest-level migration agents by (1) iteratively copying guest data from the first VM to the second VM until a switchover criteria of the migration operation is met, and (2) copying a remainder of guest data from the first VM to the second VM responsive to satisfaction of the switchover criteria. 2. The method of claim 1, wherein the migration process is completed by rebooting the second VM. 3. The method of claim 1, wherein, during the migration process, the guest-level migration agent of the first VM determines an estimated time for transferring the guest data from the first VM to the second VM. 4. The method of claim 3, wherein the switchover criteria comprises a downtime parameter indicating a duration of time, and step (2) is executed when the downtime parameter no longer exceeds the estimated time for transferring the guest data. 5. The method of claim 1, further comprising:
responsive to determining that the second cloud computing system does not expose a public network address to the second VM, generating a virtual private network (VPN) connection to the second VM, wherein the guest-level migration agent of the second VM is installed via the VPN connection. 6. The method of claim 1, wherein, during the migration process, the guest-level migration agent of the first VM excludes one or more directories from the transfer of the guest data. 7. The method of claim 1, wherein, during the migration process, the guest-level migration agent of the first VM, responsive to determining that the switchover criteria has been satisfied, restricts run-level access by the first VM to the guest data. 8. A non-transitory computer readable medium comprising instructions that are executable in a computer system to carry out a method for migrating a virtual machine, the method comprising:
establishing a first connection, as a first tenant, to a first cloud computing system executing a first virtual machine (VM) associated with the first tenant, wherein the first cloud computing system is managed by a first cloud provider; establishing a second connection, as the first tenant, to a second cloud computing system managed by a second cloud provider, wherein the first cloud provider and the second cloud provider are different; instantiating a second VM designated as a destination VM in the second cloud computing system; installing a guest-level migration agent on each of the first VM and the second VM, wherein the guest-level migration agents are configured to execute within a virtual machine at a guest level; and executing a migration process of the first VM in the first cloud computing system to the second VM in the second cloud computing system using the guest-level migration agents by (1) iteratively copying guest data from the first VM to the second VM until a switchover criteria of the migration operation is met, and (2) copying a remainder of guest data from the first VM to the second VM responsive to satisfaction of the switchover criteria. 9. The computer readable medium of claim 8, wherein the migration process is completed by rebooting the second VM. 10. The computer readable medium of claim 8, during the migration process, the guest-level migration agent of the first VM determines an estimated time for transferring the guest data from the first VM to the second VM. 11. The computer readable medium of claim 10, wherein the switchover criteria comprises a downtime parameter indicating a duration of time, and step (2) is executed when the downtime parameter no longer exceeds the estimated time for transferring the guest data. 12. The computer readable medium of claim 8, wherein the method further comprises:
responsive to determining that the second cloud computing system does not expose a public network address to the second VM, generating a virtual private network (VPN) connection to the second VM, wherein the guest-level migration agent of the second VM is installed via the VPN connection. 13. The computer readable medium of claim 8, during the migration process, the guest-level migration agent of the first VM excludes one or more directories from the transfer of the guest data. 14. The computer readable medium of claim 8, wherein, during the migration process, the guest-level migration agent of the first VM, responsive to determining that the switchover criteria has been satisfied, restricts run-level access by the first VM to the guest data. 15. A computer system comprising:
a first cloud computing system including a first host computer executing a first virtual machine (VM), the first VM having installed therein a guest-level migration agent, wherein the first cloud computing system is managed by a first cloud provider; and a second cloud computing system including a second host computer, wherein the second cloud computing system is managed by a second cloud provider that is different from the first cloud provider, wherein the second host computer is configured to instantiate a second VM designated as a destination VM, the second VM having installed therein a guest-level migration agent, and wherein the guest-level migration agents of the first VM and the second VM are configured to execute a migration process of the first VM to the second VM by (1) iteratively copying guest data from the first VM to the second VM until a switchover criteria of the migration operation is met, and (2) copying a remainder of guest data from the first VM to the second VM responsive to satisfaction of the switchover criteria. 16. The method of claim 15, wherein the migration process is completed by rebooting the second VM. 17. The method of claim 15, wherein the guest-level migration agent of the first VM is configured to determine an estimated time for transferring the guest data from the first VM to the second VM. 18. The method of claim 17, wherein the switchover criteria comprises a downtime parameter indicating a duration of time, and the guest-level migration agents are configured to iteratively copy the guest data so long as the downtime parameter exceeds the estimated time for transferring the guest data. 19. The method of claim 15, wherein the guest-level migration agent of the first VM is further configured to exclude one or more directories from the transfer of the guest data. 20. The method of claim 15, wherein the guest-level migration agent of the first VM is further configured to:
responsive to determining that the switchover criteria has been satisfied, restrict run-level access by the first VM to the guest data. | A method for migrating a virtual machine (VM) includes establishing a first connection to a first cloud computing system executing a first VM, and establishing a second connection to a second cloud computing system managed by a second cloud provider, which is different form the first cloud provider. The method further includes instantiating a second VM designated as a destination VM in the second cloud computing system, and installing a migration agent on each of the first VM and the second VM. The migration agents execute a migration process of the first VM to the second VM by (1) iteratively copying guest data from the first VM to the second VM until a switchover criteria of the migration operation is met, and (2) copying a remainder of guest data from the first VM to the second VM when the switchover criteria is met.1. A method for migrating a virtual machine, the method comprising:
establishing a first connection, as a first tenant, to a first cloud computing system executing a first virtual machine (VM) associated with the first tenant, wherein the first cloud computing system is managed by a first cloud provider; establishing a second connection, as the first tenant, to a second cloud computing system managed by a second cloud provider, wherein the first cloud provider and the second cloud provider are different; instantiating a second VM designated as a destination VM in the second cloud computing system; installing a guest-level migration agent on each of the first VM and the second VM, wherein the guest-level migration agents are configured to execute within a virtual machine at a guest level; and executing a migration process of the first VM in the first cloud computing system to the second VM in the second cloud computing system using the guest-level migration agents by (1) iteratively copying guest data from the first VM to the second VM until a switchover criteria of the migration operation is met, and (2) copying a remainder of guest data from the first VM to the second VM responsive to satisfaction of the switchover criteria. 2. The method of claim 1, wherein the migration process is completed by rebooting the second VM. 3. The method of claim 1, wherein, during the migration process, the guest-level migration agent of the first VM determines an estimated time for transferring the guest data from the first VM to the second VM. 4. The method of claim 3, wherein the switchover criteria comprises a downtime parameter indicating a duration of time, and step (2) is executed when the downtime parameter no longer exceeds the estimated time for transferring the guest data. 5. The method of claim 1, further comprising:
responsive to determining that the second cloud computing system does not expose a public network address to the second VM, generating a virtual private network (VPN) connection to the second VM, wherein the guest-level migration agent of the second VM is installed via the VPN connection. 6. The method of claim 1, wherein, during the migration process, the guest-level migration agent of the first VM excludes one or more directories from the transfer of the guest data. 7. The method of claim 1, wherein, during the migration process, the guest-level migration agent of the first VM, responsive to determining that the switchover criteria has been satisfied, restricts run-level access by the first VM to the guest data. 8. A non-transitory computer readable medium comprising instructions that are executable in a computer system to carry out a method for migrating a virtual machine, the method comprising:
establishing a first connection, as a first tenant, to a first cloud computing system executing a first virtual machine (VM) associated with the first tenant, wherein the first cloud computing system is managed by a first cloud provider; establishing a second connection, as the first tenant, to a second cloud computing system managed by a second cloud provider, wherein the first cloud provider and the second cloud provider are different; instantiating a second VM designated as a destination VM in the second cloud computing system; installing a guest-level migration agent on each of the first VM and the second VM, wherein the guest-level migration agents are configured to execute within a virtual machine at a guest level; and executing a migration process of the first VM in the first cloud computing system to the second VM in the second cloud computing system using the guest-level migration agents by (1) iteratively copying guest data from the first VM to the second VM until a switchover criteria of the migration operation is met, and (2) copying a remainder of guest data from the first VM to the second VM responsive to satisfaction of the switchover criteria. 9. The computer readable medium of claim 8, wherein the migration process is completed by rebooting the second VM. 10. The computer readable medium of claim 8, during the migration process, the guest-level migration agent of the first VM determines an estimated time for transferring the guest data from the first VM to the second VM. 11. The computer readable medium of claim 10, wherein the switchover criteria comprises a downtime parameter indicating a duration of time, and step (2) is executed when the downtime parameter no longer exceeds the estimated time for transferring the guest data. 12. The computer readable medium of claim 8, wherein the method further comprises:
responsive to determining that the second cloud computing system does not expose a public network address to the second VM, generating a virtual private network (VPN) connection to the second VM, wherein the guest-level migration agent of the second VM is installed via the VPN connection. 13. The computer readable medium of claim 8, during the migration process, the guest-level migration agent of the first VM excludes one or more directories from the transfer of the guest data. 14. The computer readable medium of claim 8, wherein, during the migration process, the guest-level migration agent of the first VM, responsive to determining that the switchover criteria has been satisfied, restricts run-level access by the first VM to the guest data. 15. A computer system comprising:
a first cloud computing system including a first host computer executing a first virtual machine (VM), the first VM having installed therein a guest-level migration agent, wherein the first cloud computing system is managed by a first cloud provider; and a second cloud computing system including a second host computer, wherein the second cloud computing system is managed by a second cloud provider that is different from the first cloud provider, wherein the second host computer is configured to instantiate a second VM designated as a destination VM, the second VM having installed therein a guest-level migration agent, and wherein the guest-level migration agents of the first VM and the second VM are configured to execute a migration process of the first VM to the second VM by (1) iteratively copying guest data from the first VM to the second VM until a switchover criteria of the migration operation is met, and (2) copying a remainder of guest data from the first VM to the second VM responsive to satisfaction of the switchover criteria. 16. The method of claim 15, wherein the migration process is completed by rebooting the second VM. 17. The method of claim 15, wherein the guest-level migration agent of the first VM is configured to determine an estimated time for transferring the guest data from the first VM to the second VM. 18. The method of claim 17, wherein the switchover criteria comprises a downtime parameter indicating a duration of time, and the guest-level migration agents are configured to iteratively copy the guest data so long as the downtime parameter exceeds the estimated time for transferring the guest data. 19. The method of claim 15, wherein the guest-level migration agent of the first VM is further configured to exclude one or more directories from the transfer of the guest data. 20. The method of claim 15, wherein the guest-level migration agent of the first VM is further configured to:
responsive to determining that the switchover criteria has been satisfied, restrict run-level access by the first VM to the guest data. | 2,100 |
6,458 | 6,458 | 14,687,975 | 2,164 | Disclosed are various embodiments for generating an index. A computing device provides a location of a requested file to a client device in response to receiving a search query of a master index from the client device. The computing device updates the master index with index data associated with the requested file, wherein the index data associated with the requested file is received from the client device. | 1. A non-transitory computer-readable medium embodying a program executable in a computing device, wherein the program is configured to cause the computinu device to at least:
authenticate access to a file by a client device; send a location of the file to the client device in response to authentication of the client device; and update a master index based at least in part on index data of the file, wherein the index data is provided by the client device. 2. The non-transitory computer-readable medium of claim 1, wherein the program is further configured to cause the computing device to at least:
identify an unindexed file; and send the unindexed file to the client device for indexing. 3. The non-transitory computer-readable medium of claim 2, wherein the client device is a first client device and the program is further configured to cause the computing device to at least:
determine that the first cheat device has failed to index the unindexed file before a threshold period of time has lapsed; and send a request to a second client device, wherein the request comprises a request for the second client device to ietrieve the unindexed file and index the unindexed file. 4. The non-transitory computer-readable medium of claim 2, wherein the client device is a first client device and the program is further configured to cause the computing device to at least:
determine that the first client device has failed to index the unindexed file before a threshold period of time has lapsed; push the unindexed file to a second client device in response to the determination that the first client device has failed to index the unindexed file before the threshold period of time has lapsed; and instruct the second client device to index the unindexed file. 5. The non-transitory computer-readable medium of claim 1, wherein the client device is one of a plurality of client devices and the program is further configured to cause the computing device to at least:
determine a number of the plurality of client devices that have retrieved the file; and instruct a subset of the plurality of client devices to index the file in response to a determination that the number of the plurality of client devices exceeds a threshold. 6. The non-transitory computer-readable medium of claim 1, wherein the master index comprises indexed metadata for the file and the index data comprises an index of content data of the file. 7. The non-transitory computer-readable medium of claim 1, wherein the program is further configured to cause the computing device to at least send a copy of at least a portion of the master index to a client device in response to a request from the client device for the copy. 8. A method comprising:
providing, by a computing device, a location of a requested file to a client device in response to receiving a search query of a master index from the client device; and updating, by the computing device, the master index with index data associated with the requested file, wherein the index data associated with the requested file is received from the client device. 9. The method of claim 8, wherein the computing device is a first computing device and the location of the requested file corresponds to a second computing device. 10. The method of claim 8, further comprising:
indexing, by the computing device, metadata of the requested file; and updating, by the computing device, the master index with the indexed metadata of the requested file. 11. The method of claim 8, wherein the index data associated with the requested file comprises an index of content data of the requested file. 12. The method of claim 8, further comprising:
identifying, by the computing device, an unrequested file; instructing, by the computing device, a client device to index the unrequested file; sending, by the computing device, the unrequested file to the client device; and updating, by the computing device, the master index with index data associated with the unrequested file, wherein the index data associated with the unrequested file is received from the client device. 13. The method of claim 12, wherein the index data associated with the unrequested file comprises an index of content data of the requested file. 14. The method of claim 8, wherein the location of the file comprises a link to the file. 15. A method, comprising:
downloading, by a client device, a file, using a link provided by a remote computing device; indexing, by the client device, the file; and uploading, by the client device, index data to the remote computing device, wherein the index data comprises an index of content data of the file. 16. The method of claim 15, further comprising:
downloading, by the client device, a copy of a master index from a remote computing device; and updating, by the client device, a local index based at least in part on the copy of the master index. 17. The method of claim 16, further comprising updating, by the client deice, the local index based at least in part on the index data of the content of the file. 18. The method of claim 16, wherein downloading, by the client device, the file using the link provided by the remote computing device occurs in response to the client device receiving a request from the remote computing device to index the file. 19. The method of claim 15, further comprising:
determining, by the client device, that the client device has entered an idle state; and wherein the indexing of the file by the client device occurs in response to determining that the client device has entered the idle state. 20. The method of claim 15, further comprising:
determining, by the computing device, that the file has not been previously indexed; and wherein indexing, by the client device, the file occurs in response to determining that the file has not been previously indexed. | Disclosed are various embodiments for generating an index. A computing device provides a location of a requested file to a client device in response to receiving a search query of a master index from the client device. The computing device updates the master index with index data associated with the requested file, wherein the index data associated with the requested file is received from the client device.1. A non-transitory computer-readable medium embodying a program executable in a computing device, wherein the program is configured to cause the computinu device to at least:
authenticate access to a file by a client device; send a location of the file to the client device in response to authentication of the client device; and update a master index based at least in part on index data of the file, wherein the index data is provided by the client device. 2. The non-transitory computer-readable medium of claim 1, wherein the program is further configured to cause the computing device to at least:
identify an unindexed file; and send the unindexed file to the client device for indexing. 3. The non-transitory computer-readable medium of claim 2, wherein the client device is a first client device and the program is further configured to cause the computing device to at least:
determine that the first cheat device has failed to index the unindexed file before a threshold period of time has lapsed; and send a request to a second client device, wherein the request comprises a request for the second client device to ietrieve the unindexed file and index the unindexed file. 4. The non-transitory computer-readable medium of claim 2, wherein the client device is a first client device and the program is further configured to cause the computing device to at least:
determine that the first client device has failed to index the unindexed file before a threshold period of time has lapsed; push the unindexed file to a second client device in response to the determination that the first client device has failed to index the unindexed file before the threshold period of time has lapsed; and instruct the second client device to index the unindexed file. 5. The non-transitory computer-readable medium of claim 1, wherein the client device is one of a plurality of client devices and the program is further configured to cause the computing device to at least:
determine a number of the plurality of client devices that have retrieved the file; and instruct a subset of the plurality of client devices to index the file in response to a determination that the number of the plurality of client devices exceeds a threshold. 6. The non-transitory computer-readable medium of claim 1, wherein the master index comprises indexed metadata for the file and the index data comprises an index of content data of the file. 7. The non-transitory computer-readable medium of claim 1, wherein the program is further configured to cause the computing device to at least send a copy of at least a portion of the master index to a client device in response to a request from the client device for the copy. 8. A method comprising:
providing, by a computing device, a location of a requested file to a client device in response to receiving a search query of a master index from the client device; and updating, by the computing device, the master index with index data associated with the requested file, wherein the index data associated with the requested file is received from the client device. 9. The method of claim 8, wherein the computing device is a first computing device and the location of the requested file corresponds to a second computing device. 10. The method of claim 8, further comprising:
indexing, by the computing device, metadata of the requested file; and updating, by the computing device, the master index with the indexed metadata of the requested file. 11. The method of claim 8, wherein the index data associated with the requested file comprises an index of content data of the requested file. 12. The method of claim 8, further comprising:
identifying, by the computing device, an unrequested file; instructing, by the computing device, a client device to index the unrequested file; sending, by the computing device, the unrequested file to the client device; and updating, by the computing device, the master index with index data associated with the unrequested file, wherein the index data associated with the unrequested file is received from the client device. 13. The method of claim 12, wherein the index data associated with the unrequested file comprises an index of content data of the requested file. 14. The method of claim 8, wherein the location of the file comprises a link to the file. 15. A method, comprising:
downloading, by a client device, a file, using a link provided by a remote computing device; indexing, by the client device, the file; and uploading, by the client device, index data to the remote computing device, wherein the index data comprises an index of content data of the file. 16. The method of claim 15, further comprising:
downloading, by the client device, a copy of a master index from a remote computing device; and updating, by the client device, a local index based at least in part on the copy of the master index. 17. The method of claim 16, further comprising updating, by the client deice, the local index based at least in part on the index data of the content of the file. 18. The method of claim 16, wherein downloading, by the client device, the file using the link provided by the remote computing device occurs in response to the client device receiving a request from the remote computing device to index the file. 19. The method of claim 15, further comprising:
determining, by the client device, that the client device has entered an idle state; and wherein the indexing of the file by the client device occurs in response to determining that the client device has entered the idle state. 20. The method of claim 15, further comprising:
determining, by the computing device, that the file has not been previously indexed; and wherein indexing, by the client device, the file occurs in response to determining that the file has not been previously indexed. | 2,100 |
6,459 | 6,459 | 15,703,894 | 2,198 | Techniques are provided herein for evolving computer-based reasoning system and include receiving a first training context-action pair that includes at least one first executable context element; receiving a second training context-action pair; evolving a candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair; testing whether a set of candidate context-action pairs (that includes candidate context-action pair) meets exit criteria; and when the set of candidate context-action pairs meets the exit criteria, selecting the set of candidate context-action pairs as an evolved set of candidate-action pairs. | 1. A non-transitory computer readable medium storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform a process of:
receiving a first training context-action pair, wherein the first training context-action pair comprises first one or more context elements, first one or more action elements representing actions taken in a first training context represented by the first one or more context elements, and at least one first executable context element; receiving a second training context-action pair, wherein the second training context-action pair comprises second one or more context elements, and second one or more action elements representing actions taken in a second training context represented by the one or more context elements; evolving a candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair by:
evolving the first one or more context elements and the second one or more context elements to produce candidate one or more context elements;
evolving the first one or more action elements and the second one or more action elements to produce candidate one or more action elements;
creating the candidate context-action pair based on the candidate one or more context elements and the candidate one or more action elements;
testing whether a set of candidate context-action pairs meets exit criteria, wherein the set of candidate context-action pairs includes the candidate context-action pair; in response to determining that the set of candidate context-action pairs meets the exit criteria, selecting the set of candidate context-action pairs as an evolved set of candidate context-action pairs. 2. The non-transitory computer readable medium of claim 1, wherein determining the candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair comprises mutating a portion of the candidate context-action pair. 3. The non-transitory computer readable medium of claim 2, wherein mutating the portion of the candidate context-action pair comprises replacing the portion of the candidate context-action pair with a function. 4. The non-transitory computer readable medium of claim 3, wherein the function is calculated based on a second portion of the candidate context-action pair different from said portion of the candidate context-action pair. 5. The non-transitory computer readable medium of claim 1, wherein the candidate context-action pair comprises at least one candidate executable context element and the process further comprises determining the at least one candidate executable context element of the candidate context-action pair based at least in part on the at least one first executable context element. 6. The non-transitory computer readable medium of claim 1, wherein the process further comprises receiving a third training context-action pair and wherein evolving the candidate context-action pair comprises evolving the candidate context-action pair based at least in part on the first training context-action pair, the second training context-action pair, and the third training context-action pair. 7. The non-transitory computer readable medium of claim 1, wherein determining the candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair comprises selecting randomly between the first training context-action pair and the second training context-action pair. 8. The non-transitory computer readable medium of claim 1, wherein determining the candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair comprises performing a resampling based at least in part on a context element of the first training context-action pair and a context element of the second training context-action pair. 9. The non-transitory computer readable medium of claim 1, wherein determining the candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair comprises comparing modifiers associated with on the first training context-action pair and the second training context-action pair. 10. The non-transitory computer readable medium of claim 1, wherein testing whether the set of candidate context-action pairs meets the exit criteria comprises determining a fitness score for the set of candidate context-action pairs. 11. The non-transitory computer readable medium of claim 1, wherein when the exit criteria are not met, the set of candidate context-action pairs is used as a training set, and is further evolved. 12. The non-transitory computer readable medium of claim 1, wherein the first training context-action pair and the second training context-action pair are part of one or more sets of training context-action pairs, and the process performed by the one or more computing devices further comprises:
comparing two or more training context-action pairs in the one or more sets of training context-action pairs; and selecting the first training context-action pair and the second training context-action pair based at least in part on the comparison of the two or more training context-action pairs in the one or more sets of training context-action pairs. 13. A method comprising:
receiving a first training context-action pair, wherein the first training context-action pair comprises first one or more context elements, first one or more action elements representing actions taken in a first training context represented by the first one or more context elements, and at least one first executable context element; receiving a second training context-action pair, wherein the second training context-action pair comprises second one or more context elements, and second one or more action elements representing actions taken in a second training context represented by the one or more context elements; evolving a candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair by:
evolving the first one or more context elements and the second one or more context elements to produce candidate one or more context elements;
evolving the first one or more action elements and the second one or more action elements to produce candidate one or more action elements;
creating the candidate context-action pair based on the candidate one or more context elements and the candidate one or more action elements;
testing whether a set of candidate context-action pairs meets exit criteria, wherein the set of candidate context-action pairs includes the candidate context-action pair; and in response to determining that the set of candidate context-action pairs meets the exit criteria, selecting the set of candidate context-action pairs as an evolved set of candidate-action pairs, wherein the method is performed by one or more computing devices. 14. The method of claim 13, wherein determining the candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair comprises mutating a portion of the candidate context-action pair. 15. The method of claim 14, wherein mutating the portion of the candidate context-action pair comprises replacing the portion of the candidate context-action pair with a function. 16. The method of claim 15, wherein the function is calculated based on a second portion of the candidate context-action pair different from said portion of the candidate context-action pair. 17. The method of claim 13, wherein the second training context-action pair comprises at least one second executable context element. 18. The method of claim 17, wherein an executable element of the candidate context-action pair is determined based at least in part on the at least one second executable context element. 19. A system for performing a machine-executed operation involving instructions, wherein said instructions are instructions which, when executed by one or more computing devices, cause performance of a process including:
receiving a first training context-action pair, wherein the first training context-action pair comprises first one or more context elements, first one or more action elements representing actions taken in a first training context represented by the first one or more context elements, and at least one first executable context element; receiving a second training context-action pair, wherein the second training context-action pair comprises second one or more context elements, and second one or more action elements representing actions taken in a second training context represented by the one or more context elements; evolving a candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair by:
evolving the first one or more context elements and the second one or more context elements to produce candidate one or more context elements;
evolving the first one or more action elements and the second one or more action elements to produce candidate one or more action elements;
creating the candidate context-action pair based on the candidate one or more context elements and the candidate one or more action elements;
testing whether a set of candidate context-action pairs meets exit criteria, wherein the set of candidate context-action pairs includes the candidate context-action pair; and in response to determining that the set of candidate context-action pairs meets the exit criteria, selecting the set of candidate context-action pairs as an evolved set of candidate-action pairs. 20. The system of claim 19, wherein when the exit criteria are not met, the set of candidate context-action pairs is used as a training set, and is further evolved. 21. The non-transitory computer readable medium of claim 1, wherein the process further comprises:
receiving an input context for a controlled system; determining one or more suggested context-action pairs from among the evolved set of candidate context-action pairs by comparing the set of candidate context-action pairs to the input context; causing control of the controlled system based on the one or more suggested context-action pairs. 22. The method of claim 13, further comprising:
receiving an input context for a controlled system; determining one or more suggested context-action pairs from among the evolved set of candidate context-action pairs by comparing the set of candidate context-action pairs to the input context; causing control of the controlled system based on the one or more suggested context-action pairs. 23. The system of claim 19, wherein the process further comprises:
receiving an input context for a controlled system; determining one or more suggested context-action pairs from among the evolved set of candidate context-action pairs by comparing the set of candidate context-action pairs to the input context; causing control of the controlled system based on the one or more suggested context-action pairs. | Techniques are provided herein for evolving computer-based reasoning system and include receiving a first training context-action pair that includes at least one first executable context element; receiving a second training context-action pair; evolving a candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair; testing whether a set of candidate context-action pairs (that includes candidate context-action pair) meets exit criteria; and when the set of candidate context-action pairs meets the exit criteria, selecting the set of candidate context-action pairs as an evolved set of candidate-action pairs.1. A non-transitory computer readable medium storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform a process of:
receiving a first training context-action pair, wherein the first training context-action pair comprises first one or more context elements, first one or more action elements representing actions taken in a first training context represented by the first one or more context elements, and at least one first executable context element; receiving a second training context-action pair, wherein the second training context-action pair comprises second one or more context elements, and second one or more action elements representing actions taken in a second training context represented by the one or more context elements; evolving a candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair by:
evolving the first one or more context elements and the second one or more context elements to produce candidate one or more context elements;
evolving the first one or more action elements and the second one or more action elements to produce candidate one or more action elements;
creating the candidate context-action pair based on the candidate one or more context elements and the candidate one or more action elements;
testing whether a set of candidate context-action pairs meets exit criteria, wherein the set of candidate context-action pairs includes the candidate context-action pair; in response to determining that the set of candidate context-action pairs meets the exit criteria, selecting the set of candidate context-action pairs as an evolved set of candidate context-action pairs. 2. The non-transitory computer readable medium of claim 1, wherein determining the candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair comprises mutating a portion of the candidate context-action pair. 3. The non-transitory computer readable medium of claim 2, wherein mutating the portion of the candidate context-action pair comprises replacing the portion of the candidate context-action pair with a function. 4. The non-transitory computer readable medium of claim 3, wherein the function is calculated based on a second portion of the candidate context-action pair different from said portion of the candidate context-action pair. 5. The non-transitory computer readable medium of claim 1, wherein the candidate context-action pair comprises at least one candidate executable context element and the process further comprises determining the at least one candidate executable context element of the candidate context-action pair based at least in part on the at least one first executable context element. 6. The non-transitory computer readable medium of claim 1, wherein the process further comprises receiving a third training context-action pair and wherein evolving the candidate context-action pair comprises evolving the candidate context-action pair based at least in part on the first training context-action pair, the second training context-action pair, and the third training context-action pair. 7. The non-transitory computer readable medium of claim 1, wherein determining the candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair comprises selecting randomly between the first training context-action pair and the second training context-action pair. 8. The non-transitory computer readable medium of claim 1, wherein determining the candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair comprises performing a resampling based at least in part on a context element of the first training context-action pair and a context element of the second training context-action pair. 9. The non-transitory computer readable medium of claim 1, wherein determining the candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair comprises comparing modifiers associated with on the first training context-action pair and the second training context-action pair. 10. The non-transitory computer readable medium of claim 1, wherein testing whether the set of candidate context-action pairs meets the exit criteria comprises determining a fitness score for the set of candidate context-action pairs. 11. The non-transitory computer readable medium of claim 1, wherein when the exit criteria are not met, the set of candidate context-action pairs is used as a training set, and is further evolved. 12. The non-transitory computer readable medium of claim 1, wherein the first training context-action pair and the second training context-action pair are part of one or more sets of training context-action pairs, and the process performed by the one or more computing devices further comprises:
comparing two or more training context-action pairs in the one or more sets of training context-action pairs; and selecting the first training context-action pair and the second training context-action pair based at least in part on the comparison of the two or more training context-action pairs in the one or more sets of training context-action pairs. 13. A method comprising:
receiving a first training context-action pair, wherein the first training context-action pair comprises first one or more context elements, first one or more action elements representing actions taken in a first training context represented by the first one or more context elements, and at least one first executable context element; receiving a second training context-action pair, wherein the second training context-action pair comprises second one or more context elements, and second one or more action elements representing actions taken in a second training context represented by the one or more context elements; evolving a candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair by:
evolving the first one or more context elements and the second one or more context elements to produce candidate one or more context elements;
evolving the first one or more action elements and the second one or more action elements to produce candidate one or more action elements;
creating the candidate context-action pair based on the candidate one or more context elements and the candidate one or more action elements;
testing whether a set of candidate context-action pairs meets exit criteria, wherein the set of candidate context-action pairs includes the candidate context-action pair; and in response to determining that the set of candidate context-action pairs meets the exit criteria, selecting the set of candidate context-action pairs as an evolved set of candidate-action pairs, wherein the method is performed by one or more computing devices. 14. The method of claim 13, wherein determining the candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair comprises mutating a portion of the candidate context-action pair. 15. The method of claim 14, wherein mutating the portion of the candidate context-action pair comprises replacing the portion of the candidate context-action pair with a function. 16. The method of claim 15, wherein the function is calculated based on a second portion of the candidate context-action pair different from said portion of the candidate context-action pair. 17. The method of claim 13, wherein the second training context-action pair comprises at least one second executable context element. 18. The method of claim 17, wherein an executable element of the candidate context-action pair is determined based at least in part on the at least one second executable context element. 19. A system for performing a machine-executed operation involving instructions, wherein said instructions are instructions which, when executed by one or more computing devices, cause performance of a process including:
receiving a first training context-action pair, wherein the first training context-action pair comprises first one or more context elements, first one or more action elements representing actions taken in a first training context represented by the first one or more context elements, and at least one first executable context element; receiving a second training context-action pair, wherein the second training context-action pair comprises second one or more context elements, and second one or more action elements representing actions taken in a second training context represented by the one or more context elements; evolving a candidate context-action pair based at least in part on the first training context-action pair and the second training context-action pair by:
evolving the first one or more context elements and the second one or more context elements to produce candidate one or more context elements;
evolving the first one or more action elements and the second one or more action elements to produce candidate one or more action elements;
creating the candidate context-action pair based on the candidate one or more context elements and the candidate one or more action elements;
testing whether a set of candidate context-action pairs meets exit criteria, wherein the set of candidate context-action pairs includes the candidate context-action pair; and in response to determining that the set of candidate context-action pairs meets the exit criteria, selecting the set of candidate context-action pairs as an evolved set of candidate-action pairs. 20. The system of claim 19, wherein when the exit criteria are not met, the set of candidate context-action pairs is used as a training set, and is further evolved. 21. The non-transitory computer readable medium of claim 1, wherein the process further comprises:
receiving an input context for a controlled system; determining one or more suggested context-action pairs from among the evolved set of candidate context-action pairs by comparing the set of candidate context-action pairs to the input context; causing control of the controlled system based on the one or more suggested context-action pairs. 22. The method of claim 13, further comprising:
receiving an input context for a controlled system; determining one or more suggested context-action pairs from among the evolved set of candidate context-action pairs by comparing the set of candidate context-action pairs to the input context; causing control of the controlled system based on the one or more suggested context-action pairs. 23. The system of claim 19, wherein the process further comprises:
receiving an input context for a controlled system; determining one or more suggested context-action pairs from among the evolved set of candidate context-action pairs by comparing the set of candidate context-action pairs to the input context; causing control of the controlled system based on the one or more suggested context-action pairs. | 2,100 |
6,460 | 6,460 | 14,286,709 | 2,173 | User inputs are received on a visual display, when a user is generating visual content. The user inputs trigger guide functionality, and guides are displayed to guide the user in creating the visual content. | 1. A computer-implemented method, comprising:
displaying an object on a user interface display; receiving a user interaction with the object; and displaying a guide, in response to the user interaction, indicative of a relationship between an orientation of the object and a predetermined characteristic of the user interface display, or a relationship between the object and a margin of the user interface display. 2. The computer-implemented method of claim 1 wherein the object is elongate along an axis, and wherein displaying a guide comprises:
displaying the guide to indicate when the axis of the object is in a predetermined orientation on the user interface display. 3. The computer-implemented method of claim 2 wherein displaying the guide comprises:
displaying the guide to indicate that the axis of the object is oriented horizontally, or vertically on the user interface display. 4. The computer-implemented method of claim 1 wherein displaying a guide further comprises:
displaying the guide to indicate a relationship between a portion of the object and a center of the user interface display. 5. The computer-implemented method of claim 4 wherein displaying the guide further comprises:
displaying the guide to indicate when a center or edge of the object is aligned with the center of the user interface display. 6. The computer-implemented method of claim 5 wherein displaying the guide further comprises:
displaying the guide to indicate when the center or edge of the object is aligned with either a horizontal center or vertical center of the user interface display. 7. The computer-implemented method of claim 1 wherein displaying a guide comprises:
displaying the guide to indicate when the object and another object are in a predetermined relationship relative to a portion of the user interface display. 8. The computer-implemented method of claim 7 wherein displaying the guide to indicate when the object and another object are in a predetermined relationship relative to a portion of the user interface display comprises:
displaying the guide to indicate when the object and the other object are equally spaced from opposite edges of the user interface display. 9. The computer-implemented method of claim 7 wherein displaying the guide to indicate when the object and another object are in a predetermined relationship relative to a portion of the user interface display comprises:
displaying the guide to indicate when the object and the other object are equally spaced from a center of the user interface display. 10. The computer-implemented method of claim 9 wherein displaying the guide to indicate when the object and the other object are equally spaced from a center of the user interface display comprises:
displaying the guide to indicate when the object and the other object are equally spaced from a horizontal center of the user interface display. 11. The computer-implemented method of claim 9 wherein displaying the guide to indicate when the object and the other object are equally spaced from a center of the user interface display comprises:
displaying the guide to indicate when the object and the other object are equally spaced from a vertical center of the user interface display. 12. A computer-implemented method, comprising:
displaying first and second objects on a user interface display; receiving a user interaction with the first object; and displaying a guide, in response to the user interaction, indicative of a positional relationship between the first and second objects on the user interface display. 13. The computer-implemented method of claim 12 wherein displaying a guide comprises:
displaying the guide to indicate when a center of one of the first and second objects is aligned with an edge of another of the first and second objects. 14. The computer-implemented method of claim 12 and further comprising:
receiving user identification of another user interface display with a set of objects displayed thereon. 15. The computer-implemented method of claim 14 and further comprising:
displaying the guide to indicate a positional relationship of the first object relative to one of the set of objects on the other user interface display. 16. The computer-implemented method of claim 15 wherein displaying the guide to indicate a positional relationship of the first object relative to one of the set of objects on the other user interface display comprises:
displaying the guide to indicate when the first object and the one object are in a predetermined relationship relative to a portion of the user interface displays. 17. The computer-implemented method of claim 16 wherein displaying the guide to indicate when the first object and the one object are in a predetermined relationship relative to a portion of the user interface displays comprises:
displaying the guide to indicate when the first object and the one object are equally spaced from opposite edges of the user interface displays. 18. The computer-implemented method of claim 16 wherein displaying the guide to indicate when the first object and the one object are in a predetermined relationship relative to a portion of the user interface displays comprises:
displaying the guide to indicate when the first object and the one object are equally spaced from a center of the user interface displays. 19. The computer-implemented method of claim 15 wherein displaying the guide to indicate a positional relationship of the first object relative to one of the set of objects on the other user interface display comprises:
displaying, as an underlay, a display of the other user interface display;
displaying, as an overlay, at least objects on the first user interface display over the underlay; and
displaying alignment guides for at least one of the set of objects on the underlay, and the objects on the overlay. 20. A computer readable storage medium that stores computer executable instructions which, when executed by a computer, cause the computer to perform a method, comprising:
receiving a user input identifying a first display with a first object displayed thereon and a second display with a second object displayed thereon; displaying, as a semi-transparent underlay, a display of the first user interface display; displaying, as an overlay, at least objects on the second user interface display over the underlay; receiving a user interaction with the second object; and displaying alignment guides, based on the user interaction, for at least one of the first object on the underlay and the second object on the overlay. | User inputs are received on a visual display, when a user is generating visual content. The user inputs trigger guide functionality, and guides are displayed to guide the user in creating the visual content.1. A computer-implemented method, comprising:
displaying an object on a user interface display; receiving a user interaction with the object; and displaying a guide, in response to the user interaction, indicative of a relationship between an orientation of the object and a predetermined characteristic of the user interface display, or a relationship between the object and a margin of the user interface display. 2. The computer-implemented method of claim 1 wherein the object is elongate along an axis, and wherein displaying a guide comprises:
displaying the guide to indicate when the axis of the object is in a predetermined orientation on the user interface display. 3. The computer-implemented method of claim 2 wherein displaying the guide comprises:
displaying the guide to indicate that the axis of the object is oriented horizontally, or vertically on the user interface display. 4. The computer-implemented method of claim 1 wherein displaying a guide further comprises:
displaying the guide to indicate a relationship between a portion of the object and a center of the user interface display. 5. The computer-implemented method of claim 4 wherein displaying the guide further comprises:
displaying the guide to indicate when a center or edge of the object is aligned with the center of the user interface display. 6. The computer-implemented method of claim 5 wherein displaying the guide further comprises:
displaying the guide to indicate when the center or edge of the object is aligned with either a horizontal center or vertical center of the user interface display. 7. The computer-implemented method of claim 1 wherein displaying a guide comprises:
displaying the guide to indicate when the object and another object are in a predetermined relationship relative to a portion of the user interface display. 8. The computer-implemented method of claim 7 wherein displaying the guide to indicate when the object and another object are in a predetermined relationship relative to a portion of the user interface display comprises:
displaying the guide to indicate when the object and the other object are equally spaced from opposite edges of the user interface display. 9. The computer-implemented method of claim 7 wherein displaying the guide to indicate when the object and another object are in a predetermined relationship relative to a portion of the user interface display comprises:
displaying the guide to indicate when the object and the other object are equally spaced from a center of the user interface display. 10. The computer-implemented method of claim 9 wherein displaying the guide to indicate when the object and the other object are equally spaced from a center of the user interface display comprises:
displaying the guide to indicate when the object and the other object are equally spaced from a horizontal center of the user interface display. 11. The computer-implemented method of claim 9 wherein displaying the guide to indicate when the object and the other object are equally spaced from a center of the user interface display comprises:
displaying the guide to indicate when the object and the other object are equally spaced from a vertical center of the user interface display. 12. A computer-implemented method, comprising:
displaying first and second objects on a user interface display; receiving a user interaction with the first object; and displaying a guide, in response to the user interaction, indicative of a positional relationship between the first and second objects on the user interface display. 13. The computer-implemented method of claim 12 wherein displaying a guide comprises:
displaying the guide to indicate when a center of one of the first and second objects is aligned with an edge of another of the first and second objects. 14. The computer-implemented method of claim 12 and further comprising:
receiving user identification of another user interface display with a set of objects displayed thereon. 15. The computer-implemented method of claim 14 and further comprising:
displaying the guide to indicate a positional relationship of the first object relative to one of the set of objects on the other user interface display. 16. The computer-implemented method of claim 15 wherein displaying the guide to indicate a positional relationship of the first object relative to one of the set of objects on the other user interface display comprises:
displaying the guide to indicate when the first object and the one object are in a predetermined relationship relative to a portion of the user interface displays. 17. The computer-implemented method of claim 16 wherein displaying the guide to indicate when the first object and the one object are in a predetermined relationship relative to a portion of the user interface displays comprises:
displaying the guide to indicate when the first object and the one object are equally spaced from opposite edges of the user interface displays. 18. The computer-implemented method of claim 16 wherein displaying the guide to indicate when the first object and the one object are in a predetermined relationship relative to a portion of the user interface displays comprises:
displaying the guide to indicate when the first object and the one object are equally spaced from a center of the user interface displays. 19. The computer-implemented method of claim 15 wherein displaying the guide to indicate a positional relationship of the first object relative to one of the set of objects on the other user interface display comprises:
displaying, as an underlay, a display of the other user interface display;
displaying, as an overlay, at least objects on the first user interface display over the underlay; and
displaying alignment guides for at least one of the set of objects on the underlay, and the objects on the overlay. 20. A computer readable storage medium that stores computer executable instructions which, when executed by a computer, cause the computer to perform a method, comprising:
receiving a user input identifying a first display with a first object displayed thereon and a second display with a second object displayed thereon; displaying, as a semi-transparent underlay, a display of the first user interface display; displaying, as an overlay, at least objects on the second user interface display over the underlay; receiving a user interaction with the second object; and displaying alignment guides, based on the user interaction, for at least one of the first object on the underlay and the second object on the overlay. | 2,100 |
6,461 | 6,461 | 16,012,990 | 2,138 | A storage system in one embodiment comprises multiple storage nodes each comprising at least one storage device. Each of the storage nodes further comprises a set of processing modules configured to communicate over one or more networks with corresponding sets of processing modules on other ones of the storage nodes. The sets of processing modules of the storage nodes each comprise at least one control module. The storage system is configured to assign portions of a logical address space of the storage system to respective ones of the control modules, to receive a plurality of tracks of data records in a count-key-data format, and to store the tracks in respective ones of the portions of the logical address space assigned to respective ones of the control modules. Each of the tracks is stored in its entirety in the portion of the logical address space assigned to a corresponding one of the control modules. | 1. An apparatus comprising:
a storage system comprising a plurality of storage nodes each comprising at least one storage device; each of the storage nodes further comprising: a processor coupled to a memory; and a set of processing modules configured to communicate over one or more networks with corresponding sets of processing modules on other ones of the storage nodes; the sets of processing modules of the storage nodes each comprising at least one control module; the storage system being configured: to assign portions of a logical address space of the storage system to respective ones of the control modules; to receive a plurality of tracks of data records in a count-key-data format; and to store the tracks in respective ones of the portions of the logical address space assigned to respective ones of the control modules; wherein each of the tracks is stored in its entirety in the portion of the logical address space assigned to a corresponding one of the control modules. 2. The apparatus of claim 1 wherein the sets of processing modules collectively comprise at least a portion of a distributed storage controller of the storage system. 3. The apparatus of claim 2 wherein the assignment of portions of the logical address space of the storage system to respective ones of the control modules is implemented at least in part by at least one system-wide management module of the distributed storage controller. 4. The apparatus of claim 1 wherein the sets of processing modules each comprise at least one routing module in addition to the control module. 5. The apparatus of claim 4 wherein the routing module is configured:
to receive a given one of the tracks of data records in one or more communications of a first count-key-data protocol; and
to route the given track of data records to the control module in one or more communications of a second count-key-data protocol different than the first count-key-data protocol for storage in at least one of the storage devices. 6. The apparatus of claim 4 wherein communications between the routing module and the control module are stateless so as to thereby permit first and second distinct routing modules to communicate with the control module without requiring transfer of state information from the first routing module to the second routing module. 7. The apparatus of claim 1 wherein a native page size of the storage system is less than a size of a given one of the tracks of data records such that the given track of data records is stored utilizing multiple pages of the portion of the logical address space assigned to the corresponding one of the control modules. 8. The apparatus of claim 7 wherein the portion of the logical address space has a size defined as a multiple of the native page size of the storage system 9. The apparatus of claim 1 wherein at least a subset of the portions of the logical address space comprise respective equal-size portions each comprising a designated number of pages in a native page size of the storage system. 10. The apparatus of claim 9 wherein the designated number of pages in each of the equal-sized portions is at least four pages. 11. The apparatus of claim 1 wherein multiple distinct portions of the logical address space are assigned to at least one of the control modules. 12. The apparatus of claim 1 wherein in conjunction with storing a given one of the tracks of data records in the count-key-data format in one of the portions of the logical address space assigned to one of the control modules, count and key information of the data records is stored in a designated page of a set of pages of the portion and data of the data records is stored in one or more other pages of the set of pages. 13. The apparatus of claim 1 wherein the storage system comprises a content addressable storage system. 14. The apparatus of claim 1 wherein the storage devices comprise respective non-volatile memory devices. 15. A method comprising:
configuring a storage system to include a plurality of storage nodes each comprising at least one storage device, each of the storage nodes further comprising a set of processing modules configured to communicate over one or more networks with corresponding sets of processing modules on other ones of the storage nodes, the sets of processing modules each comprising at least one control module; assigning portions of a logical address space of the storage system to respective ones of the control modules; receiving a plurality of tracks of data records in a count-key-data format; and storing the tracks in respective ones of the portions of the logical address space assigned to respective ones of the control modules; wherein each of the tracks is stored in its entirety in the portion of the logical address space assigned to a corresponding one of the control modules; and wherein the method is performed by at least one processing device comprising a processor coupled to a memory. 16. The method of claim 15 wherein the sets of processing modules each comprise at least one routing module in addition to the control module. 17. The method of claim 16 wherein the routing module is configured:
to receive a given one of the tracks of data records in one or more communications of a first count-key-data protocol; and
to route the given track of data records to the control module in one or more communications of a second count-key-data protocol different than the first count-key-data protocol for storage in at least one of the storage devices. 18. A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes said at least one processing device:
to configure a storage system to include a plurality of storage nodes each comprising at least one storage device, each of the storage nodes further comprising a set of processing modules configured to communicate over one or more networks with corresponding sets of processing modules on other ones of the storage nodes, the sets of processing modules each comprising at least one control module; to assign portions of a logical address space of the storage system to respective ones of the control modules; to receive a plurality of tracks of data records in a count-key-data format; and to store the tracks in respective ones of the portions of the logical address space assigned to respective ones of the control modules; wherein each of the tracks is stored in its entirety in the portion of the logical address space assigned to a corresponding one of the control modules. 19. The computer program product of claim 18 wherein the sets of processing modules each comprise at least one routing module in addition to the control module. 20. The computer program product of claim 19 wherein the routing module is configured:
to receive a given one of the tracks of data records in one or more communications of a first count-key-data protocol; and
to route the given track of data records to the control module in one or more communications of a second count-key-data protocol different than the first count-key-data protocol for storage in at least one of the storage devices. | A storage system in one embodiment comprises multiple storage nodes each comprising at least one storage device. Each of the storage nodes further comprises a set of processing modules configured to communicate over one or more networks with corresponding sets of processing modules on other ones of the storage nodes. The sets of processing modules of the storage nodes each comprise at least one control module. The storage system is configured to assign portions of a logical address space of the storage system to respective ones of the control modules, to receive a plurality of tracks of data records in a count-key-data format, and to store the tracks in respective ones of the portions of the logical address space assigned to respective ones of the control modules. Each of the tracks is stored in its entirety in the portion of the logical address space assigned to a corresponding one of the control modules.1. An apparatus comprising:
a storage system comprising a plurality of storage nodes each comprising at least one storage device; each of the storage nodes further comprising: a processor coupled to a memory; and a set of processing modules configured to communicate over one or more networks with corresponding sets of processing modules on other ones of the storage nodes; the sets of processing modules of the storage nodes each comprising at least one control module; the storage system being configured: to assign portions of a logical address space of the storage system to respective ones of the control modules; to receive a plurality of tracks of data records in a count-key-data format; and to store the tracks in respective ones of the portions of the logical address space assigned to respective ones of the control modules; wherein each of the tracks is stored in its entirety in the portion of the logical address space assigned to a corresponding one of the control modules. 2. The apparatus of claim 1 wherein the sets of processing modules collectively comprise at least a portion of a distributed storage controller of the storage system. 3. The apparatus of claim 2 wherein the assignment of portions of the logical address space of the storage system to respective ones of the control modules is implemented at least in part by at least one system-wide management module of the distributed storage controller. 4. The apparatus of claim 1 wherein the sets of processing modules each comprise at least one routing module in addition to the control module. 5. The apparatus of claim 4 wherein the routing module is configured:
to receive a given one of the tracks of data records in one or more communications of a first count-key-data protocol; and
to route the given track of data records to the control module in one or more communications of a second count-key-data protocol different than the first count-key-data protocol for storage in at least one of the storage devices. 6. The apparatus of claim 4 wherein communications between the routing module and the control module are stateless so as to thereby permit first and second distinct routing modules to communicate with the control module without requiring transfer of state information from the first routing module to the second routing module. 7. The apparatus of claim 1 wherein a native page size of the storage system is less than a size of a given one of the tracks of data records such that the given track of data records is stored utilizing multiple pages of the portion of the logical address space assigned to the corresponding one of the control modules. 8. The apparatus of claim 7 wherein the portion of the logical address space has a size defined as a multiple of the native page size of the storage system 9. The apparatus of claim 1 wherein at least a subset of the portions of the logical address space comprise respective equal-size portions each comprising a designated number of pages in a native page size of the storage system. 10. The apparatus of claim 9 wherein the designated number of pages in each of the equal-sized portions is at least four pages. 11. The apparatus of claim 1 wherein multiple distinct portions of the logical address space are assigned to at least one of the control modules. 12. The apparatus of claim 1 wherein in conjunction with storing a given one of the tracks of data records in the count-key-data format in one of the portions of the logical address space assigned to one of the control modules, count and key information of the data records is stored in a designated page of a set of pages of the portion and data of the data records is stored in one or more other pages of the set of pages. 13. The apparatus of claim 1 wherein the storage system comprises a content addressable storage system. 14. The apparatus of claim 1 wherein the storage devices comprise respective non-volatile memory devices. 15. A method comprising:
configuring a storage system to include a plurality of storage nodes each comprising at least one storage device, each of the storage nodes further comprising a set of processing modules configured to communicate over one or more networks with corresponding sets of processing modules on other ones of the storage nodes, the sets of processing modules each comprising at least one control module; assigning portions of a logical address space of the storage system to respective ones of the control modules; receiving a plurality of tracks of data records in a count-key-data format; and storing the tracks in respective ones of the portions of the logical address space assigned to respective ones of the control modules; wherein each of the tracks is stored in its entirety in the portion of the logical address space assigned to a corresponding one of the control modules; and wherein the method is performed by at least one processing device comprising a processor coupled to a memory. 16. The method of claim 15 wherein the sets of processing modules each comprise at least one routing module in addition to the control module. 17. The method of claim 16 wherein the routing module is configured:
to receive a given one of the tracks of data records in one or more communications of a first count-key-data protocol; and
to route the given track of data records to the control module in one or more communications of a second count-key-data protocol different than the first count-key-data protocol for storage in at least one of the storage devices. 18. A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes said at least one processing device:
to configure a storage system to include a plurality of storage nodes each comprising at least one storage device, each of the storage nodes further comprising a set of processing modules configured to communicate over one or more networks with corresponding sets of processing modules on other ones of the storage nodes, the sets of processing modules each comprising at least one control module; to assign portions of a logical address space of the storage system to respective ones of the control modules; to receive a plurality of tracks of data records in a count-key-data format; and to store the tracks in respective ones of the portions of the logical address space assigned to respective ones of the control modules; wherein each of the tracks is stored in its entirety in the portion of the logical address space assigned to a corresponding one of the control modules. 19. The computer program product of claim 18 wherein the sets of processing modules each comprise at least one routing module in addition to the control module. 20. The computer program product of claim 19 wherein the routing module is configured:
to receive a given one of the tracks of data records in one or more communications of a first count-key-data protocol; and
to route the given track of data records to the control module in one or more communications of a second count-key-data protocol different than the first count-key-data protocol for storage in at least one of the storage devices. | 2,100 |
6,462 | 6,462 | 15,445,228 | 2,153 | Examples of the present disclosure describe systems and methods for ontology-based graph query optimization. In an example, ontology data relating to a graph or isolated collection may be collected. The ontology data may comprise uniqueness and topology information and may be used to reformulate a query in order to yield a query that is more performant than the original query when retrieving target information from a graph. In an example, reformulating a query may comprise reordering one or more parameters of the query relating to resources, relationships, and/or properties based on uniqueness information. In another example, the query may be reformulated by modifying the resource type to which the query is anchored based on the topology information. The reformulated query may then be executed to identify target information in the isolated collection, thereby identifying the same target information as the original query, but in a manner that is more performant. | 1. A system comprising:
at least one processor; and a memory storing instructions that when executed by the at least one processor perform a set of operations comprising:
receiving a query for information stored in an isolated collection, wherein the query comprises one or more parameters;
accessing ontology data relating to the isolated collection, wherein the ontology data comprises uniqueness information;
identifying, using at least the uniqueness information, a level of uniqueness for each of the one or more parameters;
determining, for each of the one or more parameters, a query order based on the uniqueness information identified for the parameter;
generating a more performant query for the query, wherein the more performant query is comprised of the one or more parameters in the determined query order; and
executing the more performant query to identify information in the isolated collection. 2. The system of claim 1, wherein the ontology data further comprises topology information, and the set of operations further comprises:
identifying an anchor and one or more resource types for the query, wherein the anchor relates to at least one of the one or more resource types; identifying, using the topology information, an average number of relationships for each of the one or more resource types; and determining, using the average number of relationships for each of the one or more resource types, whether the anchor for the query should relate to a different group of one or more of the one or more of resource types. 3. The system of claim 2, wherein generating the more performant query further comprises:
when it is determined that the anchor query should relate to a different group of one or more of the one or more resource types, generating the more performant query such that the different group of one or more of the one or more resource types relates to the anchor of the more performant query. 4. The system of claim 1, wherein the isolated collection is associated with a related isolated collection, and the ontology data relating to the isolated collection was generated using the related isolated collection. 5. The system of claim 1, wherein the ontology data relating to the isolated collection is updated when information in the isolated collection is at least one of added, modified, and deleted. 6. The system of claim 1, wherein the ontology data relating to the isolated collection is updated periodically. 7. The system of claim 1, wherein the more performant query is more efficient than the received query when executed to identify information in the isolated collection. 8. A computer-implemented method for generating ontology data for an isolated collection, the method comprising:
receiving, from a computing device, a request comprising a change to an isolated collection; determining whether the change is related to one of a resource and a property; when it is determined that the change relates to a resource, generating a key for the resource; when it is determined that the change relates to a property, generating a key for the property; generating uniqueness information based on the change, wherein the uniqueness information comprises a uniqueness index; associating the generated uniqueness information with the key; and storing, using the key, the generated uniqueness information. 9. The computer-implemented method of claim 8, further comprising:
determining whether the change is related to a relationship; when it is determined that the change relates to a relationship, identifying a plurality of resources associated with the relationship; for each of the plurality of resources:
generating a key for the resource based on a resource type for the resource;
generating topology information, wherein the topology information indicates an average number of relationships for the resource type;
associating the generated topology information with the key; and
storing, using the key, the generated topology information. 10. The computer-implemented method of claim 8, wherein storing the generated uniqueness information comprises storing the generated uniqueness information using a PATRICIA tree. 11. The computer-implemented method of claim 9, wherein storing the generated topology information comprises storing the generated topology information using a PATRICIA tree. 12. The computer-implemented method of claim 8, wherein the uniqueness index indicates a relative level of uniqueness for one of a resource and a property as compared to at least one of a second resource and a second property. 13. The computer-implemented method of claim 9, where the topology information indicates a relative level of connectedness for a resource type as compared to a second resource type. 14. A computer-implemented method for generating a more performant query for information stored in an isolated collection, the method comprising:
receiving a query for information stored in an isolated collection; identifying an anchor and one or more resource types for the query, wherein the anchor is at least one of the one or more resource types; accessing ontology data relating to the isolated collection, wherein the ontology data comprises topology information; identifying, using the topology information, an average number of relationships for each of the one or more resource types; determining, using the average number of relationships for each of the one or more resource types, whether the anchor for the query should be a different group of one or more of the one or more resource types; when it is determined that the anchor query should be a different group of one or more of the one or more resource types, generating a more performant query such that the different group of one or more of the one or more resource types is the anchor for the more performant query; and executing the more performant query to identify information in the isolated collection. 15. The computer-implemented method of claim 14, wherein the ontology data further comprises uniqueness information, the method further comprising:
identifying one or more parameters of the query; identifying, using the uniqueness information, a level of uniqueness for each of the one or more parameters; and determining, for each of the one or more parameters, a query order based on the uniqueness information identified for the parameter. 16. The computer-implemented method of claim 15, wherein generating the more performant query further comprises reformulating the query such that one or more parameters of the more performant query are in the determined query order. 17. The computer-implemented method of claim 14, wherein the isolated collection is associated with a related isolated collection, and the ontology data relating to the isolated collection was generated from the related isolated collection. 18. The computer-implemented method of claim 14, wherein the ontology data relating to the isolated collection is updated when information in the isolated collection is at least one of added, modified, and deleted. 19. The computer-implemented method of claim 14, wherein the ontology data relating to the isolated collection is updated periodically. 20. The computer-implemented method of claim 14, wherein the ontology data is stored in a PATRICIA tree. | Examples of the present disclosure describe systems and methods for ontology-based graph query optimization. In an example, ontology data relating to a graph or isolated collection may be collected. The ontology data may comprise uniqueness and topology information and may be used to reformulate a query in order to yield a query that is more performant than the original query when retrieving target information from a graph. In an example, reformulating a query may comprise reordering one or more parameters of the query relating to resources, relationships, and/or properties based on uniqueness information. In another example, the query may be reformulated by modifying the resource type to which the query is anchored based on the topology information. The reformulated query may then be executed to identify target information in the isolated collection, thereby identifying the same target information as the original query, but in a manner that is more performant.1. A system comprising:
at least one processor; and a memory storing instructions that when executed by the at least one processor perform a set of operations comprising:
receiving a query for information stored in an isolated collection, wherein the query comprises one or more parameters;
accessing ontology data relating to the isolated collection, wherein the ontology data comprises uniqueness information;
identifying, using at least the uniqueness information, a level of uniqueness for each of the one or more parameters;
determining, for each of the one or more parameters, a query order based on the uniqueness information identified for the parameter;
generating a more performant query for the query, wherein the more performant query is comprised of the one or more parameters in the determined query order; and
executing the more performant query to identify information in the isolated collection. 2. The system of claim 1, wherein the ontology data further comprises topology information, and the set of operations further comprises:
identifying an anchor and one or more resource types for the query, wherein the anchor relates to at least one of the one or more resource types; identifying, using the topology information, an average number of relationships for each of the one or more resource types; and determining, using the average number of relationships for each of the one or more resource types, whether the anchor for the query should relate to a different group of one or more of the one or more of resource types. 3. The system of claim 2, wherein generating the more performant query further comprises:
when it is determined that the anchor query should relate to a different group of one or more of the one or more resource types, generating the more performant query such that the different group of one or more of the one or more resource types relates to the anchor of the more performant query. 4. The system of claim 1, wherein the isolated collection is associated with a related isolated collection, and the ontology data relating to the isolated collection was generated using the related isolated collection. 5. The system of claim 1, wherein the ontology data relating to the isolated collection is updated when information in the isolated collection is at least one of added, modified, and deleted. 6. The system of claim 1, wherein the ontology data relating to the isolated collection is updated periodically. 7. The system of claim 1, wherein the more performant query is more efficient than the received query when executed to identify information in the isolated collection. 8. A computer-implemented method for generating ontology data for an isolated collection, the method comprising:
receiving, from a computing device, a request comprising a change to an isolated collection; determining whether the change is related to one of a resource and a property; when it is determined that the change relates to a resource, generating a key for the resource; when it is determined that the change relates to a property, generating a key for the property; generating uniqueness information based on the change, wherein the uniqueness information comprises a uniqueness index; associating the generated uniqueness information with the key; and storing, using the key, the generated uniqueness information. 9. The computer-implemented method of claim 8, further comprising:
determining whether the change is related to a relationship; when it is determined that the change relates to a relationship, identifying a plurality of resources associated with the relationship; for each of the plurality of resources:
generating a key for the resource based on a resource type for the resource;
generating topology information, wherein the topology information indicates an average number of relationships for the resource type;
associating the generated topology information with the key; and
storing, using the key, the generated topology information. 10. The computer-implemented method of claim 8, wherein storing the generated uniqueness information comprises storing the generated uniqueness information using a PATRICIA tree. 11. The computer-implemented method of claim 9, wherein storing the generated topology information comprises storing the generated topology information using a PATRICIA tree. 12. The computer-implemented method of claim 8, wherein the uniqueness index indicates a relative level of uniqueness for one of a resource and a property as compared to at least one of a second resource and a second property. 13. The computer-implemented method of claim 9, where the topology information indicates a relative level of connectedness for a resource type as compared to a second resource type. 14. A computer-implemented method for generating a more performant query for information stored in an isolated collection, the method comprising:
receiving a query for information stored in an isolated collection; identifying an anchor and one or more resource types for the query, wherein the anchor is at least one of the one or more resource types; accessing ontology data relating to the isolated collection, wherein the ontology data comprises topology information; identifying, using the topology information, an average number of relationships for each of the one or more resource types; determining, using the average number of relationships for each of the one or more resource types, whether the anchor for the query should be a different group of one or more of the one or more resource types; when it is determined that the anchor query should be a different group of one or more of the one or more resource types, generating a more performant query such that the different group of one or more of the one or more resource types is the anchor for the more performant query; and executing the more performant query to identify information in the isolated collection. 15. The computer-implemented method of claim 14, wherein the ontology data further comprises uniqueness information, the method further comprising:
identifying one or more parameters of the query; identifying, using the uniqueness information, a level of uniqueness for each of the one or more parameters; and determining, for each of the one or more parameters, a query order based on the uniqueness information identified for the parameter. 16. The computer-implemented method of claim 15, wherein generating the more performant query further comprises reformulating the query such that one or more parameters of the more performant query are in the determined query order. 17. The computer-implemented method of claim 14, wherein the isolated collection is associated with a related isolated collection, and the ontology data relating to the isolated collection was generated from the related isolated collection. 18. The computer-implemented method of claim 14, wherein the ontology data relating to the isolated collection is updated when information in the isolated collection is at least one of added, modified, and deleted. 19. The computer-implemented method of claim 14, wherein the ontology data relating to the isolated collection is updated periodically. 20. The computer-implemented method of claim 14, wherein the ontology data is stored in a PATRICIA tree. | 2,100 |
6,463 | 6,463 | 15,605,373 | 2,162 | A sending device receives a user input indicating that the user wishes to share and open item of content with a receiving device. A near field communication link is opened between the sending device and the receiving device. Metadata for sharing the open data is gathered on the sending device and a permission setting user interface display is displayed, with the user input mechanism that allows a user to set permissions corresponding to the open item. User actuation of the permission setting user input mechanism is received, the permissions are added to the open item, and the metadata is sent to the receiving device over the near field communication link. The metadata includes a location of the open item. The open item can then be accessed by the receiving device, with the permissions applied to the open item. | 1-20. (canceled) 21. A mobile device, comprising:
a display device; an application component configured to access an item from a location in a remote server system and display the item on the display device; an open section identifier component configured to identify a current location, within the item, that is currently displayed on the display device; a metadata gathering system configured to:
receive a share user input indicating the item is to be shared with another mobile device;
obtain location metadata that identifies the location of the item in the remote server system; and
obtain navigation metadata indicative of the current location;
a permission setting component configured to generate a permission setting user input mechanism, on the display device, that is actuated to set a permission for the item, relative to a user of the other mobile device, the permission setting component configured to send the permission to a permission system in the remote server system; and a near field communication system configured to:
send the location of the item in the remote server system to the other mobile device over a near field communication link; and
send the navigation metadata to the other mobile device over the near field communication link. 22. The mobile device of claim 21, wherein the permission setting user input mechanism displays a plurality of different permission levels, each permission level defining a different level of user access relative to a user of the other mobile device, and wherein the permission setting component is configured to:
receive a user input through the permission setting user input mechanism, the user input selecting one of the plurality of different permission levels; and send an indication of the selected permission level to the permission system in the remote server system. 23. The mobile device of claim 22 wherein the remote server system is configured to share the item to the other mobile device with functionality that is identified, based on the selected permission level. 24. The mobile device of claim 21 and further comprising:
a messaging system configured to generate a message including a link to the item in the remote server system and send the message to the other mobile device. 25. The mobile device of claim 21 wherein the metadata gathering system is configured to obtain account metadata indicative of account information for a user of the mobile device and account information for the user of the other mobile device. 26. The mobile device of claim 21 wherein the item comprises a word processing document and wherein the permission setting component is configured to generate the permission setting user input mechanism that is actuated to set the permission to a read only permission or an edit permission for the word processing document, relative to the user of the other mobile device. 27. The mobile device of claim 21 wherein the item comprises a spreadsheet document and wherein the permission setting component is configured to generate the permission setting user input mechanism that is actuated to set the permission to a read only permission or an edit permission for the spreadsheet document, relative to the user of the other mobile device. 28. The mobile device of claim 21 wherein the item comprises a slide presentation document and wherein the permission setting component is configured to generate the permission setting user input mechanism that is actuated to set the permission to a read only permission or an edit permission for the slide presentation document, relative to the user of the other mobile device. 29. The mobile device of claim 21 wherein the item comprises a folder and wherein the permission setting component is configured to generate the permission, setting user input mechanism that is actuated to set the permission to a read only permission or an edit permission for any other items in the folder, relative to the user of the other mobile device. 30. The mobile device of claim 21 wherein the item comprises an album and wherein the permission setting component is configured to generate the permission setting user input mechanism that is actuated to set the permission to a read only permission or an edit permission for any items in the album, relative to the user of the other mobile device. 31. The mobile device of claim 21 and further comprising:
a sharing system configured to, in response to the share user input, use the near field communication system to send a request to the other mobile device, requesting permission to share the item, that receives a response from the other mobile device, and that controls whether the item is shared based on the response. 32. A mobile device, comprising:
an application component configured to access an item from a location in a remote server system and display the item on the display device a display device; a sharing system configured to:
receive a share user input indicating the item is to be shared with another mobile device;
obtain location metadata that identifies the location of the item in the remote server system;
generate a permission setting user input mechanism, on the display device, that displays a plurality of different permission levels, each permission level defining a different level of user access relative to a user of the other mobile device;
receive a user input through the permission setting user input mechanism, the user input selecting one of the plurality of different permission levels; and
send an indication of the selected permission level to a permission system in the remote server system;
a near field communication system configured to send the location of the item in the remote server system to the other mobile device over a near field communication link. 33. The mobile device of claim 32 wherein the remote server system is configured to share the item to the other mobile device with functionality that is identified based on the selected permission level. 34. The mobile device of claim 32, wherein the sharing system is configured to:
identify a current location, within the item, that is currently displayed on the display device; and obtain navigation metadata indicative of the location within the item to be shared, wherein the near field communication system is configured to send the navigation metadata to the other mobile device over the near field communication link. 35. A method, comprising:
identifying, by a first mobile device, a shared item that is stored on a remote server system that is remote from the first mobile device after identifying the shared item that is stored on the remote server system, receiving a share user input at the first mobile device, the share user input being indicative of a request to share the share item with a user of a second mobile device; identifying, by the first mobile device, a location within the remote server system where the shared item is stored; displaying a permission setting user input mechanism on the first mobile device; receiving actuation, of the permission setting user input mechanism indicative of a permission corresponding to the shared item, relative to the user of the second mobile device, with which the shared item is to be shared; sending the permission from the first mobile device to the remote server system for application to the shared item; and sending the location where the shared item is stored from the first mobile device to the second mobile device over a near field communication link. 36. The method of claim 35 and further comprising:
sending a message from the first mobile device to the second mobile device that includes a link to the shared item in the remote server system. 37. The method of claim 35 and further comprising:
identifying a location within the shared item to which the first mobile device has navigated; and
sending the location within the shared item to the second mobile device over the near field communication link. 38. The method of claim 35 and further comprising:
prior to sending the location where the shared item is stored from the first mobile device to the second mobile device over the near field communication link, sending a request to share the shared item, from the first mobile device to the second mobile device over the near field communication
receiving a response from the second mobile device; and
controlling whether the location where the shared item is stored is sent to the second mobile device based on the response received from the second mobile device. 39. The method of claim 35 wherein displaying a permission setting user input mechanism comprises:
displaying a plurality of different permission user input mechanisms, each corresponding to a different permission to be applied to the shared item, relative to the user of the second mobile device. 40. The method of claim 39 wherein displaying a plurality of different permission user input mechanisms comprises:
displaying a read only permission user input mechanism that is actuated to apply a read only permission to the shared item, relative to the user of the second mobile device; and
displaying an edit permission user input mechanism that is actuated to apply an edit permission to the shared item, relative to the user of the second mobile device. | A sending device receives a user input indicating that the user wishes to share and open item of content with a receiving device. A near field communication link is opened between the sending device and the receiving device. Metadata for sharing the open data is gathered on the sending device and a permission setting user interface display is displayed, with the user input mechanism that allows a user to set permissions corresponding to the open item. User actuation of the permission setting user input mechanism is received, the permissions are added to the open item, and the metadata is sent to the receiving device over the near field communication link. The metadata includes a location of the open item. The open item can then be accessed by the receiving device, with the permissions applied to the open item.1-20. (canceled) 21. A mobile device, comprising:
a display device; an application component configured to access an item from a location in a remote server system and display the item on the display device; an open section identifier component configured to identify a current location, within the item, that is currently displayed on the display device; a metadata gathering system configured to:
receive a share user input indicating the item is to be shared with another mobile device;
obtain location metadata that identifies the location of the item in the remote server system; and
obtain navigation metadata indicative of the current location;
a permission setting component configured to generate a permission setting user input mechanism, on the display device, that is actuated to set a permission for the item, relative to a user of the other mobile device, the permission setting component configured to send the permission to a permission system in the remote server system; and a near field communication system configured to:
send the location of the item in the remote server system to the other mobile device over a near field communication link; and
send the navigation metadata to the other mobile device over the near field communication link. 22. The mobile device of claim 21, wherein the permission setting user input mechanism displays a plurality of different permission levels, each permission level defining a different level of user access relative to a user of the other mobile device, and wherein the permission setting component is configured to:
receive a user input through the permission setting user input mechanism, the user input selecting one of the plurality of different permission levels; and send an indication of the selected permission level to the permission system in the remote server system. 23. The mobile device of claim 22 wherein the remote server system is configured to share the item to the other mobile device with functionality that is identified, based on the selected permission level. 24. The mobile device of claim 21 and further comprising:
a messaging system configured to generate a message including a link to the item in the remote server system and send the message to the other mobile device. 25. The mobile device of claim 21 wherein the metadata gathering system is configured to obtain account metadata indicative of account information for a user of the mobile device and account information for the user of the other mobile device. 26. The mobile device of claim 21 wherein the item comprises a word processing document and wherein the permission setting component is configured to generate the permission setting user input mechanism that is actuated to set the permission to a read only permission or an edit permission for the word processing document, relative to the user of the other mobile device. 27. The mobile device of claim 21 wherein the item comprises a spreadsheet document and wherein the permission setting component is configured to generate the permission setting user input mechanism that is actuated to set the permission to a read only permission or an edit permission for the spreadsheet document, relative to the user of the other mobile device. 28. The mobile device of claim 21 wherein the item comprises a slide presentation document and wherein the permission setting component is configured to generate the permission setting user input mechanism that is actuated to set the permission to a read only permission or an edit permission for the slide presentation document, relative to the user of the other mobile device. 29. The mobile device of claim 21 wherein the item comprises a folder and wherein the permission setting component is configured to generate the permission, setting user input mechanism that is actuated to set the permission to a read only permission or an edit permission for any other items in the folder, relative to the user of the other mobile device. 30. The mobile device of claim 21 wherein the item comprises an album and wherein the permission setting component is configured to generate the permission setting user input mechanism that is actuated to set the permission to a read only permission or an edit permission for any items in the album, relative to the user of the other mobile device. 31. The mobile device of claim 21 and further comprising:
a sharing system configured to, in response to the share user input, use the near field communication system to send a request to the other mobile device, requesting permission to share the item, that receives a response from the other mobile device, and that controls whether the item is shared based on the response. 32. A mobile device, comprising:
an application component configured to access an item from a location in a remote server system and display the item on the display device a display device; a sharing system configured to:
receive a share user input indicating the item is to be shared with another mobile device;
obtain location metadata that identifies the location of the item in the remote server system;
generate a permission setting user input mechanism, on the display device, that displays a plurality of different permission levels, each permission level defining a different level of user access relative to a user of the other mobile device;
receive a user input through the permission setting user input mechanism, the user input selecting one of the plurality of different permission levels; and
send an indication of the selected permission level to a permission system in the remote server system;
a near field communication system configured to send the location of the item in the remote server system to the other mobile device over a near field communication link. 33. The mobile device of claim 32 wherein the remote server system is configured to share the item to the other mobile device with functionality that is identified based on the selected permission level. 34. The mobile device of claim 32, wherein the sharing system is configured to:
identify a current location, within the item, that is currently displayed on the display device; and obtain navigation metadata indicative of the location within the item to be shared, wherein the near field communication system is configured to send the navigation metadata to the other mobile device over the near field communication link. 35. A method, comprising:
identifying, by a first mobile device, a shared item that is stored on a remote server system that is remote from the first mobile device after identifying the shared item that is stored on the remote server system, receiving a share user input at the first mobile device, the share user input being indicative of a request to share the share item with a user of a second mobile device; identifying, by the first mobile device, a location within the remote server system where the shared item is stored; displaying a permission setting user input mechanism on the first mobile device; receiving actuation, of the permission setting user input mechanism indicative of a permission corresponding to the shared item, relative to the user of the second mobile device, with which the shared item is to be shared; sending the permission from the first mobile device to the remote server system for application to the shared item; and sending the location where the shared item is stored from the first mobile device to the second mobile device over a near field communication link. 36. The method of claim 35 and further comprising:
sending a message from the first mobile device to the second mobile device that includes a link to the shared item in the remote server system. 37. The method of claim 35 and further comprising:
identifying a location within the shared item to which the first mobile device has navigated; and
sending the location within the shared item to the second mobile device over the near field communication link. 38. The method of claim 35 and further comprising:
prior to sending the location where the shared item is stored from the first mobile device to the second mobile device over the near field communication link, sending a request to share the shared item, from the first mobile device to the second mobile device over the near field communication
receiving a response from the second mobile device; and
controlling whether the location where the shared item is stored is sent to the second mobile device based on the response received from the second mobile device. 39. The method of claim 35 wherein displaying a permission setting user input mechanism comprises:
displaying a plurality of different permission user input mechanisms, each corresponding to a different permission to be applied to the shared item, relative to the user of the second mobile device. 40. The method of claim 39 wherein displaying a plurality of different permission user input mechanisms comprises:
displaying a read only permission user input mechanism that is actuated to apply a read only permission to the shared item, relative to the user of the second mobile device; and
displaying an edit permission user input mechanism that is actuated to apply an edit permission to the shared item, relative to the user of the second mobile device. | 2,100 |
6,464 | 6,464 | 15,660,006 | 2,135 | Filtering insertion of evicted cache entries predicted as dead-on-arrival (DOA) into a last level cache (LLC) memory is disclosed. A lower-level cache memory updates a DOA prediction value associated with a requested cache entry in a DOA prediction circuit indicating a cache entry reuse history. The DOA prediction value is updated to indicate if the requested cache entry was reused in the LLC memory or not based on whether a cache miss in the lower-level cache memory for the requested cache entry was serviced by the LLC memory. Subsequently, upon eviction of the requested cache entry from the lower-level cache memory, the associated DOA prediction value can be consulted to predict if the cache entry will be DOA. If so, the LLC memory is filtered to store the evicted cache entry in system memory or to insert in a less recently used location in the LLC memory. | 1. A cache memory system, comprising:
a lower-level cache memory configured to store a plurality of lower-level cache entries each representing a system data entry in a system memory, the lower-level cache memory configured to:
evict a lower-level cache entry among the plurality of lower-level cache entries to a last level cache (LLC) memory; and
receive a last level cache entry from the LLC memory in response to a cache miss to a lower-level cache;
the LLC memory configured to store a plurality of last level cache entries each representing the system data entry in the system memory, the LLC memory configured to:
insert the evicted lower-level cache entry from the lower-level cache memory in a last level cache entry among the plurality of last level cache entries based on an address of the evicted lower-level cache entry;
evict the last level cache entry to the system memory; and
receive the system data entry from the system memory in response to a cache miss to the LLC memory;
a dead-on-arrival (DOA) prediction circuit comprising one or more DOA prediction registers associated with the plurality of lower-level cache entries each configured to store a DOA prediction value indicative of a whether the plurality of lower-level cache entries are predicted to be dead from the LLC memory; and in response to eviction of the lower-level cache entry from the lower-level cache memory, the cache memory system configured to:
access a DOA prediction value in a DOA prediction register among the one or more DOA prediction registers associated with the evicted lower-level cache entry;
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value; and
in response to determining that the evicted lower-level cache entry is predicted to be dead from the LLC memory, filter the evicted lower-level cache entry in the LLC memory. 2. The cache memory system of claim 1, wherein in response to determining the evicted lower-level cache entry is predicted to be dead from the LLC memory, the cache memory system is configured to filter the evicted lower-level cache entry by being configured to not insert the evicted lower-level cache entry into the LLC memory. 3. The cache memory system of claim 1, wherein in response to determining the evicted lower-level cache entry is predicted to be dead from the LLC memory, the cache memory system is configured to filter the evicted lower-level cache entry by being configured to insert the evicted lower-level cache entry into a less recently used cache entry in the LLC memory. 4. The cache memory system of claim 1, further configured to, in response to determining the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value;
determine if the evicted lower-level cache entry is dirty; and in response to determining that the evicted lower-level cache entry is dirty, insert the evicted lower-level cache entry into the system memory. 5. The cache memory system of claim 1, further configured to, in response to determining that the evicted lower-level cache entry is not predicted to be dead from the LLC memory, insert the evicted lower-level cache entry in the LLC memory. 6. The cache memory system of claim 1, wherein the DOA prediction circuit is not included in the plurality of last level cache entries of the LLC memory. 7. The cache memory system of claim 1, wherein the one or more DOA prediction registers comprises one or more DOA prediction counters each configured to store the DOA prediction value comprising a DOA prediction count;
wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory:
access a DOA prediction count in a DOA prediction counter among the one or more DOA prediction counters associated with the evicted lower-level cache entry; and
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction count. 8. The cache memory system of claim 7, wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory:
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction count exceeding a predefined prediction count value. 9. The cache memory system of claim 8, wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory:
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction count exceeding below the predefined prediction count value. 10. The cache memory system of claim 1, wherein the one or more DOA prediction registers are each associated with at least one memory address; and
wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory, access a DOA prediction value in a DOA prediction register among the one or more DOA prediction registers associated with a memory address of the evicted lower-level cache entry. 11. The cache memory system of claim 10, wherein:
the cache memory system is further configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory, generate a hash value based on the memory address of the evicted lower-level cache entry; and the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory, access the DOA prediction value in the DOA prediction register among the one or more DOA prediction registers based on the hash value of the memory address of the evicted lower-level cache entry. 12. The cache memory system of claim 1, wherein the one or more DOA prediction registers are each associated with at least one memory address;
wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory:
access a DOA prediction value in a DOA prediction register among the one or more DOA prediction registers associated with a program counter of a load instruction that generated the evicted lower-level cache entry. 13. The cache memory system of claim 1, wherein the DOA prediction circuit further comprises one or more DOA prediction tags each associated with a DOA prediction register among the one or more DOA prediction registers;
wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory, access the DOA prediction value by being configured to:
access a DOA prediction tag among the one or more DOA prediction tags associated with the evicted lower-level cache entry; and
access the DOA prediction value in the DOA prediction register among the one or more DOA prediction registers associated with the accessed DOA prediction tag. 14. The cache memory system of claim 1, wherein:
the lower-level cache memory is configured to:
receive a request to access a lower-level cache entry among the plurality of lower-level cache entries; and
generate a lower-level cache miss in response to the requested lower-level cache entry not being present in the lower-level cache memory; and
in response to the lower-level cache miss, the cache memory system is further configured to update a DOA prediction value in a DOA prediction register among the one or more DOA prediction registers associated with the requested lower-level cache entry in the DOA prediction circuit. 15. The cache memory system of claim 14, wherein in response to the lower-level cache miss, the cache memory system is further configured to determine if a received data entry associated with a memory address of the requested lower-level cache entry was serviced by the system memory; and
wherein the cache memory system is configured to update the DOA prediction value in the DOA prediction register among the one or more DOA prediction registers associated with the requested lower-level cache entry based on the determination of whether the received data entry was serviced by the system memory. 16. The cache memory system of claim 15, wherein the one or more DOA prediction registers comprises one or more DOA prediction counters each configured to store the DOA prediction value comprising a DOA prediction count; and
wherein the cache memory system is configured to update the DOA prediction count in DOA prediction counter among the one or more DOA prediction counters associated with the requested lower-level cache entry if the received data entry was serviced by the system memory. 17. The cache memory system of claim 16, wherein, in response to a first instance of the lower-level cache miss in the lower-level cache memory, the cache memory system is configured to initialize the DOA prediction count in the DOA prediction counter among the one or more DOA prediction counters associated with the requested lower-level cache entry with a saturation count. 18. The cache memory system of claim 1, wherein:
the LLC memory comprises:
an LLC cache comprising a plurality of cache sets comprising a plurality of follower cache sets and a plurality of dedicated cache sets comprising at least one first dedicated cache set comprising a first dedicated subset of the plurality of dedicated cache sets in the LLC cache for which at least one first DOA prediction policy is applied, and at least one second dedicated cache set comprising a second dedicated subset of the plurality of dedicated cache sets in the LLC cache for which at least one second DOA prediction policy, different from the at least one first DOA prediction policy, is applied;
the LLC memory configured to update a DOA prediction value in a DOA prediction register based on a cache miss resulting from an accessed cache entry only in a dedicated cache set among the plurality of dedicated cache sets in the LLC cache; the lower-level cache memory configured to:
access the DOA prediction value in the DOA prediction register associated with the evicted lower-level cache entry;
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value; and
in response to determining that the evicted lower-level cache entry is predicted to be dead from the LLC memory, communicate a DOA prediction for the evicted lower-level cache entry to the LLC memory; and
the LLC memory is further configured to:
access a DOA prediction value in the DOA prediction register;
determine whether the at least one first DOA prediction policy or the at least one second DOA prediction policy should be applied to the evicted lower-level cache entry based on the accessed DOA prediction value; and
filter the evicted lower-level cache entry in the LLC memory based on the determined DOA prediction policy among the at least one first DOA prediction policy and the at least one second DOA prediction policy. 19. The cache memory system of claim 1, wherein the plurality of last level cache entries stored in the LLC memory are exclusive of the plurality of lower-level cache entries stored in the lower-level cache memory. 20. The cache memory system of claim 1, wherein the plurality of last level cache entries stored in the LLC memory are inclusive of the plurality of lower-level cache entries stored in the lower-level cache memory. 21. The cache memory system of claim 1 integrated into a system-on-a-chip (SoC). 22. The cache memory system of claim 1 integrated into a device selected from the group consisting of: a set top box; an entertainment unit; a navigation device; a communications device; a fixed location data unit; a mobile location data unit; a global positioning system (GPS) device; a mobile phone; a cellular phone; a smart phone; a session initiation protocol (SIP) phone; a tablet; a phablet; a server; a computer; a portable computer; a mobile computing device; a wearable computing device; a desktop computer; a personal digital assistant (PDA); a monitor; a computer monitor; a television; a tuner; a radio; a satellite radio; a music player; a digital music player; a portable music player; a digital video player; a video player; a digital video disc (DVD) player; a portable digital video player; an automobile; a vehicle component; avionics systems; a drone; and a multicopter. 23. A method of evicting a lower-level cache entry in a cache memory system, comprising:
evicting a lower-level cache entry among a plurality of lower-level cache entries from a lower-level cache memory to a last level cache (LLC) memory; accessing a dead-on-arrival (DOA) prediction value in a DOA prediction register among one or more DOA prediction registers associated with the evicted lower-level cache entry; determining if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value; and in response to determining that the evicted lower-level cache entry is predicted to be dead from the LLC memory, filtering the evicted lower-level cache entry in the LLC memory. 24. The method of claim 23, wherein filtering the lower-level cache entry comprises not inserting the evicted lower-level cache entry into the LLC memory. 25. The method of claim 23, wherein filtering the lower-level cache entry comprises inserting the evicted lower-level cache entry into a less recently used cache entry in the LLC memory. 26. The method of claim 23, wherein, in response to determining the evicted lower-level cache entry is not predicted to be dead from the LLC memory, inserting the evicted lower-level cache entry in the LLC memory. 27. A last level cache (LLC) memory, comprising:
a last level cache configured to store a plurality of last level cache entries each representing a data entry in a system memory; and an LLC controller configured to:
receive an evicted lower-level cache entry from a lower-level cache memory;
insert the received evicted lower-level cache entry in a last level cache entry among the plurality of last level cache entries based on an address of the evicted lower-level cache entry;
evict a last level cache entry among the plurality of last level cache entries to system memory;
receive a system data entry from the system memory in response to a cache miss to the LLC memory; and
in response to the received evicted lower-level cache entry from the lower-level cache memory:
access a dead-on-arrival (DOA) prediction value in a DOA prediction register among one or more DOA prediction registers associated with the evicted lower-level cache entry;
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value; and
in response to determining that the evicted lower-level cache entry is predicted to be dead from the LLC memory, filter the evicted lower-level cache entry in the lower-level cache among the plurality of lower-level cache entries. 28. The LLC memory of claim 27, wherein the LLC controller is further configured to, in response to determining that the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value;
determine if the evicted lower-level cache entry is dirty; and in response to determining that the evicted lower-level cache entry is dirty, insert the evicted lower-level cache entry into the system memory. 29. The LLC memory of claim 27, wherein the LLC controller is further configured to, in response to determining that the evicted lower-level cache entry is not predicted to be dead from the LLC memory, insert the evicted lower-level cache entry to the LLC memory. 30. A lower-level cache memory, comprising:
a plurality of lower-level cache entries each representing a system data entry in a system memory; and the lower-level cache memory configured to:
evict a lower-level cache entry among the plurality of lower-level cache entries to a last level cache (LLC) memory;
receive a last level cache entry from the LLC memory in response to a cache miss to the lower-level cache;
receive a request to access the lower-level cache entry among the plurality of lower-level cache entries in the lower-level cache;
generate a lower-level cache miss in response to the requested lower-level cache entry not being present in the lower-level cache; and
in response to the lower-level cache miss:
determine if a received data entry associated with a memory address of the requested lower-level cache entry was serviced by the system memory; and
update a dead-on-arrival (DOA) prediction value in a DOA prediction register among one or more DOA prediction registers associated with the requested lower-level cache entry based on the determination of whether the received data entry was serviced by the system memory. 31. The lower-level cache memory of claim 30, wherein the one or more DOA prediction registers comprises one or more DOA prediction counters each configured to store the DOA prediction value comprising a DOA prediction count; and
wherein the lower-level cache memory is configured to update the DOA prediction count in a DOA prediction counter among the one or more DOA prediction counters associated with the requested lower-level cache entry if the received data entry was serviced by the system memory. | Filtering insertion of evicted cache entries predicted as dead-on-arrival (DOA) into a last level cache (LLC) memory is disclosed. A lower-level cache memory updates a DOA prediction value associated with a requested cache entry in a DOA prediction circuit indicating a cache entry reuse history. The DOA prediction value is updated to indicate if the requested cache entry was reused in the LLC memory or not based on whether a cache miss in the lower-level cache memory for the requested cache entry was serviced by the LLC memory. Subsequently, upon eviction of the requested cache entry from the lower-level cache memory, the associated DOA prediction value can be consulted to predict if the cache entry will be DOA. If so, the LLC memory is filtered to store the evicted cache entry in system memory or to insert in a less recently used location in the LLC memory.1. A cache memory system, comprising:
a lower-level cache memory configured to store a plurality of lower-level cache entries each representing a system data entry in a system memory, the lower-level cache memory configured to:
evict a lower-level cache entry among the plurality of lower-level cache entries to a last level cache (LLC) memory; and
receive a last level cache entry from the LLC memory in response to a cache miss to a lower-level cache;
the LLC memory configured to store a plurality of last level cache entries each representing the system data entry in the system memory, the LLC memory configured to:
insert the evicted lower-level cache entry from the lower-level cache memory in a last level cache entry among the plurality of last level cache entries based on an address of the evicted lower-level cache entry;
evict the last level cache entry to the system memory; and
receive the system data entry from the system memory in response to a cache miss to the LLC memory;
a dead-on-arrival (DOA) prediction circuit comprising one or more DOA prediction registers associated with the plurality of lower-level cache entries each configured to store a DOA prediction value indicative of a whether the plurality of lower-level cache entries are predicted to be dead from the LLC memory; and in response to eviction of the lower-level cache entry from the lower-level cache memory, the cache memory system configured to:
access a DOA prediction value in a DOA prediction register among the one or more DOA prediction registers associated with the evicted lower-level cache entry;
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value; and
in response to determining that the evicted lower-level cache entry is predicted to be dead from the LLC memory, filter the evicted lower-level cache entry in the LLC memory. 2. The cache memory system of claim 1, wherein in response to determining the evicted lower-level cache entry is predicted to be dead from the LLC memory, the cache memory system is configured to filter the evicted lower-level cache entry by being configured to not insert the evicted lower-level cache entry into the LLC memory. 3. The cache memory system of claim 1, wherein in response to determining the evicted lower-level cache entry is predicted to be dead from the LLC memory, the cache memory system is configured to filter the evicted lower-level cache entry by being configured to insert the evicted lower-level cache entry into a less recently used cache entry in the LLC memory. 4. The cache memory system of claim 1, further configured to, in response to determining the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value;
determine if the evicted lower-level cache entry is dirty; and in response to determining that the evicted lower-level cache entry is dirty, insert the evicted lower-level cache entry into the system memory. 5. The cache memory system of claim 1, further configured to, in response to determining that the evicted lower-level cache entry is not predicted to be dead from the LLC memory, insert the evicted lower-level cache entry in the LLC memory. 6. The cache memory system of claim 1, wherein the DOA prediction circuit is not included in the plurality of last level cache entries of the LLC memory. 7. The cache memory system of claim 1, wherein the one or more DOA prediction registers comprises one or more DOA prediction counters each configured to store the DOA prediction value comprising a DOA prediction count;
wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory:
access a DOA prediction count in a DOA prediction counter among the one or more DOA prediction counters associated with the evicted lower-level cache entry; and
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction count. 8. The cache memory system of claim 7, wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory:
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction count exceeding a predefined prediction count value. 9. The cache memory system of claim 8, wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory:
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction count exceeding below the predefined prediction count value. 10. The cache memory system of claim 1, wherein the one or more DOA prediction registers are each associated with at least one memory address; and
wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory, access a DOA prediction value in a DOA prediction register among the one or more DOA prediction registers associated with a memory address of the evicted lower-level cache entry. 11. The cache memory system of claim 10, wherein:
the cache memory system is further configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory, generate a hash value based on the memory address of the evicted lower-level cache entry; and the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory, access the DOA prediction value in the DOA prediction register among the one or more DOA prediction registers based on the hash value of the memory address of the evicted lower-level cache entry. 12. The cache memory system of claim 1, wherein the one or more DOA prediction registers are each associated with at least one memory address;
wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory:
access a DOA prediction value in a DOA prediction register among the one or more DOA prediction registers associated with a program counter of a load instruction that generated the evicted lower-level cache entry. 13. The cache memory system of claim 1, wherein the DOA prediction circuit further comprises one or more DOA prediction tags each associated with a DOA prediction register among the one or more DOA prediction registers;
wherein the cache memory system is configured to, in response to the eviction of the lower-level cache entry from the lower-level cache memory, access the DOA prediction value by being configured to:
access a DOA prediction tag among the one or more DOA prediction tags associated with the evicted lower-level cache entry; and
access the DOA prediction value in the DOA prediction register among the one or more DOA prediction registers associated with the accessed DOA prediction tag. 14. The cache memory system of claim 1, wherein:
the lower-level cache memory is configured to:
receive a request to access a lower-level cache entry among the plurality of lower-level cache entries; and
generate a lower-level cache miss in response to the requested lower-level cache entry not being present in the lower-level cache memory; and
in response to the lower-level cache miss, the cache memory system is further configured to update a DOA prediction value in a DOA prediction register among the one or more DOA prediction registers associated with the requested lower-level cache entry in the DOA prediction circuit. 15. The cache memory system of claim 14, wherein in response to the lower-level cache miss, the cache memory system is further configured to determine if a received data entry associated with a memory address of the requested lower-level cache entry was serviced by the system memory; and
wherein the cache memory system is configured to update the DOA prediction value in the DOA prediction register among the one or more DOA prediction registers associated with the requested lower-level cache entry based on the determination of whether the received data entry was serviced by the system memory. 16. The cache memory system of claim 15, wherein the one or more DOA prediction registers comprises one or more DOA prediction counters each configured to store the DOA prediction value comprising a DOA prediction count; and
wherein the cache memory system is configured to update the DOA prediction count in DOA prediction counter among the one or more DOA prediction counters associated with the requested lower-level cache entry if the received data entry was serviced by the system memory. 17. The cache memory system of claim 16, wherein, in response to a first instance of the lower-level cache miss in the lower-level cache memory, the cache memory system is configured to initialize the DOA prediction count in the DOA prediction counter among the one or more DOA prediction counters associated with the requested lower-level cache entry with a saturation count. 18. The cache memory system of claim 1, wherein:
the LLC memory comprises:
an LLC cache comprising a plurality of cache sets comprising a plurality of follower cache sets and a plurality of dedicated cache sets comprising at least one first dedicated cache set comprising a first dedicated subset of the plurality of dedicated cache sets in the LLC cache for which at least one first DOA prediction policy is applied, and at least one second dedicated cache set comprising a second dedicated subset of the plurality of dedicated cache sets in the LLC cache for which at least one second DOA prediction policy, different from the at least one first DOA prediction policy, is applied;
the LLC memory configured to update a DOA prediction value in a DOA prediction register based on a cache miss resulting from an accessed cache entry only in a dedicated cache set among the plurality of dedicated cache sets in the LLC cache; the lower-level cache memory configured to:
access the DOA prediction value in the DOA prediction register associated with the evicted lower-level cache entry;
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value; and
in response to determining that the evicted lower-level cache entry is predicted to be dead from the LLC memory, communicate a DOA prediction for the evicted lower-level cache entry to the LLC memory; and
the LLC memory is further configured to:
access a DOA prediction value in the DOA prediction register;
determine whether the at least one first DOA prediction policy or the at least one second DOA prediction policy should be applied to the evicted lower-level cache entry based on the accessed DOA prediction value; and
filter the evicted lower-level cache entry in the LLC memory based on the determined DOA prediction policy among the at least one first DOA prediction policy and the at least one second DOA prediction policy. 19. The cache memory system of claim 1, wherein the plurality of last level cache entries stored in the LLC memory are exclusive of the plurality of lower-level cache entries stored in the lower-level cache memory. 20. The cache memory system of claim 1, wherein the plurality of last level cache entries stored in the LLC memory are inclusive of the plurality of lower-level cache entries stored in the lower-level cache memory. 21. The cache memory system of claim 1 integrated into a system-on-a-chip (SoC). 22. The cache memory system of claim 1 integrated into a device selected from the group consisting of: a set top box; an entertainment unit; a navigation device; a communications device; a fixed location data unit; a mobile location data unit; a global positioning system (GPS) device; a mobile phone; a cellular phone; a smart phone; a session initiation protocol (SIP) phone; a tablet; a phablet; a server; a computer; a portable computer; a mobile computing device; a wearable computing device; a desktop computer; a personal digital assistant (PDA); a monitor; a computer monitor; a television; a tuner; a radio; a satellite radio; a music player; a digital music player; a portable music player; a digital video player; a video player; a digital video disc (DVD) player; a portable digital video player; an automobile; a vehicle component; avionics systems; a drone; and a multicopter. 23. A method of evicting a lower-level cache entry in a cache memory system, comprising:
evicting a lower-level cache entry among a plurality of lower-level cache entries from a lower-level cache memory to a last level cache (LLC) memory; accessing a dead-on-arrival (DOA) prediction value in a DOA prediction register among one or more DOA prediction registers associated with the evicted lower-level cache entry; determining if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value; and in response to determining that the evicted lower-level cache entry is predicted to be dead from the LLC memory, filtering the evicted lower-level cache entry in the LLC memory. 24. The method of claim 23, wherein filtering the lower-level cache entry comprises not inserting the evicted lower-level cache entry into the LLC memory. 25. The method of claim 23, wherein filtering the lower-level cache entry comprises inserting the evicted lower-level cache entry into a less recently used cache entry in the LLC memory. 26. The method of claim 23, wherein, in response to determining the evicted lower-level cache entry is not predicted to be dead from the LLC memory, inserting the evicted lower-level cache entry in the LLC memory. 27. A last level cache (LLC) memory, comprising:
a last level cache configured to store a plurality of last level cache entries each representing a data entry in a system memory; and an LLC controller configured to:
receive an evicted lower-level cache entry from a lower-level cache memory;
insert the received evicted lower-level cache entry in a last level cache entry among the plurality of last level cache entries based on an address of the evicted lower-level cache entry;
evict a last level cache entry among the plurality of last level cache entries to system memory;
receive a system data entry from the system memory in response to a cache miss to the LLC memory; and
in response to the received evicted lower-level cache entry from the lower-level cache memory:
access a dead-on-arrival (DOA) prediction value in a DOA prediction register among one or more DOA prediction registers associated with the evicted lower-level cache entry;
determine if the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value; and
in response to determining that the evicted lower-level cache entry is predicted to be dead from the LLC memory, filter the evicted lower-level cache entry in the lower-level cache among the plurality of lower-level cache entries. 28. The LLC memory of claim 27, wherein the LLC controller is further configured to, in response to determining that the evicted lower-level cache entry is predicted to be dead from the LLC memory based on the accessed DOA prediction value;
determine if the evicted lower-level cache entry is dirty; and in response to determining that the evicted lower-level cache entry is dirty, insert the evicted lower-level cache entry into the system memory. 29. The LLC memory of claim 27, wherein the LLC controller is further configured to, in response to determining that the evicted lower-level cache entry is not predicted to be dead from the LLC memory, insert the evicted lower-level cache entry to the LLC memory. 30. A lower-level cache memory, comprising:
a plurality of lower-level cache entries each representing a system data entry in a system memory; and the lower-level cache memory configured to:
evict a lower-level cache entry among the plurality of lower-level cache entries to a last level cache (LLC) memory;
receive a last level cache entry from the LLC memory in response to a cache miss to the lower-level cache;
receive a request to access the lower-level cache entry among the plurality of lower-level cache entries in the lower-level cache;
generate a lower-level cache miss in response to the requested lower-level cache entry not being present in the lower-level cache; and
in response to the lower-level cache miss:
determine if a received data entry associated with a memory address of the requested lower-level cache entry was serviced by the system memory; and
update a dead-on-arrival (DOA) prediction value in a DOA prediction register among one or more DOA prediction registers associated with the requested lower-level cache entry based on the determination of whether the received data entry was serviced by the system memory. 31. The lower-level cache memory of claim 30, wherein the one or more DOA prediction registers comprises one or more DOA prediction counters each configured to store the DOA prediction value comprising a DOA prediction count; and
wherein the lower-level cache memory is configured to update the DOA prediction count in a DOA prediction counter among the one or more DOA prediction counters associated with the requested lower-level cache entry if the received data entry was serviced by the system memory. | 2,100 |
6,465 | 6,465 | 15,454,252 | 2,113 | Systems and methods for a self-learning event response engine of systems are described. In one embodiment, the systems and methods may include identifying two or more patterns of events among a plurality of detected events stored in a database, identifying an adverse condition of the storage system that occurs as a result of a particular pattern of events from the identified patterns of events, identifying a corrective action that resolves the adverse condition of the storage system, detecting an occurrence of one or more events from the particular pattern of events, and implementing the corrective action based at least in part on detecting the occurrence of the one or more events from the particular pattern of events. | 1. A storage system comprising:
a hardware controller configured to:
identify two or more patterns of events among a plurality of detected events and an adverse condition of the storage system that occurs as a result of a particular pattern of events from the identified patterns of events;
select a corrective action that resolves the adverse condition;
detect an occurrence of one or more events from the particular pattern of events; and
implement the corrective action based at least in part on detecting the occurrence of the one or more events from the particular pattern of events. 2. The storage system of claim 1, wherein each pattern of events includes a sequence of two or more events in a given order related to operations of the storage system, the storage system comprising a storage drive, a storage server, a storage enclosure enclosing two or more storage drives, a distributed data storage system, a cloud storage system, or any combination thereof. 3. The storage system of claim 1, wherein the adverse condition includes an abnormal operation of the storage system, an abnormal operating condition of the storage system, a hardware failure, a software bug, a firmware bug, unavailability of the storage system, a loss of data stored on the storage system, or any combination thereof. 4. The storage system of claim 1, wherein the plurality of detected events comprises an event type, an event trigger, an event severity level, a pattern severity level, or any combination thereof. 5. The storage system of claim 4, wherein the hardware controller is further configured to:
rank the identified patterns of events based at least in part on their frequency of occurrence, the event severity level, the pattern severity level, or any combination thereof. 6. The storage system of claim 5, wherein the hardware controller is further configured to:
detect the occurrence of the one or more events being based at least in part on the ranking of the identified patterns of events. 7. The storage system of claim 5, wherein the hardware controller is further configured to:
calculate a time period expected to lapse between two events in the particular pattern of events; and estimate, based at least in part on the time period, a mean time before the adverse condition occurs in relation to detecting the occurrence of the one or more events from the particular pattern of events. 8. The storage system of claim 7, wherein the hardware controller is further configured to:
implement the corrective action based at least in part on the event severity level, the pattern severity level, the rank of the particular pattern of events, the time period, the estimated mean time before the adverse condition occurs, a cost of the corrective action, a cost of implementing the corrective action immediately versus a cost of implementing the corrective action after waiting a predetermined time period, current storage system performance, a service agreement, a device warranty, or any combination thereof. 9. The storage system of claim 4, wherein the event severity level of the particular pattern of events is based at least in part on a position of a specific event from the particular pattern of events relative to other events in the particular patterns of events, and the pattern severity level of the particular pattern of events being based at least in part on a severity of the adverse condition caused by the particular pattern of events. 10. The storage system of claim 1, wherein the corrective action includes at least one of deleting a file, downloading a file, implementing a file, saving a file in a file system folder stored on a storage medium of the storage system, saving a file in a certain location of the storage medium of the storage system, installing a program, updating a program, installing firmware, upgrading firmware, repairing a hardware component, replacing a hardware component, sending a notification, or any combination thereof. 11. A storage system method, comprising:
identifying two or more patterns of events among a plurality of detected events stored in a database; identifying an adverse condition of the storage system that occurs as a result of a particular pattern of events from the identified patterns of events; identifying a corrective action that resolves the adverse condition of the storage system; detecting an occurrence of one or more events from the particular pattern of events; and implementing the corrective action based at least in part on detecting the occurrence of the one or more events from the particular pattern of events. 12. The storage system method of claim 11, wherein each pattern of events includes a sequence of two or more events in a given order related to operations of the storage system, the storage system comprising a storage drive, a storage server, a storage enclosure enclosing two or more storage drives, a distributed data storage system, a cloud storage system, or any combination thereof. 13. The storage system method of claim 11, wherein the adverse condition includes an abnormal operation of the storage system, an abnormal operating condition of the storage system, a hardware failure, a software bug, a firmware bug, unavailability of the storage system, a loss of data stored on the storage system, or any combination thereof. 14. The storage system method of claim 11, wherein the plurality of detected events stored in the database comprises an event type, an event trigger, an event severity level, a pattern severity level, or any combination thereof. 15. The storage system method of claim 14, further comprising:
ranking the identified patterns of events based at least in part on their frequency of occurrence, the event severity level, the pattern severity level, or any combination thereof; and detecting the occurrence of the one or more events being based at least in part on the ranking of the identified patterns of events. 16. The storage system method of claim 15, further comprising:
calculating a time period expected to lapse between two events in the particular pattern of events; and estimating, based at least in part on the calculated time period, a mean time before the adverse condition occurs in relation to detecting the occurrence of the one or more events from the particular pattern of events. 17. The storage system method of claim 16, further comprising:
implementing the identified corrective action based at least in part on the event severity level, the pattern severity level, the rank of the particular pattern of events, the time period, the estimated mean time before the adverse condition occurs, a cost of the corrective action, a cost of implementing the corrective action immediately versus a cost of implementing the corrective action after waiting a predetermined time period, current storage system performance, a service agreement, a device warranty, or any combination thereof. 18. The storage system method of claim 14, wherein the event severity level of the particular pattern of events is based at least in part on a position of a specific event from the particular pattern of events relative to other events in the particular patterns of events, and the pattern severity level of the particular pattern of events being based at least in part on a severity of the adverse condition caused by the particular pattern of events. 19. A non-transitory computer-readable storage medium storing computer executable instructions to improve a computer system that when executed by a processor cause the processor to perform the steps of:
identifying two or more patterns of events among a plurality of detected events stored in a database, each pattern of events including a sequence of two or more events in a given order related to operations of a storage system; identifying an adverse condition of the storage system that occurs as a result of the identified patterns of events; selecting a corrective action that resolves the adverse condition of the storage system; and implementing the corrective action. 20. The storage medium of claim 19, wherein the storage system comprises a storage drive, a storage server, a storage enclosure enclosing two or more storage drives, a distributed data storage system, a cloud storage system, or any combination thereof. | Systems and methods for a self-learning event response engine of systems are described. In one embodiment, the systems and methods may include identifying two or more patterns of events among a plurality of detected events stored in a database, identifying an adverse condition of the storage system that occurs as a result of a particular pattern of events from the identified patterns of events, identifying a corrective action that resolves the adverse condition of the storage system, detecting an occurrence of one or more events from the particular pattern of events, and implementing the corrective action based at least in part on detecting the occurrence of the one or more events from the particular pattern of events.1. A storage system comprising:
a hardware controller configured to:
identify two or more patterns of events among a plurality of detected events and an adverse condition of the storage system that occurs as a result of a particular pattern of events from the identified patterns of events;
select a corrective action that resolves the adverse condition;
detect an occurrence of one or more events from the particular pattern of events; and
implement the corrective action based at least in part on detecting the occurrence of the one or more events from the particular pattern of events. 2. The storage system of claim 1, wherein each pattern of events includes a sequence of two or more events in a given order related to operations of the storage system, the storage system comprising a storage drive, a storage server, a storage enclosure enclosing two or more storage drives, a distributed data storage system, a cloud storage system, or any combination thereof. 3. The storage system of claim 1, wherein the adverse condition includes an abnormal operation of the storage system, an abnormal operating condition of the storage system, a hardware failure, a software bug, a firmware bug, unavailability of the storage system, a loss of data stored on the storage system, or any combination thereof. 4. The storage system of claim 1, wherein the plurality of detected events comprises an event type, an event trigger, an event severity level, a pattern severity level, or any combination thereof. 5. The storage system of claim 4, wherein the hardware controller is further configured to:
rank the identified patterns of events based at least in part on their frequency of occurrence, the event severity level, the pattern severity level, or any combination thereof. 6. The storage system of claim 5, wherein the hardware controller is further configured to:
detect the occurrence of the one or more events being based at least in part on the ranking of the identified patterns of events. 7. The storage system of claim 5, wherein the hardware controller is further configured to:
calculate a time period expected to lapse between two events in the particular pattern of events; and estimate, based at least in part on the time period, a mean time before the adverse condition occurs in relation to detecting the occurrence of the one or more events from the particular pattern of events. 8. The storage system of claim 7, wherein the hardware controller is further configured to:
implement the corrective action based at least in part on the event severity level, the pattern severity level, the rank of the particular pattern of events, the time period, the estimated mean time before the adverse condition occurs, a cost of the corrective action, a cost of implementing the corrective action immediately versus a cost of implementing the corrective action after waiting a predetermined time period, current storage system performance, a service agreement, a device warranty, or any combination thereof. 9. The storage system of claim 4, wherein the event severity level of the particular pattern of events is based at least in part on a position of a specific event from the particular pattern of events relative to other events in the particular patterns of events, and the pattern severity level of the particular pattern of events being based at least in part on a severity of the adverse condition caused by the particular pattern of events. 10. The storage system of claim 1, wherein the corrective action includes at least one of deleting a file, downloading a file, implementing a file, saving a file in a file system folder stored on a storage medium of the storage system, saving a file in a certain location of the storage medium of the storage system, installing a program, updating a program, installing firmware, upgrading firmware, repairing a hardware component, replacing a hardware component, sending a notification, or any combination thereof. 11. A storage system method, comprising:
identifying two or more patterns of events among a plurality of detected events stored in a database; identifying an adverse condition of the storage system that occurs as a result of a particular pattern of events from the identified patterns of events; identifying a corrective action that resolves the adverse condition of the storage system; detecting an occurrence of one or more events from the particular pattern of events; and implementing the corrective action based at least in part on detecting the occurrence of the one or more events from the particular pattern of events. 12. The storage system method of claim 11, wherein each pattern of events includes a sequence of two or more events in a given order related to operations of the storage system, the storage system comprising a storage drive, a storage server, a storage enclosure enclosing two or more storage drives, a distributed data storage system, a cloud storage system, or any combination thereof. 13. The storage system method of claim 11, wherein the adverse condition includes an abnormal operation of the storage system, an abnormal operating condition of the storage system, a hardware failure, a software bug, a firmware bug, unavailability of the storage system, a loss of data stored on the storage system, or any combination thereof. 14. The storage system method of claim 11, wherein the plurality of detected events stored in the database comprises an event type, an event trigger, an event severity level, a pattern severity level, or any combination thereof. 15. The storage system method of claim 14, further comprising:
ranking the identified patterns of events based at least in part on their frequency of occurrence, the event severity level, the pattern severity level, or any combination thereof; and detecting the occurrence of the one or more events being based at least in part on the ranking of the identified patterns of events. 16. The storage system method of claim 15, further comprising:
calculating a time period expected to lapse between two events in the particular pattern of events; and estimating, based at least in part on the calculated time period, a mean time before the adverse condition occurs in relation to detecting the occurrence of the one or more events from the particular pattern of events. 17. The storage system method of claim 16, further comprising:
implementing the identified corrective action based at least in part on the event severity level, the pattern severity level, the rank of the particular pattern of events, the time period, the estimated mean time before the adverse condition occurs, a cost of the corrective action, a cost of implementing the corrective action immediately versus a cost of implementing the corrective action after waiting a predetermined time period, current storage system performance, a service agreement, a device warranty, or any combination thereof. 18. The storage system method of claim 14, wherein the event severity level of the particular pattern of events is based at least in part on a position of a specific event from the particular pattern of events relative to other events in the particular patterns of events, and the pattern severity level of the particular pattern of events being based at least in part on a severity of the adverse condition caused by the particular pattern of events. 19. A non-transitory computer-readable storage medium storing computer executable instructions to improve a computer system that when executed by a processor cause the processor to perform the steps of:
identifying two or more patterns of events among a plurality of detected events stored in a database, each pattern of events including a sequence of two or more events in a given order related to operations of a storage system; identifying an adverse condition of the storage system that occurs as a result of the identified patterns of events; selecting a corrective action that resolves the adverse condition of the storage system; and implementing the corrective action. 20. The storage medium of claim 19, wherein the storage system comprises a storage drive, a storage server, a storage enclosure enclosing two or more storage drives, a distributed data storage system, a cloud storage system, or any combination thereof. | 2,100 |
6,466 | 6,466 | 14,711,711 | 2,173 | Many users may share information, such as messages, photos, and/or links to websites or other content, through social networks as social network posts. As provided herein, a commentating user may be provided with the ability to overlay content, such as images, icons, text, links to websites, and/or other content visually and positionally onto a social network post to create an overlaid social network post. The commentating user may resize, reposition, and/or tag the content that is overlaid the social network post. In this way, various users may overlay content onto social network posts for an improved user interface and interaction for social network interfacing. | 1. A method for overlaying content items onto social network posts, comprising:
displaying a social network post, created by an originating user, to a commentating user; receiving user input, from the commentating user, at a position within the social network post, the user input corresponding to a content overlay command associated with content; and overlaying the content onto the social network post at the position to create an overlaid social network post. 2. The method of claim 1, the content comprising at least one of an image, text, a shape, a sticker, an animation, a user interface element, or a thought bubble. 3. The method of claim 1, the content comprising a link to at least one of a website, audio content, a video, execution of an application, a coupon, a location within a map interface, a document, or task completion functionality. 4. The method of claim 1, comprising:
exposing a create content interface to the commentating user; and receiving user generated content, based upon user interaction with the create content interface, as the content. 5. The method of claim 1, comprising:
exposing a content selection interface, populated with a plurality of available content, to the commentating user; and receiving a selection, based upon user interaction with the create content interface, of available content as the content. 6. The method of claim 1, the overlaying comprising:
responsive to receiving a reposition command, moving the content from the position to a new position within the overlaid social network post. 7. The method of claim 1, comprising:
displaying the overlaid social network post to a second user. 8. The method of claim 7, comprising:
receiving second user input, from the second user, at a second position within the overlaid social network post, the second user input corresponding to a second content overlay command associated with second content; and overlaying the second content onto the overlaid social network post at the second position to create an updated overlaid social network post. 9. The method of claim 7, comprising:
responsive to receiving a content filter command from the second user, hiding one or more content overlaid the overlaid social network post based upon the content filter command. 10. The method of claim 1, comprising:
responsive to receiving a resize command, adjusting a size of the content within the overlaid social network post. 11. The method of claim 1, the content comprising a thought bubble, and the method comprising:
displaying a text entry interface to the commentating user; and responsive to receiving text through the text entry interface, populating the thought bubble with the text. 12. The method of claim 10, comprising:
displaying the overlaid social network post to a second user where the text of the thought bubble is hidden; and responsive to user interaction with the thought bubble, displaying the text. 13. The method of claim 1, comprising:
responsive to receiving a tag command for the content within the overlaid social network post, tagging the content with a tag of a social network user specified by the tag command. 14. The method of claim 7, comprising:
responsive to user interaction with the content, displaying at least one of a textual description for the content, a timestamp of the content, or a user identification of the commentator user. 15. The method of claim 1, comprising:
displaying the overlaid social network post to the originating user; and responsive to receiving a response command from the originating user, overlaying response content, of the response command, onto the content of the overlaid social network post to create an updated overlaid social network post. 16. The method of claim 1, comprising:
displaying the overlaid social network post to the originating user; and responsive to receiving a remove command from the originating user, removing the content from the overlaid social network post. 17. The method of claim 1, comprising:
responsive to receiving a share command associated with the overlaid social network post, sending the overlaid social network post to a second user based upon the share command. 18. The method of claim 1, comprising:
responsive to receiving a create file command associated with the overlaid social network post, generating a file depicting the overlaid social network post. 19. A system for overlaying content items onto social network posts, comprising:
an overlay component configured to:
display a social network post, created by an originating user, to a commentating user;
receive user input, from the commentating user, at a position within the social network post, the user input corresponding to a content overlay command associated with content;
overlay the content onto the social network post at the position to create an overlaid social network post; and
display the overlaid social network post to a second user. 20. A non-transitory computer readable medium comprising computer executable instructions that when executed by a processor perform a method for overlaying content items onto social network posts, comprising:
displaying a social network post, created by an originating user, to a commentating user; receiving user input, from the commentating user, at a position within the social network post, the user input corresponding to a content overlay command associated with content, the receiving user input comprising:
exposing a create content interface to the commentating user; and
receiving at least one of user markup, user uploaded content, user specified text, or user generated content, through the create content interface, as the content; and
overlaying the content onto the social network post at the position to create an overlaid social network post. | Many users may share information, such as messages, photos, and/or links to websites or other content, through social networks as social network posts. As provided herein, a commentating user may be provided with the ability to overlay content, such as images, icons, text, links to websites, and/or other content visually and positionally onto a social network post to create an overlaid social network post. The commentating user may resize, reposition, and/or tag the content that is overlaid the social network post. In this way, various users may overlay content onto social network posts for an improved user interface and interaction for social network interfacing.1. A method for overlaying content items onto social network posts, comprising:
displaying a social network post, created by an originating user, to a commentating user; receiving user input, from the commentating user, at a position within the social network post, the user input corresponding to a content overlay command associated with content; and overlaying the content onto the social network post at the position to create an overlaid social network post. 2. The method of claim 1, the content comprising at least one of an image, text, a shape, a sticker, an animation, a user interface element, or a thought bubble. 3. The method of claim 1, the content comprising a link to at least one of a website, audio content, a video, execution of an application, a coupon, a location within a map interface, a document, or task completion functionality. 4. The method of claim 1, comprising:
exposing a create content interface to the commentating user; and receiving user generated content, based upon user interaction with the create content interface, as the content. 5. The method of claim 1, comprising:
exposing a content selection interface, populated with a plurality of available content, to the commentating user; and receiving a selection, based upon user interaction with the create content interface, of available content as the content. 6. The method of claim 1, the overlaying comprising:
responsive to receiving a reposition command, moving the content from the position to a new position within the overlaid social network post. 7. The method of claim 1, comprising:
displaying the overlaid social network post to a second user. 8. The method of claim 7, comprising:
receiving second user input, from the second user, at a second position within the overlaid social network post, the second user input corresponding to a second content overlay command associated with second content; and overlaying the second content onto the overlaid social network post at the second position to create an updated overlaid social network post. 9. The method of claim 7, comprising:
responsive to receiving a content filter command from the second user, hiding one or more content overlaid the overlaid social network post based upon the content filter command. 10. The method of claim 1, comprising:
responsive to receiving a resize command, adjusting a size of the content within the overlaid social network post. 11. The method of claim 1, the content comprising a thought bubble, and the method comprising:
displaying a text entry interface to the commentating user; and responsive to receiving text through the text entry interface, populating the thought bubble with the text. 12. The method of claim 10, comprising:
displaying the overlaid social network post to a second user where the text of the thought bubble is hidden; and responsive to user interaction with the thought bubble, displaying the text. 13. The method of claim 1, comprising:
responsive to receiving a tag command for the content within the overlaid social network post, tagging the content with a tag of a social network user specified by the tag command. 14. The method of claim 7, comprising:
responsive to user interaction with the content, displaying at least one of a textual description for the content, a timestamp of the content, or a user identification of the commentator user. 15. The method of claim 1, comprising:
displaying the overlaid social network post to the originating user; and responsive to receiving a response command from the originating user, overlaying response content, of the response command, onto the content of the overlaid social network post to create an updated overlaid social network post. 16. The method of claim 1, comprising:
displaying the overlaid social network post to the originating user; and responsive to receiving a remove command from the originating user, removing the content from the overlaid social network post. 17. The method of claim 1, comprising:
responsive to receiving a share command associated with the overlaid social network post, sending the overlaid social network post to a second user based upon the share command. 18. The method of claim 1, comprising:
responsive to receiving a create file command associated with the overlaid social network post, generating a file depicting the overlaid social network post. 19. A system for overlaying content items onto social network posts, comprising:
an overlay component configured to:
display a social network post, created by an originating user, to a commentating user;
receive user input, from the commentating user, at a position within the social network post, the user input corresponding to a content overlay command associated with content;
overlay the content onto the social network post at the position to create an overlaid social network post; and
display the overlaid social network post to a second user. 20. A non-transitory computer readable medium comprising computer executable instructions that when executed by a processor perform a method for overlaying content items onto social network posts, comprising:
displaying a social network post, created by an originating user, to a commentating user; receiving user input, from the commentating user, at a position within the social network post, the user input corresponding to a content overlay command associated with content, the receiving user input comprising:
exposing a create content interface to the commentating user; and
receiving at least one of user markup, user uploaded content, user specified text, or user generated content, through the create content interface, as the content; and
overlaying the content onto the social network post at the position to create an overlaid social network post. | 2,100 |
6,467 | 6,467 | 14,156,651 | 2,153 | Architecture that obtains and utilizes collections of geographically-tagged data to discover optimal vantage points for viewsheds of entities of interest such as physical entities and conceptual entities such as landmarks, sunset, skyline, etc. The disclosed architecture discloses the utilization of at least geo-tagged image data to discover relationships between a combination of concrete entities and/or abstract concepts, and techniques for surfacing such relationships to users. The data can be crowd-sourced geo-tagged image data that are mined from social content and which can be observed or experienced from a certain location/area. | 1. A system, comprising:
an analysis component configured to analyze geographically-tagged data associated with an entity of interest and to discover relationships between location-based entities and the entity of interest, the entity of interest is an object or an abstract concept; a view generation component configured to generate vantage-point information from which to view the entity of interest based on the discovered relationships; and at least one microprocessor that executes computer-executable instructions in a memory associated with the analysis component and the view generation component. 2. The system of claim 1, wherein the geographically-tagged data is crowd-sourced image data. 3. The system of claim 1, further comprising an augmentation component configured to augment the vantage-point information with popularity data and source credibility data. 4. The system of claim 1, further comprising an augmentation component configured to augment the vantage-point information with emotional data derived from an association with the geographically-tagged data. 5. The system of claim 1, further comprising a recommendation component configured to recommend a new entity of interest based on the relationships and relationship conditions, as part of exploring the entity of interest. 6. The system of claim 1, further comprising a conditions component configured to detect at least one of spatial or temporal conditions under which the relationships are active. 7. The system of claim 1, further comprising a graphing component configured to generate graphs of entities of interest and relationships between the entities of interest to discover new entities. 8. The system of claim 1, wherein the geographically-tagged data is obtained from a social network and the analysis component employs machine-learning techniques to identify and de-noise the geographically tagged data. 9. A method, comprising acts of:
accessing geographically-tagged data associated with an entity of interest that is an object or an abstract concept; analyzing the geographically-tagged data to discover relationships between location-based entities and the entity of interest; and generating vantage-point information from which to view the entity of interest based on the discovered relationships. 10. The method of claim 9, further comprising deriving conditions under which the relationships are active. 11. The method of claim 9, further comprising augmenting the vantage-point information with popularity data and source credibility data. 12. The method of claim 9, further comprising augmenting the relationships with emotional data associated with the geographically-tagged data. 13. The method of claim 9, further comprising constructing and presenting hybrid graphs related to the entity of interest that enable discovery of new entities of interest. 14. The method of claim 9, further comprising recommending new entities of interest based on the relationships and relationship conditions. 15. The method of claim 9, further comprising recommending related entities of interest while exploring the entity of interest. 16. A computer-readable storage medium comprising computer-executable instructions that when executed by a microprocessor, cause the microprocessor to perform acts of:
accessing geographically-tagged data associated with an entity of interest, the entity of interest a physical object or an abstract concept; analyzing the geographically-tagged data to discover relationships between location-based entities and the entity of interest; generating vantage-point information from which to view the entity of interest based on the discovered relationships; and constructing and presenting hybrid graphs related to the entity of interest that enable discovery of new entities of interest. 17. The computer-readable storage medium of claim 16, further comprising augmenting the vantage-point information with popularity data and source credibility data, and augmenting the relationships with emotional data associated with the geographically-tagged data. 18. The computer-readable storage medium of claim 16, further comprising recommending new entities of interest based on the relationships and relationship conditions. 19. The computer-readable storage medium of claim 16, further comprising recommending related entities of interest while exploring the entity of interest. 20. The computer-readable storage medium of claim 16, further comprising deriving conditions under which the relationships are active. | Architecture that obtains and utilizes collections of geographically-tagged data to discover optimal vantage points for viewsheds of entities of interest such as physical entities and conceptual entities such as landmarks, sunset, skyline, etc. The disclosed architecture discloses the utilization of at least geo-tagged image data to discover relationships between a combination of concrete entities and/or abstract concepts, and techniques for surfacing such relationships to users. The data can be crowd-sourced geo-tagged image data that are mined from social content and which can be observed or experienced from a certain location/area.1. A system, comprising:
an analysis component configured to analyze geographically-tagged data associated with an entity of interest and to discover relationships between location-based entities and the entity of interest, the entity of interest is an object or an abstract concept; a view generation component configured to generate vantage-point information from which to view the entity of interest based on the discovered relationships; and at least one microprocessor that executes computer-executable instructions in a memory associated with the analysis component and the view generation component. 2. The system of claim 1, wherein the geographically-tagged data is crowd-sourced image data. 3. The system of claim 1, further comprising an augmentation component configured to augment the vantage-point information with popularity data and source credibility data. 4. The system of claim 1, further comprising an augmentation component configured to augment the vantage-point information with emotional data derived from an association with the geographically-tagged data. 5. The system of claim 1, further comprising a recommendation component configured to recommend a new entity of interest based on the relationships and relationship conditions, as part of exploring the entity of interest. 6. The system of claim 1, further comprising a conditions component configured to detect at least one of spatial or temporal conditions under which the relationships are active. 7. The system of claim 1, further comprising a graphing component configured to generate graphs of entities of interest and relationships between the entities of interest to discover new entities. 8. The system of claim 1, wherein the geographically-tagged data is obtained from a social network and the analysis component employs machine-learning techniques to identify and de-noise the geographically tagged data. 9. A method, comprising acts of:
accessing geographically-tagged data associated with an entity of interest that is an object or an abstract concept; analyzing the geographically-tagged data to discover relationships between location-based entities and the entity of interest; and generating vantage-point information from which to view the entity of interest based on the discovered relationships. 10. The method of claim 9, further comprising deriving conditions under which the relationships are active. 11. The method of claim 9, further comprising augmenting the vantage-point information with popularity data and source credibility data. 12. The method of claim 9, further comprising augmenting the relationships with emotional data associated with the geographically-tagged data. 13. The method of claim 9, further comprising constructing and presenting hybrid graphs related to the entity of interest that enable discovery of new entities of interest. 14. The method of claim 9, further comprising recommending new entities of interest based on the relationships and relationship conditions. 15. The method of claim 9, further comprising recommending related entities of interest while exploring the entity of interest. 16. A computer-readable storage medium comprising computer-executable instructions that when executed by a microprocessor, cause the microprocessor to perform acts of:
accessing geographically-tagged data associated with an entity of interest, the entity of interest a physical object or an abstract concept; analyzing the geographically-tagged data to discover relationships between location-based entities and the entity of interest; generating vantage-point information from which to view the entity of interest based on the discovered relationships; and constructing and presenting hybrid graphs related to the entity of interest that enable discovery of new entities of interest. 17. The computer-readable storage medium of claim 16, further comprising augmenting the vantage-point information with popularity data and source credibility data, and augmenting the relationships with emotional data associated with the geographically-tagged data. 18. The computer-readable storage medium of claim 16, further comprising recommending new entities of interest based on the relationships and relationship conditions. 19. The computer-readable storage medium of claim 16, further comprising recommending related entities of interest while exploring the entity of interest. 20. The computer-readable storage medium of claim 16, further comprising deriving conditions under which the relationships are active. | 2,100 |
6,468 | 6,468 | 15,175,751 | 2,143 | Embodiments of the present invention provide a system for querying a graph based on applying filters to a visual representation of the graph. The system allows complicated graph query operations to be performed with ease visually. During operation, the system obtains data indicating vertices and edges of a graph. The system displays a visual representation of the graph for a user. The system receives, from the user, a command defining a local graph filter comprising a region in the visual representation. The system then filters a representation of the graph, and stores the filtered representation. | 1. A computer-implemented method for querying a graph, comprising:
obtaining, by a computer system comprising a set of processors, a data structure representing a graph comprising vertices and edges; displaying, for a user, a visual representation of the graph; receiving, from the user, a command defining a local graph filter, wherein the local graph filter comprises a region in the visual representation of the graph; filtering a representation of the graph to select a subset of vertices visually represented within the region, and edges connecting vertices in the subset; and storing the selected vertices and edges of the filtered representation of the graph in a non-transitory storage medium. 2. The method of claim 1, further comprising:
receiving, from the user, an additional local graph filter comprising an additional region in the visual representation of the graph; determining a combined region in the visual representation of the graph as a union or an intersection of the region and the additional region; and filtering the representation of the graph to select a combined set of vertices visually represented within the combined region, and edges connecting vertices in the combined set. 3. The method of claim 1, wherein receiving the command defining the local graph filter further comprises receiving, from the user via a pointing device, a boundary delimiting the region in the visual representation of the graph. 4. The method of claim 1, wherein the received local graph filter further specifies a set of constraints, and wherein filtering the representation of the graph comprises further filtering the subset of vertices visually represented within the region to select vertices satisfying the constraints, and edges connecting the selected vertices. 5. The method of claim 4, wherein receiving the command defining the local graph filter further comprises:
displaying, for the user, a slider control associated with the local graph filter and representing a property of vertices in the graph; receiving, from the user via a pointing device and according to a position of the slider control, a value for the property; and setting a respective constraint based on the received value for the property. 6. The method of claim 4, wherein a respective constraint defines a range of values for one or more of:
a degree of a respective vertex in the graph; a number of triangles associated with a respective vertex in the graph; a number of cliques associated with a respective vertex in the graph; a number of graphlets associated with a respective vertex in the graph; a k-core number of a respective vertex in the graph; a measure of graph distance of a respective edge in the graph; and a measure of graph connectivity of a respective vertex in the graph. 7. The method of claim 4, wherein a respective vertex in the graph is associated with auxiliary properties, and a respective constraint defines a range of values for an auxiliary property of the respective vertex. 8. The method of claim 7, wherein the auxiliary property includes one or more of:
an age of a person; a wealth or income level of a person; a geographic location of a person; a purchase history of a person; a person's friends or social network; a time of a transaction; an amount of a transaction; spatial or temporal information of a commercial activity; a derived property of the graph; a property obtained from a matrix factorization of the graph; an attribute based on a classification or regression method for prediction in the graph; and an attribute representing whether a vertex or edge has been correctly classified. 9. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for querying a graph, the method comprising:
obtaining, by the computer, a data structure representing a graph comprising vertices and edges; displaying, for a user, a visual representation of the graph; receiving, from the user, a command defining a local graph filter, wherein the local graph filter comprises a region in the visual representation of the graph; filtering a representation of the graph to select a subset of vertices visually represented within the region, and edges connecting vertices in the subset; and storing the selected vertices and edges of the filtered representation of the graph in a non-transitory storage medium. 10. The computer-readable storage medium of claim 9, wherein the method further comprises:
receiving, from the user, an additional local graph filter comprising an additional region in the visual representation of the graph; determining a combined region in the visual representation of the graph as a union or an intersection of the region and the additional region; and filtering the representation of the graph to select a combined set of vertices visually represented within the combined region, and edges connecting vertices in the combined set. 11. The computer-readable storage medium of claim 9, wherein the received local graph filter further specifies a set of constraints, and wherein filtering the representation of the graph comprises further filtering the subset of vertices visually represented within the region to select vertices satisfying the constraints, and edges connecting the selected vertices. 12. The computer-readable storage medium of claim 11, wherein receiving the command defining the local graph filter further comprises:
displaying, for the user, a slider control associated with the local graph filter and representing a property of vertices in the graph; receiving, from the user via a pointing device and according to a position of the slider control, a value for the property; and setting a respective constraint based on the received value for the property. 13. The computer-readable storage medium of claim 11, wherein a respective vertex in the graph is associated with auxiliary properties, and a respective constraint defines a range of values for an auxiliary property of the respective vertex. 14. The computer-readable storage medium of claim 13, wherein the property includes one or more of:
an age of a person; a wealth or income level of a person; a geographic location of a person; a purchase history of a person; a person's friends or social network; a time of a transaction; an amount of a transaction; spatial or temporal information of a commercial activity; a derived property of the graph; a property obtained from a matrix factorization of the graph; an attribute based on a classification or regression method for prediction in the graph; and an attribute representing whether a vertex or edge has been correctly classified. 15. A computing system for querying a graph, the system comprising:
a set of multiple processors, and a non-transitory computer-readable medium coupled to the set of processors having instructions stored thereon that, when executed by the set of processors, cause the set of processors to perform a method for querying a graph, the method comprising: obtaining a data structure representing a graph comprising vertices and edges; displaying, for a user, a visual representation of the graph; receiving, from the user, a command defining a local graph filter, wherein the local graph filter comprises a region in the visual representation of the graph; filtering a representation of the graph to select a subset of vertices visually represented within the region, and edges connecting vertices in the subset; and storing the selected vertices and edges of the filtered representation of the graph in a non-transitory storage medium. 16. The computing system of claim 15, wherein the method further comprises:
receiving, from the user, an additional local graph filter comprising an additional region in the visual representation of the graph; determining a combined region in the visual representation of the graph as a union or an intersection of the region and the additional region; and filtering the representation of the graph to select a combined set of vertices visually represented within the combined region, and edges connecting vertices in the combined set. 17. The computing system of claim 15, wherein the received local graph filter further specifies a set of constraints, and wherein filtering the representation of the graph comprises further filtering the subset of vertices visually represented within the region to select vertices satisfying the constraints, and edges connecting the selected vertices. 18. The computing system of claim 17, wherein receiving the command defining the local graph filter further comprises:
displaying, for the user, a slider control associated with the local graph filter and representing a property of vertices in the graph; receiving, from the user via a pointing device and according to a position of the slider control, a value for the property; and setting a respective constraint based on the received value for the property. 19. The computing system of claim 17, wherein a respective vertex in the graph is associated with auxiliary properties, and a respective constraint defines a range of values for an auxiliary property of the respective vertex. 20. The computing system of claim 19, wherein the auxiliary property includes one or more of:
an age of a person; a wealth or income level of a person; a geographic location of a person; a purchase history of a person; a person's friends or social network; a time of a transaction; an amount of a transaction; spatial or temporal information of a commercial activity; a derived property of the graph; a property obtained from a matrix factorization of the graph; an attribute based on a classification or regression method for prediction in the graph; and an attribute representing whether a vertex or edge has been correctly classified. | Embodiments of the present invention provide a system for querying a graph based on applying filters to a visual representation of the graph. The system allows complicated graph query operations to be performed with ease visually. During operation, the system obtains data indicating vertices and edges of a graph. The system displays a visual representation of the graph for a user. The system receives, from the user, a command defining a local graph filter comprising a region in the visual representation. The system then filters a representation of the graph, and stores the filtered representation.1. A computer-implemented method for querying a graph, comprising:
obtaining, by a computer system comprising a set of processors, a data structure representing a graph comprising vertices and edges; displaying, for a user, a visual representation of the graph; receiving, from the user, a command defining a local graph filter, wherein the local graph filter comprises a region in the visual representation of the graph; filtering a representation of the graph to select a subset of vertices visually represented within the region, and edges connecting vertices in the subset; and storing the selected vertices and edges of the filtered representation of the graph in a non-transitory storage medium. 2. The method of claim 1, further comprising:
receiving, from the user, an additional local graph filter comprising an additional region in the visual representation of the graph; determining a combined region in the visual representation of the graph as a union or an intersection of the region and the additional region; and filtering the representation of the graph to select a combined set of vertices visually represented within the combined region, and edges connecting vertices in the combined set. 3. The method of claim 1, wherein receiving the command defining the local graph filter further comprises receiving, from the user via a pointing device, a boundary delimiting the region in the visual representation of the graph. 4. The method of claim 1, wherein the received local graph filter further specifies a set of constraints, and wherein filtering the representation of the graph comprises further filtering the subset of vertices visually represented within the region to select vertices satisfying the constraints, and edges connecting the selected vertices. 5. The method of claim 4, wherein receiving the command defining the local graph filter further comprises:
displaying, for the user, a slider control associated with the local graph filter and representing a property of vertices in the graph; receiving, from the user via a pointing device and according to a position of the slider control, a value for the property; and setting a respective constraint based on the received value for the property. 6. The method of claim 4, wherein a respective constraint defines a range of values for one or more of:
a degree of a respective vertex in the graph; a number of triangles associated with a respective vertex in the graph; a number of cliques associated with a respective vertex in the graph; a number of graphlets associated with a respective vertex in the graph; a k-core number of a respective vertex in the graph; a measure of graph distance of a respective edge in the graph; and a measure of graph connectivity of a respective vertex in the graph. 7. The method of claim 4, wherein a respective vertex in the graph is associated with auxiliary properties, and a respective constraint defines a range of values for an auxiliary property of the respective vertex. 8. The method of claim 7, wherein the auxiliary property includes one or more of:
an age of a person; a wealth or income level of a person; a geographic location of a person; a purchase history of a person; a person's friends or social network; a time of a transaction; an amount of a transaction; spatial or temporal information of a commercial activity; a derived property of the graph; a property obtained from a matrix factorization of the graph; an attribute based on a classification or regression method for prediction in the graph; and an attribute representing whether a vertex or edge has been correctly classified. 9. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for querying a graph, the method comprising:
obtaining, by the computer, a data structure representing a graph comprising vertices and edges; displaying, for a user, a visual representation of the graph; receiving, from the user, a command defining a local graph filter, wherein the local graph filter comprises a region in the visual representation of the graph; filtering a representation of the graph to select a subset of vertices visually represented within the region, and edges connecting vertices in the subset; and storing the selected vertices and edges of the filtered representation of the graph in a non-transitory storage medium. 10. The computer-readable storage medium of claim 9, wherein the method further comprises:
receiving, from the user, an additional local graph filter comprising an additional region in the visual representation of the graph; determining a combined region in the visual representation of the graph as a union or an intersection of the region and the additional region; and filtering the representation of the graph to select a combined set of vertices visually represented within the combined region, and edges connecting vertices in the combined set. 11. The computer-readable storage medium of claim 9, wherein the received local graph filter further specifies a set of constraints, and wherein filtering the representation of the graph comprises further filtering the subset of vertices visually represented within the region to select vertices satisfying the constraints, and edges connecting the selected vertices. 12. The computer-readable storage medium of claim 11, wherein receiving the command defining the local graph filter further comprises:
displaying, for the user, a slider control associated with the local graph filter and representing a property of vertices in the graph; receiving, from the user via a pointing device and according to a position of the slider control, a value for the property; and setting a respective constraint based on the received value for the property. 13. The computer-readable storage medium of claim 11, wherein a respective vertex in the graph is associated with auxiliary properties, and a respective constraint defines a range of values for an auxiliary property of the respective vertex. 14. The computer-readable storage medium of claim 13, wherein the property includes one or more of:
an age of a person; a wealth or income level of a person; a geographic location of a person; a purchase history of a person; a person's friends or social network; a time of a transaction; an amount of a transaction; spatial or temporal information of a commercial activity; a derived property of the graph; a property obtained from a matrix factorization of the graph; an attribute based on a classification or regression method for prediction in the graph; and an attribute representing whether a vertex or edge has been correctly classified. 15. A computing system for querying a graph, the system comprising:
a set of multiple processors, and a non-transitory computer-readable medium coupled to the set of processors having instructions stored thereon that, when executed by the set of processors, cause the set of processors to perform a method for querying a graph, the method comprising: obtaining a data structure representing a graph comprising vertices and edges; displaying, for a user, a visual representation of the graph; receiving, from the user, a command defining a local graph filter, wherein the local graph filter comprises a region in the visual representation of the graph; filtering a representation of the graph to select a subset of vertices visually represented within the region, and edges connecting vertices in the subset; and storing the selected vertices and edges of the filtered representation of the graph in a non-transitory storage medium. 16. The computing system of claim 15, wherein the method further comprises:
receiving, from the user, an additional local graph filter comprising an additional region in the visual representation of the graph; determining a combined region in the visual representation of the graph as a union or an intersection of the region and the additional region; and filtering the representation of the graph to select a combined set of vertices visually represented within the combined region, and edges connecting vertices in the combined set. 17. The computing system of claim 15, wherein the received local graph filter further specifies a set of constraints, and wherein filtering the representation of the graph comprises further filtering the subset of vertices visually represented within the region to select vertices satisfying the constraints, and edges connecting the selected vertices. 18. The computing system of claim 17, wherein receiving the command defining the local graph filter further comprises:
displaying, for the user, a slider control associated with the local graph filter and representing a property of vertices in the graph; receiving, from the user via a pointing device and according to a position of the slider control, a value for the property; and setting a respective constraint based on the received value for the property. 19. The computing system of claim 17, wherein a respective vertex in the graph is associated with auxiliary properties, and a respective constraint defines a range of values for an auxiliary property of the respective vertex. 20. The computing system of claim 19, wherein the auxiliary property includes one or more of:
an age of a person; a wealth or income level of a person; a geographic location of a person; a purchase history of a person; a person's friends or social network; a time of a transaction; an amount of a transaction; spatial or temporal information of a commercial activity; a derived property of the graph; a property obtained from a matrix factorization of the graph; an attribute based on a classification or regression method for prediction in the graph; and an attribute representing whether a vertex or edge has been correctly classified. | 2,100 |
6,469 | 6,469 | 14,533,277 | 2,162 | A system includes definition of a remote data source, definition of a virtual function specifying executable job code, a return data format and a data location in the remote data source, reception of a structured language query including the virtual function as a data source, and, in response to the received query, instruction of the remote data source to execute the job code based on data in the data location and return data in the return data format. | 1. A system comprising:
a memory storing processor-executable program code; and a processor to execute the processor-executable program code in order to cause the first computing system to:
define a remote data source;
define a virtual function specifying a return data format and a location of a file stored in the remote data source;
receive a structured language query including the virtual function as a data source; and
in response to the received query, retrieve the file from the remote data source and return data of the file in the return data format. 2. A system according to claim 1, wherein return of the data of the file in the return data format comprises return of the data in columnar and compressed format. 3. A system according to claim 1, wherein the virtual function specifies executable job code and a data location in the remote data source, and
wherein, in response to the received query, the remote data source is instructed to execute the executable job code based on data in the data location. 4. A system according to claim 1, wherein the virtual function specifies executable code of a plurality of jobs and a sequence of the plurality of jobs, and
wherein, in response to the received query, the remote data source is instructed to execute the executable code of the plurality of jobs according to the sequence. 5. A system comprising:
a memory storing processor-executable program code; and a processor to execute the processor-executable program code in order to cause the first computing system to:
define a remote data source;
define a virtual function specifying executable job code, a return data format and a data location in the remote data source;
receive a structured language query including the virtual function as a data source; and
in response to the received query, instruct the remote data source to execute the job code based on data in the data location and return data in the return data format. 6. A system according to claim 5, wherein the returned data is in columnar and compressed format. 7. A system according to claim 4, wherein the virtual function specifies executable code of a plurality of jobs and a sequence of the plurality of jobs, and
wherein, in response to the received query, the remote data source is instructed to execute the executable code of the plurality of jobs according to the sequence. 8. A non-transitory computer-readable medium storing program code, the program code executable by a processor of a computing system to cause the computing system to:
define a remote data source; define a virtual function specifying a return data format and a location of a file stored in the remote data source; receive a structured language query including the virtual function as a data source; and in response to the received query, retrieve the file from the remote data source and return data of the file in the return data format. 9. A medium according to claim 8, wherein return of the data of the file in the return data format comprises return of the data in columnar and compressed format. 10. A medium according to claim 9, wherein the virtual function specifies executable job code and a data location in the remote data source, and
wherein, in response to the received query, the remote data source is instructed to execute the executable job code based on data in the data location. 11. A medium according to claim 9, wherein the virtual function specifies executable code of a plurality of jobs and a sequence of the plurality of jobs, and
wherein, in response to the received query, the remote data source is instructed to execute the executable code of the plurality of jobs according to the sequence. | A system includes definition of a remote data source, definition of a virtual function specifying executable job code, a return data format and a data location in the remote data source, reception of a structured language query including the virtual function as a data source, and, in response to the received query, instruction of the remote data source to execute the job code based on data in the data location and return data in the return data format.1. A system comprising:
a memory storing processor-executable program code; and a processor to execute the processor-executable program code in order to cause the first computing system to:
define a remote data source;
define a virtual function specifying a return data format and a location of a file stored in the remote data source;
receive a structured language query including the virtual function as a data source; and
in response to the received query, retrieve the file from the remote data source and return data of the file in the return data format. 2. A system according to claim 1, wherein return of the data of the file in the return data format comprises return of the data in columnar and compressed format. 3. A system according to claim 1, wherein the virtual function specifies executable job code and a data location in the remote data source, and
wherein, in response to the received query, the remote data source is instructed to execute the executable job code based on data in the data location. 4. A system according to claim 1, wherein the virtual function specifies executable code of a plurality of jobs and a sequence of the plurality of jobs, and
wherein, in response to the received query, the remote data source is instructed to execute the executable code of the plurality of jobs according to the sequence. 5. A system comprising:
a memory storing processor-executable program code; and a processor to execute the processor-executable program code in order to cause the first computing system to:
define a remote data source;
define a virtual function specifying executable job code, a return data format and a data location in the remote data source;
receive a structured language query including the virtual function as a data source; and
in response to the received query, instruct the remote data source to execute the job code based on data in the data location and return data in the return data format. 6. A system according to claim 5, wherein the returned data is in columnar and compressed format. 7. A system according to claim 4, wherein the virtual function specifies executable code of a plurality of jobs and a sequence of the plurality of jobs, and
wherein, in response to the received query, the remote data source is instructed to execute the executable code of the plurality of jobs according to the sequence. 8. A non-transitory computer-readable medium storing program code, the program code executable by a processor of a computing system to cause the computing system to:
define a remote data source; define a virtual function specifying a return data format and a location of a file stored in the remote data source; receive a structured language query including the virtual function as a data source; and in response to the received query, retrieve the file from the remote data source and return data of the file in the return data format. 9. A medium according to claim 8, wherein return of the data of the file in the return data format comprises return of the data in columnar and compressed format. 10. A medium according to claim 9, wherein the virtual function specifies executable job code and a data location in the remote data source, and
wherein, in response to the received query, the remote data source is instructed to execute the executable job code based on data in the data location. 11. A medium according to claim 9, wherein the virtual function specifies executable code of a plurality of jobs and a sequence of the plurality of jobs, and
wherein, in response to the received query, the remote data source is instructed to execute the executable code of the plurality of jobs according to the sequence. | 2,100 |
6,470 | 6,470 | 15,680,092 | 2,193 | In some examples, test relevancy prediction for code changes may include ascertaining files for a commit for a build, and for each test of a plurality of tests, determining a score based on a weight assigned to a file of the ascertained files. Test relevancy prediction for code changes may further include ordering each test of the plurality of tests according to the determined score, and identifying, based on the ordering of each test of the plurality of tests, tests from the plurality of tests for which the score exceeds a specified threshold. The identified tests may represent tests that are to be applied to the build. | 1. An apparatus comprising:
a processor; and a non-transitory computer readable medium storing machine readable instructions that when executed by the processor cause the processor to:
ascertain files for a commit for a build;
for each test of a plurality of tests, determine a score based on a weight assigned to a file of the ascertained files;
order each test of the plurality of tests according to the determined score; and
identify, based on the ordering of each test of the plurality of tests, tests from the plurality of tests for which the score exceeds a specified threshold, wherein the identified tests represent tests that are to be applied to the build. 2. The apparatus according to claim 1, wherein the instructions are further to cause the processor to:
determine the weight based on a term frequency-inverse document frequency (TF-IDF) statistic. 3. The apparatus according to claim 1, wherein the instructions are further to cause the processor to:
determine, for a file of the ascertained files that does not include an associated test, the weight based on a dice measure. 4. The apparatus according to claim 1, wherein the instructions are further to cause the processor to:
train a model to determine the score by
identifying a test and file pair in a set of builds, wherein the set of builds is associated with a failed test and a passed test, and
for the identified test and file pair, logging an occurrence of a file included in the test and file pair, the logging associated with the failed test and the passed test. 5. The apparatus according to claim 4, wherein the set of builds is associated with the failed test and the passed test that is subsequent to the failed test. 6. The apparatus according to claim 4, wherein the instructions are further to cause the processor to:
maintain a count of a number of times the file included in the test and file pair appears in any commit in a build, of the set of builds, associated with the passed test. 7. The apparatus according to claim 4, wherein the instructions are further to cause the processor to:
maintain a pairwise count of a number of times the file included in the test and file pair appears with any other file in a same commit in the set of builds. 8. The apparatus according to claim 4, wherein the instructions are further to cause the processor to:
identify the test and file pair in the set of builds by determining whether a test and the file of the test and file pair are related by using a heuristic. 9. The apparatus according to claim 8, wherein the heuristic is at least one of:
file commonality between the test and the file of the test and file pair; committer commonality between the test and the file of the test and file pair; or a commit message associated with the test and the file of the test and file pair. 10. A method comprising:
identifying, by a processor, a test and file pair in a set of builds, wherein the set of builds is associated with a failed test and a passed test; and for the identified test and file pair, logging an occurrence of a file included in the test and file pair, the logging associated with the failed test and the passed test. 11. The method according to claim 10, wherein the set of builds is associated with the failed test and the passed test that is subsequent to the failed test. 12. The method according to claim 10, further comprising:
maintaining a count of a number of times the file appears in any commit in a build, of the set of builds, associated with the passed test. 13. The method according to claim 10, further comprising:
maintaining a pairwise count of a number of times the file included in the test and file pair appears with any other file in a same commit in the set of builds. 14. The method according to claim 10, further comprising:
identifying the test and file pair in the set of builds by determining whether a test and the file of the test and file pair are related by using a heuristic. 15. The method according to claim 14, wherein the heuristic is at least one of:
file commonality between the test and the file of the test and file pair; committer commonality between the test and the file of the test and file pair; or a commit message associated with the test and the file of the test and file pair. 16. A non-transitory computer readable medium having stored thereon machine readable instructions, the machine readable instructions, when executed, cause a processor to:
ascertain files for each commit for a build; for each test of a plurality of tests, determine, based on a trained model that accounts for a failed test and passed test, a score based on a weight assigned to a file of the ascertained files; order each test of the plurality of tests according to the determined score; and identify, based on the ordering of each test of the plurality of tests, tests from the plurality of tests for which the score exceeds a specified threshold, wherein the identified tests represent tests that are to be applied to the build. 17. The non-transitory computer readable medium according to claim 16, wherein the machine readable instructions, when executed, further cause the processor to:
determine the weight based on a term frequency-inverse document frequency (TF-IDF) statistic. 18. The non-transitory computer readable medium according to claim 16, wherein the machine readable instructions, when executed, further cause the processor to:
determine, for a file of the ascertained files that does not include an associated test, the weight based on a dice measure. 19. The non-transitory computer readable medium according to claim 16, wherein the machine readable instructions, when executed, further cause the processor to:
train the model by
identifying a test and file pair in a set of builds, wherein the set of builds is associated with the failed test and the passed test, and
for the identified test and file pair, logging an occurrence of a file included in the test and file pair, the logging associated with the failed test and the passed test. 20. The non-transitory computer readable medium according to claim 19, wherein the set of builds is associated with the failed test and the passed test that is subsequent to the failed test. | In some examples, test relevancy prediction for code changes may include ascertaining files for a commit for a build, and for each test of a plurality of tests, determining a score based on a weight assigned to a file of the ascertained files. Test relevancy prediction for code changes may further include ordering each test of the plurality of tests according to the determined score, and identifying, based on the ordering of each test of the plurality of tests, tests from the plurality of tests for which the score exceeds a specified threshold. The identified tests may represent tests that are to be applied to the build.1. An apparatus comprising:
a processor; and a non-transitory computer readable medium storing machine readable instructions that when executed by the processor cause the processor to:
ascertain files for a commit for a build;
for each test of a plurality of tests, determine a score based on a weight assigned to a file of the ascertained files;
order each test of the plurality of tests according to the determined score; and
identify, based on the ordering of each test of the plurality of tests, tests from the plurality of tests for which the score exceeds a specified threshold, wherein the identified tests represent tests that are to be applied to the build. 2. The apparatus according to claim 1, wherein the instructions are further to cause the processor to:
determine the weight based on a term frequency-inverse document frequency (TF-IDF) statistic. 3. The apparatus according to claim 1, wherein the instructions are further to cause the processor to:
determine, for a file of the ascertained files that does not include an associated test, the weight based on a dice measure. 4. The apparatus according to claim 1, wherein the instructions are further to cause the processor to:
train a model to determine the score by
identifying a test and file pair in a set of builds, wherein the set of builds is associated with a failed test and a passed test, and
for the identified test and file pair, logging an occurrence of a file included in the test and file pair, the logging associated with the failed test and the passed test. 5. The apparatus according to claim 4, wherein the set of builds is associated with the failed test and the passed test that is subsequent to the failed test. 6. The apparatus according to claim 4, wherein the instructions are further to cause the processor to:
maintain a count of a number of times the file included in the test and file pair appears in any commit in a build, of the set of builds, associated with the passed test. 7. The apparatus according to claim 4, wherein the instructions are further to cause the processor to:
maintain a pairwise count of a number of times the file included in the test and file pair appears with any other file in a same commit in the set of builds. 8. The apparatus according to claim 4, wherein the instructions are further to cause the processor to:
identify the test and file pair in the set of builds by determining whether a test and the file of the test and file pair are related by using a heuristic. 9. The apparatus according to claim 8, wherein the heuristic is at least one of:
file commonality between the test and the file of the test and file pair; committer commonality between the test and the file of the test and file pair; or a commit message associated with the test and the file of the test and file pair. 10. A method comprising:
identifying, by a processor, a test and file pair in a set of builds, wherein the set of builds is associated with a failed test and a passed test; and for the identified test and file pair, logging an occurrence of a file included in the test and file pair, the logging associated with the failed test and the passed test. 11. The method according to claim 10, wherein the set of builds is associated with the failed test and the passed test that is subsequent to the failed test. 12. The method according to claim 10, further comprising:
maintaining a count of a number of times the file appears in any commit in a build, of the set of builds, associated with the passed test. 13. The method according to claim 10, further comprising:
maintaining a pairwise count of a number of times the file included in the test and file pair appears with any other file in a same commit in the set of builds. 14. The method according to claim 10, further comprising:
identifying the test and file pair in the set of builds by determining whether a test and the file of the test and file pair are related by using a heuristic. 15. The method according to claim 14, wherein the heuristic is at least one of:
file commonality between the test and the file of the test and file pair; committer commonality between the test and the file of the test and file pair; or a commit message associated with the test and the file of the test and file pair. 16. A non-transitory computer readable medium having stored thereon machine readable instructions, the machine readable instructions, when executed, cause a processor to:
ascertain files for each commit for a build; for each test of a plurality of tests, determine, based on a trained model that accounts for a failed test and passed test, a score based on a weight assigned to a file of the ascertained files; order each test of the plurality of tests according to the determined score; and identify, based on the ordering of each test of the plurality of tests, tests from the plurality of tests for which the score exceeds a specified threshold, wherein the identified tests represent tests that are to be applied to the build. 17. The non-transitory computer readable medium according to claim 16, wherein the machine readable instructions, when executed, further cause the processor to:
determine the weight based on a term frequency-inverse document frequency (TF-IDF) statistic. 18. The non-transitory computer readable medium according to claim 16, wherein the machine readable instructions, when executed, further cause the processor to:
determine, for a file of the ascertained files that does not include an associated test, the weight based on a dice measure. 19. The non-transitory computer readable medium according to claim 16, wherein the machine readable instructions, when executed, further cause the processor to:
train the model by
identifying a test and file pair in a set of builds, wherein the set of builds is associated with the failed test and the passed test, and
for the identified test and file pair, logging an occurrence of a file included in the test and file pair, the logging associated with the failed test and the passed test. 20. The non-transitory computer readable medium according to claim 19, wherein the set of builds is associated with the failed test and the passed test that is subsequent to the failed test. | 2,100 |
6,471 | 6,471 | 15,331,212 | 2,148 | A method, apparatus, system, and computer program product generates construction metrics. Building means, methods, and limitations of construction for one or more companies are gathered in a computer. A digital building information model (BIM) is acquired. Fabrication and construction parameters are extracted from the BIM. Construction metrics (for the BIM) are generated by combining the building means, methods, and limitations with the extracted fabrication and construction parameters. The construction metrics are then visualized in a modeling application and/or used to output construction process documentation. | 1. A computer-implemented method for generating construction metrics, comprising:
gathering, in a computer, building means, methods, and limitations of construction for one or more companies; acquiring, in the computer, a digital building information model (BIM); extracting, in the computer, fabrication and construction parameters from the BIM; generating, in the computer, the construction metrics for the BIM by combining the building means, methods, and limitations with the extracted fabrication and construction parameters; and visualizing the construction metrics in a modeling application. 2. The computer-implemented method of claim 1, wherein the gathering comprises:
accepting input into a website from the one or more companies. 3. The computer-implemented method of claim 1, wherein the means, methods, and limitations of construction further comprise times and pay rate ranges for each step of a construction process. 4. The computer-implemented method of claim 1, wherein the gathering further comprises:
storing the building means, methods, and limitations of construction in a structured database. 5. The computer-implemented method of claim 1, wherein the fabrication and construction parameters comprise every geometric element in the BIM that requires a unit of construction or fabrication work. 6. The computer-implemented method of claim 1, wherein the extracting is performed by a set of one or more plugins to one or more modeling programs. 7. The computer-implemented method of claim 1, wherein the extracting further comprises:
uploading the fabrication and construction parameters to a cloud-based server. 8. The computer-implemented method of claim 1, wherein the generating is performed entirely within a cloud-based server system. 9. The computer-implemented method of claim 1, wherein the generating is performed in real-time for a particular selected BIM. 10. The computer-implemented method of claim 1, wherein the generating further comprises:
optimizing the BIM by:
adjusting the fabrication and construction parameters for the BIM;
combining the adjusted fabrication and construction parameters for the BIM with one or more of the one or more companies; and
iterating the adjusting and combining steps to determine an optimal configuration based on cost. 11. The computer-implemented method of claim 1, wherein the generating is based on an average rate across the one or more companies and an average building logic across multiple BIMs. 12. The computer-implemented method of claim 1, further comprising:
generating, based on the construction metrics for the BIM, construction process documentation for each step of a fabrication, transportation, erection, and construction process of a building. 13. A computer-implemented system for generating construction metrics, comprising:
(a) a website that gathers building means, methods, and limitations of construction from one or more companies; (b) a modeling application executing on a computer that acquires a digital building information model (BIM); (c) a set of one or more plugins to the modeling program that:
(1) extract fabrication and construction parameters from the BIM;
(d) an estimator that generates the construction metrics for the BIM by combining the building means, methods, and limitations with the extracted fabrication and construction parameters; and wherein the modeling application is further configured to visualize the construction metrics. 14. The computer-implemented system of claim 13, wherein the means, methods, and limitations of construction further comprise times and pay rate ranges for each step of a construction process. 15. The computer-implemented system of claim 13, further comprising:
a structured database communicatively coupled to the website, wherein the building means, methods, and limitations of construction are stored in the structured database. 16. The computer-implemented system of claim 13, wherein the fabrication and construction parameters comprise every geometric element in the BIM that requires a unit of construction or fabrication work. 17. The computer-implemented system of claim 13, wherein set of one or more plugins:
upload the fabrication and construction parameters to a cloud-based server. 18. The computer-implemented system of claim 13, wherein the estimator is within a cloud-based server system. 19. The computer-implemented system of claim 13, wherein the estimator generates the construction metrics in real-time for a particular selected BIM. 20. The computer-implemented system of claim 13, wherein the estimator:
optimizes the BIM by:
adjusting the fabrication and construction parameters for the BIM;
combining the adjusted fabrication and construction parameters for the BIM with one or more of the one or more companies; and
iterating the adjusting and combining steps to determine an optimal configuration based on cost. 21. The computer-implemented system of claim 13, wherein the estimator generates based on an average rate across the one or more companies and an average building logic across multiple BIMs. 22. The computer-implemented system of claim 13, wherein the estimator:
generates, based on the construction metrics for the BIM, construction process documentation for each step of a fabrication, transportation, erection, and construction process of a building. | A method, apparatus, system, and computer program product generates construction metrics. Building means, methods, and limitations of construction for one or more companies are gathered in a computer. A digital building information model (BIM) is acquired. Fabrication and construction parameters are extracted from the BIM. Construction metrics (for the BIM) are generated by combining the building means, methods, and limitations with the extracted fabrication and construction parameters. The construction metrics are then visualized in a modeling application and/or used to output construction process documentation.1. A computer-implemented method for generating construction metrics, comprising:
gathering, in a computer, building means, methods, and limitations of construction for one or more companies; acquiring, in the computer, a digital building information model (BIM); extracting, in the computer, fabrication and construction parameters from the BIM; generating, in the computer, the construction metrics for the BIM by combining the building means, methods, and limitations with the extracted fabrication and construction parameters; and visualizing the construction metrics in a modeling application. 2. The computer-implemented method of claim 1, wherein the gathering comprises:
accepting input into a website from the one or more companies. 3. The computer-implemented method of claim 1, wherein the means, methods, and limitations of construction further comprise times and pay rate ranges for each step of a construction process. 4. The computer-implemented method of claim 1, wherein the gathering further comprises:
storing the building means, methods, and limitations of construction in a structured database. 5. The computer-implemented method of claim 1, wherein the fabrication and construction parameters comprise every geometric element in the BIM that requires a unit of construction or fabrication work. 6. The computer-implemented method of claim 1, wherein the extracting is performed by a set of one or more plugins to one or more modeling programs. 7. The computer-implemented method of claim 1, wherein the extracting further comprises:
uploading the fabrication and construction parameters to a cloud-based server. 8. The computer-implemented method of claim 1, wherein the generating is performed entirely within a cloud-based server system. 9. The computer-implemented method of claim 1, wherein the generating is performed in real-time for a particular selected BIM. 10. The computer-implemented method of claim 1, wherein the generating further comprises:
optimizing the BIM by:
adjusting the fabrication and construction parameters for the BIM;
combining the adjusted fabrication and construction parameters for the BIM with one or more of the one or more companies; and
iterating the adjusting and combining steps to determine an optimal configuration based on cost. 11. The computer-implemented method of claim 1, wherein the generating is based on an average rate across the one or more companies and an average building logic across multiple BIMs. 12. The computer-implemented method of claim 1, further comprising:
generating, based on the construction metrics for the BIM, construction process documentation for each step of a fabrication, transportation, erection, and construction process of a building. 13. A computer-implemented system for generating construction metrics, comprising:
(a) a website that gathers building means, methods, and limitations of construction from one or more companies; (b) a modeling application executing on a computer that acquires a digital building information model (BIM); (c) a set of one or more plugins to the modeling program that:
(1) extract fabrication and construction parameters from the BIM;
(d) an estimator that generates the construction metrics for the BIM by combining the building means, methods, and limitations with the extracted fabrication and construction parameters; and wherein the modeling application is further configured to visualize the construction metrics. 14. The computer-implemented system of claim 13, wherein the means, methods, and limitations of construction further comprise times and pay rate ranges for each step of a construction process. 15. The computer-implemented system of claim 13, further comprising:
a structured database communicatively coupled to the website, wherein the building means, methods, and limitations of construction are stored in the structured database. 16. The computer-implemented system of claim 13, wherein the fabrication and construction parameters comprise every geometric element in the BIM that requires a unit of construction or fabrication work. 17. The computer-implemented system of claim 13, wherein set of one or more plugins:
upload the fabrication and construction parameters to a cloud-based server. 18. The computer-implemented system of claim 13, wherein the estimator is within a cloud-based server system. 19. The computer-implemented system of claim 13, wherein the estimator generates the construction metrics in real-time for a particular selected BIM. 20. The computer-implemented system of claim 13, wherein the estimator:
optimizes the BIM by:
adjusting the fabrication and construction parameters for the BIM;
combining the adjusted fabrication and construction parameters for the BIM with one or more of the one or more companies; and
iterating the adjusting and combining steps to determine an optimal configuration based on cost. 21. The computer-implemented system of claim 13, wherein the estimator generates based on an average rate across the one or more companies and an average building logic across multiple BIMs. 22. The computer-implemented system of claim 13, wherein the estimator:
generates, based on the construction metrics for the BIM, construction process documentation for each step of a fabrication, transportation, erection, and construction process of a building. | 2,100 |
6,472 | 6,472 | 15,827,674 | 2,167 | Aggregation in a computing system can include receiving, at a service node of the computing system, a first query specifying aggregation and translating the first query into a second query having a first canonical format and specifying the aggregation. The method can include forwarding the second query to a first subset of a plurality of endpoint nodes and translating, at each endpoint node of the first subset, the second query into a third query having a format executable by a data source connected to the endpoint node. The third query can specify a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source. The endpoint nodes can initiate execution of the third query by the data sources and provide an aggregated result including a result from the data source(s) to the service node. | 1. A computer-implemented method, comprising:
in response to receiving, at a service node of a computing system, a first query specifying aggregation, translating the first query into a second query having a first canonical format and specifying the aggregation; forwarding the second query to a first subset of a plurality of endpoint nodes of the computing system; translating, at each endpoint node of the first subset, the second query into a third query having a format executable by a data source connected to the endpoint node and specifying a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source, wherein each endpoint node initiates execution of the third query by the data source; and providing, from each endpoint node of the first subset to the service node, an aggregated result including a result from execution of the third query. 2. The computer-implemented method of claim 1, further comprising:
translating, at each endpoint node of the first subset, the second query into a fourth query having a second canonical format; and forwarding, from each endpoint node of the first subset, the fourth query to an endpoint node of a second subset of the plurality of endpoint nodes; wherein a version of the first query in one of the canonical formats is propagated to each endpoint node of the computing system. 3. The computer-implemented method of claim 2, wherein each endpoint node of the second plurality of endpoint nodes has not received a version of the first query in one of the canonical formats. 4. The computer-implemented method of claim 2, wherein each endpoint node is configured to aggregate results received from each child endpoint node that provides results thereto and forward the aggregated results to a parent endpoint node. 5. The computer-implemented method of claim 1, further comprising:
in response to determining, at a selected endpoint node of the plurality of endpoint nodes, that the data source coupled to the selected endpoint node does not support the aggregation specified by the second query, generating the third query to specify a lesser level of the aggregation than specified by the second query; and performing, at the selected endpoint node, an operation on the result from the data source coupled to the selected endpoint node, wherein the operation is part of the aggregation specified by the second query. 6. The computer-implemented method of claim 1, wherein the translating the second query into the third query comprises:
breaking the aggregation specified by the second query into a plurality of constituent operations specified by the third query. 7. The computer-implemented method of claim 1, wherein the first canonical format is specified using structured query language. 8. A computing system, comprising:
a service node; and a plurality of endpoint nodes; wherein the service node is configured to initiate operations including:
in response to receiving a first query specifying aggregation, translating the first query into a second query having a first canonical format and specifying the aggregation;
forwarding the second query to a first subset of the plurality of endpoint nodes;
wherein each endpoint node of the first subset is configured to initiate operations including:
translating the second query into a third query having a format executable by a data source connected to the selected endpoint node and specifying a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source;
initiating execution of the third query by the data source; and
providing an aggregated result including a result from the data source to the service node. 9. The computing system of claim 8, wherein each endpoint node of the first subset is configured to initiate operations further comprising:
translating the second query into a fourth query having a second canonical format; and forwarding the fourth query to an endpoint node of a second subset of the plurality of endpoint nodes; wherein a version of the first query in one of the canonical formats is propagated to each endpoint node of the computing system. 10. The computing system of claim 9, wherein each endpoint node of the second plurality of endpoint nodes has not received a version of the first query in one of the canonical formats. 11. The computing system of claim 9, wherein each endpoint node is configured to aggregate results received from each child endpoint node that provides results thereto and forward the aggregated results to a parent endpoint node. 12. The computing system of claim 8, wherein a selected endpoint node of the plurality of endpoint nodes is configured to initiate operations further comprising:
in response to determining that the data source coupled to the thereto does not support the aggregation specified by the second query, generating the third query to specify a lesser level of the aggregation than specified by the second query; and performing an operation on the result from the data source coupled to the selected endpoint node, wherein the operation is part of the aggregation specified by the second query. 13. The computing system of claim 8, wherein the translating the second query into the third query comprises:
breaking the aggregation of the second query into a plurality of constituent operations specified by the third query. 14. The computing system of claim 8, wherein the first canonical format is specified using structured query language. 15. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to initiate operations comprising:
in response to receiving, at a service node of a computing system, a first query specifying aggregation, translating the first query into a second query having a first canonical format and specifying the aggregation; forwarding the second query to a first subset of a plurality of endpoint nodes of the computing system; translating, at each endpoint node of the first subset, the second query into a third query having a format executable by a data source connected to the endpoint node and specifying a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source, wherein each endpoint node initiates execution of the third query by the data source; and providing, from each endpoint node of the first subset to the service node, an aggregated result including a result from execution of the third query. 16. The computer program product of claim 15, wherein the program instructions cause the one or more processors to initiate operations further comprising:
translating, at each endpoint node of the first subset, the second query into a fourth query having a second canonical format; and forwarding, from each endpoint node of the first subset, the fourth query to an endpoint node of a second subset of the plurality of endpoint nodes; wherein a version of the first query in one of the canonical formats is propagated to each endpoint node of the computing system. 17. The computer program product of claim 16, wherein each endpoint node of the second plurality of endpoint nodes has not received a version of the first query in one of the canonical formats. 18. The computer program product of claim 16, wherein the program instructions cause the one or more processors to initiate operations further comprising:
configuring each endpoint node to aggregate results received from each child endpoint node that provides results thereto and forward the aggregated results to a parent endpoint node. 19. The computer program product of claim 15, wherein the program instructions cause the one or more processors to initiate operations further comprising:
in response to determining, at a selected endpoint node of the plurality of endpoint nodes, that the data source coupled to the selected endpoint node does not support the aggregation specified by the second query, generating the third query to specify a lesser level of the aggregation than specified by the second query; and performing, at the selected endpoint node of the first subset, an operation on the result from the data source coupled to the selected endpoint node, wherein the operation is part of the aggregation specified by the second query. 20. The computer program product of claim 15, wherein the translating the second query into the third query comprises:
breaking the aggregation of the second query into a plurality of constituent operations specified by the third query. | Aggregation in a computing system can include receiving, at a service node of the computing system, a first query specifying aggregation and translating the first query into a second query having a first canonical format and specifying the aggregation. The method can include forwarding the second query to a first subset of a plurality of endpoint nodes and translating, at each endpoint node of the first subset, the second query into a third query having a format executable by a data source connected to the endpoint node. The third query can specify a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source. The endpoint nodes can initiate execution of the third query by the data sources and provide an aggregated result including a result from the data source(s) to the service node.1. A computer-implemented method, comprising:
in response to receiving, at a service node of a computing system, a first query specifying aggregation, translating the first query into a second query having a first canonical format and specifying the aggregation; forwarding the second query to a first subset of a plurality of endpoint nodes of the computing system; translating, at each endpoint node of the first subset, the second query into a third query having a format executable by a data source connected to the endpoint node and specifying a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source, wherein each endpoint node initiates execution of the third query by the data source; and providing, from each endpoint node of the first subset to the service node, an aggregated result including a result from execution of the third query. 2. The computer-implemented method of claim 1, further comprising:
translating, at each endpoint node of the first subset, the second query into a fourth query having a second canonical format; and forwarding, from each endpoint node of the first subset, the fourth query to an endpoint node of a second subset of the plurality of endpoint nodes; wherein a version of the first query in one of the canonical formats is propagated to each endpoint node of the computing system. 3. The computer-implemented method of claim 2, wherein each endpoint node of the second plurality of endpoint nodes has not received a version of the first query in one of the canonical formats. 4. The computer-implemented method of claim 2, wherein each endpoint node is configured to aggregate results received from each child endpoint node that provides results thereto and forward the aggregated results to a parent endpoint node. 5. The computer-implemented method of claim 1, further comprising:
in response to determining, at a selected endpoint node of the plurality of endpoint nodes, that the data source coupled to the selected endpoint node does not support the aggregation specified by the second query, generating the third query to specify a lesser level of the aggregation than specified by the second query; and performing, at the selected endpoint node, an operation on the result from the data source coupled to the selected endpoint node, wherein the operation is part of the aggregation specified by the second query. 6. The computer-implemented method of claim 1, wherein the translating the second query into the third query comprises:
breaking the aggregation specified by the second query into a plurality of constituent operations specified by the third query. 7. The computer-implemented method of claim 1, wherein the first canonical format is specified using structured query language. 8. A computing system, comprising:
a service node; and a plurality of endpoint nodes; wherein the service node is configured to initiate operations including:
in response to receiving a first query specifying aggregation, translating the first query into a second query having a first canonical format and specifying the aggregation;
forwarding the second query to a first subset of the plurality of endpoint nodes;
wherein each endpoint node of the first subset is configured to initiate operations including:
translating the second query into a third query having a format executable by a data source connected to the selected endpoint node and specifying a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source;
initiating execution of the third query by the data source; and
providing an aggregated result including a result from the data source to the service node. 9. The computing system of claim 8, wherein each endpoint node of the first subset is configured to initiate operations further comprising:
translating the second query into a fourth query having a second canonical format; and forwarding the fourth query to an endpoint node of a second subset of the plurality of endpoint nodes; wherein a version of the first query in one of the canonical formats is propagated to each endpoint node of the computing system. 10. The computing system of claim 9, wherein each endpoint node of the second plurality of endpoint nodes has not received a version of the first query in one of the canonical formats. 11. The computing system of claim 9, wherein each endpoint node is configured to aggregate results received from each child endpoint node that provides results thereto and forward the aggregated results to a parent endpoint node. 12. The computing system of claim 8, wherein a selected endpoint node of the plurality of endpoint nodes is configured to initiate operations further comprising:
in response to determining that the data source coupled to the thereto does not support the aggregation specified by the second query, generating the third query to specify a lesser level of the aggregation than specified by the second query; and performing an operation on the result from the data source coupled to the selected endpoint node, wherein the operation is part of the aggregation specified by the second query. 13. The computing system of claim 8, wherein the translating the second query into the third query comprises:
breaking the aggregation of the second query into a plurality of constituent operations specified by the third query. 14. The computing system of claim 8, wherein the first canonical format is specified using structured query language. 15. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to initiate operations comprising:
in response to receiving, at a service node of a computing system, a first query specifying aggregation, translating the first query into a second query having a first canonical format and specifying the aggregation; forwarding the second query to a first subset of a plurality of endpoint nodes of the computing system; translating, at each endpoint node of the first subset, the second query into a third query having a format executable by a data source connected to the endpoint node and specifying a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source, wherein each endpoint node initiates execution of the third query by the data source; and providing, from each endpoint node of the first subset to the service node, an aggregated result including a result from execution of the third query. 16. The computer program product of claim 15, wherein the program instructions cause the one or more processors to initiate operations further comprising:
translating, at each endpoint node of the first subset, the second query into a fourth query having a second canonical format; and forwarding, from each endpoint node of the first subset, the fourth query to an endpoint node of a second subset of the plurality of endpoint nodes; wherein a version of the first query in one of the canonical formats is propagated to each endpoint node of the computing system. 17. The computer program product of claim 16, wherein each endpoint node of the second plurality of endpoint nodes has not received a version of the first query in one of the canonical formats. 18. The computer program product of claim 16, wherein the program instructions cause the one or more processors to initiate operations further comprising:
configuring each endpoint node to aggregate results received from each child endpoint node that provides results thereto and forward the aggregated results to a parent endpoint node. 19. The computer program product of claim 15, wherein the program instructions cause the one or more processors to initiate operations further comprising:
in response to determining, at a selected endpoint node of the plurality of endpoint nodes, that the data source coupled to the selected endpoint node does not support the aggregation specified by the second query, generating the third query to specify a lesser level of the aggregation than specified by the second query; and performing, at the selected endpoint node of the first subset, an operation on the result from the data source coupled to the selected endpoint node, wherein the operation is part of the aggregation specified by the second query. 20. The computer program product of claim 15, wherein the translating the second query into the third query comprises:
breaking the aggregation of the second query into a plurality of constituent operations specified by the third query. | 2,100 |
6,473 | 6,473 | 14,921,682 | 2,125 | This document discloses methods, systems, computer readable medium, and a machine for describing, mapping, modeling, generating, recreating, maintaining, archiving, and incorporating emotion or the immersive first-person experiences of self-awareness as data in a computing environment. In a preferable embodiment of the present invention, the method includes analyzing a body, obtaining information regarding at least some of the one or more relationships corresponding to location, topological, directional, distance, or temporal references, and generating a representation of the experience. In a preferable embodiment of the present invention, one or more methods also include the representation of immersive first-person experiences of emotion with other systems and data, the step of using comprising at least one of editing, generating, storing, converting, encoding, transmitting, displaying, editing, and incorporating data from input, output, outcome, result, or derivative values with applications, systems, or instance relevant computing environments. | 1. A machine having a memory containing data representing either of or both data structures and program instructions for editing, storing, converting, encoding, generating, or maintaining said data structures representing one or more immersive first-person experiences of emotion with zero, one, or more representations of immersive first-person physical sensations of self-awareness and zero, one, or more representations of a vantage using a hierarchy of coordinate systems being generated by a method comprising the steps of: analyzing one or more bodies; obtaining information about a body; generating one or more hierarchical representation. 2. The machine of claim 1, wherein data structures representing one or more immersive first-person experiences of emotion consisting of zero, one, or more members with each member representing zero, one, or more locations or subordinate coordinate systems and having zero, one, or more relationships, references, properties, descriptions, or dimensions of interest representing a nuanced emotion experience related whole with zero, one, or more dependently, interdependently, or independently corresponding or non-corresponding representations of immersive first-person physical sensations of self-awareness consisting of zero, one, or more members with each physical sensations related member representing zero, one, or more locations or subordinate coordinate systems and having zero, one, or more relationships, references, properties, descriptions, or dimensions of interest representing a nuanced physical sensations related whole and zero, one, or more representations of a corresponding or non-corresponding vantage using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 1. 3. The machine of claim 2, wherein hierarchy of coordinate systems is being generated by a method comprising the steps of: analyzing one or more biological, non-biological, virtual, or theoretical body in whole, in part, or both in part and in whole; obtaining information about each individual biological, non-biological, virtual, or theoretical body regarding one or more locations and zero, one, or more of each of the following: relationships, references, properties, descriptions, and dimensions of interest; generating one or more hierarchical representation with each having at least one member being represented by zero, one, or more of each of the following: locations, relationships, references, properties, descriptions, and dimensions of interest. 4. The machine of claim 3, wherein each data structure representing one or more immersive first-person experiences of emotion independently, interdependently, or dependently with zero, one, or more representations of immersive first-person physical sensations of self-awareness, and zero, one, or more representations of a vantage while either in a grouped or ungrouped state using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 3. 5. The machine of claim 4, wherein one or more data structure representations having input values, output values, derivatives, outcomes, or results for representing electronically, visually, graphically, programmatically, computationally, or as data. 6. The machine of claim 5, wherein one or more data structures representing nuanced immersive first-person subjective experiences and relevant conditions, states, components, events, properties, relationships, references, attributes, descriptions, or transitions either graphically, textually, numerically, symbolically, sequentially, or conceptually from first, second, or third person perspectives. 7. The machine of claim 6, wherein at least one data structure having one or more representation of an immersive first-person experience of emotion and zero, one, or more representations of immersive first-person physical sensations of self-awareness in a grouped state and is situated in relation to one or more representations of a corresponding vantage using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 6. 8. The machine of claim 7, wherein each data structure representing immersive first-person emotion, immersive first-person physical sensations of self-awareness, or vantage, in whole or in part, acting or residing in, as, or among one or more hierarchy of coordinate systems either expanded, combined, or collapsed as one or more coordinate systems. 9. The machine of claim 8, wherein one or more data structures representing awareness, sentience, subjectivity, or consciousness, in part or in whole, and zero, one, or more components of vantage representing as Center of Consciousness, Consciousness, Cybernetic Consciousness, Artificial Consciousness, Machine Consciousness, or Synthetic Consciousness. 10. The machine of claim 9, wherein at least one data structure representing one or more immersive first-person experiences of emotion, each in whole or in part, having alignment and configuration to model or maintain one or more data structures representing static or kinetic immersive first-person components of zero, one, or more of the following: intuition, empathy, anger, joy, euphoria, excitement, happiness, sadness, fear, love, mood, pain, nausea, headache, melancholy, depression, anxiety, grief, dysthymia, bipolar disorder, mania, psychosis, intoxication, and hallucination. 11. A non-transitory computer readable medium containing data representing either of or both data structures and program instructions for editing, storing, converting, encoding, generating, or maintaining said data structures representing zero, one, or more values for subjectively held ideas including self-concept, preconception, belief, and tenet for combining with data storage, processing, calculation, or decision models and one or more input values, output values, derivatives, outcomes, or results representing for or from one or more immersive first-person experiences of emotion with zero, one, or more representations of immersive first-person physical sensations of self-awareness and zero, one, or more representations of a vantage using a hierarchy of coordinate systems being generated by a method comprising the steps of: analyzing one or more bodies; obtaining information about a body; generating one or more representation. 12. The non-transitory computer readable medium of claim 11, wherein hierarchy of coordinate systems is being generated by a method comprising the steps of: analyzing one or more biological, non-biological, virtual, or theoretical body in whole, in part, or both in part and in whole; obtaining information about each individual biological, non-biological, virtual, or theoretical body regarding one or more locations and zero, one, or more of each of the following: relationships, references, properties, descriptions, and dimensions of interest; generating one or more hierarchical representation with each having at least one member being represented by zero, one, or more of each of the following: locations, relationships, references, properties, descriptions, and dimensions of interest. 13. The non-transitory computer readable medium of claim 12, wherein at least one representation of an immersive first-person experience of emotion and zero, one, or more representations of an immersive first-person physical sensations of self-awareness are representing in a grouped state and is situated in relation to one or more representations of a corresponding vantage using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 12. 14. The non-transitory computer readable medium of claim 13, wherein one or more data structure representation having input values, output values, derivatives, outcomes, or results for representing electronically, visually, graphically, programmatically, computationally, or as data. 15. The non-transitory computer readable medium of claim 14, wherein each of the one or more representations of immersive first-person experiences of emotion, each of the zero, one, or more representations of immersive first-person physical sensations of self-awareness, and the zero, one, or more representations of a vantage being represented from first-person, second-person, or third-person perspectives in the manner of one or more of each of the following: graphically, programmatically, computationally, textually, numerically, symbolically, sequentially, or conceptually. 16. The non-transitory computer readable medium of claim 15, wherein one or more data structures representing awareness, sentience, subjectivity, or consciousness, in part or in whole, and zero, one, or more components of vantage representing as Center of Consciousness, Consciousness, Cybernetic Consciousness, Artificial Consciousness, Machine Consciousness, or Synthetic Consciousness. 17. The non-transitory computer readable medium of claim 16, wherein at least one data structure representing one or more immersive first-person experiences of emotion, each in whole or in part, having alignment and configuration to model or maintain one or more data structures representing static or kinetic immersive first-person components of zero, one, or more of the following: intuition, empathy, anger, joy, euphoria, excitement, happiness, sadness, fear, love, mood, pain, nausea, headache, melancholy, depression, anxiety, grief, dysthymia, bipolar disorder, mania, psychosis, intoxication, and hallucination. 18. A system having a memory containing data representing either of or both data structures and program instructions for editing, storing, converting, encoding, generating, or maintaining said data structures wherein one or more data structures representing zero, one, or more values for subjectively held ideas including self-concept, preconception, belief, and tenet for combining with data storage, processing, calculation, or decision models and one or more input values, output values, derivatives, outcomes, or results representing for or from one or more coordinate system hierarchies using one or more coordinate representations of immersive first-person experiences of emotion, zero, one, or more coordinate representations of immersive first-person physical sensations of self-awareness, and zero, one, or more coordinate representations of a vantage using a hierarchy of coordinate systems being generated by a method comprising the steps of: analyzing one or more bodies; obtaining information about a body; generating one or more hierarchical representation. 19. The system of claim 18, wherein data structures representing one or more immersive first-person experiences of emotion consisting of zero, one, or more members with each member representing zero, one, or more locations or subordinate coordinate systems and having zero, one, or more relationships, references, properties, descriptions, or dimensions of interest representing a nuanced emotion experience related whole with zero, one, or more dependently, interdependently, or independently corresponding or non-corresponding representations of immersive first-person physical sensations of self-awareness consisting of zero, one, or more members with each physical sensations related member representing zero, one, or more locations or subordinate coordinate systems and having zero, one, or more relationships, references, properties, descriptions, or dimensions of interest representing a nuanced physical sensations related whole and zero, one, or more representations of a corresponding or non-corresponding vantage using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 18. 20. The system of claim 19, wherein hierarchy of coordinate systems is generated by a method comprising the steps of: analyzing one or more biological, non-biological, virtual, or theoretical body in whole, in part, or both in part and in whole; obtaining information about each individual biological, non-biological, virtual, or theoretical body regarding one or more locations and zero, one, or more of each of the following: relationships, references, properties, descriptions, and dimensions of interest; generating one or more hierarchical representation with each having at least one member being represented by zero, one, or more of each of the following: locations, relationships, references, properties, descriptions, and dimensions of interest. 21. The system of claim 20, wherein each data structure representing one or more immersive first-person experiences of emotion independently, interdependently, or dependently with zero, one, or more representations of immersive first-person physical sensations of self-awareness, and zero, one, or more representations of a vantage while either in a grouped or ungrouped state using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 20. 22. The system of claim 21, wherein one or more data structure representation having input values, output values, derivatives, outcomes, or results for representing electronically, visually, graphically, programmatically, computationally, or as data. 23. The system of claim 22, wherein each data structure representation being graphically, textually, numerically, symbolically, sequentially, or conceptually representing nuanced immersive first-person subjective experiences and relevant conditions, states, components, events, properties, relationships, references, attributes, descriptions, or transitions from first, second, or third person perspectives. 24. The system of claim 23, wherein at least one data structure representing an immersive first-person experience of emotion and zero, one, or more representations of immersive first-person physical sensations of self-awareness are in a grouped state and is situated in relation to one or more representations of a corresponding vantage using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 23. 25. The system of claim 24, wherein one or more data structures representing awareness, sentience, subjectivity, or consciousness, in part or in whole, and zero, one, or more components of vantage representing as Center of Consciousness, Consciousness, Cybernetic Consciousness, Artificial Consciousness, Machine Consciousness, or Synthetic Consciousness. 26. The system of claim 25, wherein at least one data structure representing one or more immersive first-person experiences of emotion, each in whole or in part, having alignment and configuration to model or maintain one or more data structures representing static or kinetic immersive first-person components of zero, one, or more of the following: intuition, empathy, anger, joy, euphoria, excitement, happiness, sadness, fear, love, mood, pain, nausea, headache, melancholy, depression, anxiety, grief, dysthymia, bipolar disorder, mania, psychosis, intoxication, and hallucination. | This document discloses methods, systems, computer readable medium, and a machine for describing, mapping, modeling, generating, recreating, maintaining, archiving, and incorporating emotion or the immersive first-person experiences of self-awareness as data in a computing environment. In a preferable embodiment of the present invention, the method includes analyzing a body, obtaining information regarding at least some of the one or more relationships corresponding to location, topological, directional, distance, or temporal references, and generating a representation of the experience. In a preferable embodiment of the present invention, one or more methods also include the representation of immersive first-person experiences of emotion with other systems and data, the step of using comprising at least one of editing, generating, storing, converting, encoding, transmitting, displaying, editing, and incorporating data from input, output, outcome, result, or derivative values with applications, systems, or instance relevant computing environments.1. A machine having a memory containing data representing either of or both data structures and program instructions for editing, storing, converting, encoding, generating, or maintaining said data structures representing one or more immersive first-person experiences of emotion with zero, one, or more representations of immersive first-person physical sensations of self-awareness and zero, one, or more representations of a vantage using a hierarchy of coordinate systems being generated by a method comprising the steps of: analyzing one or more bodies; obtaining information about a body; generating one or more hierarchical representation. 2. The machine of claim 1, wherein data structures representing one or more immersive first-person experiences of emotion consisting of zero, one, or more members with each member representing zero, one, or more locations or subordinate coordinate systems and having zero, one, or more relationships, references, properties, descriptions, or dimensions of interest representing a nuanced emotion experience related whole with zero, one, or more dependently, interdependently, or independently corresponding or non-corresponding representations of immersive first-person physical sensations of self-awareness consisting of zero, one, or more members with each physical sensations related member representing zero, one, or more locations or subordinate coordinate systems and having zero, one, or more relationships, references, properties, descriptions, or dimensions of interest representing a nuanced physical sensations related whole and zero, one, or more representations of a corresponding or non-corresponding vantage using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 1. 3. The machine of claim 2, wherein hierarchy of coordinate systems is being generated by a method comprising the steps of: analyzing one or more biological, non-biological, virtual, or theoretical body in whole, in part, or both in part and in whole; obtaining information about each individual biological, non-biological, virtual, or theoretical body regarding one or more locations and zero, one, or more of each of the following: relationships, references, properties, descriptions, and dimensions of interest; generating one or more hierarchical representation with each having at least one member being represented by zero, one, or more of each of the following: locations, relationships, references, properties, descriptions, and dimensions of interest. 4. The machine of claim 3, wherein each data structure representing one or more immersive first-person experiences of emotion independently, interdependently, or dependently with zero, one, or more representations of immersive first-person physical sensations of self-awareness, and zero, one, or more representations of a vantage while either in a grouped or ungrouped state using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 3. 5. The machine of claim 4, wherein one or more data structure representations having input values, output values, derivatives, outcomes, or results for representing electronically, visually, graphically, programmatically, computationally, or as data. 6. The machine of claim 5, wherein one or more data structures representing nuanced immersive first-person subjective experiences and relevant conditions, states, components, events, properties, relationships, references, attributes, descriptions, or transitions either graphically, textually, numerically, symbolically, sequentially, or conceptually from first, second, or third person perspectives. 7. The machine of claim 6, wherein at least one data structure having one or more representation of an immersive first-person experience of emotion and zero, one, or more representations of immersive first-person physical sensations of self-awareness in a grouped state and is situated in relation to one or more representations of a corresponding vantage using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 6. 8. The machine of claim 7, wherein each data structure representing immersive first-person emotion, immersive first-person physical sensations of self-awareness, or vantage, in whole or in part, acting or residing in, as, or among one or more hierarchy of coordinate systems either expanded, combined, or collapsed as one or more coordinate systems. 9. The machine of claim 8, wherein one or more data structures representing awareness, sentience, subjectivity, or consciousness, in part or in whole, and zero, one, or more components of vantage representing as Center of Consciousness, Consciousness, Cybernetic Consciousness, Artificial Consciousness, Machine Consciousness, or Synthetic Consciousness. 10. The machine of claim 9, wherein at least one data structure representing one or more immersive first-person experiences of emotion, each in whole or in part, having alignment and configuration to model or maintain one or more data structures representing static or kinetic immersive first-person components of zero, one, or more of the following: intuition, empathy, anger, joy, euphoria, excitement, happiness, sadness, fear, love, mood, pain, nausea, headache, melancholy, depression, anxiety, grief, dysthymia, bipolar disorder, mania, psychosis, intoxication, and hallucination. 11. A non-transitory computer readable medium containing data representing either of or both data structures and program instructions for editing, storing, converting, encoding, generating, or maintaining said data structures representing zero, one, or more values for subjectively held ideas including self-concept, preconception, belief, and tenet for combining with data storage, processing, calculation, or decision models and one or more input values, output values, derivatives, outcomes, or results representing for or from one or more immersive first-person experiences of emotion with zero, one, or more representations of immersive first-person physical sensations of self-awareness and zero, one, or more representations of a vantage using a hierarchy of coordinate systems being generated by a method comprising the steps of: analyzing one or more bodies; obtaining information about a body; generating one or more representation. 12. The non-transitory computer readable medium of claim 11, wherein hierarchy of coordinate systems is being generated by a method comprising the steps of: analyzing one or more biological, non-biological, virtual, or theoretical body in whole, in part, or both in part and in whole; obtaining information about each individual biological, non-biological, virtual, or theoretical body regarding one or more locations and zero, one, or more of each of the following: relationships, references, properties, descriptions, and dimensions of interest; generating one or more hierarchical representation with each having at least one member being represented by zero, one, or more of each of the following: locations, relationships, references, properties, descriptions, and dimensions of interest. 13. The non-transitory computer readable medium of claim 12, wherein at least one representation of an immersive first-person experience of emotion and zero, one, or more representations of an immersive first-person physical sensations of self-awareness are representing in a grouped state and is situated in relation to one or more representations of a corresponding vantage using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 12. 14. The non-transitory computer readable medium of claim 13, wherein one or more data structure representation having input values, output values, derivatives, outcomes, or results for representing electronically, visually, graphically, programmatically, computationally, or as data. 15. The non-transitory computer readable medium of claim 14, wherein each of the one or more representations of immersive first-person experiences of emotion, each of the zero, one, or more representations of immersive first-person physical sensations of self-awareness, and the zero, one, or more representations of a vantage being represented from first-person, second-person, or third-person perspectives in the manner of one or more of each of the following: graphically, programmatically, computationally, textually, numerically, symbolically, sequentially, or conceptually. 16. The non-transitory computer readable medium of claim 15, wherein one or more data structures representing awareness, sentience, subjectivity, or consciousness, in part or in whole, and zero, one, or more components of vantage representing as Center of Consciousness, Consciousness, Cybernetic Consciousness, Artificial Consciousness, Machine Consciousness, or Synthetic Consciousness. 17. The non-transitory computer readable medium of claim 16, wherein at least one data structure representing one or more immersive first-person experiences of emotion, each in whole or in part, having alignment and configuration to model or maintain one or more data structures representing static or kinetic immersive first-person components of zero, one, or more of the following: intuition, empathy, anger, joy, euphoria, excitement, happiness, sadness, fear, love, mood, pain, nausea, headache, melancholy, depression, anxiety, grief, dysthymia, bipolar disorder, mania, psychosis, intoxication, and hallucination. 18. A system having a memory containing data representing either of or both data structures and program instructions for editing, storing, converting, encoding, generating, or maintaining said data structures wherein one or more data structures representing zero, one, or more values for subjectively held ideas including self-concept, preconception, belief, and tenet for combining with data storage, processing, calculation, or decision models and one or more input values, output values, derivatives, outcomes, or results representing for or from one or more coordinate system hierarchies using one or more coordinate representations of immersive first-person experiences of emotion, zero, one, or more coordinate representations of immersive first-person physical sensations of self-awareness, and zero, one, or more coordinate representations of a vantage using a hierarchy of coordinate systems being generated by a method comprising the steps of: analyzing one or more bodies; obtaining information about a body; generating one or more hierarchical representation. 19. The system of claim 18, wherein data structures representing one or more immersive first-person experiences of emotion consisting of zero, one, or more members with each member representing zero, one, or more locations or subordinate coordinate systems and having zero, one, or more relationships, references, properties, descriptions, or dimensions of interest representing a nuanced emotion experience related whole with zero, one, or more dependently, interdependently, or independently corresponding or non-corresponding representations of immersive first-person physical sensations of self-awareness consisting of zero, one, or more members with each physical sensations related member representing zero, one, or more locations or subordinate coordinate systems and having zero, one, or more relationships, references, properties, descriptions, or dimensions of interest representing a nuanced physical sensations related whole and zero, one, or more representations of a corresponding or non-corresponding vantage using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 18. 20. The system of claim 19, wherein hierarchy of coordinate systems is generated by a method comprising the steps of: analyzing one or more biological, non-biological, virtual, or theoretical body in whole, in part, or both in part and in whole; obtaining information about each individual biological, non-biological, virtual, or theoretical body regarding one or more locations and zero, one, or more of each of the following: relationships, references, properties, descriptions, and dimensions of interest; generating one or more hierarchical representation with each having at least one member being represented by zero, one, or more of each of the following: locations, relationships, references, properties, descriptions, and dimensions of interest. 21. The system of claim 20, wherein each data structure representing one or more immersive first-person experiences of emotion independently, interdependently, or dependently with zero, one, or more representations of immersive first-person physical sensations of self-awareness, and zero, one, or more representations of a vantage while either in a grouped or ungrouped state using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 20. 22. The system of claim 21, wherein one or more data structure representation having input values, output values, derivatives, outcomes, or results for representing electronically, visually, graphically, programmatically, computationally, or as data. 23. The system of claim 22, wherein each data structure representation being graphically, textually, numerically, symbolically, sequentially, or conceptually representing nuanced immersive first-person subjective experiences and relevant conditions, states, components, events, properties, relationships, references, attributes, descriptions, or transitions from first, second, or third person perspectives. 24. The system of claim 23, wherein at least one data structure representing an immersive first-person experience of emotion and zero, one, or more representations of immersive first-person physical sensations of self-awareness are in a grouped state and is situated in relation to one or more representations of a corresponding vantage using a hierarchy of coordinate systems being generated by a method comprising the steps recited in claim 23. 25. The system of claim 24, wherein one or more data structures representing awareness, sentience, subjectivity, or consciousness, in part or in whole, and zero, one, or more components of vantage representing as Center of Consciousness, Consciousness, Cybernetic Consciousness, Artificial Consciousness, Machine Consciousness, or Synthetic Consciousness. 26. The system of claim 25, wherein at least one data structure representing one or more immersive first-person experiences of emotion, each in whole or in part, having alignment and configuration to model or maintain one or more data structures representing static or kinetic immersive first-person components of zero, one, or more of the following: intuition, empathy, anger, joy, euphoria, excitement, happiness, sadness, fear, love, mood, pain, nausea, headache, melancholy, depression, anxiety, grief, dysthymia, bipolar disorder, mania, psychosis, intoxication, and hallucination. | 2,100 |
6,474 | 6,474 | 15,052,253 | 2,154 | Some aspects of this disclosure involve generation of crowd-based results based on measurements of affective response of users. In some embodiments described herein, sensors are used to take measurements of affective response of at least ten users who were at a certain location. The measurements may include various values indicative of physiological signals and/or behavioral cues of the at least ten users. Some examples of locations in this disclosure include vacation destinations, businesses, establishments that provide entertainment, certain geographical regions, and virtual environments. User interfaces are configured to receive data describing a location score computed based on the measurements of the at least ten users, which represents the affective response of the at least ten users to being at the certain location. The user interfaces may be used to report the location score (e.g., to a user who may be interested in visiting the certain location). | 1. A system configured to report a location score for a certain location based on measurements of affective response, comprising:
sensors configured to take measurements of affective response of users; the measurements comprising measurements of affective response of at least ten users; wherein each measurement of a user is taken at most ten minutes after leaving the certain location; and user interfaces configured to receive data describing the location score; wherein the location score is computed based on the measurements of the at least ten users and represents the affective response of the at least ten users to visiting the certain location; and the user interfaces are further configured to report the location score. 2. The system of claim 1, wherein the sensors comprise a sensor implanted in a body of a user from among the at least ten users. 3. The system of claim 1, wherein the sensors comprise a sensor embedded in a device used by a user from among the at least ten users. 4. The system of claim 1, wherein at least some of the sensors are embedded in at least one of: clothing items, footwear, jewelry items, and wearable artifacts. 5. The system of claim 1, wherein the sensors comprise a sensor that is not in physical contact with the user of whom the sensor takes a measurement of affective response. 6. The system of claim 5, wherein the sensor is an image capturing device used to take a measurement of affective response of a user comprising one or more images of the user. 7. The system of claim 1, wherein at least some of the sensors are configured to take measurements of at least one of the following: physiological signals of the at least ten users, and behavioral cues of the at least ten users. 8. The system of claim 1, wherein the at least ten users consist a number of users that falls into one of the following ranges: 10 to 24, 25-99, 100-999, 1000-9999, 10000-99999, 100000-1000000, and more than one million. 9. The system of claim 1, wherein the certain location is an establishment in which entertainment is provided that is one or more of the following establishments: a club, a bar, a movie theater, a theater, a casino, a stadium, and a concert venue. 10. The system of claim 1, wherein the certain location is a place of business that is one or more of the following places of business: a store, a restaurant, a booth, a shopping mall, a shopping center, a market, a supermarket, a beauty salon, a spa, and a hospital clinic. 11. The system of claim 1, wherein the certain location is a vacation destination that is one or more of the following: a continent, a country, a county, a city, a resort, a neighborhood, and a hotel. 12. The system of claim 1, wherein the certain location a certain region of a larger location; and wherein the certain region is one or more of the following: a certain wing of a hotel, a certain floor of a hotel, a certain room in a hotel, a certain room in a resort, a certain cabin in a ship, a certain seat in a vehicle, a certain class of seats in a vehicle, a certain type of seating location in a vehicle. 13. The system of claim 1, wherein the certain location is a virtual environment in a virtual world, with at least one instantiation of the virtual environment stored in a memory of a computer; wherein a user is considered to be in the virtual environment by virtue of having a value stored in the memory of the computer indicating a presence of a representation of the user in the virtual environment. 14. The system of claim 1, wherein the at least ten users comprise a user that receives an indication of the scores via a user interface from among the user interfaces
wherein the measurements of the at least ten users comprise first and second measurements, such that the first measurement is taken at least 24 hours before the second measurement is taken. 15. A method for reporting a location score for a certain location based on measurements of affective response, comprising:
taking measurements of affective response of users with sensors; the measurements comprising measurements of affective response of at least ten users; wherein each measurement of a user is taken at most ten minutes after leaving the certain location; receiving data describing the location score; wherein the location score is computed based on the measurements of the at least ten users and represents the affective response of the at least ten users to visiting the certain location; and reporting the location score via user interfaces. 16. The method of claim 15, further comprising computing the location score based on the measurements. 17. The method of claim 15, further comprising receiving baseline affective response value for the at least ten users, measurements of affective response of the at least ten users, and normalizing the measurements of the at least ten users with respect to the baseline affective response values. 18. A non-transitory computer-readable medium having instructions stored thereon that, in response to execution by a system including a processor and memory, cause the system to perform operations comprising:
taking measurements of affective response of users with sensors; the measurements comprising measurements of affective response of at least ten users; wherein each measurement of a user is taken at most ten minutes after leaving a certain location; receiving data describing a location score; wherein the location score is computed based on the measurements of the at least ten users and represents the affective response of the at least ten users to visiting the certain location; and reporting the location score via user interfaces. 19. The non-transitory computer-readable medium of claim 18, further comprising instructions defining the step of computing the location score based on the measurements. 20. The non-transitory computer-readable medium of claim 18, further comprising additional instructions that, in response to execution, cause the system to perform operations comprising: receiving baseline affective response value for the at least ten users, measurements of affective response of the at least ten users, and normalizing the measurements of the at least some of the at least ten users with respect to the baseline affective response values. | Some aspects of this disclosure involve generation of crowd-based results based on measurements of affective response of users. In some embodiments described herein, sensors are used to take measurements of affective response of at least ten users who were at a certain location. The measurements may include various values indicative of physiological signals and/or behavioral cues of the at least ten users. Some examples of locations in this disclosure include vacation destinations, businesses, establishments that provide entertainment, certain geographical regions, and virtual environments. User interfaces are configured to receive data describing a location score computed based on the measurements of the at least ten users, which represents the affective response of the at least ten users to being at the certain location. The user interfaces may be used to report the location score (e.g., to a user who may be interested in visiting the certain location).1. A system configured to report a location score for a certain location based on measurements of affective response, comprising:
sensors configured to take measurements of affective response of users; the measurements comprising measurements of affective response of at least ten users; wherein each measurement of a user is taken at most ten minutes after leaving the certain location; and user interfaces configured to receive data describing the location score; wherein the location score is computed based on the measurements of the at least ten users and represents the affective response of the at least ten users to visiting the certain location; and the user interfaces are further configured to report the location score. 2. The system of claim 1, wherein the sensors comprise a sensor implanted in a body of a user from among the at least ten users. 3. The system of claim 1, wherein the sensors comprise a sensor embedded in a device used by a user from among the at least ten users. 4. The system of claim 1, wherein at least some of the sensors are embedded in at least one of: clothing items, footwear, jewelry items, and wearable artifacts. 5. The system of claim 1, wherein the sensors comprise a sensor that is not in physical contact with the user of whom the sensor takes a measurement of affective response. 6. The system of claim 5, wherein the sensor is an image capturing device used to take a measurement of affective response of a user comprising one or more images of the user. 7. The system of claim 1, wherein at least some of the sensors are configured to take measurements of at least one of the following: physiological signals of the at least ten users, and behavioral cues of the at least ten users. 8. The system of claim 1, wherein the at least ten users consist a number of users that falls into one of the following ranges: 10 to 24, 25-99, 100-999, 1000-9999, 10000-99999, 100000-1000000, and more than one million. 9. The system of claim 1, wherein the certain location is an establishment in which entertainment is provided that is one or more of the following establishments: a club, a bar, a movie theater, a theater, a casino, a stadium, and a concert venue. 10. The system of claim 1, wherein the certain location is a place of business that is one or more of the following places of business: a store, a restaurant, a booth, a shopping mall, a shopping center, a market, a supermarket, a beauty salon, a spa, and a hospital clinic. 11. The system of claim 1, wherein the certain location is a vacation destination that is one or more of the following: a continent, a country, a county, a city, a resort, a neighborhood, and a hotel. 12. The system of claim 1, wherein the certain location a certain region of a larger location; and wherein the certain region is one or more of the following: a certain wing of a hotel, a certain floor of a hotel, a certain room in a hotel, a certain room in a resort, a certain cabin in a ship, a certain seat in a vehicle, a certain class of seats in a vehicle, a certain type of seating location in a vehicle. 13. The system of claim 1, wherein the certain location is a virtual environment in a virtual world, with at least one instantiation of the virtual environment stored in a memory of a computer; wherein a user is considered to be in the virtual environment by virtue of having a value stored in the memory of the computer indicating a presence of a representation of the user in the virtual environment. 14. The system of claim 1, wherein the at least ten users comprise a user that receives an indication of the scores via a user interface from among the user interfaces
wherein the measurements of the at least ten users comprise first and second measurements, such that the first measurement is taken at least 24 hours before the second measurement is taken. 15. A method for reporting a location score for a certain location based on measurements of affective response, comprising:
taking measurements of affective response of users with sensors; the measurements comprising measurements of affective response of at least ten users; wherein each measurement of a user is taken at most ten minutes after leaving the certain location; receiving data describing the location score; wherein the location score is computed based on the measurements of the at least ten users and represents the affective response of the at least ten users to visiting the certain location; and reporting the location score via user interfaces. 16. The method of claim 15, further comprising computing the location score based on the measurements. 17. The method of claim 15, further comprising receiving baseline affective response value for the at least ten users, measurements of affective response of the at least ten users, and normalizing the measurements of the at least ten users with respect to the baseline affective response values. 18. A non-transitory computer-readable medium having instructions stored thereon that, in response to execution by a system including a processor and memory, cause the system to perform operations comprising:
taking measurements of affective response of users with sensors; the measurements comprising measurements of affective response of at least ten users; wherein each measurement of a user is taken at most ten minutes after leaving a certain location; receiving data describing a location score; wherein the location score is computed based on the measurements of the at least ten users and represents the affective response of the at least ten users to visiting the certain location; and reporting the location score via user interfaces. 19. The non-transitory computer-readable medium of claim 18, further comprising instructions defining the step of computing the location score based on the measurements. 20. The non-transitory computer-readable medium of claim 18, further comprising additional instructions that, in response to execution, cause the system to perform operations comprising: receiving baseline affective response value for the at least ten users, measurements of affective response of the at least ten users, and normalizing the measurements of the at least some of the at least ten users with respect to the baseline affective response values. | 2,100 |
6,475 | 6,475 | 15,412,092 | 2,164 | A data request workflow system comprises vehicle operational data. A file delivery request comprises a data request for a subset of the vehicle operational data. A file processing system receives the vehicle operational data and includes at least one set of predefined file editing rules. The file processing system automatically applies at least one set of predefined file editing rules to filter out a predefined sub-set of the vehicle operational data and to automatically edit data that is part of the data request per predefined criteria for each type of vehicle operational data to provide a converted data file to be transmitted to a user that submitted the file delivery request. | 1. A data request workflow system comprising:
vehicle operational data; a file delivery request comprising a data request for a subset of the vehicle operational data; a file processing system that receives the vehicle operational data and includes at least one set of predefined file editing rules; and wherein the file processing system automatically applies the at least one set of predefined file editing rules to filter out a predefined sub-set of the vehicle operational data and to automatically edit data that is part of the data request per predefined criteria for each type of vehicle operational data to provide a converted data file to be transmitted to a user that submitted the file delivery request. 2. The data request workflow system according to claim 1 including a data acquisition system mounted within a vehicle that collects the vehicle operational data in a raw format. 3. The data request workflow system according to claim 2 wherein the file processing system automatically converts raw vehicle operational data into a useable format for the user. 4. The data request workflow system according to claim 3 wherein the file delivery request comprises at least an identification of the user, a request for a desired file format output, and a desired notification process when the file delivery request is completed. 5. The data request workflow system according to claim 2 wherein the data acquisition system comprises an avionics computer system that is mounted within an aircraft. 6. The data request workflow system according to claim 5 wherein airline personnel define the predefined editing rules and wherein airline personnel approve the converted data file prior to transmission to the user. 7. The data request workflow system according to claim 5 wherein the data request for a subset of the vehicle operational data comprises at least a request for gas turbine engine data and associated flight data. 8. The data request workflow system according to claim 7 wherein the predefined editing rules include rules that automatically edit flight data such that vehicle operational data cannot be linked to specific flights. 9. The data request workflow system according to claim 1 wherein the file processing system includes a control module with at least one processor, a memory portion to store the predefined file editing rules, one or more input device interfaces to receive the vehicle operational data and the file delivery request, and one or more output devices interfaces to communicate the converted data filed to the user. 10. A method for processing a data request in a workflow system comprising:
providing vehicle operational data comprising vehicle data and vehicle sub-system data; obtaining predefined file editing rules for the vehicle operational data; providing a file processing system that receives the vehicle operational data and the predefined file editing rules; submitting a file delivery request to the file processing system, wherein the file delivery request comprises a data request for a subset of the vehicle operational data; and wherein the file processing system automatically applies the predefined file editing rules to filter out a predefined sub-set of the vehicle operational data and to automatically edit data that is part of the data request per predefined criteria for each type of vehicle operational data to provide a converted data file as an output. 11. The method according to claim 10 including transmitting the converted data file to a user that submitted the file delivery request in a desired format defined by the user in the file delivery request. 12. The method according to claim 11 including automatically notifying the user that the converted data file is available. 13. The method according to claim 10 including communicating with a data acquisition system mounted within a vehicle that collects the vehicle operational data in a raw format. 14. The method according to claim 13 including receiving raw data and automatically converting the raw data into a useable format for the user. 15. The method according to claim 13 wherein the data acquisition system comprises an avionics computer system that is mounted within an aircraft. 16. The method according to claim 15 wherein the data request for a subset of the vehicle operational data comprises requesting at least gas turbine engine data and associated flight data. 17. The method according to claim 16 including having airline personnel define the predefined file editing rules, and having at least one airline personnel approve the converted data file prior to transmission to a user that submitted the file delivery request. 18. The method according to claim 15 wherein the predefined editing rules include rules that automatically edit flight data such that vehicle operational data cannot be linked to specific flights. 19. The method according to claim 10 wherein the file delivery request includes at least identifying at least one vehicle sub-system, requesting specific data feeds from the vehicle operational data related to the vehicle sub-system, and identifying a desired file format output. 20. The method according to claim 19 wherein the predefined file editing rules automatically filter out all data feeds not related to a specified vehicle sub-system, flag any new data feed requests not previously requested for the specified vehicle sub-system, and automatically transmit any flagged data feed request to an owner of the vehicle operational data. | A data request workflow system comprises vehicle operational data. A file delivery request comprises a data request for a subset of the vehicle operational data. A file processing system receives the vehicle operational data and includes at least one set of predefined file editing rules. The file processing system automatically applies at least one set of predefined file editing rules to filter out a predefined sub-set of the vehicle operational data and to automatically edit data that is part of the data request per predefined criteria for each type of vehicle operational data to provide a converted data file to be transmitted to a user that submitted the file delivery request.1. A data request workflow system comprising:
vehicle operational data; a file delivery request comprising a data request for a subset of the vehicle operational data; a file processing system that receives the vehicle operational data and includes at least one set of predefined file editing rules; and wherein the file processing system automatically applies the at least one set of predefined file editing rules to filter out a predefined sub-set of the vehicle operational data and to automatically edit data that is part of the data request per predefined criteria for each type of vehicle operational data to provide a converted data file to be transmitted to a user that submitted the file delivery request. 2. The data request workflow system according to claim 1 including a data acquisition system mounted within a vehicle that collects the vehicle operational data in a raw format. 3. The data request workflow system according to claim 2 wherein the file processing system automatically converts raw vehicle operational data into a useable format for the user. 4. The data request workflow system according to claim 3 wherein the file delivery request comprises at least an identification of the user, a request for a desired file format output, and a desired notification process when the file delivery request is completed. 5. The data request workflow system according to claim 2 wherein the data acquisition system comprises an avionics computer system that is mounted within an aircraft. 6. The data request workflow system according to claim 5 wherein airline personnel define the predefined editing rules and wherein airline personnel approve the converted data file prior to transmission to the user. 7. The data request workflow system according to claim 5 wherein the data request for a subset of the vehicle operational data comprises at least a request for gas turbine engine data and associated flight data. 8. The data request workflow system according to claim 7 wherein the predefined editing rules include rules that automatically edit flight data such that vehicle operational data cannot be linked to specific flights. 9. The data request workflow system according to claim 1 wherein the file processing system includes a control module with at least one processor, a memory portion to store the predefined file editing rules, one or more input device interfaces to receive the vehicle operational data and the file delivery request, and one or more output devices interfaces to communicate the converted data filed to the user. 10. A method for processing a data request in a workflow system comprising:
providing vehicle operational data comprising vehicle data and vehicle sub-system data; obtaining predefined file editing rules for the vehicle operational data; providing a file processing system that receives the vehicle operational data and the predefined file editing rules; submitting a file delivery request to the file processing system, wherein the file delivery request comprises a data request for a subset of the vehicle operational data; and wherein the file processing system automatically applies the predefined file editing rules to filter out a predefined sub-set of the vehicle operational data and to automatically edit data that is part of the data request per predefined criteria for each type of vehicle operational data to provide a converted data file as an output. 11. The method according to claim 10 including transmitting the converted data file to a user that submitted the file delivery request in a desired format defined by the user in the file delivery request. 12. The method according to claim 11 including automatically notifying the user that the converted data file is available. 13. The method according to claim 10 including communicating with a data acquisition system mounted within a vehicle that collects the vehicle operational data in a raw format. 14. The method according to claim 13 including receiving raw data and automatically converting the raw data into a useable format for the user. 15. The method according to claim 13 wherein the data acquisition system comprises an avionics computer system that is mounted within an aircraft. 16. The method according to claim 15 wherein the data request for a subset of the vehicle operational data comprises requesting at least gas turbine engine data and associated flight data. 17. The method according to claim 16 including having airline personnel define the predefined file editing rules, and having at least one airline personnel approve the converted data file prior to transmission to a user that submitted the file delivery request. 18. The method according to claim 15 wherein the predefined editing rules include rules that automatically edit flight data such that vehicle operational data cannot be linked to specific flights. 19. The method according to claim 10 wherein the file delivery request includes at least identifying at least one vehicle sub-system, requesting specific data feeds from the vehicle operational data related to the vehicle sub-system, and identifying a desired file format output. 20. The method according to claim 19 wherein the predefined file editing rules automatically filter out all data feeds not related to a specified vehicle sub-system, flag any new data feed requests not previously requested for the specified vehicle sub-system, and automatically transmit any flagged data feed request to an owner of the vehicle operational data. | 2,100 |
6,476 | 6,476 | 16,181,529 | 2,179 | Methods are described for operating a social network server wherein the server receives a plurality of annotations. Candidate tags are determined from the annotations by removing commonly occurring words. Tag probabilities are determined based on social distance between an annotation contributor and an owner of the image, geographical distance between an annotation contributor and capture location of the image, and the size and position of an annotation contributor. | 1. A method of operating a server, the method comprising:
receiving at a social network server, for an image, a plurality of annotations from a plurality of devices, corresponding to a plurality of annotation contributors; determining a plurality of candidate tags from the plurality of annotations; excluding, from the plurality of candidate tags, commonly occurring words; modifying, a first candidate tag probability of a first candidate tag, based on a social distance between a first annotation contributor and an owner of the image; modifying, a second candidate tag probability of a second candidate tag, based on a geographical distance between a geographical location of a second annotation contributor as indicated by the geographical location of a second annotation contributor device, and a capture location of the image as indicated by GPS coordinates, stored in the image by a capture device at a time of capture; and modifying, a third candidate tag probability of a third candidate tag, based on a size and location of a third annotation contributor subject face appearing in the image; and selecting for the image, by the social network server, a recommended tag from the plurality of candidate tags based on the first candidate tag probability, second candidate tag probability, and third candidate tag probability, wherein the social network server is operable to create symmetric friend relationships through an invitation and acceptance process, and to restrict annotation contribution to users appearing in the image and friends of the users appearing in the image, and wherein the first candidate tag, the second candidate tag, and the third candidate tag are different tags. 2. The method of claim 1 wherein the invitation is to form a friend connection between a first user associated first user account of the social network server and a second user associated with a second user account of the social network server. 3. The method of claim 1 wherein the first annotation contributor, the second annotation contributor, and the third annotation contributor are a same annotation contributor. 4. The method of claim 1 wherein the first annotation contributor, the second annotation contributor, and the third annotation contributor are different annotation contributors. 5. The method of claim 1 wherein at least one of the plurality of annotations is a sentence. 6. A method of operating a server, the method comprising:
receiving at a social network server, for an image, a plurality of annotations from a plurality of devices corresponding to a plurality of annotation contributors; determining a plurality of candidate tags, and a corresponding plurality of candidate tag probabilities, from the plurality of annotations; excluding, from the plurality of candidate tags, commonly occurring words; modifying, a first candidate tag probability of a first candidate tag of the corresponding plurality of candidate tag probabilities, based on a social distance between a first annotation contributor and an owner of the image; modifying, a second candidate tag probability of a second candidate tag of the corresponding plurality of candidate tag probabilities, based on a geographical distance between a geographical location of a second annotation contributor as indicated by the geographical location of a second annotation contributor device, and a capture location of the image as indicated by GPS coordinates, stored in the image, by a capture device at a time of capture; and modifying, a third candidate tag probability of a third candidate tag of the corresponding plurality of candidate tag probabilities, based on an occurrence of a third annotation contributor associated with the third candidate tag as a subject face in the image; and selecting for the image, by the social network server, a recommended tag from the plurality of candidate tags based on the corresponding plurality of candidate tag probabilities, wherein the social network server is operable to create symmetric friend relationships through an invitation and acceptance process, and to restrict annotation contribution to users appearing in the image, and friends of the users appearing in the image. 7. The method of claim 6 wherein the invitation is to form a friend connection between a first user associated first user account of the social network server and a second user associated with a second user account of the social network server. 8. The method of claim 6 wherein the first candidate tag, the second candidate tag, and the third candidate tag are a same tag. 9. The method of claim 6 wherein the first candidate tag, the second candidate tag, and the third candidate tag are different tags. 10. The method of claim 6 wherein the first annotation contributor, the second annotation contributor, and the third annotation contributor are a same annotation contributor. 11. The method of claim 6 wherein the first annotation contributor, the second annotation contributor, and the third annotation contributor are different annotation contributors. 12. The method of claim 6 wherein modifying the third candidate tag probability of the third candidate tag further comprises:
modifying the third candidate tag probability based on a size and location of the third annotation contributor subject face appearing the image. 13. The method of claim 6 wherein modifying the third candidate tag probability of the third candidate tag further comprises:
modifying the third candidate tag probability based on a size and location of the third annotation contributor subject face appearing the image,
wherein the first candidate tag, the second candidate tag, and the third candidate tag are different tags. 14. The method of claim 6 wherein the image comprises a plurality of subject faces and one of the plurality of subject faces is of the third annotation contributor and is a current tag. 15. The method of claim 6 wherein the image consists of a single subject face appearing in the image and the single subject face is currently tagged as the third annotation contributor. 16. The method of claim 6 wherein at least one of the plurality of annotations is a sentence. 17. A method of operating a server, the method comprising:
receiving at a social network server, for an image consisting of a single subject face, an annotation from a device, the device corresponding to an annotation contributor, the annotation comprised of a plurality of words; determining a plurality of candidate tags, and corresponding candidate tag probabilities, from the annotation, the plurality of candidate tags determined by excluding commonly occurring words in the annotation and treating remaining words as candidate tags, and modifying the candidate tag probabilities based on a social distance between the annotation contributor and an owner of the image and a geographical location of the annotation contributor; modifying, a candidate tag probability of a candidate tag of plurality of candidate tags, based on the annotation contributor associated with the candidate tag being the single subject face identified in the image; and selecting for the image, by the social network server, a recommended tag from the plurality of candidate tags based on the corresponding candidate tag probabilities, wherein the social network server is operable to create symmetric friend relationships through an invitation and acceptance process, and to restrict annotation to a user appearing as the single subject face in the image and friends of the user appearing as the single subject face in the image. 18. The method of claim 17 wherein the invitation is to form a friend connection between a first user associated first user account of the social network server and a second user associated with a second user account of the social network server. 19. The method of claim 17 wherein the annotation is a sentence. | Methods are described for operating a social network server wherein the server receives a plurality of annotations. Candidate tags are determined from the annotations by removing commonly occurring words. Tag probabilities are determined based on social distance between an annotation contributor and an owner of the image, geographical distance between an annotation contributor and capture location of the image, and the size and position of an annotation contributor.1. A method of operating a server, the method comprising:
receiving at a social network server, for an image, a plurality of annotations from a plurality of devices, corresponding to a plurality of annotation contributors; determining a plurality of candidate tags from the plurality of annotations; excluding, from the plurality of candidate tags, commonly occurring words; modifying, a first candidate tag probability of a first candidate tag, based on a social distance between a first annotation contributor and an owner of the image; modifying, a second candidate tag probability of a second candidate tag, based on a geographical distance between a geographical location of a second annotation contributor as indicated by the geographical location of a second annotation contributor device, and a capture location of the image as indicated by GPS coordinates, stored in the image by a capture device at a time of capture; and modifying, a third candidate tag probability of a third candidate tag, based on a size and location of a third annotation contributor subject face appearing in the image; and selecting for the image, by the social network server, a recommended tag from the plurality of candidate tags based on the first candidate tag probability, second candidate tag probability, and third candidate tag probability, wherein the social network server is operable to create symmetric friend relationships through an invitation and acceptance process, and to restrict annotation contribution to users appearing in the image and friends of the users appearing in the image, and wherein the first candidate tag, the second candidate tag, and the third candidate tag are different tags. 2. The method of claim 1 wherein the invitation is to form a friend connection between a first user associated first user account of the social network server and a second user associated with a second user account of the social network server. 3. The method of claim 1 wherein the first annotation contributor, the second annotation contributor, and the third annotation contributor are a same annotation contributor. 4. The method of claim 1 wherein the first annotation contributor, the second annotation contributor, and the third annotation contributor are different annotation contributors. 5. The method of claim 1 wherein at least one of the plurality of annotations is a sentence. 6. A method of operating a server, the method comprising:
receiving at a social network server, for an image, a plurality of annotations from a plurality of devices corresponding to a plurality of annotation contributors; determining a plurality of candidate tags, and a corresponding plurality of candidate tag probabilities, from the plurality of annotations; excluding, from the plurality of candidate tags, commonly occurring words; modifying, a first candidate tag probability of a first candidate tag of the corresponding plurality of candidate tag probabilities, based on a social distance between a first annotation contributor and an owner of the image; modifying, a second candidate tag probability of a second candidate tag of the corresponding plurality of candidate tag probabilities, based on a geographical distance between a geographical location of a second annotation contributor as indicated by the geographical location of a second annotation contributor device, and a capture location of the image as indicated by GPS coordinates, stored in the image, by a capture device at a time of capture; and modifying, a third candidate tag probability of a third candidate tag of the corresponding plurality of candidate tag probabilities, based on an occurrence of a third annotation contributor associated with the third candidate tag as a subject face in the image; and selecting for the image, by the social network server, a recommended tag from the plurality of candidate tags based on the corresponding plurality of candidate tag probabilities, wherein the social network server is operable to create symmetric friend relationships through an invitation and acceptance process, and to restrict annotation contribution to users appearing in the image, and friends of the users appearing in the image. 7. The method of claim 6 wherein the invitation is to form a friend connection between a first user associated first user account of the social network server and a second user associated with a second user account of the social network server. 8. The method of claim 6 wherein the first candidate tag, the second candidate tag, and the third candidate tag are a same tag. 9. The method of claim 6 wherein the first candidate tag, the second candidate tag, and the third candidate tag are different tags. 10. The method of claim 6 wherein the first annotation contributor, the second annotation contributor, and the third annotation contributor are a same annotation contributor. 11. The method of claim 6 wherein the first annotation contributor, the second annotation contributor, and the third annotation contributor are different annotation contributors. 12. The method of claim 6 wherein modifying the third candidate tag probability of the third candidate tag further comprises:
modifying the third candidate tag probability based on a size and location of the third annotation contributor subject face appearing the image. 13. The method of claim 6 wherein modifying the third candidate tag probability of the third candidate tag further comprises:
modifying the third candidate tag probability based on a size and location of the third annotation contributor subject face appearing the image,
wherein the first candidate tag, the second candidate tag, and the third candidate tag are different tags. 14. The method of claim 6 wherein the image comprises a plurality of subject faces and one of the plurality of subject faces is of the third annotation contributor and is a current tag. 15. The method of claim 6 wherein the image consists of a single subject face appearing in the image and the single subject face is currently tagged as the third annotation contributor. 16. The method of claim 6 wherein at least one of the plurality of annotations is a sentence. 17. A method of operating a server, the method comprising:
receiving at a social network server, for an image consisting of a single subject face, an annotation from a device, the device corresponding to an annotation contributor, the annotation comprised of a plurality of words; determining a plurality of candidate tags, and corresponding candidate tag probabilities, from the annotation, the plurality of candidate tags determined by excluding commonly occurring words in the annotation and treating remaining words as candidate tags, and modifying the candidate tag probabilities based on a social distance between the annotation contributor and an owner of the image and a geographical location of the annotation contributor; modifying, a candidate tag probability of a candidate tag of plurality of candidate tags, based on the annotation contributor associated with the candidate tag being the single subject face identified in the image; and selecting for the image, by the social network server, a recommended tag from the plurality of candidate tags based on the corresponding candidate tag probabilities, wherein the social network server is operable to create symmetric friend relationships through an invitation and acceptance process, and to restrict annotation to a user appearing as the single subject face in the image and friends of the user appearing as the single subject face in the image. 18. The method of claim 17 wherein the invitation is to form a friend connection between a first user associated first user account of the social network server and a second user associated with a second user account of the social network server. 19. The method of claim 17 wherein the annotation is a sentence. | 2,100 |
6,477 | 6,477 | 15,796,395 | 2,191 | Various aspects described herein are directed to a method or system that implements an integrated platform for continuous deployment of software application delivery models. One or more releases and pertinent information of a software application delivery model may be identified or created at a portal on a remote computing system. One or more data structures may be populated for one or more modules hosted on the remote computing system. Tracking records may be generated at least by tracking the one or more releases with at least some of aggregated information identified from a plurality of tenants connected to the portal; and a release of the one or more releases or a portion thereof may be propagated along a release pipeline based in part or in whole upon the tracking records. | 1. A computer implemented method for implementing an integrated platform for continuous deployment of software application delivery models, comprising:
creating, by a release creation module comprising computer-executable instructions stored at partially in memory and executed by at least one microprocessor, one or more releases and pertinent information of a software application delivery model at a portal on a remote computing system; populating one or more data structures for one or more modules hosted on the remote computing system with the pertinent information; generating tracking records at least by tracking the one or more releases with at least some of aggregated information identified from a plurality of tenants connected to the portal; and propagating a release of the one or more releases or a portion thereof along a release pipeline based in part or in whole upon the tracking records. 2. The computer implemented method of claim 1, further comprising:
identifying or determining the portal on the remote computing system; identifying the plurality of tenants connected to the portal on the remote computing system; identifying one or more versions of the software application delivery model; and identifying respective pertinent information about the one or more versions from at least one tenant of the plurality of tenants. 3. The computer implemented method of claim 2, further comprising:
generating the aggregated information at least by aggregating the pertinent information about the one or more releases into a first data structure managed by a release train module. 4. The computer implemented method of claim 3, further comprising:
classifying a plurality of code modules, artifacts, or the pertinent information into one or more clusters. 5. The computer implemented method of claim 4, further comprising:
identifying first information pertaining to an artifact, a code module, or at least a portion of the pertinent information; and normalizing the first information into normalized information. 6. The computer implemented method of claim 5, further comprising:
identifying or determining the one or more clusters at least by applying word or term embedding techniques to the normalized information; and identifying or determining one or more recommendations for the one or more clusters. 7. The computer implemented method of claim 4, further comprising:
determining dependencies among the one or more releases or one or more portions thereof. 8. The computer implemented method of claim 7, further comprising:
tracking the one or more releases along respective release pipelines to generate at least the tracking records; and populating a calendar with at least a portion of the respective pertinent information or the aggregate information based at least in part upon at least some of the tracking records. 9. The computer implemented method of claim 8, further comprising:
identifying a release activity or information thereof associated with a release of the one or more releases; accessing an impact of the release activity or information thereof on one or more other release activities; and determining a score for the release activity or the information thereof. 10. The computer implemented method of claim 9, further comprising:
identifying or determining one or more other pieces of pertinent information affected by or affecting the release activity or the information thereof; and determining an extent of influence of the release activity or the information thereof. 11. The computer implemented method of claim 10, further comprising:
determining a first level of impact of the release activity or the information thereof on the one or more other pieces of pertinent information; and determining one or more second levels of impact of the release activity or the information thereof on the release activity of the information thereof. 12. The computer implemented method of claim 9, further comprising:
identifying a release from the one or more releases of the software application delivery model; identifying one or more tenants and release activities corresponding to the release; and determining respective states of the release activities. 13. The computer implemented method of claim 12, further comprising:
identifying a hindering state that hinders the release of the software application delivery model; and determining one or more issues resulting in the hindering state and one or more corresponding tenants that are associated with the one or more issues. 14. The computer implemented method of claim 13, further comprising:
identifying issue descriptions or issue resolutions concerning the one or more issues from a database table, an expert system, or a knowledge base; determining respective numeric or symbolic scores for the release activities; and cross-linking information concerning the one or more tenants, the release activities, the respective states, the hindering state, the one or more issues, the one or more corresponding tenants, the issue descriptions, the issue resolutions, or the respective numeric or symbolic scores. 15. An article of manufacture comprising a non-transitory computer accessible storage medium having stored thereupon a sequence of instructions which, when executed by at least one processor or at least one processor core executing one or more threads, causes the at least one processor or the at least one processor core to perform a set of acts for implementing an integrated platform for continuous deployment of software application delivery models, the set of acts comprising:
identifying or creating, by a release creation module comprising computer-executable instructions stored at partially in memory and executed by at least one microprocessor, one or more releases and pertinent information of a software application delivery model at a portal on a remote computing system; populating one or more data structures for one or more modules hosted on the remote computing system; generating tracking records at least by tracking the one or more releases with at least some of aggregated information identified from a plurality of tenants connected to the portal; and propagating a release of the one or more releases or a portion thereof along a release pipeline based in part or in whole upon the tracking records. 16. The article of manufacture of claim 15, wherein the set of acts further comprises:
identifying or determining the portal on the remote computing system; identifying the plurality of tenants connected to the remote computing system; identifying one or more versions of the software application delivery model; and identifying respective pertinent information about the one or more versions. 17. The article of manufacture of claim 16, wherein the set of acts further comprises:
populating one or more data structures with the respective pertinent information for the one or more releases; and generating the aggregated information at least by aggregating the pertinent information about the one or more releases into a first data structure managed by a release train module. 18. The article of manufacture of claim 16, wherein the set of acts further comprises:
classifying a plurality of code modules, artifacts, or the pertinent information into one or more clusters; identifying first information pertaining to an artifact, a code module, or at least a portion of the pertinent information; normalizing the first information into normalized information; identifying or determining the one or more clusters at least by applying word or term embedding techniques to the normalized information; and identifying or determining one or more recommendations for the one or more clusters. 19. The article of manufacture of claim 18, wherein the set of acts further comprises:
identifying first information pertaining to an artifact, a code module, or at least a portion of the pertinent information; normalizing the first information into normalized information; identifying or determining the one or more clusters at least by applying word or term embedding techniques to the normalized information; and identifying or determining one or more recommendations for the one or more clusters. 20. The article of manufacture of claim 18, wherein the set of acts further comprises:
determining dependencies among the one or more releases or one or more portions thereof. tracking the one or more releases along respective release pipelines to generate at least the tracking records; populating a calendar with at least a portion of the respective pertinent information or the aggregate information; identifying a release activity or information thereof associated with a release of the one or more releases; accessing an impact of the release activity or information thereof on one or more other release activities; and determining a score for the release activity or the information thereof. 21. The article of manufacture of claim 20, wherein the set of acts further comprises:
identifying or determining one or more other pieces of pertinent information affected by or affecting the release activity or the information thereof; determining an extent of influence of the release activity or the information thereof; determining a first level of impact of the release activity or the information thereof on the one or more other pieces of pertinent information; and determining one or more second levels of impact of the release activity or the information thereof on the release activity of the information thereof. 22. A system for implementing an integrated platform for continuous deployment of software application delivery models, comprising:
a plurality of modules, at least one of which is stored at least partially in memory and comprises at least one microprocessor including one or more processor cores executing one or more threads; a non-transitory computer accessible storage medium storing thereupon program code that includes a sequence of instructions that, when executed by the at least one microprocessor, causes the at least one microprocessor at least to: identify or create, by a release creation module comprising computer-executable instructions stored at partially in memory and executed by at least one microprocessor, one or more releases and pertinent information of a software application delivery model at a portal on a remote computing system; populate one or more data structures for one or more modules hosted on the remote computing system; generate tracking records at least by tracking the one or more releases with at least some of aggregated information identified from a plurality of tenants connected to the portal; and propagate a release of the one or more releases or a portion thereof along a release pipeline based in part or in whole upon the tracking records. 23. The system of claim 22, wherein the program code includes further instructions that, when executed by the at least one microprocessor or processor core, cause the at least one processor or processor core at least further to:
identify a release activity or information thereof associated with a release of the one or more releases; access an impact of the release activity or information thereof on one or more other release activities; and determine a score for the release activity or the information thereof. 24. The system of claim 23, wherein the program code includes further instructions that, when executed by the at least one microprocessor or processor core, cause the at least one processor or processor core at least further to:
identify a release from the one or more releases of the software application delivery model; identify one or more tenants and release activities corresponding to the release; and determine respective states of the release activities. 25. The system of claim 24, wherein the program code includes further instructions that, when executed by the at least one microprocessor or processor core, cause the at least one processor or processor core at least further to:
identify a hindering state that hinders the release of the software application delivery model; and determine one or more issues resulting in the hindering state and one or more corresponding tenants that are associated with the one or more issues. 26. The system of claim 25, wherein the program code includes further instructions that, when executed by the at least one microprocessor or processor core, cause the at least one processor or processor core at least further to:
identify issue descriptions or issue resolutions concerning the one or more issues from a database table, an expert system, or a knowledge base; determine respective numeric or symbolic scores for the release activities; and cross-link information concerning the one or more tenants, the release activities, the respective states, the hindering state, the one or more issues, the one or more corresponding tenants, the issue descriptions, the issue resolutions, or the respective numeric or symbolic scores. | Various aspects described herein are directed to a method or system that implements an integrated platform for continuous deployment of software application delivery models. One or more releases and pertinent information of a software application delivery model may be identified or created at a portal on a remote computing system. One or more data structures may be populated for one or more modules hosted on the remote computing system. Tracking records may be generated at least by tracking the one or more releases with at least some of aggregated information identified from a plurality of tenants connected to the portal; and a release of the one or more releases or a portion thereof may be propagated along a release pipeline based in part or in whole upon the tracking records.1. A computer implemented method for implementing an integrated platform for continuous deployment of software application delivery models, comprising:
creating, by a release creation module comprising computer-executable instructions stored at partially in memory and executed by at least one microprocessor, one or more releases and pertinent information of a software application delivery model at a portal on a remote computing system; populating one or more data structures for one or more modules hosted on the remote computing system with the pertinent information; generating tracking records at least by tracking the one or more releases with at least some of aggregated information identified from a plurality of tenants connected to the portal; and propagating a release of the one or more releases or a portion thereof along a release pipeline based in part or in whole upon the tracking records. 2. The computer implemented method of claim 1, further comprising:
identifying or determining the portal on the remote computing system; identifying the plurality of tenants connected to the portal on the remote computing system; identifying one or more versions of the software application delivery model; and identifying respective pertinent information about the one or more versions from at least one tenant of the plurality of tenants. 3. The computer implemented method of claim 2, further comprising:
generating the aggregated information at least by aggregating the pertinent information about the one or more releases into a first data structure managed by a release train module. 4. The computer implemented method of claim 3, further comprising:
classifying a plurality of code modules, artifacts, or the pertinent information into one or more clusters. 5. The computer implemented method of claim 4, further comprising:
identifying first information pertaining to an artifact, a code module, or at least a portion of the pertinent information; and normalizing the first information into normalized information. 6. The computer implemented method of claim 5, further comprising:
identifying or determining the one or more clusters at least by applying word or term embedding techniques to the normalized information; and identifying or determining one or more recommendations for the one or more clusters. 7. The computer implemented method of claim 4, further comprising:
determining dependencies among the one or more releases or one or more portions thereof. 8. The computer implemented method of claim 7, further comprising:
tracking the one or more releases along respective release pipelines to generate at least the tracking records; and populating a calendar with at least a portion of the respective pertinent information or the aggregate information based at least in part upon at least some of the tracking records. 9. The computer implemented method of claim 8, further comprising:
identifying a release activity or information thereof associated with a release of the one or more releases; accessing an impact of the release activity or information thereof on one or more other release activities; and determining a score for the release activity or the information thereof. 10. The computer implemented method of claim 9, further comprising:
identifying or determining one or more other pieces of pertinent information affected by or affecting the release activity or the information thereof; and determining an extent of influence of the release activity or the information thereof. 11. The computer implemented method of claim 10, further comprising:
determining a first level of impact of the release activity or the information thereof on the one or more other pieces of pertinent information; and determining one or more second levels of impact of the release activity or the information thereof on the release activity of the information thereof. 12. The computer implemented method of claim 9, further comprising:
identifying a release from the one or more releases of the software application delivery model; identifying one or more tenants and release activities corresponding to the release; and determining respective states of the release activities. 13. The computer implemented method of claim 12, further comprising:
identifying a hindering state that hinders the release of the software application delivery model; and determining one or more issues resulting in the hindering state and one or more corresponding tenants that are associated with the one or more issues. 14. The computer implemented method of claim 13, further comprising:
identifying issue descriptions or issue resolutions concerning the one or more issues from a database table, an expert system, or a knowledge base; determining respective numeric or symbolic scores for the release activities; and cross-linking information concerning the one or more tenants, the release activities, the respective states, the hindering state, the one or more issues, the one or more corresponding tenants, the issue descriptions, the issue resolutions, or the respective numeric or symbolic scores. 15. An article of manufacture comprising a non-transitory computer accessible storage medium having stored thereupon a sequence of instructions which, when executed by at least one processor or at least one processor core executing one or more threads, causes the at least one processor or the at least one processor core to perform a set of acts for implementing an integrated platform for continuous deployment of software application delivery models, the set of acts comprising:
identifying or creating, by a release creation module comprising computer-executable instructions stored at partially in memory and executed by at least one microprocessor, one or more releases and pertinent information of a software application delivery model at a portal on a remote computing system; populating one or more data structures for one or more modules hosted on the remote computing system; generating tracking records at least by tracking the one or more releases with at least some of aggregated information identified from a plurality of tenants connected to the portal; and propagating a release of the one or more releases or a portion thereof along a release pipeline based in part or in whole upon the tracking records. 16. The article of manufacture of claim 15, wherein the set of acts further comprises:
identifying or determining the portal on the remote computing system; identifying the plurality of tenants connected to the remote computing system; identifying one or more versions of the software application delivery model; and identifying respective pertinent information about the one or more versions. 17. The article of manufacture of claim 16, wherein the set of acts further comprises:
populating one or more data structures with the respective pertinent information for the one or more releases; and generating the aggregated information at least by aggregating the pertinent information about the one or more releases into a first data structure managed by a release train module. 18. The article of manufacture of claim 16, wherein the set of acts further comprises:
classifying a plurality of code modules, artifacts, or the pertinent information into one or more clusters; identifying first information pertaining to an artifact, a code module, or at least a portion of the pertinent information; normalizing the first information into normalized information; identifying or determining the one or more clusters at least by applying word or term embedding techniques to the normalized information; and identifying or determining one or more recommendations for the one or more clusters. 19. The article of manufacture of claim 18, wherein the set of acts further comprises:
identifying first information pertaining to an artifact, a code module, or at least a portion of the pertinent information; normalizing the first information into normalized information; identifying or determining the one or more clusters at least by applying word or term embedding techniques to the normalized information; and identifying or determining one or more recommendations for the one or more clusters. 20. The article of manufacture of claim 18, wherein the set of acts further comprises:
determining dependencies among the one or more releases or one or more portions thereof. tracking the one or more releases along respective release pipelines to generate at least the tracking records; populating a calendar with at least a portion of the respective pertinent information or the aggregate information; identifying a release activity or information thereof associated with a release of the one or more releases; accessing an impact of the release activity or information thereof on one or more other release activities; and determining a score for the release activity or the information thereof. 21. The article of manufacture of claim 20, wherein the set of acts further comprises:
identifying or determining one or more other pieces of pertinent information affected by or affecting the release activity or the information thereof; determining an extent of influence of the release activity or the information thereof; determining a first level of impact of the release activity or the information thereof on the one or more other pieces of pertinent information; and determining one or more second levels of impact of the release activity or the information thereof on the release activity of the information thereof. 22. A system for implementing an integrated platform for continuous deployment of software application delivery models, comprising:
a plurality of modules, at least one of which is stored at least partially in memory and comprises at least one microprocessor including one or more processor cores executing one or more threads; a non-transitory computer accessible storage medium storing thereupon program code that includes a sequence of instructions that, when executed by the at least one microprocessor, causes the at least one microprocessor at least to: identify or create, by a release creation module comprising computer-executable instructions stored at partially in memory and executed by at least one microprocessor, one or more releases and pertinent information of a software application delivery model at a portal on a remote computing system; populate one or more data structures for one or more modules hosted on the remote computing system; generate tracking records at least by tracking the one or more releases with at least some of aggregated information identified from a plurality of tenants connected to the portal; and propagate a release of the one or more releases or a portion thereof along a release pipeline based in part or in whole upon the tracking records. 23. The system of claim 22, wherein the program code includes further instructions that, when executed by the at least one microprocessor or processor core, cause the at least one processor or processor core at least further to:
identify a release activity or information thereof associated with a release of the one or more releases; access an impact of the release activity or information thereof on one or more other release activities; and determine a score for the release activity or the information thereof. 24. The system of claim 23, wherein the program code includes further instructions that, when executed by the at least one microprocessor or processor core, cause the at least one processor or processor core at least further to:
identify a release from the one or more releases of the software application delivery model; identify one or more tenants and release activities corresponding to the release; and determine respective states of the release activities. 25. The system of claim 24, wherein the program code includes further instructions that, when executed by the at least one microprocessor or processor core, cause the at least one processor or processor core at least further to:
identify a hindering state that hinders the release of the software application delivery model; and determine one or more issues resulting in the hindering state and one or more corresponding tenants that are associated with the one or more issues. 26. The system of claim 25, wherein the program code includes further instructions that, when executed by the at least one microprocessor or processor core, cause the at least one processor or processor core at least further to:
identify issue descriptions or issue resolutions concerning the one or more issues from a database table, an expert system, or a knowledge base; determine respective numeric or symbolic scores for the release activities; and cross-link information concerning the one or more tenants, the release activities, the respective states, the hindering state, the one or more issues, the one or more corresponding tenants, the issue descriptions, the issue resolutions, or the respective numeric or symbolic scores. | 2,100 |
6,478 | 6,478 | 14,527,926 | 2,167 | A system for a product navigator is provided. The system receives a search string at a business-type classification interface on an interactive user display. A search type associated with the search string may be determined based on a search type selection from a plurality of search types. A search of a database may be initiated for an entry matching the search string according to the search type, where the entry has corresponding business-type classification data including a brief classification description, an extended classification description, and an associated classification code. The brief classification description and the extended classification description may be output on the interactive user display based on receiving the corresponding business-type classification data from the database. A confirmation request may be presented on the interactive user display and a classification code field populated with the associated classification code based on receiving an affirmative response to the confirmation request. | 1. A system, comprising:
a processing device; and a memory device in communication with the processing device, the memory device storing instructions that when executed by the processing device result in:
receiving a search string at a business-type classification interface on an interactive user display;
determining a search type associated with the search string based on a search type selection from a plurality of search types;
initiating a search of a database for an entry matching the search string according to the search type, the entry having corresponding business-type classification data comprising: a brief classification description, an extended classification description, and an associated classification code;
outputting the brief classification description and the extended classification description on the interactive user display based on receiving the corresponding business-type classification data from the database;
presenting a confirmation request on the interactive user display; and
populating a classification code field with the associated classification code based on receiving an affirmative response to the confirmation request. 2. The system of claim 1, wherein the search is initiated as a keyword search of one or more of brief classification descriptions and extended classification descriptions that include the search string in the database based on determining that the requested search type is the keyword search. 3. The system of claim 1, wherein the search is initiated as an industry segment search that matches the search string in the database based on determining that the requested search type is the industry segment search. 4. The system of claim 1, wherein the search is initiated as a code search that matches the search string in the database based on determining that the requested search type is the code search. 5. The system of claim 4, wherein the code search comprises one of: a Standard Industry Classification (SIC) code search, a North American Industry Classification System (NAICS) code search, and a numeric classification code search. 6. The system of claim 1, further comprising instructions that when executed by the processing device result in:
based on multiple entries in the database matching the search string, creating a list of the business-type classification data corresponding to the matching entries; providing a selection interface to enable a user selection of one of the matching entries; and extracting the brief classification description, the extended classification description, and the associated classification code from the list of the business-type classification data based on the user selection of one of the matching entries. 7. The system of claim 6, wherein the selection interface is a drop-down box that defaults to an alpha-numerically ordered first entry of the matching entries and displays additional entries of the matching entries in response to a user-initiated drop-down request. 8. The system of claim 1, wherein the business-type classification data further comprises a classification guide defining classification separation examples between business types identified as being similar to a business type defined in the brief classification description. 9. The system of claim 8, further comprising instructions that when executed by the processing device result in:
outputting the classification guide on the interactive user display based on receiving the corresponding business-type classification data from the database and prior to receiving a response to the confirmation request. 10. The system of claim 1, wherein the business-type classification data further comprises a list of additional product provisioning opportunities associated with a business type defined in the brief classification description. 11. The system of claim 10, further comprising instructions that when executed by the processing device result in:
outputting the list of additional product provisioning opportunities on the interactive user display based on receiving the corresponding business-type classification data from the database. 12. The system of claim 11, wherein the list of additional product provisioning opportunities is displayed in a portion of the interactive user display that remains visible as processing advances to subsequent input interfaces. 13. The system of claim 1, further comprising instructions that when executed by the processing device result in:
presenting a new search request option in combination with the confirmation request; and clearing the brief classification description, the extended classification description, and the search string from the interactive user display based on receiving an affirmative response to the new search request option. 14. The system of claim 1, further comprising instructions that when executed by the processing device result in:
providing a visual command to launch the business-type classification interface, wherein the visual command is located on a portion of the interactive user display that remains visible as processing advances to subsequent input interfaces. 15. The system of claim 1, wherein the interactive user display is generated by an application server coupled to a mediation layer gateway, and the business-type classification data received in response to the search is retrieved from the database by a query processor and passed through the mediation layer gateway. 16. The system of claim 1, wherein the business-type classification interface supports type-ahead searching. 17. The system of claim 1, wherein the search of the database includes searching for one or more of: exact string matches, partial string matches, and synonym matches. 18. A computer program product comprising a storage medium embodied with computer program instructions that when executed by a computer cause the computer to implement:
receiving a search string at a business-type classification interface on an interactive user display; determining a search type associated with the search string based on a search type selection from a plurality of search types; initiating a search of a database for an entry matching the search string according to the search type, the entry having corresponding business-type classification data comprising: a brief classification description, an extended classification description, and an associated classification code; outputting the brief classification description and the extended classification description on the interactive user display based on receiving the corresponding business-type classification data from the database; presenting a confirmation request on the interactive user display; and populating a classification code field with the associated classification code based on receiving an affirmative response to the confirmation request. 19. The computer program product of claim 18, further comprising computer program instructions that when executed by the computer cause the computer to implement:
based on multiple entries in the database matching the search string, creating a list of the business-type classification data corresponding to the matching entries; providing a selection interface to enable a user selection of one of the matching entries; and extracting the brief classification description, the extended classification description, and the associated classification code from the list of the business-type classification data based on the user selection of one of the matching entries. 20. The computer program product of claim 18, wherein the business-type classification data further comprises a classification guide defining classification separation examples between business types identified as being similar to a business type defined in the brief classification description. 21. The computer program product of claim 18, wherein the business-type classification data further comprises a list of additional product provisioning opportunities associated with a business type defined in the brief classification description. 22. The computer program product of claim 21, further comprising computer program instructions that when executed by the computer cause the computer to implement:
outputting the list of additional product provisioning opportunities on the interactive user display based on receiving the corresponding business-type classification data from the database. 23. The computer program product of claim 18, further comprising computer program instructions that when executed by the computer cause the computer to implement:
presenting a new search request option in combination with the confirmation request; and clearing the brief classification description, the extended classification description, and the search string from the interactive user display based on receiving an affirmative response to the new search request option. 24. The computer program product of claim 18, further comprising computer program instructions that when executed by the computer cause the computer to implement:
providing a visual command to launch the business-type classification interface, wherein the visual command is located on a portion of the interactive user display that remains visible as processing advances to subsequent input interfaces. | A system for a product navigator is provided. The system receives a search string at a business-type classification interface on an interactive user display. A search type associated with the search string may be determined based on a search type selection from a plurality of search types. A search of a database may be initiated for an entry matching the search string according to the search type, where the entry has corresponding business-type classification data including a brief classification description, an extended classification description, and an associated classification code. The brief classification description and the extended classification description may be output on the interactive user display based on receiving the corresponding business-type classification data from the database. A confirmation request may be presented on the interactive user display and a classification code field populated with the associated classification code based on receiving an affirmative response to the confirmation request.1. A system, comprising:
a processing device; and a memory device in communication with the processing device, the memory device storing instructions that when executed by the processing device result in:
receiving a search string at a business-type classification interface on an interactive user display;
determining a search type associated with the search string based on a search type selection from a plurality of search types;
initiating a search of a database for an entry matching the search string according to the search type, the entry having corresponding business-type classification data comprising: a brief classification description, an extended classification description, and an associated classification code;
outputting the brief classification description and the extended classification description on the interactive user display based on receiving the corresponding business-type classification data from the database;
presenting a confirmation request on the interactive user display; and
populating a classification code field with the associated classification code based on receiving an affirmative response to the confirmation request. 2. The system of claim 1, wherein the search is initiated as a keyword search of one or more of brief classification descriptions and extended classification descriptions that include the search string in the database based on determining that the requested search type is the keyword search. 3. The system of claim 1, wherein the search is initiated as an industry segment search that matches the search string in the database based on determining that the requested search type is the industry segment search. 4. The system of claim 1, wherein the search is initiated as a code search that matches the search string in the database based on determining that the requested search type is the code search. 5. The system of claim 4, wherein the code search comprises one of: a Standard Industry Classification (SIC) code search, a North American Industry Classification System (NAICS) code search, and a numeric classification code search. 6. The system of claim 1, further comprising instructions that when executed by the processing device result in:
based on multiple entries in the database matching the search string, creating a list of the business-type classification data corresponding to the matching entries; providing a selection interface to enable a user selection of one of the matching entries; and extracting the brief classification description, the extended classification description, and the associated classification code from the list of the business-type classification data based on the user selection of one of the matching entries. 7. The system of claim 6, wherein the selection interface is a drop-down box that defaults to an alpha-numerically ordered first entry of the matching entries and displays additional entries of the matching entries in response to a user-initiated drop-down request. 8. The system of claim 1, wherein the business-type classification data further comprises a classification guide defining classification separation examples between business types identified as being similar to a business type defined in the brief classification description. 9. The system of claim 8, further comprising instructions that when executed by the processing device result in:
outputting the classification guide on the interactive user display based on receiving the corresponding business-type classification data from the database and prior to receiving a response to the confirmation request. 10. The system of claim 1, wherein the business-type classification data further comprises a list of additional product provisioning opportunities associated with a business type defined in the brief classification description. 11. The system of claim 10, further comprising instructions that when executed by the processing device result in:
outputting the list of additional product provisioning opportunities on the interactive user display based on receiving the corresponding business-type classification data from the database. 12. The system of claim 11, wherein the list of additional product provisioning opportunities is displayed in a portion of the interactive user display that remains visible as processing advances to subsequent input interfaces. 13. The system of claim 1, further comprising instructions that when executed by the processing device result in:
presenting a new search request option in combination with the confirmation request; and clearing the brief classification description, the extended classification description, and the search string from the interactive user display based on receiving an affirmative response to the new search request option. 14. The system of claim 1, further comprising instructions that when executed by the processing device result in:
providing a visual command to launch the business-type classification interface, wherein the visual command is located on a portion of the interactive user display that remains visible as processing advances to subsequent input interfaces. 15. The system of claim 1, wherein the interactive user display is generated by an application server coupled to a mediation layer gateway, and the business-type classification data received in response to the search is retrieved from the database by a query processor and passed through the mediation layer gateway. 16. The system of claim 1, wherein the business-type classification interface supports type-ahead searching. 17. The system of claim 1, wherein the search of the database includes searching for one or more of: exact string matches, partial string matches, and synonym matches. 18. A computer program product comprising a storage medium embodied with computer program instructions that when executed by a computer cause the computer to implement:
receiving a search string at a business-type classification interface on an interactive user display; determining a search type associated with the search string based on a search type selection from a plurality of search types; initiating a search of a database for an entry matching the search string according to the search type, the entry having corresponding business-type classification data comprising: a brief classification description, an extended classification description, and an associated classification code; outputting the brief classification description and the extended classification description on the interactive user display based on receiving the corresponding business-type classification data from the database; presenting a confirmation request on the interactive user display; and populating a classification code field with the associated classification code based on receiving an affirmative response to the confirmation request. 19. The computer program product of claim 18, further comprising computer program instructions that when executed by the computer cause the computer to implement:
based on multiple entries in the database matching the search string, creating a list of the business-type classification data corresponding to the matching entries; providing a selection interface to enable a user selection of one of the matching entries; and extracting the brief classification description, the extended classification description, and the associated classification code from the list of the business-type classification data based on the user selection of one of the matching entries. 20. The computer program product of claim 18, wherein the business-type classification data further comprises a classification guide defining classification separation examples between business types identified as being similar to a business type defined in the brief classification description. 21. The computer program product of claim 18, wherein the business-type classification data further comprises a list of additional product provisioning opportunities associated with a business type defined in the brief classification description. 22. The computer program product of claim 21, further comprising computer program instructions that when executed by the computer cause the computer to implement:
outputting the list of additional product provisioning opportunities on the interactive user display based on receiving the corresponding business-type classification data from the database. 23. The computer program product of claim 18, further comprising computer program instructions that when executed by the computer cause the computer to implement:
presenting a new search request option in combination with the confirmation request; and clearing the brief classification description, the extended classification description, and the search string from the interactive user display based on receiving an affirmative response to the new search request option. 24. The computer program product of claim 18, further comprising computer program instructions that when executed by the computer cause the computer to implement:
providing a visual command to launch the business-type classification interface, wherein the visual command is located on a portion of the interactive user display that remains visible as processing advances to subsequent input interfaces. | 2,100 |
6,479 | 6,479 | 15,720,750 | 2,178 | A human interface technique is disclosed for industrial automation systems. The technique allows for reduced data set visualizations to be created for components that may include identifying information, operational data, real or near real time data, and so forth, such as in the form of a faceplate or brief summary. The content may be made available to users on interface devices, such as thin client devices. The access to the content may be distributed based upon relevant criteria that are stored as policies in a visualization manager, such as user, location, device, event triggers, and so forth. | 1. A system comprising:
a visualization manager that, in operation, communicates with a thin client HMI to permit the thin client HMI to access and display a limited visualization based upon data from an industrial automation visualization source of a controlled machine or process, wherein the limited visualization comprises a subset of a visualization generated by the industrial automation visualization source. 2. The system of claim 1, wherein the industrial automation visualization source comprises an automation controller. 3. The system of claim 2, wherein the industrial automation visualization source comprises a motor drive, and the subset comprises real time or near-real time data for a motor controlled by the motor drive. 4. The system of claim 1, wherein the subset comprises data for ongoing operation of an automation component controlled by the industrial automation visualization source. 5. The system of claim 1, wherein the limited visualization comprises a template having fields completed by the data from the industrial automation visualization source. 6. The system of claim 5, wherein the fields comprise identification fields and real or near-real time operational parameter fields. 7. The system of claim 1, wherein the visualization manager is configured to permit the access and display based upon a location of the thin client HMI. 8. The system of claim 1, wherein the visualization manager is configured to permit user interaction with the limited visualization to obtain additional information from either the visualization manager or from the industrial automation visualization source. 9. The system of claim 1, wherein the visualization manager permits access and display of the limited visualization in response to an event trigger generated by the industrial automation visualization source. 10. The system of claim 1, wherein the limited visualization comprises a link to an external source of additional data for a controlled component. 11. The system of claim 1, wherein the limited visualization comprises an enhanced camera view of at least a portion of the controlled machine or process. 12. The system of claim 1, wherein the limited visualization comprises an augmented reality view of at least a portion of the controlled machine or process, or of a product made or handled by the controlled machine or process. 13. The system of claim 1, where the limited visualization comprises streaming data that is updated during display of the limited visualization on the thin client HMI. 14. A system comprising:
an industrial automation visualization source associated with a controlled machine or process; a thin client HMI configured to display a visualization from the industrial automation visualization source in real or near-real time during operation of the controlled machine or process; a visualization manager that, in operation, communicates with a thin client HMI to permit the thin client HMI to access and display a limited visualization based upon data from an industrial automation visualization source of a controlled machine or process, wherein the limited visualization comprises a subset of a visualization generated by the industrial automation visualization source. 15. The system of claim 14, wherein the industrial automation visualization source comprises a motor drive, and the subset comprises real time or near-real time data for a motor controlled by the motor drive. 16. The system of claim 14, wherein the subset comprises streaming data for ongoing operation of an automation component controlled by the industrial automation visualization source. 17. The system of claim 14, wherein the limited visualization comprises a template having fields completed by the data from the industrial automation visualization source. 18. The system of claim 17, wherein the fields comprise identification fields and real or near-real time operational parameter fields. 19. The system of claim 14, wherein the visualization manager is configured to permit the access and display based upon a location of the thin client HMI. 20. A system comprising:
an industrial automation visualization source associated with a controlled machine or process; a thin client HMI configured to display a visualization from the industrial automation visualization source in real or near-real time during operation of the controlled machine or process; a visualization manager that, in operation, communicates with a thin client HMI to permit the thin client HMI to access and display a limited visualization based upon data from an industrial automation visualization source of a controlled machine or process, wherein the limited visualization comprises a subset of a visualization generated by the industrial automation visualization source including component identification data and updating operational parameter data, wherein the visualization manager is configured to permit user interaction with the limited visualization to obtain additional information from either the visualization manager or from the industrial automation visualization source. | A human interface technique is disclosed for industrial automation systems. The technique allows for reduced data set visualizations to be created for components that may include identifying information, operational data, real or near real time data, and so forth, such as in the form of a faceplate or brief summary. The content may be made available to users on interface devices, such as thin client devices. The access to the content may be distributed based upon relevant criteria that are stored as policies in a visualization manager, such as user, location, device, event triggers, and so forth.1. A system comprising:
a visualization manager that, in operation, communicates with a thin client HMI to permit the thin client HMI to access and display a limited visualization based upon data from an industrial automation visualization source of a controlled machine or process, wherein the limited visualization comprises a subset of a visualization generated by the industrial automation visualization source. 2. The system of claim 1, wherein the industrial automation visualization source comprises an automation controller. 3. The system of claim 2, wherein the industrial automation visualization source comprises a motor drive, and the subset comprises real time or near-real time data for a motor controlled by the motor drive. 4. The system of claim 1, wherein the subset comprises data for ongoing operation of an automation component controlled by the industrial automation visualization source. 5. The system of claim 1, wherein the limited visualization comprises a template having fields completed by the data from the industrial automation visualization source. 6. The system of claim 5, wherein the fields comprise identification fields and real or near-real time operational parameter fields. 7. The system of claim 1, wherein the visualization manager is configured to permit the access and display based upon a location of the thin client HMI. 8. The system of claim 1, wherein the visualization manager is configured to permit user interaction with the limited visualization to obtain additional information from either the visualization manager or from the industrial automation visualization source. 9. The system of claim 1, wherein the visualization manager permits access and display of the limited visualization in response to an event trigger generated by the industrial automation visualization source. 10. The system of claim 1, wherein the limited visualization comprises a link to an external source of additional data for a controlled component. 11. The system of claim 1, wherein the limited visualization comprises an enhanced camera view of at least a portion of the controlled machine or process. 12. The system of claim 1, wherein the limited visualization comprises an augmented reality view of at least a portion of the controlled machine or process, or of a product made or handled by the controlled machine or process. 13. The system of claim 1, where the limited visualization comprises streaming data that is updated during display of the limited visualization on the thin client HMI. 14. A system comprising:
an industrial automation visualization source associated with a controlled machine or process; a thin client HMI configured to display a visualization from the industrial automation visualization source in real or near-real time during operation of the controlled machine or process; a visualization manager that, in operation, communicates with a thin client HMI to permit the thin client HMI to access and display a limited visualization based upon data from an industrial automation visualization source of a controlled machine or process, wherein the limited visualization comprises a subset of a visualization generated by the industrial automation visualization source. 15. The system of claim 14, wherein the industrial automation visualization source comprises a motor drive, and the subset comprises real time or near-real time data for a motor controlled by the motor drive. 16. The system of claim 14, wherein the subset comprises streaming data for ongoing operation of an automation component controlled by the industrial automation visualization source. 17. The system of claim 14, wherein the limited visualization comprises a template having fields completed by the data from the industrial automation visualization source. 18. The system of claim 17, wherein the fields comprise identification fields and real or near-real time operational parameter fields. 19. The system of claim 14, wherein the visualization manager is configured to permit the access and display based upon a location of the thin client HMI. 20. A system comprising:
an industrial automation visualization source associated with a controlled machine or process; a thin client HMI configured to display a visualization from the industrial automation visualization source in real or near-real time during operation of the controlled machine or process; a visualization manager that, in operation, communicates with a thin client HMI to permit the thin client HMI to access and display a limited visualization based upon data from an industrial automation visualization source of a controlled machine or process, wherein the limited visualization comprises a subset of a visualization generated by the industrial automation visualization source including component identification data and updating operational parameter data, wherein the visualization manager is configured to permit user interaction with the limited visualization to obtain additional information from either the visualization manager or from the industrial automation visualization source. | 2,100 |
6,480 | 6,480 | 14,414,000 | 2,146 | A method and apparatus for estimating a rock physics model anisotropic parameter for a geological subsurface. A volume fraction of dry clay minerals present in the geological subsurface is determined. A total porosity of the geological subsurface is also determined. A value for the anisotropic parameter is determined using the volume fraction of dry clay minerals, the total porosity and empirically derived constants. The resultant anisotropy parameters can be used in rock physics models where, for example, estimates of the anisotropy parameters cannot be obtained from other sources. | 1-17. (canceled) 18. A method of estimating a rock physics model anisotropic parameter for a geological subsurface, the method comprising:
determining a volume fraction of dry clay minerals present in the geological subsurface; determining a total porosity of the geological subsurface; determining a value for the anisotropic parameter using the volume fraction of dry clay minerals, the total porosity and empirically derived constants. 19. The method according to claim 18, wherein the anisotropic parameter is a Thomsen γ parameter. 20. The method according to claim 19 wherein the anisotropic parameter is estimated according to the equation
γ=aV cldry b e −cφ t
where a, b and c are said empirically derived constants, Vcldry is the volume fraction of dry clay minerals and Φt is the total porosity. 21. The method according to claim 19, further comprising estimating any of Thomsen parameters ε and δ using the estimated value of γ and at least one further empirically derived constant. 22. The method according to claim 18, further comprising determining the empirically derived constants using well log data selected from any of refracted shear data, cross-dipole shear data, low frequency Stoneley data and compressional data. 23. The method according to claim 22, further comprising:
determining an elastic modulus tensor element C44 value for the subsurface using any of dipole shear data and refracted shear data obtained from a vertical or near vertical well; determining an elastic modulus tensor element C66 value for the subsurface using low frequency Stoneley shear data obtained from a vertical or near vertical well; determining a calibration value for the anisotropic parameter using elastic modulus tensor elements C44 and C66; calibrating any of the empirically derived constants using the determined calibration value of the anisotropic parameter. 24. The method according to claim 22, further comprising:
determining an elastic modulus tensor element C44 value for the subsurface using any of dipole shear data and refracted shear data obtained from a vertical or near vertical well; determining an elastic modulus tensor element C66 value for the subsurface using low frequency Stoneley shear data obtained from a vertical or near vertical well; determining a calibration value for the anisotropic parameter using elastic modulus tensor elements C44 and C66; calibrating any of the empirically derived constants using the determined calibration value of the anisotropic parameter; wherein the empirically derived constants are calibrated using the determined calibration value of the anisotropic parameter by performing a regression. 25. The method according to claim 22, further comprising, in the event that the empirically derived constants cannot be derived using well log data, using default values for the empirically derived constants. 26. The method according to claim 18, further comprising determining the volume fraction of dry clay minerals by using a clay index and an additional empirically derived constant. 27. A computer apparatus arranged to estimate a rock physics model anisotropic parameter for a geological subsurface, the apparatus comprising:
a processor for determining a value for the anisotropic parameter using a volume fraction of dry clay minerals in the geological subsurface, a total porosity value of the geological subsurface, and empirically derived constants. 28. The computer apparatus according to claim 27, wherein the anisotropic parameter is a Thomsen γ parameter and the processor is arranged to estimate γ according to the equation
γ=aV cldry b e −cφ t
where a, b and c are said empirically derived constants, Vcldry is the volume fraction of dry clay minerals and Φt is the total porosity. 29. The computer apparatus according to claim 28, wherein the processor is further arranged to estimate any of Thomsen parameters ε and δ using the estimated value of γ and at least one further empirically derived constant. 30. The computer apparatus according to claim 27, wherein the processor is further arranged to determine the empirically derived constants using well log data selected from any of refracted shear data, cross-dipole shear data, low frequency Stoneley data and compressional data. 31. The computer apparatus according to claim 30, wherein the processor is arranged to determine an elastic modulus tensor element C44 value for the subsurface using any of dipole shear data and refracted shear data, determine an elastic modulus tensor element C66 value for the subsurface using low frequency Stoneley shear data, determine a calibration value for the anisotropic parameter using elastic modulus tensors element C44 and C66, and calibrate any of the empirically derived constants using the determined calibration value of the anisotropic parameter. 32. The computer apparatus according to claim 27, further comprising a database, the database arranged to store values for any of the empirically derived constants. 33. A computer program, comprising computer readable code which, when run on a computer apparatus, causes the computer apparatus to perform the method of claim 18. 34. A computer program product comprising a computer readable medium and a computer program according to claim 33, wherein the computer program is stored on the computer readable medium. | A method and apparatus for estimating a rock physics model anisotropic parameter for a geological subsurface. A volume fraction of dry clay minerals present in the geological subsurface is determined. A total porosity of the geological subsurface is also determined. A value for the anisotropic parameter is determined using the volume fraction of dry clay minerals, the total porosity and empirically derived constants. The resultant anisotropy parameters can be used in rock physics models where, for example, estimates of the anisotropy parameters cannot be obtained from other sources.1-17. (canceled) 18. A method of estimating a rock physics model anisotropic parameter for a geological subsurface, the method comprising:
determining a volume fraction of dry clay minerals present in the geological subsurface; determining a total porosity of the geological subsurface; determining a value for the anisotropic parameter using the volume fraction of dry clay minerals, the total porosity and empirically derived constants. 19. The method according to claim 18, wherein the anisotropic parameter is a Thomsen γ parameter. 20. The method according to claim 19 wherein the anisotropic parameter is estimated according to the equation
γ=aV cldry b e −cφ t
where a, b and c are said empirically derived constants, Vcldry is the volume fraction of dry clay minerals and Φt is the total porosity. 21. The method according to claim 19, further comprising estimating any of Thomsen parameters ε and δ using the estimated value of γ and at least one further empirically derived constant. 22. The method according to claim 18, further comprising determining the empirically derived constants using well log data selected from any of refracted shear data, cross-dipole shear data, low frequency Stoneley data and compressional data. 23. The method according to claim 22, further comprising:
determining an elastic modulus tensor element C44 value for the subsurface using any of dipole shear data and refracted shear data obtained from a vertical or near vertical well; determining an elastic modulus tensor element C66 value for the subsurface using low frequency Stoneley shear data obtained from a vertical or near vertical well; determining a calibration value for the anisotropic parameter using elastic modulus tensor elements C44 and C66; calibrating any of the empirically derived constants using the determined calibration value of the anisotropic parameter. 24. The method according to claim 22, further comprising:
determining an elastic modulus tensor element C44 value for the subsurface using any of dipole shear data and refracted shear data obtained from a vertical or near vertical well; determining an elastic modulus tensor element C66 value for the subsurface using low frequency Stoneley shear data obtained from a vertical or near vertical well; determining a calibration value for the anisotropic parameter using elastic modulus tensor elements C44 and C66; calibrating any of the empirically derived constants using the determined calibration value of the anisotropic parameter; wherein the empirically derived constants are calibrated using the determined calibration value of the anisotropic parameter by performing a regression. 25. The method according to claim 22, further comprising, in the event that the empirically derived constants cannot be derived using well log data, using default values for the empirically derived constants. 26. The method according to claim 18, further comprising determining the volume fraction of dry clay minerals by using a clay index and an additional empirically derived constant. 27. A computer apparatus arranged to estimate a rock physics model anisotropic parameter for a geological subsurface, the apparatus comprising:
a processor for determining a value for the anisotropic parameter using a volume fraction of dry clay minerals in the geological subsurface, a total porosity value of the geological subsurface, and empirically derived constants. 28. The computer apparatus according to claim 27, wherein the anisotropic parameter is a Thomsen γ parameter and the processor is arranged to estimate γ according to the equation
γ=aV cldry b e −cφ t
where a, b and c are said empirically derived constants, Vcldry is the volume fraction of dry clay minerals and Φt is the total porosity. 29. The computer apparatus according to claim 28, wherein the processor is further arranged to estimate any of Thomsen parameters ε and δ using the estimated value of γ and at least one further empirically derived constant. 30. The computer apparatus according to claim 27, wherein the processor is further arranged to determine the empirically derived constants using well log data selected from any of refracted shear data, cross-dipole shear data, low frequency Stoneley data and compressional data. 31. The computer apparatus according to claim 30, wherein the processor is arranged to determine an elastic modulus tensor element C44 value for the subsurface using any of dipole shear data and refracted shear data, determine an elastic modulus tensor element C66 value for the subsurface using low frequency Stoneley shear data, determine a calibration value for the anisotropic parameter using elastic modulus tensors element C44 and C66, and calibrate any of the empirically derived constants using the determined calibration value of the anisotropic parameter. 32. The computer apparatus according to claim 27, further comprising a database, the database arranged to store values for any of the empirically derived constants. 33. A computer program, comprising computer readable code which, when run on a computer apparatus, causes the computer apparatus to perform the method of claim 18. 34. A computer program product comprising a computer readable medium and a computer program according to claim 33, wherein the computer program is stored on the computer readable medium. | 2,100 |
6,481 | 6,481 | 15,234,199 | 2,195 | A method and an apparatus that schedule a plurality of executables in a schedule queue for execution in one or more physical compute devices such as CPUs or GPUs concurrently are described. One or more executables are compiled online from a source having an existing executable for a type of physical compute devices different from the one or more physical compute devices. Dependency relations among elements corresponding to scheduled executables are determined to select an executable to be executed by a plurality of threads concurrently in more than one of the physical compute devices. A thread initialized for executing an executable in a GPU of the physical compute devices are initialized for execution in another CPU of the physical compute devices if the GPU is busy with graphics processing threads Sources and existing executables for an API function are stored in an API library to execute a plurality of executables in a plurality of physical compute devices, including the existing executables and online compiled executables from the sources. | 1. A computer implemented method comprising:
loading, in response to an first API request from an application running in a first processing unit, one or more executables for a data processing task of the application; and selecting, in response to a second API request from the application, one of the one or more executables for a second processing unit. | A method and an apparatus that schedule a plurality of executables in a schedule queue for execution in one or more physical compute devices such as CPUs or GPUs concurrently are described. One or more executables are compiled online from a source having an existing executable for a type of physical compute devices different from the one or more physical compute devices. Dependency relations among elements corresponding to scheduled executables are determined to select an executable to be executed by a plurality of threads concurrently in more than one of the physical compute devices. A thread initialized for executing an executable in a GPU of the physical compute devices are initialized for execution in another CPU of the physical compute devices if the GPU is busy with graphics processing threads Sources and existing executables for an API function are stored in an API library to execute a plurality of executables in a plurality of physical compute devices, including the existing executables and online compiled executables from the sources.1. A computer implemented method comprising:
loading, in response to an first API request from an application running in a first processing unit, one or more executables for a data processing task of the application; and selecting, in response to a second API request from the application, one of the one or more executables for a second processing unit. | 2,100 |
6,482 | 6,482 | 14,578,461 | 2,145 | Technologies are generally provided for creating content by detecting user intent and providing suggestions associated with content actions. User intent may be determined from a number of different factors associated with the user, a document, and an environment of the user and/or content. Suggestions on content actions such as placement, style, formatting, or extent of content may be automatically made to the user. Suggestions may also be provided based on other factors such as crowd sourcing. In addition to automatic emphasis and connection, content transformation may be enabled prior to consumption after the author has created the content. | 1. A method to be executed in a computing device to create content through author intent based suggestions, the method comprising:
determining an author intent based on one or more of an author attribute, a content context, a content attribute, a collaboration attribute, a trend, and a computing device attribute; identifying one or more content actions based on the author intent; displaying the one or more content actions as suggestions for creating the content; and implementing one or more selected content actions in response to receiving a selection. 2. The method of claim 1, further comprising:
displaying the one or more content actions in an implemented mode such that the author views each content action as performed. 3. The method of claim 1, further comprising:
displaying the one or more content actions in galleries based on multiple properties of the content and content elements by applying changes directly on the content. 4. The method of claim 1, wherein determining the author intent comprises analyzing one or more of an organizational position of the author, a professional status of the author, an identity of the author, a social status of the author, a storage location of the content, one or more documents related to the content, one or more prior versions of the content, a type of the content, a restriction imposed on the content, and one or more modifications on the content. 5. The method of claim 4, wherein determining the author intent further comprises analyzing one or more of collaborators, a collaboration project, a type of computing device associated with the author, a trend among peers of the author, an organizational norm, and a trend in the Internet. 6. The method of claim 1, wherein identifying the content actions comprises determining one or more of a placement of the content, a formatting of the content, a style of the content, one or more relationships between content elements, a size of one or more content elements, an attribute of one or more content elements, a layout of the content elements, an animation associated with the one or more content elements, and a motion path for the one or more content elements. 7. The method of claim 6, wherein identifying the content actions further comprises determining one or more accessibility options. 8. The method of claim 1, further comprising:
replacing one or more content portions with each other to emphasize the determined author intent. 9. The method of claim 8, wherein the content portions include text, an image, a graphic, and an embedded object. 10. A computing device to create content through author intent based suggestions, the computing device comprising:
a memory; a display; and a processing unit coupled to the memory and the display, the processing unit executing an authoring application, wherein the authoring application is configured to:
determine an author intent based on one or more of an author attribute, a content context, a content attribute, a collaboration attribute, a trend, and a computing device attribute;
identify one or more content actions based on the author intent;
display the one or more content actions as suggestions to create the content in an implemented mode such that the author views each content action as performed; and
implement one or more selected content actions in response to receiving a selection. 11. The computing device of claim 10, wherein the authoring application is further configured to enable transformation of the created content based on one or more of a consumer attribute and a consumer intent subsequent to publication of the content to potential consumers of the content. 12. The computing device of claim 11, wherein the transformation is performed at one or more of a cloud storing the created content, a hosted application providing access to the created content, and a local application associated with a consumer of the content. 13. The computing device of claim 10, wherein the authoring application is further configured to connect one or more portions of the content such that a consumer of the created content is prevented from one of modifying and deleting the connected portions of the content. 14. The computing device of claim 10, wherein the content actions include one or more of a placement of the content, a formatting of the content, a style of the content, one or more relationships between content elements, a size of one or more content elements, an attribute of one or more content elements, a layout of the content elements, an animation associated with the one or more content elements, a motion path for the one or more content elements, one or more accessibility options, and a replacement of two or more content portions. 15. The computing device of claim 10, wherein the author is enabled to interact with the authoring application through one or more of a touch input, a gesture input, a keyboard input, a mouse input, a pen input, a voice command, and an eye tracking input. 16. The computing device of claim 10, wherein the authoring application is one a locally installed application and a hosted service, and the computing device is one of: a server, a desktop computer, a laptop computer, a tablet, a smart whiteboard, and a smart phone. 17. A computer-readable memory device with instructions stored thereon to create content through author intent based suggestions, the instructions comprising:
determining an author intent based on one or more of an author attribute, a content context, a content attribute, a collaboration attribute, a trend, and a computing device attribute; identifying one or more content actions based on the author intent; displaying the one or more content actions as suggestions to create the content in an implemented mode such that the author views each content action as performed; implementing one or more selected content actions in response to receiving a selection; and enabling transformation of the created content based on one or more of a consumer attribute and a consumer intent subsequent to publication of the content to potential consumers of the content. 18. The computer-readable memory device of claim 17, wherein the instructions further comprise determining a theme of the content and emphasizing at least one portion of the content based on the determined theme employing one or more of a textual scheme, a graphic scheme, a shading scheme, a placement scheme, and a color scheme. 19. The computer-readable memory device of claim 17, wherein enabling transformation of the content comprises enabling modification of one or more of the created content, an attribute of the created content, and an attribute of a content element. 20. The computer-readable memory device of claim 17, wherein the instructions further comprise employing a learning algorithm to dynamically adjust intent determination and content action identification. | Technologies are generally provided for creating content by detecting user intent and providing suggestions associated with content actions. User intent may be determined from a number of different factors associated with the user, a document, and an environment of the user and/or content. Suggestions on content actions such as placement, style, formatting, or extent of content may be automatically made to the user. Suggestions may also be provided based on other factors such as crowd sourcing. In addition to automatic emphasis and connection, content transformation may be enabled prior to consumption after the author has created the content.1. A method to be executed in a computing device to create content through author intent based suggestions, the method comprising:
determining an author intent based on one or more of an author attribute, a content context, a content attribute, a collaboration attribute, a trend, and a computing device attribute; identifying one or more content actions based on the author intent; displaying the one or more content actions as suggestions for creating the content; and implementing one or more selected content actions in response to receiving a selection. 2. The method of claim 1, further comprising:
displaying the one or more content actions in an implemented mode such that the author views each content action as performed. 3. The method of claim 1, further comprising:
displaying the one or more content actions in galleries based on multiple properties of the content and content elements by applying changes directly on the content. 4. The method of claim 1, wherein determining the author intent comprises analyzing one or more of an organizational position of the author, a professional status of the author, an identity of the author, a social status of the author, a storage location of the content, one or more documents related to the content, one or more prior versions of the content, a type of the content, a restriction imposed on the content, and one or more modifications on the content. 5. The method of claim 4, wherein determining the author intent further comprises analyzing one or more of collaborators, a collaboration project, a type of computing device associated with the author, a trend among peers of the author, an organizational norm, and a trend in the Internet. 6. The method of claim 1, wherein identifying the content actions comprises determining one or more of a placement of the content, a formatting of the content, a style of the content, one or more relationships between content elements, a size of one or more content elements, an attribute of one or more content elements, a layout of the content elements, an animation associated with the one or more content elements, and a motion path for the one or more content elements. 7. The method of claim 6, wherein identifying the content actions further comprises determining one or more accessibility options. 8. The method of claim 1, further comprising:
replacing one or more content portions with each other to emphasize the determined author intent. 9. The method of claim 8, wherein the content portions include text, an image, a graphic, and an embedded object. 10. A computing device to create content through author intent based suggestions, the computing device comprising:
a memory; a display; and a processing unit coupled to the memory and the display, the processing unit executing an authoring application, wherein the authoring application is configured to:
determine an author intent based on one or more of an author attribute, a content context, a content attribute, a collaboration attribute, a trend, and a computing device attribute;
identify one or more content actions based on the author intent;
display the one or more content actions as suggestions to create the content in an implemented mode such that the author views each content action as performed; and
implement one or more selected content actions in response to receiving a selection. 11. The computing device of claim 10, wherein the authoring application is further configured to enable transformation of the created content based on one or more of a consumer attribute and a consumer intent subsequent to publication of the content to potential consumers of the content. 12. The computing device of claim 11, wherein the transformation is performed at one or more of a cloud storing the created content, a hosted application providing access to the created content, and a local application associated with a consumer of the content. 13. The computing device of claim 10, wherein the authoring application is further configured to connect one or more portions of the content such that a consumer of the created content is prevented from one of modifying and deleting the connected portions of the content. 14. The computing device of claim 10, wherein the content actions include one or more of a placement of the content, a formatting of the content, a style of the content, one or more relationships between content elements, a size of one or more content elements, an attribute of one or more content elements, a layout of the content elements, an animation associated with the one or more content elements, a motion path for the one or more content elements, one or more accessibility options, and a replacement of two or more content portions. 15. The computing device of claim 10, wherein the author is enabled to interact with the authoring application through one or more of a touch input, a gesture input, a keyboard input, a mouse input, a pen input, a voice command, and an eye tracking input. 16. The computing device of claim 10, wherein the authoring application is one a locally installed application and a hosted service, and the computing device is one of: a server, a desktop computer, a laptop computer, a tablet, a smart whiteboard, and a smart phone. 17. A computer-readable memory device with instructions stored thereon to create content through author intent based suggestions, the instructions comprising:
determining an author intent based on one or more of an author attribute, a content context, a content attribute, a collaboration attribute, a trend, and a computing device attribute; identifying one or more content actions based on the author intent; displaying the one or more content actions as suggestions to create the content in an implemented mode such that the author views each content action as performed; implementing one or more selected content actions in response to receiving a selection; and enabling transformation of the created content based on one or more of a consumer attribute and a consumer intent subsequent to publication of the content to potential consumers of the content. 18. The computer-readable memory device of claim 17, wherein the instructions further comprise determining a theme of the content and emphasizing at least one portion of the content based on the determined theme employing one or more of a textual scheme, a graphic scheme, a shading scheme, a placement scheme, and a color scheme. 19. The computer-readable memory device of claim 17, wherein enabling transformation of the content comprises enabling modification of one or more of the created content, an attribute of the created content, and an attribute of a content element. 20. The computer-readable memory device of claim 17, wherein the instructions further comprise employing a learning algorithm to dynamically adjust intent determination and content action identification. | 2,100 |
6,483 | 6,483 | 16,005,978 | 2,111 | On-chip spread spectrum characterization including obtaining, from a skitter circuit, skitter data comprising a spread width corresponding to an amplitude of a spread of a spread spectrum clock signal; setting an offset pointer to a center of the spread width corresponding to the amplitude of the spread; retrieving, for each of a number of reference clock cycles, edge data indicating a location, within the spread width, of an edge of the spread spectrum during the reference clock cycle; incrementing, using the edge data, an offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer; and calculating a frequency of the spread spectrum using the offset counter and the number of reference clock cycles. | 1. A method of on-chip spread spectrum characterization, the method comprising:
obtaining, from a skitter circuit, skitter data comprising a spread width corresponding to an amplitude of a spread of a spread spectrum clock signal; setting an offset pointer to a center of the spread width corresponding to the amplitude of the spread; retrieving, for each of a number of reference clock cycles, edge data indicating a location, within the spread width, of an edge of the spread spectrum during the reference clock cycle; incrementing, using the edge data, an offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer; and calculating a frequency of the spread spectrum using the offset counter and the number of reference clock cycles. 2. The method of claim 1, further comprising:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and adjusting a frequency setting of the PLL in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 3. The method of claim 1, further comprising:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and generating an alert in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 4. The method of claim 1, wherein the edge data is retrieved from the skitter circuit for a single clock cycle. 5. The method of claim 1, wherein incrementing the offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer comprises eliminating random jitter using hysteresis protection on the offset counter. 6. The method of claim 1, wherein retrieving, for each of the number of reference clock cycles, the edge data indicating the location, within the spread width, of the edge of the spread spectrum during the reference clock cycle comprises one selected from a group consisting of:
retrieving, for each of the number of reference clock cycles, rising edge data indicating the location, within the spread width, of only the rising edge of the spread spectrum during the reference clock cycle; and retrieving, for each of the number of reference clock cycles, falling edge data indicating the location, within the spread width, of only the falling edge of the spread spectrum during the reference clock cycle. 7. The method of claim 1, wherein the spread spectrum is one selected from a group consisting of a deterministic spread spectrum intentionally added to the spread spectrum clock signal by a phase-locked loop (PLL) and a deterministic jitter unintentionally added to the spread spectrum clock signal by the PLL. 8. An integrated circuit for on-chip spread spectrum characterization, the integrated circuit configured to carry out the steps of:
obtaining, from a skitter circuit, skitter data comprising a spread width corresponding to an amplitude of a spread of a spread spectrum clock signal; setting an offset pointer to a center of the spread width corresponding to the amplitude of the spread; retrieving, for each of a number of reference clock cycles, edge data indicating a location, within the spread width, of an edge of the spread spectrum during the reference clock cycle; incrementing, using the edge data, an offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer; and calculating a frequency of the spread spectrum using the offset counter and the number of reference clock cycles. 9. The integrated circuit of claim 8, wherein the integrated circuit is further configured to carry out the steps of:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and adjusting a frequency setting of the PLL in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 10. The integrated circuit of claim 8, wherein the integrated circuit is further configured to carry out the steps of:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and generating an alert in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 11. The integrated circuit of claim 8, wherein the edge data is retrieved from the skitter circuit for a single clock cycle. 12. The integrated circuit of claim 8, wherein incrementing the offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer comprises eliminating random jitter using hysteresis protection on the offset counter. 13. The integrated circuit of claim 8, wherein retrieving, for each of the number of reference clock cycles, the edge data indicating the location, within the spread width, of the edge of the spread spectrum during the reference clock cycle comprises one selected from a group consisting of:
retrieving, for each of the number of reference clock cycles, rising edge data indicating the location, within the spread width, of only the rising edge of the spread spectrum during the reference clock cycle; and retrieving, for each of the number of reference clock cycles, falling edge data indicating the location, within the spread width, of only the falling edge of the spread spectrum during the reference clock cycle. 14. The integrated circuit of claim 8, wherein the spread spectrum is one selected from a group consisting of a deterministic spread spectrum intentionally added to the spread spectrum clock signal by a phase-locked loop (PLL) and a deterministic jitter unintentionally added to the spread spectrum clock signal by the PLL. 15. A computer program product for on-chip spread spectrum characterization, the computer program product disposed upon a computer readable medium, the computer program product comprising computer program instructions that, when executed, cause a computer to carry out the steps of:
obtaining, from a skitter circuit, skitter data comprising a spread width corresponding to an amplitude of a spread of a spread spectrum clock signal; setting an offset pointer to a center of the spread width corresponding to the amplitude of the spread; retrieving, for each of a number of reference clock cycles, edge data indicating a location, within the spread width, of an edge of the spread spectrum during the reference clock cycle; incrementing, using the edge data, an offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer; and calculating a frequency of the spread spectrum using the offset counter and the number of reference clock cycles. 16. The computer program product of claim 15, wherein the computer program instructions, when executed, further cause the computer to carry out the steps of:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and adjusting a frequency setting of the PLL in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 17. The computer program product of claim 15, wherein the computer program instructions, when executed, further cause the computer to carry out the steps of:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and generating an alert in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 18. The computer program product of claim 15, wherein the edge data is retrieved from the skitter circuit for a single clock cycle. 19. The computer program product of claim 15, wherein incrementing the offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer comprises eliminating random jitter using hysteresis protection on the offset counter. 20. The computer program product of claim 15, wherein retrieving, for each of the number of reference clock cycles, the edge data indicating the location, within the spread width, of the edge of the spread spectrum during the reference clock cycle comprises one selected from a group consisting of:
retrieving, for each of the number of reference clock cycles, rising edge data indicating the location, within the spread width, of only the rising edge of the spread spectrum during the reference clock cycle; and retrieving, for each of the number of reference clock cycles, falling edge data indicating the location, within the spread width, of only the falling edge of the spread spectrum during the reference clock cycle. | On-chip spread spectrum characterization including obtaining, from a skitter circuit, skitter data comprising a spread width corresponding to an amplitude of a spread of a spread spectrum clock signal; setting an offset pointer to a center of the spread width corresponding to the amplitude of the spread; retrieving, for each of a number of reference clock cycles, edge data indicating a location, within the spread width, of an edge of the spread spectrum during the reference clock cycle; incrementing, using the edge data, an offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer; and calculating a frequency of the spread spectrum using the offset counter and the number of reference clock cycles.1. A method of on-chip spread spectrum characterization, the method comprising:
obtaining, from a skitter circuit, skitter data comprising a spread width corresponding to an amplitude of a spread of a spread spectrum clock signal; setting an offset pointer to a center of the spread width corresponding to the amplitude of the spread; retrieving, for each of a number of reference clock cycles, edge data indicating a location, within the spread width, of an edge of the spread spectrum during the reference clock cycle; incrementing, using the edge data, an offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer; and calculating a frequency of the spread spectrum using the offset counter and the number of reference clock cycles. 2. The method of claim 1, further comprising:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and adjusting a frequency setting of the PLL in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 3. The method of claim 1, further comprising:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and generating an alert in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 4. The method of claim 1, wherein the edge data is retrieved from the skitter circuit for a single clock cycle. 5. The method of claim 1, wherein incrementing the offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer comprises eliminating random jitter using hysteresis protection on the offset counter. 6. The method of claim 1, wherein retrieving, for each of the number of reference clock cycles, the edge data indicating the location, within the spread width, of the edge of the spread spectrum during the reference clock cycle comprises one selected from a group consisting of:
retrieving, for each of the number of reference clock cycles, rising edge data indicating the location, within the spread width, of only the rising edge of the spread spectrum during the reference clock cycle; and retrieving, for each of the number of reference clock cycles, falling edge data indicating the location, within the spread width, of only the falling edge of the spread spectrum during the reference clock cycle. 7. The method of claim 1, wherein the spread spectrum is one selected from a group consisting of a deterministic spread spectrum intentionally added to the spread spectrum clock signal by a phase-locked loop (PLL) and a deterministic jitter unintentionally added to the spread spectrum clock signal by the PLL. 8. An integrated circuit for on-chip spread spectrum characterization, the integrated circuit configured to carry out the steps of:
obtaining, from a skitter circuit, skitter data comprising a spread width corresponding to an amplitude of a spread of a spread spectrum clock signal; setting an offset pointer to a center of the spread width corresponding to the amplitude of the spread; retrieving, for each of a number of reference clock cycles, edge data indicating a location, within the spread width, of an edge of the spread spectrum during the reference clock cycle; incrementing, using the edge data, an offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer; and calculating a frequency of the spread spectrum using the offset counter and the number of reference clock cycles. 9. The integrated circuit of claim 8, wherein the integrated circuit is further configured to carry out the steps of:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and adjusting a frequency setting of the PLL in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 10. The integrated circuit of claim 8, wherein the integrated circuit is further configured to carry out the steps of:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and generating an alert in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 11. The integrated circuit of claim 8, wherein the edge data is retrieved from the skitter circuit for a single clock cycle. 12. The integrated circuit of claim 8, wherein incrementing the offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer comprises eliminating random jitter using hysteresis protection on the offset counter. 13. The integrated circuit of claim 8, wherein retrieving, for each of the number of reference clock cycles, the edge data indicating the location, within the spread width, of the edge of the spread spectrum during the reference clock cycle comprises one selected from a group consisting of:
retrieving, for each of the number of reference clock cycles, rising edge data indicating the location, within the spread width, of only the rising edge of the spread spectrum during the reference clock cycle; and retrieving, for each of the number of reference clock cycles, falling edge data indicating the location, within the spread width, of only the falling edge of the spread spectrum during the reference clock cycle. 14. The integrated circuit of claim 8, wherein the spread spectrum is one selected from a group consisting of a deterministic spread spectrum intentionally added to the spread spectrum clock signal by a phase-locked loop (PLL) and a deterministic jitter unintentionally added to the spread spectrum clock signal by the PLL. 15. A computer program product for on-chip spread spectrum characterization, the computer program product disposed upon a computer readable medium, the computer program product comprising computer program instructions that, when executed, cause a computer to carry out the steps of:
obtaining, from a skitter circuit, skitter data comprising a spread width corresponding to an amplitude of a spread of a spread spectrum clock signal; setting an offset pointer to a center of the spread width corresponding to the amplitude of the spread; retrieving, for each of a number of reference clock cycles, edge data indicating a location, within the spread width, of an edge of the spread spectrum during the reference clock cycle; incrementing, using the edge data, an offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer; and calculating a frequency of the spread spectrum using the offset counter and the number of reference clock cycles. 16. The computer program product of claim 15, wherein the computer program instructions, when executed, further cause the computer to carry out the steps of:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and adjusting a frequency setting of the PLL in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 17. The computer program product of claim 15, wherein the computer program instructions, when executed, further cause the computer to carry out the steps of:
comparing the frequency of the spread spectrum to a targeted spread spectrum frequency of a phase-locked loop (PLL) generating the spread spectrum; and generating an alert in response to determining that the frequency of the spread spectrum does not match the targeted spread spectrum frequency of the PLL. 18. The computer program product of claim 15, wherein the edge data is retrieved from the skitter circuit for a single clock cycle. 19. The computer program product of claim 15, wherein incrementing the offset counter for each reference clock cycle during which the edge of the spread spectrum crosses the offset pointer comprises eliminating random jitter using hysteresis protection on the offset counter. 20. The computer program product of claim 15, wherein retrieving, for each of the number of reference clock cycles, the edge data indicating the location, within the spread width, of the edge of the spread spectrum during the reference clock cycle comprises one selected from a group consisting of:
retrieving, for each of the number of reference clock cycles, rising edge data indicating the location, within the spread width, of only the rising edge of the spread spectrum during the reference clock cycle; and retrieving, for each of the number of reference clock cycles, falling edge data indicating the location, within the spread width, of only the falling edge of the spread spectrum during the reference clock cycle. | 2,100 |
6,484 | 6,484 | 16,136,178 | 2,176 | An electronic device includes instructions for receiving a first communication; in response to receiving the first communication, generating a first notification for the first communication received at the device, the first notification including content of the first communication; receiving a second communication at the device; and in response to receiving the second communication: determining whether the second communication and the first communication are received from the same sender. The instructions include, in accordance with a determination that the second communication and the first communication are from the same sender, updating the first notification such that the updated first notification concurrently includes the content of the first communication and the second communication; and in accordance with a determination that the second communication and the first communication are not from the same sender, generating a second notification, for concurrent display with the first notification. | 1. A method, comprising:
at an electronic device with a display:
receiving a communication associated with an application;
displaying a notification that corresponds to the communication, wherein the displayed notification includes a user interface that provides a subset of functionalities available in the application;
while displaying the notification, detecting user interaction with the user interface of the notification, wherein the user interaction causes changes to an initial local state of the user interface of the notification;
in response to detecting the user interaction with the user interface of the notification, displaying a current local state of the user interface of the notification that is different from the initial local state of the user interface of the notification;
while displaying the current local state of the user interface of the notification, receiving a user input invoking the application from the user interface of the notification; and,
in response to receiving the user input invoking the application from the user interface of the notification, starting the application in a modified state, different from a default initial state of the application, wherein the modified state of the application is generated based on the current local state of the user interface of the notification. 2. The method of claim 1, wherein detecting the user interaction with the user interface of the notification further includes:
detecting entry of content in the user interface of the notification by the user. 3. The method of claim 2, wherein the communication is an instant message associated with an instant messaging application, the initial local state of the user interface includes a reply input field configured to receive a reply message input, the current local state of the user interface includes the reply input field and first message input provided in the reply input field by a user, and starting the application in the modified state includes displaying the first message input in the user interface of the instant messaging application. 4. The method of claim 1, wherein detecting the user interaction with the user interface of the notification includes:
detecting a change in user interface configuration of the user interface of the notification in response to manipulation of the user interface by a user. 5. The method of claim 4, wherein the communication is a calendar invitation associated with a calendar application, the initial local state of the user interface includes a first portion of a calendar in a first view, the current local state of the user interface includes a second portion of the calendar in a second view that is different from the first portion of the calendar in the first view, and starting the application in the modified state includes displaying the second portion of the calendar in the second view. 6. The method of claim 1, wherein displaying the notification that corresponds to the communication further comprises displaying the notification on a lock screen, and wherein the method includes:
requesting and processing an authentication input before starting the application in a modified state. 7. The method of claim 1, including:
providing data regarding the current local state of the user interface of the notification to the application. 8. The method of claim 7, including:
storing the data regarding the current local state of the user interface of the notification in a data store accessible by the application. 9. The method of claim 7, wherein providing data regarding the current local state of user interface of the notification to the application further comprises:
sending the data regarding the current local state of the user interface of the notification to the application. 10. An electronic device, comprising:
a display; one or more processors; memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for:
receiving a communication associated with an application;
displaying a notification that corresponds to the communication, wherein the displayed notification includes a user interface that provides a subset of functionalities available in the application;
while displaying the notification, detecting user interaction with the user interface of the notification, wherein the user interaction causes changes to an initial local state of the user interface of the notification;
in response to detecting the user interaction with the user interface of the notification, displaying a current local state of the user interface of the notification that is different from the initial local state of the user interface of the notification;
while displaying the current local state of the user interface of the notification, receiving a user input invoking the application from the user interface of the notification; and,
in response to receiving the user input invoking the application from the user interface of the notification, starting the application in a modified state, different from a default initial state of the application, wherein the modified state of the application is generated based on the current local state of the user interface of the notification. 11. A computer readable storage medium storing one or more programs, which, when executed by an electronic device having a display and one or more processors configured to executed the one or more programs, cause the electronic device to:
receive a communication associated with an application; display a notification that corresponds to the communication, wherein the displayed notification includes a user interface that provides a subset of functionalities available in the application; while displaying the notification, detect user interaction with the user interface of the notification, wherein the user interaction causes changes to an initial local state of the user interface of the notification; in response to detecting the user interaction with the user interface of the notification, display a current local state of the user interface of the notification that is different from the initial local state of the user interface of the notification; while displaying the current local state of the user interface of the notification, receive a user input invoking the application from the user interface of the notification; and, in response to receiving the user input invoking the application from the user interface of the notification, start the application in a modified state, different from a default initial state of the application, wherein the modified state of the application is generated based on the current local state of the user interface of the notification. | An electronic device includes instructions for receiving a first communication; in response to receiving the first communication, generating a first notification for the first communication received at the device, the first notification including content of the first communication; receiving a second communication at the device; and in response to receiving the second communication: determining whether the second communication and the first communication are received from the same sender. The instructions include, in accordance with a determination that the second communication and the first communication are from the same sender, updating the first notification such that the updated first notification concurrently includes the content of the first communication and the second communication; and in accordance with a determination that the second communication and the first communication are not from the same sender, generating a second notification, for concurrent display with the first notification.1. A method, comprising:
at an electronic device with a display:
receiving a communication associated with an application;
displaying a notification that corresponds to the communication, wherein the displayed notification includes a user interface that provides a subset of functionalities available in the application;
while displaying the notification, detecting user interaction with the user interface of the notification, wherein the user interaction causes changes to an initial local state of the user interface of the notification;
in response to detecting the user interaction with the user interface of the notification, displaying a current local state of the user interface of the notification that is different from the initial local state of the user interface of the notification;
while displaying the current local state of the user interface of the notification, receiving a user input invoking the application from the user interface of the notification; and,
in response to receiving the user input invoking the application from the user interface of the notification, starting the application in a modified state, different from a default initial state of the application, wherein the modified state of the application is generated based on the current local state of the user interface of the notification. 2. The method of claim 1, wherein detecting the user interaction with the user interface of the notification further includes:
detecting entry of content in the user interface of the notification by the user. 3. The method of claim 2, wherein the communication is an instant message associated with an instant messaging application, the initial local state of the user interface includes a reply input field configured to receive a reply message input, the current local state of the user interface includes the reply input field and first message input provided in the reply input field by a user, and starting the application in the modified state includes displaying the first message input in the user interface of the instant messaging application. 4. The method of claim 1, wherein detecting the user interaction with the user interface of the notification includes:
detecting a change in user interface configuration of the user interface of the notification in response to manipulation of the user interface by a user. 5. The method of claim 4, wherein the communication is a calendar invitation associated with a calendar application, the initial local state of the user interface includes a first portion of a calendar in a first view, the current local state of the user interface includes a second portion of the calendar in a second view that is different from the first portion of the calendar in the first view, and starting the application in the modified state includes displaying the second portion of the calendar in the second view. 6. The method of claim 1, wherein displaying the notification that corresponds to the communication further comprises displaying the notification on a lock screen, and wherein the method includes:
requesting and processing an authentication input before starting the application in a modified state. 7. The method of claim 1, including:
providing data regarding the current local state of the user interface of the notification to the application. 8. The method of claim 7, including:
storing the data regarding the current local state of the user interface of the notification in a data store accessible by the application. 9. The method of claim 7, wherein providing data regarding the current local state of user interface of the notification to the application further comprises:
sending the data regarding the current local state of the user interface of the notification to the application. 10. An electronic device, comprising:
a display; one or more processors; memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for:
receiving a communication associated with an application;
displaying a notification that corresponds to the communication, wherein the displayed notification includes a user interface that provides a subset of functionalities available in the application;
while displaying the notification, detecting user interaction with the user interface of the notification, wherein the user interaction causes changes to an initial local state of the user interface of the notification;
in response to detecting the user interaction with the user interface of the notification, displaying a current local state of the user interface of the notification that is different from the initial local state of the user interface of the notification;
while displaying the current local state of the user interface of the notification, receiving a user input invoking the application from the user interface of the notification; and,
in response to receiving the user input invoking the application from the user interface of the notification, starting the application in a modified state, different from a default initial state of the application, wherein the modified state of the application is generated based on the current local state of the user interface of the notification. 11. A computer readable storage medium storing one or more programs, which, when executed by an electronic device having a display and one or more processors configured to executed the one or more programs, cause the electronic device to:
receive a communication associated with an application; display a notification that corresponds to the communication, wherein the displayed notification includes a user interface that provides a subset of functionalities available in the application; while displaying the notification, detect user interaction with the user interface of the notification, wherein the user interaction causes changes to an initial local state of the user interface of the notification; in response to detecting the user interaction with the user interface of the notification, display a current local state of the user interface of the notification that is different from the initial local state of the user interface of the notification; while displaying the current local state of the user interface of the notification, receive a user input invoking the application from the user interface of the notification; and, in response to receiving the user input invoking the application from the user interface of the notification, start the application in a modified state, different from a default initial state of the application, wherein the modified state of the application is generated based on the current local state of the user interface of the notification. | 2,100 |
6,485 | 6,485 | 14,850,517 | 2,178 | A method and apparatus for indicating the status of an ancillary embedded system in an electronic device. In one exemplary embodiment, the method includes starting an initialization process of a high-level embedded system in the electronic device. The method further includes determining the status of the ancillary embedded system. The method further includes generating display information for the status of the ancillary embedded system. The method further includes storing the display information in a manner retrievable by the high-level embedded system. The method further includes reading the stored set of display information and displaying an indication of the status on a user display prior to completion of the high-level embedded system's initialization process. The method further includes periodically updating the stored set of display information by the ancillary embedded system to provide a real-time indication of status. | 1. A device, comprising:
a user display; a first embedded system running a first operating system, the first operating system having a first boot time; and a second embedded system running a second operating system, the second operating system having a second boot time, the second boot time being shorter than the first boot time; wherein the second embedded system is configured to perform a user function after start-up of the second embedded system is complete, and the first embedded system is configured to cause the user display to display at least one status indication of the second embedded system after the start-up of the second embedded system is complete and before start-up of the first embedded system is complete. 2. The device of claim 1, wherein the first embedded system is further configured to cause the user display to display real-time updates of the at least one status indication during start-up of the first embedded system. 3. The device of claim 1, wherein the first embedded system is configured to cause the user display to display the at least one status indication as at least part of a splash screen. 4. The device of claim 3, wherein the first embedded system is configured to cause the user display to display the splash screen partitioned into a plurality of sections with a first section displaying the at least one status indication of the first embedded system and with a second section displaying information associated with the second embedded system. 5. The device of claim 3, wherein the first embedded system is configured to cause the user display to display the splash screen that is substantially similar in appearance to an operational screen associated with the first embedded system. 6. The device of claim 5, wherein the first embedded system is configured to cause the user display to display the at least one status indication as a pop-up window at least partially covering on-screen interface objects of the operational screen associated with the first embedded system that are not available for use during the start-up of the first embedded system. 7. The device of claim 3, wherein the first embedded system is configured to cause the user display to display the splash screen as a partially functional operational screen associated with the first embedded system, wherein on-screen interface objects that are not available for use during start-up of the first embedded system are not selectable, and wherein on-screen interface objects that are available for use during start-up of the first embedded system are selectable. 8. The device of claim 1, wherein the first embedded system is configured to cause the user display to display the at least one status indication of the second embedded system by causing the user display to display an on-screen image generated by the second embedded system. 9. The device of claim 1, wherein the user function of the second embedded system is available for use after the start-up of the second embedded system is complete and before the start-up of the first embedded system is complete. 10. The device of claim 9, wherein the at least one status indication provides a visual indication that the user function is available for use. 11. The device of claim 1, wherein the second embedded system includes a land-mobile radio modem. 12. The device of claim 11, wherein the at least one status indication displayed on the user display includes a mode number of the land-mobile radio modem. 13. The device of claim 12, further comprising a push-to-talk button, wherein the second embedded system is configured to operate the land-mobile radio modem based on selective activation of the push-to-talk button after the start-up of the second embedded system is complete, and wherein the first embedded system is configured to provide additional user interface and data access functionality through the user display after the start-up of the first embedded system is complete. 14. The device of claim 1, wherein the first embedded system is configured to initiate a system restart in response to a fault condition, and wherein the first embedded system is configured to cause the user display to display the at least one status indication of the second embedded system during the system restart of the first embedded system. 15. A method for indicating a status of an ancillary embedded system in an electronic device, the method comprising:
starting an initialization process of a high-level embedded system; determining at least one status of the ancillary embedded system; generating a set of display information for the at least one status of the ancillary embedded system; storing the set of display information in a manner retrievable by the high-level embedded system; during the initialization process of the high-level embedded system, reading the stored set of display information; and displaying on a user display an indication of the at least one status of the ancillary embedded system based on the set of display information prior to completion of the initialization process by the high-level embedded system. 16. The method of claim 15, further comprising:
periodically updating the stored set of display information by the ancillary embedded system to provide a real-time indication of status; and periodically updating the set of display information on the user display by the high-level embedded system during power initialization of the high-level embedded system. 17. The method of claim 15, wherein storing the set of display information includes transferring the set of display information from the ancillary embedded system to the high-level embedded system over an inter-processor communication (IPC) data link. 18. The method of claim 15, wherein storing the set of display information includes storing the set of display information from the ancillary embedded system to a memory device commonly accessible by the high-level embedded system. 19. The method of claim 15, further comprising displaying an initialization status of the high-level embedded system on the user display in a manner spatially distinct from the displayed set of display information for the at least one status of the ancillary embedded system. 20. The method of claim 15, further comprising displaying a splash screen that is substantially similar in appearance to an operational screen of the high-level embedded system, wherein the displayed splash screen provides an indication of the at least one status of the ancillary embedded system based on the set of display information. | A method and apparatus for indicating the status of an ancillary embedded system in an electronic device. In one exemplary embodiment, the method includes starting an initialization process of a high-level embedded system in the electronic device. The method further includes determining the status of the ancillary embedded system. The method further includes generating display information for the status of the ancillary embedded system. The method further includes storing the display information in a manner retrievable by the high-level embedded system. The method further includes reading the stored set of display information and displaying an indication of the status on a user display prior to completion of the high-level embedded system's initialization process. The method further includes periodically updating the stored set of display information by the ancillary embedded system to provide a real-time indication of status.1. A device, comprising:
a user display; a first embedded system running a first operating system, the first operating system having a first boot time; and a second embedded system running a second operating system, the second operating system having a second boot time, the second boot time being shorter than the first boot time; wherein the second embedded system is configured to perform a user function after start-up of the second embedded system is complete, and the first embedded system is configured to cause the user display to display at least one status indication of the second embedded system after the start-up of the second embedded system is complete and before start-up of the first embedded system is complete. 2. The device of claim 1, wherein the first embedded system is further configured to cause the user display to display real-time updates of the at least one status indication during start-up of the first embedded system. 3. The device of claim 1, wherein the first embedded system is configured to cause the user display to display the at least one status indication as at least part of a splash screen. 4. The device of claim 3, wherein the first embedded system is configured to cause the user display to display the splash screen partitioned into a plurality of sections with a first section displaying the at least one status indication of the first embedded system and with a second section displaying information associated with the second embedded system. 5. The device of claim 3, wherein the first embedded system is configured to cause the user display to display the splash screen that is substantially similar in appearance to an operational screen associated with the first embedded system. 6. The device of claim 5, wherein the first embedded system is configured to cause the user display to display the at least one status indication as a pop-up window at least partially covering on-screen interface objects of the operational screen associated with the first embedded system that are not available for use during the start-up of the first embedded system. 7. The device of claim 3, wherein the first embedded system is configured to cause the user display to display the splash screen as a partially functional operational screen associated with the first embedded system, wherein on-screen interface objects that are not available for use during start-up of the first embedded system are not selectable, and wherein on-screen interface objects that are available for use during start-up of the first embedded system are selectable. 8. The device of claim 1, wherein the first embedded system is configured to cause the user display to display the at least one status indication of the second embedded system by causing the user display to display an on-screen image generated by the second embedded system. 9. The device of claim 1, wherein the user function of the second embedded system is available for use after the start-up of the second embedded system is complete and before the start-up of the first embedded system is complete. 10. The device of claim 9, wherein the at least one status indication provides a visual indication that the user function is available for use. 11. The device of claim 1, wherein the second embedded system includes a land-mobile radio modem. 12. The device of claim 11, wherein the at least one status indication displayed on the user display includes a mode number of the land-mobile radio modem. 13. The device of claim 12, further comprising a push-to-talk button, wherein the second embedded system is configured to operate the land-mobile radio modem based on selective activation of the push-to-talk button after the start-up of the second embedded system is complete, and wherein the first embedded system is configured to provide additional user interface and data access functionality through the user display after the start-up of the first embedded system is complete. 14. The device of claim 1, wherein the first embedded system is configured to initiate a system restart in response to a fault condition, and wherein the first embedded system is configured to cause the user display to display the at least one status indication of the second embedded system during the system restart of the first embedded system. 15. A method for indicating a status of an ancillary embedded system in an electronic device, the method comprising:
starting an initialization process of a high-level embedded system; determining at least one status of the ancillary embedded system; generating a set of display information for the at least one status of the ancillary embedded system; storing the set of display information in a manner retrievable by the high-level embedded system; during the initialization process of the high-level embedded system, reading the stored set of display information; and displaying on a user display an indication of the at least one status of the ancillary embedded system based on the set of display information prior to completion of the initialization process by the high-level embedded system. 16. The method of claim 15, further comprising:
periodically updating the stored set of display information by the ancillary embedded system to provide a real-time indication of status; and periodically updating the set of display information on the user display by the high-level embedded system during power initialization of the high-level embedded system. 17. The method of claim 15, wherein storing the set of display information includes transferring the set of display information from the ancillary embedded system to the high-level embedded system over an inter-processor communication (IPC) data link. 18. The method of claim 15, wherein storing the set of display information includes storing the set of display information from the ancillary embedded system to a memory device commonly accessible by the high-level embedded system. 19. The method of claim 15, further comprising displaying an initialization status of the high-level embedded system on the user display in a manner spatially distinct from the displayed set of display information for the at least one status of the ancillary embedded system. 20. The method of claim 15, further comprising displaying a splash screen that is substantially similar in appearance to an operational screen of the high-level embedded system, wherein the displayed splash screen provides an indication of the at least one status of the ancillary embedded system based on the set of display information. | 2,100 |
6,486 | 6,486 | 14,045,461 | 2,198 | Techniques to employ emoji for text predictions are described herein. In one or more implementations, entry of characters is detected during interaction with a device. Prediction candidates corresponding to the detected characters are generated according to a language model that is configured to consider emoji along with words and phrases. The language model may make use of a mapping table that maps a plurality of emoji to corresponding words. The mapping table enables a text prediction engine to offer the emoji as alternatives for matching words. In addition or alternatively, the text prediction engine may be configured to analyze emoji as words within the model and generate probabilities and candidate rankings for predictions that include both emoji and words. User-specific emoji use may also be learned by monitoring a user's typing activity to adapt predictions to the user's particular usage of emoji. | 1. A method, comprising:
detecting entry of characters during interaction with a device; generating one or more prediction candidates including one or more predicted emoji corresponding to the detected characters according to a language model; and employing the one or more prediction candidates to facilitate further character entry for the interaction with the device. 2. A method as recited in claim 1, wherein the language model is configured to adapt predictions made by a text prediction engine to typing styles of users on an individual basis including user-specific emoji usage. 3. A method as recited in claim 1, wherein the language model is designed to make use of multiple language model dictionaries as sources of words, emoji, and corresponding scoring data, the scoring data tuned based on collection and analysis of word and emoji usage data for collective typing activities of a population of users. 4. A method as recited in claim 1, wherein generating the one or more prediction candidates comprises computing a weighted combination of scoring data associated with words and emoji contained in multiple dictionaries associated with the language model to compute scores for the prediction candidates. 5. A method as recited in claim 4, wherein generating the one or more text prediction candidates further comprises ranking the prediction candidates including words and emoji one to another based on the computed scores. 6. A method as recited in claim 1, further comprising collecting data regarding usage of emoji on a user-specific basis to create a user-specific dictionary for the language model that reflects usage of emoji. 7. A method as recited in claim 1, wherein generating the one or more prediction candidates comprises identifying one or more emoji as prediction candidates that correspond to predicted words based upon a mapping table for the language model that directly maps emoji to words. 8. A method as recited in claim 1, further comprising enabling switching between emoji and words during interaction with the device based upon a mapping table associated with the language model that directly maps emoji to words including:
switching between a particular word and a corresponding emoji responsive to a selection of the particular word to cause the switching; and switching between a particular emoji and a corresponding word responsive to a selection of the particular emoji to cause the switching. 9. A method as recited in claim 1, wherein employing the one or more prediction candidates comprises presenting representations of one or more prediction candidates via a user interface of the device for selection by a user to automatically insert a selected candidate to modify the detected characters by replacement or insertion after the detected characters. 10. A method as recited in claim 1, wherein employing the one or more prediction candidates comprises presenting representations of one or more prediction candidates for selection by a user via a prediction bar exposed in connection with an on-screen keyboard of the device. 11. A method as recited in claim 10, wherein the prediction candidates that are emoji and prediction candidates that are words are interspersed in the prediction bar. 12. A method as recited in claim 10, wherein the prediction bar is configured to present prediction candidates that are emoji and prediction candidates that are words as separate groups of prediction candidates. 13. A method as recited in claim 1, wherein employing the one or more prediction candidates comprises exposing a ranked list of prediction candidates for selection by a user to modify the detected characters, the ranked list of prediction candidates including at least one predicted emoji. 14. A method as recited in claim 14, further comprising:
responsive to interaction with the at least one predicted emoji configured to access an emoji picker, displaying the emoji picker configured to present and enable selection of a plurality of emoji options that relate to the at least one predicted emoji. 15. One or more computer-readable storage media storing instructions that, when executed by a computing device, cause the computing device to perform operations comprising:
identifying one or more dictionaries to use as sources for predictions based on one or more detected characters; ranking emoji along with words one to another as prediction candidates for the detected characters using scoring data contained in the one or more dictionaries; selecting one or more top ranking emoji and words according to the ranking as prediction candidates for the detected characters; and utilizing selected emoji along with selected words to facilitate character entry. 16. One or more computer-readable storage media as recited in claim 15, wherein the multiple dictionaries comprise a general population dictionary representative of common usage across a community of users and at least one other dictionary generated dynamically based on input of words and emoji by a particular user of the computing device to reflect the particular user's individual typing style. 17. One or more computer-readable storage media as recited in claim 15, wherein utilizing the selected emoji along with the words to facilitate character entry comprises:
representing multiple different emoji that are determined as top ranking prediction candidates along with words that are determined as top ranking prediction candidates via a user interface instrumentality configured to enable a selection from among the prediction candidates to modify the one or more detected characters. and enabling switching between words of the detected one or more characters and emoji that are directly mapped to the words responsive to selection of the words to cause the switching. 18. A mobile computing device, comprising:
a processing system; and one or more computer-readable media storing instructions that, when executed by the processing system, implement a text prediction engine operable to:
generate one or more prediction candidates for characters detected in an interaction scenario according to one or more dictionaries of a language model that support emoji, the prediction candidates that are generated including emoji and words predicted using the language model;
exposing the prediction candidates that are generated to enable selection from among the prediction candidates to modify the one or more detected characters. 19. A computing device as recited in claim 18, wherein exposing the prediction candidates comprises exposing at least one predicted word in connection with a corresponding emoji that is determined based upon a mapping table of the language model configured to directly map a plurality of words to corresponding emoji. 20. A computing device as recited in claim 18, wherein:
the one or more dictionaries are configured to include conditional usage probabilities for language-specific usage of emoji for in relation to the interaction scenario and; and the one or more prediction candidates are generated and ranked one to another based at least in part upon the conditional usage probabilities for language-specific usage of emoji. | Techniques to employ emoji for text predictions are described herein. In one or more implementations, entry of characters is detected during interaction with a device. Prediction candidates corresponding to the detected characters are generated according to a language model that is configured to consider emoji along with words and phrases. The language model may make use of a mapping table that maps a plurality of emoji to corresponding words. The mapping table enables a text prediction engine to offer the emoji as alternatives for matching words. In addition or alternatively, the text prediction engine may be configured to analyze emoji as words within the model and generate probabilities and candidate rankings for predictions that include both emoji and words. User-specific emoji use may also be learned by monitoring a user's typing activity to adapt predictions to the user's particular usage of emoji.1. A method, comprising:
detecting entry of characters during interaction with a device; generating one or more prediction candidates including one or more predicted emoji corresponding to the detected characters according to a language model; and employing the one or more prediction candidates to facilitate further character entry for the interaction with the device. 2. A method as recited in claim 1, wherein the language model is configured to adapt predictions made by a text prediction engine to typing styles of users on an individual basis including user-specific emoji usage. 3. A method as recited in claim 1, wherein the language model is designed to make use of multiple language model dictionaries as sources of words, emoji, and corresponding scoring data, the scoring data tuned based on collection and analysis of word and emoji usage data for collective typing activities of a population of users. 4. A method as recited in claim 1, wherein generating the one or more prediction candidates comprises computing a weighted combination of scoring data associated with words and emoji contained in multiple dictionaries associated with the language model to compute scores for the prediction candidates. 5. A method as recited in claim 4, wherein generating the one or more text prediction candidates further comprises ranking the prediction candidates including words and emoji one to another based on the computed scores. 6. A method as recited in claim 1, further comprising collecting data regarding usage of emoji on a user-specific basis to create a user-specific dictionary for the language model that reflects usage of emoji. 7. A method as recited in claim 1, wherein generating the one or more prediction candidates comprises identifying one or more emoji as prediction candidates that correspond to predicted words based upon a mapping table for the language model that directly maps emoji to words. 8. A method as recited in claim 1, further comprising enabling switching between emoji and words during interaction with the device based upon a mapping table associated with the language model that directly maps emoji to words including:
switching between a particular word and a corresponding emoji responsive to a selection of the particular word to cause the switching; and switching between a particular emoji and a corresponding word responsive to a selection of the particular emoji to cause the switching. 9. A method as recited in claim 1, wherein employing the one or more prediction candidates comprises presenting representations of one or more prediction candidates via a user interface of the device for selection by a user to automatically insert a selected candidate to modify the detected characters by replacement or insertion after the detected characters. 10. A method as recited in claim 1, wherein employing the one or more prediction candidates comprises presenting representations of one or more prediction candidates for selection by a user via a prediction bar exposed in connection with an on-screen keyboard of the device. 11. A method as recited in claim 10, wherein the prediction candidates that are emoji and prediction candidates that are words are interspersed in the prediction bar. 12. A method as recited in claim 10, wherein the prediction bar is configured to present prediction candidates that are emoji and prediction candidates that are words as separate groups of prediction candidates. 13. A method as recited in claim 1, wherein employing the one or more prediction candidates comprises exposing a ranked list of prediction candidates for selection by a user to modify the detected characters, the ranked list of prediction candidates including at least one predicted emoji. 14. A method as recited in claim 14, further comprising:
responsive to interaction with the at least one predicted emoji configured to access an emoji picker, displaying the emoji picker configured to present and enable selection of a plurality of emoji options that relate to the at least one predicted emoji. 15. One or more computer-readable storage media storing instructions that, when executed by a computing device, cause the computing device to perform operations comprising:
identifying one or more dictionaries to use as sources for predictions based on one or more detected characters; ranking emoji along with words one to another as prediction candidates for the detected characters using scoring data contained in the one or more dictionaries; selecting one or more top ranking emoji and words according to the ranking as prediction candidates for the detected characters; and utilizing selected emoji along with selected words to facilitate character entry. 16. One or more computer-readable storage media as recited in claim 15, wherein the multiple dictionaries comprise a general population dictionary representative of common usage across a community of users and at least one other dictionary generated dynamically based on input of words and emoji by a particular user of the computing device to reflect the particular user's individual typing style. 17. One or more computer-readable storage media as recited in claim 15, wherein utilizing the selected emoji along with the words to facilitate character entry comprises:
representing multiple different emoji that are determined as top ranking prediction candidates along with words that are determined as top ranking prediction candidates via a user interface instrumentality configured to enable a selection from among the prediction candidates to modify the one or more detected characters. and enabling switching between words of the detected one or more characters and emoji that are directly mapped to the words responsive to selection of the words to cause the switching. 18. A mobile computing device, comprising:
a processing system; and one or more computer-readable media storing instructions that, when executed by the processing system, implement a text prediction engine operable to:
generate one or more prediction candidates for characters detected in an interaction scenario according to one or more dictionaries of a language model that support emoji, the prediction candidates that are generated including emoji and words predicted using the language model;
exposing the prediction candidates that are generated to enable selection from among the prediction candidates to modify the one or more detected characters. 19. A computing device as recited in claim 18, wherein exposing the prediction candidates comprises exposing at least one predicted word in connection with a corresponding emoji that is determined based upon a mapping table of the language model configured to directly map a plurality of words to corresponding emoji. 20. A computing device as recited in claim 18, wherein:
the one or more dictionaries are configured to include conditional usage probabilities for language-specific usage of emoji for in relation to the interaction scenario and; and the one or more prediction candidates are generated and ranked one to another based at least in part upon the conditional usage probabilities for language-specific usage of emoji. | 2,100 |
6,487 | 6,487 | 15,343,458 | 2,122 | Methods, systems, and apparatus, including computer programs encoded on computer storage medium, for training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels. The method includes actions of obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with a respective label from the predetermined set of multiple labels; and modifying the training data to generate regularizing training data, comprising: for each training item, determining whether to modify the label associated with the training item, and changing the label associated with the training item to a different label from the predetermined set of labels, and training the neural network on the regularizing data. | 1. A method of training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels, the method comprising:
obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with an initial target label distribution that assigns a respective target score to each label in the predetermined set of labels; modifying the training data to generate regularizing training data that regularizes the training of the neural network, comprising, for each training item, modifying the initial target label distribution to generate a modified target label distribution by combining the initial target label distribution with a smoothing label distribution; and training the neural network on the regularizing training data. 2. The method of claim 1, wherein combining the initial target label distribution with a smoothing label distribution includes:
calculating a weighted sum of the initial target label distribution and the smoothing label distribution. 3. The method of claim 1,
wherein, for each training item:
the target score for a known label for the training item is assigned a predetermined positive value in the initial target distribution for the training item, and
the target score for each label other than the known label is set to 0 in the initial target distribution. 4. The method of claim 1, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein each smoothing score is the same predetermined value. 5. The method of claim 1, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein the smoothing scores are non-uniform. 6. A system for training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels, the system comprising:
obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with an initial target label distribution that assigns a respective target score to each label in the predetermined set of labels; modifying the training data to generate regularizing training data that regularizes the training of the neural network, comprising, for each training item, modifying the initial target label distribution to generate a modified target label distribution by combining the initial target label distribution with a smoothing label distribution; and training the neural network on the regularizing training data. 7. The system of claim 6, wherein combining the initial target label distribution with a smoothing label distribution includes:
calculating a weighted sum of the initial target label distribution and the smoothing label distribution. 8. The system of claim 6,
wherein, for each training item:
the target score for a known label for the training item is assigned a predetermined positive value in the initial target distribution for the training item, and
the target score for each label other than the known label is set to 0 in the initial target distribution. 9. The system of claim 6, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein each smoothing score is the same predetermined value. 10. The system of claim 6, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein the smoothing scores are non-uniform. 11. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations for training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels, the operations comprising:
obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with an initial target label distribution that assigns a respective target score to each label in the predetermined set of labels; modifying the training data to generate regularizing training data that regularizes the training of the neural network, comprising, for each training item, modifying the initial target label distribution to generate a modified target label distribution by combining the initial target label distribution with a smoothing label distribution; and training the neural network on the regularizing training data. 12. The system of claim 11, wherein combining the initial target label distribution with a smoothing label distribution includes:
calculating a weighted sum of the initial target label distribution and the smoothing label distribution. 13. The system of claim 11,
wherein, for each training item:
the target score for a known label for the training item is assigned a predetermined positive value in the initial target distribution for the training item, and
the target score for each label other than the known label is set to 0 in the initial target distribution. 14. The system of claim 11, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein each smoothing score is the same predetermined value. 15. The system of claim 11, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein the smoothing scores are non-uniform. 16. A method of training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels, the method comprising:
obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with a respective label from the predetermined set of multiple labels; and modifying the training data to generate regularizing training data that regularizes the training of the neural network, comprising:
for each training item, determining whether or not to modify the label associated with the training item; and
in response to determining to modify the label associated with the training item, changing the label associated with the training item to a different label from the predetermined set of labels; and
training the neural network on the regularizing training data. 17. The method claim 16, wherein changing the label associated with training item to a different label from the predetermined set of labels includes:
changing the label from a label that correctly describes the training item to a label that incorrectly describes the training item. 18. The method of claim 16, wherein the different label is randomly selected from the predetermined set of labels. 19. The method of claim 16, wherein the label includes a training label distribution that includes a score for the training item for each label in a predetermined set of labels associated with a set of training images. 20. The method of claim 19, wherein changing the label associated with the training item to a different label from the predetermined set of labels includes changing the distribution of scores in a training data item's training label distribution from a distribution of scores representing a correct label to a distribution of scores representing an incorrect label. 21. The method of claim 16, wherein determining whether or not to modify the label associated with the training item is based on a predetermined probability. 22. The method of claim 21, wherein the predetermined probability is 10%. 23. A method of training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels, the method comprising:
receiving a request to train the neural network to optimize a loss function comprising a first error term; and training the neural network to optimize a regularized loss function, the regularized loss function comprising the first error term and a regularizing error term that penalizes the neural network based on the error between a predicted distribution and a smoothing distribution. 24. The method of claim 23, wherein the smoothing distribution is a uniform distribution. 25. The method of claim 23, wherein the smoothing distribution is a distribution that was used prior to the predicted distribution. | Methods, systems, and apparatus, including computer programs encoded on computer storage medium, for training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels. The method includes actions of obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with a respective label from the predetermined set of multiple labels; and modifying the training data to generate regularizing training data, comprising: for each training item, determining whether to modify the label associated with the training item, and changing the label associated with the training item to a different label from the predetermined set of labels, and training the neural network on the regularizing data.1. A method of training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels, the method comprising:
obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with an initial target label distribution that assigns a respective target score to each label in the predetermined set of labels; modifying the training data to generate regularizing training data that regularizes the training of the neural network, comprising, for each training item, modifying the initial target label distribution to generate a modified target label distribution by combining the initial target label distribution with a smoothing label distribution; and training the neural network on the regularizing training data. 2. The method of claim 1, wherein combining the initial target label distribution with a smoothing label distribution includes:
calculating a weighted sum of the initial target label distribution and the smoothing label distribution. 3. The method of claim 1,
wherein, for each training item:
the target score for a known label for the training item is assigned a predetermined positive value in the initial target distribution for the training item, and
the target score for each label other than the known label is set to 0 in the initial target distribution. 4. The method of claim 1, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein each smoothing score is the same predetermined value. 5. The method of claim 1, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein the smoothing scores are non-uniform. 6. A system for training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels, the system comprising:
obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with an initial target label distribution that assigns a respective target score to each label in the predetermined set of labels; modifying the training data to generate regularizing training data that regularizes the training of the neural network, comprising, for each training item, modifying the initial target label distribution to generate a modified target label distribution by combining the initial target label distribution with a smoothing label distribution; and training the neural network on the regularizing training data. 7. The system of claim 6, wherein combining the initial target label distribution with a smoothing label distribution includes:
calculating a weighted sum of the initial target label distribution and the smoothing label distribution. 8. The system of claim 6,
wherein, for each training item:
the target score for a known label for the training item is assigned a predetermined positive value in the initial target distribution for the training item, and
the target score for each label other than the known label is set to 0 in the initial target distribution. 9. The system of claim 6, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein each smoothing score is the same predetermined value. 10. The system of claim 6, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein the smoothing scores are non-uniform. 11. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations for training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels, the operations comprising:
obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with an initial target label distribution that assigns a respective target score to each label in the predetermined set of labels; modifying the training data to generate regularizing training data that regularizes the training of the neural network, comprising, for each training item, modifying the initial target label distribution to generate a modified target label distribution by combining the initial target label distribution with a smoothing label distribution; and training the neural network on the regularizing training data. 12. The system of claim 11, wherein combining the initial target label distribution with a smoothing label distribution includes:
calculating a weighted sum of the initial target label distribution and the smoothing label distribution. 13. The system of claim 11,
wherein, for each training item:
the target score for a known label for the training item is assigned a predetermined positive value in the initial target distribution for the training item, and
the target score for each label other than the known label is set to 0 in the initial target distribution. 14. The system of claim 11, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein each smoothing score is the same predetermined value. 15. The system of claim 11, wherein the smoothing label distribution includes a respective smoothing score for each label in the predetermined set of labels, and wherein the smoothing scores are non-uniform. 16. A method of training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels, the method comprising:
obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with a respective label from the predetermined set of multiple labels; and modifying the training data to generate regularizing training data that regularizes the training of the neural network, comprising:
for each training item, determining whether or not to modify the label associated with the training item; and
in response to determining to modify the label associated with the training item, changing the label associated with the training item to a different label from the predetermined set of labels; and
training the neural network on the regularizing training data. 17. The method claim 16, wherein changing the label associated with training item to a different label from the predetermined set of labels includes:
changing the label from a label that correctly describes the training item to a label that incorrectly describes the training item. 18. The method of claim 16, wherein the different label is randomly selected from the predetermined set of labels. 19. The method of claim 16, wherein the label includes a training label distribution that includes a score for the training item for each label in a predetermined set of labels associated with a set of training images. 20. The method of claim 19, wherein changing the label associated with the training item to a different label from the predetermined set of labels includes changing the distribution of scores in a training data item's training label distribution from a distribution of scores representing a correct label to a distribution of scores representing an incorrect label. 21. The method of claim 16, wherein determining whether or not to modify the label associated with the training item is based on a predetermined probability. 22. The method of claim 21, wherein the predetermined probability is 10%. 23. A method of training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels, the method comprising:
receiving a request to train the neural network to optimize a loss function comprising a first error term; and training the neural network to optimize a regularized loss function, the regularized loss function comprising the first error term and a regularizing error term that penalizes the neural network based on the error between a predicted distribution and a smoothing distribution. 24. The method of claim 23, wherein the smoothing distribution is a uniform distribution. 25. The method of claim 23, wherein the smoothing distribution is a distribution that was used prior to the predicted distribution. | 2,100 |
6,488 | 6,488 | 15,825,887 | 2,166 | A method, system and computer program product for forecasting a storage requirement of a database management system (DBMS). The storage-related operations (e.g., create, delete, update) of the applications connected to the DBMS are monitored. The impact on the storage usage of the DBMS based on these storage-related operations performed by the applications is monitored. Furthermore, the applications are categorized into groups of applications based on the monitored storage-related operations. A mathematical model is then built to forecast the storage requirement of the DBMS based on the monitored impact on the storage usage of the DBMS by the monitored storage-related operations of the applications and the categorization of the applications. The storage requirement of the DBMS is then forecasted based on the built mathematical model. In this manner, the storage requirements of the DBMS may be accurately predicted to ensure that there is available storage thereby preventing performance degradation. | 1. A method for forecasting a storage requirement of a database management system, the method comprising:
monitoring storage-related operations of applications connected to said database management system; monitoring an impact on storage usage of said database management system by said monitored storage-related operations of said applications; categorizing said applications into groups of applications based on said monitored storage-related operations, wherein each group of applications comprises one or more applications exhibiting similar monitored storage-related operations within a threshold degree of variance; building, by a processor, a mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications and based on said categorization of said applications; and forecasting, by said processor, said storage requirement of said database management system based on said mathematical model. 2. The method as recited in claim 1 further comprising:
self-tuning said mathematical model based on accuracy of said forecasted storage requirement against an actual storage usage. 3. The method as recited in claim 1 further comprising:
monitoring performance behavior of queries on database objects of said database management system. 4. The method as recited in claim 3 further comprising:
adding one or more objects to said database management system in response to determining said one or more objects will improve performance of said database management system. 5. The method as recited in claim 4 further comprising:
monitoring an impact on storage usage of said database management system based on utilization of said added one or more objects of said database management system. 6. The method as recited in claim 5 further comprising:
building said mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications, said categorization of said applications and said monitored impact on storage usage of said database management system by said utilization of said added one or more objects of said database management system. 7. The method as recited in claim 4, wherein said objects comprise an index. 8. A computer program product for forecasting a storage requirement of a database management system, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code comprising the programming instructions for:
monitoring storage-related operations of applications connected to said database management system; monitoring an impact on storage usage of said database management system by said monitored storage-related operations of said applications; categorizing said applications into groups of applications based on said monitored storage-related operations, wherein each group of applications comprises one or more applications exhibiting similar monitored storage-related operations within a threshold degree of variance; building a mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications and based on said categorization of said applications; and forecasting said storage requirement of said database management system based on said mathematical model. 9. The computer program product as recited in claim 8, wherein the program code further comprises the programming instructions for:
self-tuning said mathematical model based on accuracy of said forecasted storage requirement against an actual storage usage. 10. The computer program product as recited in claim 8, wherein the program code further comprises the programming instructions for:
monitoring performance behavior of queries on database objects of said database management system. 11. The computer program product as recited in claim 10, wherein the program code further comprises the programming instructions for:
adding one or more objects to said database management system in response to determining said one or more objects will improve performance of said database management system. 12. The computer program product as recited in claim 11, wherein the program code further comprises the programming instructions for:
monitoring an impact on storage usage of said database management system based on utilization of said added one or more objects of said database management system. 13. The computer program product as recited in claim 12, wherein the program code further comprises the programming instructions for:
building said mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications, said categorization of said applications and said monitored impact on storage usage of said database management system by said utilization of said added one or more objects of said database management system. 14. The computer program product as recited in claim 11, wherein said objects comprise an index. 15. A system, comprising:
a memory unit for storing a computer program for forecasting a storage requirement of a database management system; and a processor coupled to the memory unit, wherein the processor is configured to execute the program instructions of the computer program comprising:
monitoring storage-related operations of applications connected to said database management system;
monitoring an impact on storage usage of said database management system by said monitored storage-related operations of said applications;
categorizing said applications into groups of applications based on said monitored storage-related operations, wherein each group of applications comprises one or more applications exhibiting similar monitored storage-related operations within a threshold degree of variance;
building a mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications and based on said categorization of said applications; and
forecasting said storage requirement of said database management system based on said mathematical model. 16. The system as recited in claim 15, wherein the program instructions of the computer program further comprise:
self-tuning said mathematical model based on accuracy of said forecasted storage requirement against an actual storage usage. 17. The system as recited in claim 15, wherein the program instructions of the computer program further comprise:
monitoring performance behavior of queries on database objects of said database management system. 18. The system as recited in claim 17, wherein the program instructions of the computer program further comprise:
adding one or more objects to said database management system in response to determining said one or more objects will improve performance of said database management system. 19. The system as recited in claim 18, wherein the program instructions of the computer program further comprise:
monitoring an impact on storage usage of said database management system based on utilization of said added one or more objects of said database management system. 20. The system as recited in claim 19, wherein the program instructions of the computer program further comprise:
building said mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications, said categorization of said applications and said monitored impact on storage usage of said database management system by said utilization of said added one or more objects of said database management system. | A method, system and computer program product for forecasting a storage requirement of a database management system (DBMS). The storage-related operations (e.g., create, delete, update) of the applications connected to the DBMS are monitored. The impact on the storage usage of the DBMS based on these storage-related operations performed by the applications is monitored. Furthermore, the applications are categorized into groups of applications based on the monitored storage-related operations. A mathematical model is then built to forecast the storage requirement of the DBMS based on the monitored impact on the storage usage of the DBMS by the monitored storage-related operations of the applications and the categorization of the applications. The storage requirement of the DBMS is then forecasted based on the built mathematical model. In this manner, the storage requirements of the DBMS may be accurately predicted to ensure that there is available storage thereby preventing performance degradation.1. A method for forecasting a storage requirement of a database management system, the method comprising:
monitoring storage-related operations of applications connected to said database management system; monitoring an impact on storage usage of said database management system by said monitored storage-related operations of said applications; categorizing said applications into groups of applications based on said monitored storage-related operations, wherein each group of applications comprises one or more applications exhibiting similar monitored storage-related operations within a threshold degree of variance; building, by a processor, a mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications and based on said categorization of said applications; and forecasting, by said processor, said storage requirement of said database management system based on said mathematical model. 2. The method as recited in claim 1 further comprising:
self-tuning said mathematical model based on accuracy of said forecasted storage requirement against an actual storage usage. 3. The method as recited in claim 1 further comprising:
monitoring performance behavior of queries on database objects of said database management system. 4. The method as recited in claim 3 further comprising:
adding one or more objects to said database management system in response to determining said one or more objects will improve performance of said database management system. 5. The method as recited in claim 4 further comprising:
monitoring an impact on storage usage of said database management system based on utilization of said added one or more objects of said database management system. 6. The method as recited in claim 5 further comprising:
building said mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications, said categorization of said applications and said monitored impact on storage usage of said database management system by said utilization of said added one or more objects of said database management system. 7. The method as recited in claim 4, wherein said objects comprise an index. 8. A computer program product for forecasting a storage requirement of a database management system, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code comprising the programming instructions for:
monitoring storage-related operations of applications connected to said database management system; monitoring an impact on storage usage of said database management system by said monitored storage-related operations of said applications; categorizing said applications into groups of applications based on said monitored storage-related operations, wherein each group of applications comprises one or more applications exhibiting similar monitored storage-related operations within a threshold degree of variance; building a mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications and based on said categorization of said applications; and forecasting said storage requirement of said database management system based on said mathematical model. 9. The computer program product as recited in claim 8, wherein the program code further comprises the programming instructions for:
self-tuning said mathematical model based on accuracy of said forecasted storage requirement against an actual storage usage. 10. The computer program product as recited in claim 8, wherein the program code further comprises the programming instructions for:
monitoring performance behavior of queries on database objects of said database management system. 11. The computer program product as recited in claim 10, wherein the program code further comprises the programming instructions for:
adding one or more objects to said database management system in response to determining said one or more objects will improve performance of said database management system. 12. The computer program product as recited in claim 11, wherein the program code further comprises the programming instructions for:
monitoring an impact on storage usage of said database management system based on utilization of said added one or more objects of said database management system. 13. The computer program product as recited in claim 12, wherein the program code further comprises the programming instructions for:
building said mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications, said categorization of said applications and said monitored impact on storage usage of said database management system by said utilization of said added one or more objects of said database management system. 14. The computer program product as recited in claim 11, wherein said objects comprise an index. 15. A system, comprising:
a memory unit for storing a computer program for forecasting a storage requirement of a database management system; and a processor coupled to the memory unit, wherein the processor is configured to execute the program instructions of the computer program comprising:
monitoring storage-related operations of applications connected to said database management system;
monitoring an impact on storage usage of said database management system by said monitored storage-related operations of said applications;
categorizing said applications into groups of applications based on said monitored storage-related operations, wherein each group of applications comprises one or more applications exhibiting similar monitored storage-related operations within a threshold degree of variance;
building a mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications and based on said categorization of said applications; and
forecasting said storage requirement of said database management system based on said mathematical model. 16. The system as recited in claim 15, wherein the program instructions of the computer program further comprise:
self-tuning said mathematical model based on accuracy of said forecasted storage requirement against an actual storage usage. 17. The system as recited in claim 15, wherein the program instructions of the computer program further comprise:
monitoring performance behavior of queries on database objects of said database management system. 18. The system as recited in claim 17, wherein the program instructions of the computer program further comprise:
adding one or more objects to said database management system in response to determining said one or more objects will improve performance of said database management system. 19. The system as recited in claim 18, wherein the program instructions of the computer program further comprise:
monitoring an impact on storage usage of said database management system based on utilization of said added one or more objects of said database management system. 20. The system as recited in claim 19, wherein the program instructions of the computer program further comprise:
building said mathematical model to forecast said storage requirement of said database management system based on said monitored impact on storage usage of said database management system by said monitored storage-related operations of said applications, said categorization of said applications and said monitored impact on storage usage of said database management system by said utilization of said added one or more objects of said database management system. | 2,100 |
6,489 | 6,489 | 15,214,082 | 2,164 | Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving a query from an application, providing a query execution plan (QEP) associated with the query, the QEP including a plurality of operators executed in a first order to provide a query result, one or more operators of the plurality of operators requiring input from computer-readable memory, determining an execution time for each operator in the plurality of operators, re-ordering at least one operator of the plurality of operators to provide a re-ordered QEP (rQEP) based on a respective execution time, the rQEP including the plurality of operators executed in a second order to provide the query result, and storing the rQEP in computer-readable memory. | 1. A computer-implemented method executed by one or more processors, the method comprising:
receiving, by the one or more processors, a query from an application; providing, by the one or more processors, a query execution plan (QEP) associated with the query, the QEP comprising a plurality of operators executed in a first order to provide a query result, one or more operators of the plurality of operators requiring input from computer-readable memory; determining, by the one or more processors, an execution time for each operator in the plurality of operators; re-ordering at least one operator of the plurality of operators to provide a re-ordered QEP (rQEP) based on a respective execution time, the rQEP comprising the plurality of operators executed in a second order to provide the query result; and storing the rQEP in computer-readable memory. 2. The method of claim 1, wherein re-ordering at least one operator comprises:
identifying an operator as a time-consuming operator, the operator providing an intermediate result that is provided as input to the at least one operator; and moving the at least one operator relative to the operator, such that a number of operators between the operator and the at least one operator is reduced. 3. The method of claim 2, wherein the at least one operator is moved relative to the operator, such that the number of operators between the operator and the at least one operator is reduced to zero. 4. The method of claim 1, wherein during execution of the QEP, the at least one operator receives input data from non-cache memory. 5. The method of claim 1, wherein during execution of the rQEP, the at least one operator receives input data from a cache. 6. The method of claim 1, wherein execution of the rQEP results in fewer cache misses than execution of the QEP. 7. The method of claim 1, further comprising:
receiving source code of the application; providing an instrumented application that includes the source code and instrumentation code, the instrumented application comprising at least one instruction for profiling the plurality of operators; executing the instrumented application to process the QEP to provide a profiling file, the profiling file indicating, for each operator in the plurality of operators, respective start times and end time; and for each operator in the plurality of operators, determining a respective execution time based on a respective start time and a respective end time. 8. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving a query from an application; providing a query execution plan (QEP) associated with the query, the QEP comprising a plurality of operators executed in a first order to provide a query result, one or more operators of the plurality of operators requiring input from computer-readable memory; determining an execution time for each operator in the plurality of operators; re-ordering at least one operator of the plurality of operators to provide a re-ordered QEP (rQEP) based on a respective execution time, the rQEP comprising the plurality of operators executed in a second order to provide the query result; and storing the rQEP in computer-readable memory. 9. The computer-readable storage medium of claim 8, wherein re-ordering at least one operator comprises:
identifying an operator as a time-consuming operator, the operator providing an intermediate result that is provided as input to the at least one operator; and moving the at least one operator relative to the operator, such that a number of operators between the operator and the at least one operator is reduced. 10. The computer-readable storage medium of claim 9, wherein the at least one operator is moved relative to the operator, such that the number of operators between the operator and the at least one operator is reduced to zero. 11. The computer-readable storage medium of claim 8, wherein during execution of the QEP, the at least one operator receives input data from non-cache memory. 12. The computer-readable storage medium of claim 8, wherein during execution of the rQEP, the at least one operator receives input data from a cache. 13. The computer-readable storage medium of claim 8, wherein execution of the rQEP results in fewer cache misses than execution of the QEP. 14. The computer-readable storage medium of claim 8, wherein operations further comprise:
receiving source code of the application; providing an instrumented application that includes the source code and instrumentation code, the instrumented application comprising at least one instruction for profiling the plurality of operators; executing the instrumented application to process the QEP to provide a profiling file, the profiling file indicating, for each operator in the plurality of operators, respective start times and end time; and for each operator in the plurality of operators, determining a respective execution time based on a respective start time and a respective end time. 15. A system, comprising:
a computing device; and a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations comprising:
receiving a query from an application;
providing a query execution plan (QEP) associated with the query, the QEP comprising a plurality of operators executed in a first order to provide a query result, one or more operators of the plurality of operators requiring input from computer-readable memory;
determining an execution time for each operator in the plurality of operators;
re-ordering at least one operator of the plurality of operators to provide a re-ordered QEP (rQEP) based on a respective execution time, the rQEP comprising the plurality of operators executed in a second order to provide the query result; and
storing the rQEP in computer-readable memory. 16. The system of claim 15, wherein re-ordering at least one operator comprises:
identifying an operator as a time-consuming operator, the operator providing an intermediate result that is provided as input to the at least one operator; and moving the at least one operator relative to the operator, such that a number of operators between the operator and the at least one operator is reduced. 17. The system of claim 16, wherein the at least one operator is moved relative to the operator, such that the number of operators between the operator and the at least one operator is reduced to zero. 18. The system of claim 15, wherein during execution of the QEP, the at least one operator receives input data from non-cache memory. 19. The system of claim 15, wherein during execution of the rQEP, the at least one operator receives input data from a cache. 20. The system of claim 15, wherein execution of the rQEP results in fewer cache misses than execution of the QEP. | Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for receiving a query from an application, providing a query execution plan (QEP) associated with the query, the QEP including a plurality of operators executed in a first order to provide a query result, one or more operators of the plurality of operators requiring input from computer-readable memory, determining an execution time for each operator in the plurality of operators, re-ordering at least one operator of the plurality of operators to provide a re-ordered QEP (rQEP) based on a respective execution time, the rQEP including the plurality of operators executed in a second order to provide the query result, and storing the rQEP in computer-readable memory.1. A computer-implemented method executed by one or more processors, the method comprising:
receiving, by the one or more processors, a query from an application; providing, by the one or more processors, a query execution plan (QEP) associated with the query, the QEP comprising a plurality of operators executed in a first order to provide a query result, one or more operators of the plurality of operators requiring input from computer-readable memory; determining, by the one or more processors, an execution time for each operator in the plurality of operators; re-ordering at least one operator of the plurality of operators to provide a re-ordered QEP (rQEP) based on a respective execution time, the rQEP comprising the plurality of operators executed in a second order to provide the query result; and storing the rQEP in computer-readable memory. 2. The method of claim 1, wherein re-ordering at least one operator comprises:
identifying an operator as a time-consuming operator, the operator providing an intermediate result that is provided as input to the at least one operator; and moving the at least one operator relative to the operator, such that a number of operators between the operator and the at least one operator is reduced. 3. The method of claim 2, wherein the at least one operator is moved relative to the operator, such that the number of operators between the operator and the at least one operator is reduced to zero. 4. The method of claim 1, wherein during execution of the QEP, the at least one operator receives input data from non-cache memory. 5. The method of claim 1, wherein during execution of the rQEP, the at least one operator receives input data from a cache. 6. The method of claim 1, wherein execution of the rQEP results in fewer cache misses than execution of the QEP. 7. The method of claim 1, further comprising:
receiving source code of the application; providing an instrumented application that includes the source code and instrumentation code, the instrumented application comprising at least one instruction for profiling the plurality of operators; executing the instrumented application to process the QEP to provide a profiling file, the profiling file indicating, for each operator in the plurality of operators, respective start times and end time; and for each operator in the plurality of operators, determining a respective execution time based on a respective start time and a respective end time. 8. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving a query from an application; providing a query execution plan (QEP) associated with the query, the QEP comprising a plurality of operators executed in a first order to provide a query result, one or more operators of the plurality of operators requiring input from computer-readable memory; determining an execution time for each operator in the plurality of operators; re-ordering at least one operator of the plurality of operators to provide a re-ordered QEP (rQEP) based on a respective execution time, the rQEP comprising the plurality of operators executed in a second order to provide the query result; and storing the rQEP in computer-readable memory. 9. The computer-readable storage medium of claim 8, wherein re-ordering at least one operator comprises:
identifying an operator as a time-consuming operator, the operator providing an intermediate result that is provided as input to the at least one operator; and moving the at least one operator relative to the operator, such that a number of operators between the operator and the at least one operator is reduced. 10. The computer-readable storage medium of claim 9, wherein the at least one operator is moved relative to the operator, such that the number of operators between the operator and the at least one operator is reduced to zero. 11. The computer-readable storage medium of claim 8, wherein during execution of the QEP, the at least one operator receives input data from non-cache memory. 12. The computer-readable storage medium of claim 8, wherein during execution of the rQEP, the at least one operator receives input data from a cache. 13. The computer-readable storage medium of claim 8, wherein execution of the rQEP results in fewer cache misses than execution of the QEP. 14. The computer-readable storage medium of claim 8, wherein operations further comprise:
receiving source code of the application; providing an instrumented application that includes the source code and instrumentation code, the instrumented application comprising at least one instruction for profiling the plurality of operators; executing the instrumented application to process the QEP to provide a profiling file, the profiling file indicating, for each operator in the plurality of operators, respective start times and end time; and for each operator in the plurality of operators, determining a respective execution time based on a respective start time and a respective end time. 15. A system, comprising:
a computing device; and a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations comprising:
receiving a query from an application;
providing a query execution plan (QEP) associated with the query, the QEP comprising a plurality of operators executed in a first order to provide a query result, one or more operators of the plurality of operators requiring input from computer-readable memory;
determining an execution time for each operator in the plurality of operators;
re-ordering at least one operator of the plurality of operators to provide a re-ordered QEP (rQEP) based on a respective execution time, the rQEP comprising the plurality of operators executed in a second order to provide the query result; and
storing the rQEP in computer-readable memory. 16. The system of claim 15, wherein re-ordering at least one operator comprises:
identifying an operator as a time-consuming operator, the operator providing an intermediate result that is provided as input to the at least one operator; and moving the at least one operator relative to the operator, such that a number of operators between the operator and the at least one operator is reduced. 17. The system of claim 16, wherein the at least one operator is moved relative to the operator, such that the number of operators between the operator and the at least one operator is reduced to zero. 18. The system of claim 15, wherein during execution of the QEP, the at least one operator receives input data from non-cache memory. 19. The system of claim 15, wherein during execution of the rQEP, the at least one operator receives input data from a cache. 20. The system of claim 15, wherein execution of the rQEP results in fewer cache misses than execution of the QEP. | 2,100 |
6,490 | 6,490 | 15,091,526 | 2,169 | Users of electronic audio and video playback devices have become familiar with listening and viewing media from stored memory. Music may be listened to and any art may be viewed including television, motion pictures, still images and any other copyrightable works of audible or visual art, the types of which are vast. Traditional indexing criteria for such stored memory may include artist or author identification, track or work of art title, genre, era or origin, style of art, and other criteria pertaining to the work itself. According to the present invention, media elements stored in memory may now be characterized by entering criteria based on the qualitative attributes and emotive features ascertained upon playback or subsequent evaluation which are then associated with each stored audio, video, image or other file, for subsequent indexing, searching and recommendation operations. | 1. A method for recommending a subsequent related data file item within a specified category wherein said subsequent related data file corresponds in a specific manner with a primary data file stored within said specified category data comprising the steps of:
a. receiving user input data corresponding to a relationship between said subsequent related data file and said primary data file, wherein said user input data pertains to said specified category data for recommendation of particular said subsequent related data files within a specified category; b. recalling discrete stored criteria data in relation to said primary data file, wherein said discrete stored data is descriptive of the character of said primary data file; c. comparing said discrete stored criteria data with said specified category database wherein said specified category database relates to defined characteristics indicative of the quality or character of data stored within said specified category database; d. recalling information regarding said subsequent related data file within said specified category database whose discrete criteria most closely match said primary data file; and e. presenting said information regarding said subsequent related data file to said user. 2. A method according to claim 1 wherein said discrete stored criteria includes qualitative criteria and emotive features specific to said specified category. 3. A method according to claim 2 wherein said qualitative criteria specific to said specified category further includes one or more sub-set designations, and wherein said emotive features specific to said specified category further includes one or more sub-set designations. 4. A method according to claim 3, wherein said qualitative criteria sub-set designations specific to said specified category of said primary data file being further defined as each having an assigned numerical score, and wherein said emotive features specific to said specified category of said primary data file being further defined as having one of said sub-set designations being defined as said primary emotive feature and as said second of said sub-set designations being defined as the secondary emotive feature. 5. A method according to claim 4 wherein said qualitative criteria sub-set designations include an outstanding designation including an assigned numerical score exceeding a sub-set designation specific threshold. 6. A method according to claim 5 wherein said specified category database of items includes genre sub-sets. 7. A method according to claim 6 wherein said genre sub-sets are formed by establishing variable threshold levels in response to user input, and including thresholds relating to music selection genre. 8. A method according to claim 1 wherein said step of comparing said discrete stored criteria data with said specified category data further comprises the steps of:
a. searching said specified category database genre sub-sets of said primary data files and matching said sub-sets with data items having the same qualitative criteria sub-set designations defined as outstanding and
b. comparing said primary and secondary emotive features of said primary data file to the primary and secondary emotive features of those items in said specified category database genre sub-set of said primary data file to locate items in said specified category database genre sub-set that match emotive feature criteria sub-set designations of said primary data file. 9. A method according to claim 1 wherein local time of day is included as a data element of said primary and said subsequent related data files. 10. A method according to claim 1 wherein local time of year is included as a data element of said primary and said subsequent related data files. 11. A method according to claim 1 wherein local geographic location is included as a data element of said primary and said subsequent related data files. 12. A method according to claim 1 wherein proximity of a device to other devices or persons is included as a data element of said primary and said subsequent related data files. 13. A method according to claim 1 wherein time spent using a device and/or listening to a specific musical selection is included as a data element of said primary and said subsequent related data files. 14. A method according to claim 1 wherein velocity or acceleration of a device is included as a data element of said primary and said subsequent related data files. 15. A method according to claim 1 wherein local lighting or illumination levels is included as a data element of said primary and said subsequent related data files. 16. A method according to claim 1 wherein local weather is included as a data element of said primary and said subsequent related data files. 17. A method according to claim 1 wherein said specified category data is further defined as videos. 18. A method for recommending a second musical selection in relation to a first musical selection comprising the steps of:
a. requesting a recommendation for a second musical selection in relation to a first musical selection; b. recalling discrete stored criteria and genre information in relation to the first musical selection; c. comparing said first musical selection discrete criteria and genre information to a database of musical selections with related discrete criteria; d. recalling information regarding said second musical selection in said database whose discrete criteria and genre information most closely match said first musical selection discrete criteria and genre information, and e. presenting said information regarding said second musical selection. 19. A method according to claim 18 wherein of said discrete criteria is further defined as qualitative criteria and emotive criteria specific to music. 20. A method for creating a searchable database of musical selections comprising the steps of:
a. categorizing each musical selection according to criteria, and b. aggregating results of said categorization relating to discrete criteria corresponding to each of said musical selections, wherein said discrete criteria being further defined as comprising qualitative criteria and emotive features corresponding specifically to said musical selections, said qualitative criteria being further defined as comprising one or more of the categories of: 1. song-writing, arrangement and instrumentation; 2. vocals; 3. originality; 4. lyrics; 5. musicianship, or 6. production; and said emotive features being further defined as comprising one or more of the categories of: 1. romantic, emotional, sentimental, heart-felt or dramatic; 2. sad, morose, melancholic or dark; 3. message driven, story-telling or poetic; 4. trippy or dreamy; 5. energizing or upbeat; 6. mellow or laid-back; 7. danceable; 8. groove; 9. mixed tempo when referring to albums; 10. mid tempo when referring to tracks; 11. gritty or raw; 12. subversive or rebellious; 13. quirky, or 14. uplifting or joyful; wherein said aggregated results being operable to be further refined by preferences regarding said qualitative criteria, said emotive features, or a combination of said qualitative criteria and said emotive features. | Users of electronic audio and video playback devices have become familiar with listening and viewing media from stored memory. Music may be listened to and any art may be viewed including television, motion pictures, still images and any other copyrightable works of audible or visual art, the types of which are vast. Traditional indexing criteria for such stored memory may include artist or author identification, track or work of art title, genre, era or origin, style of art, and other criteria pertaining to the work itself. According to the present invention, media elements stored in memory may now be characterized by entering criteria based on the qualitative attributes and emotive features ascertained upon playback or subsequent evaluation which are then associated with each stored audio, video, image or other file, for subsequent indexing, searching and recommendation operations.1. A method for recommending a subsequent related data file item within a specified category wherein said subsequent related data file corresponds in a specific manner with a primary data file stored within said specified category data comprising the steps of:
a. receiving user input data corresponding to a relationship between said subsequent related data file and said primary data file, wherein said user input data pertains to said specified category data for recommendation of particular said subsequent related data files within a specified category; b. recalling discrete stored criteria data in relation to said primary data file, wherein said discrete stored data is descriptive of the character of said primary data file; c. comparing said discrete stored criteria data with said specified category database wherein said specified category database relates to defined characteristics indicative of the quality or character of data stored within said specified category database; d. recalling information regarding said subsequent related data file within said specified category database whose discrete criteria most closely match said primary data file; and e. presenting said information regarding said subsequent related data file to said user. 2. A method according to claim 1 wherein said discrete stored criteria includes qualitative criteria and emotive features specific to said specified category. 3. A method according to claim 2 wherein said qualitative criteria specific to said specified category further includes one or more sub-set designations, and wherein said emotive features specific to said specified category further includes one or more sub-set designations. 4. A method according to claim 3, wherein said qualitative criteria sub-set designations specific to said specified category of said primary data file being further defined as each having an assigned numerical score, and wherein said emotive features specific to said specified category of said primary data file being further defined as having one of said sub-set designations being defined as said primary emotive feature and as said second of said sub-set designations being defined as the secondary emotive feature. 5. A method according to claim 4 wherein said qualitative criteria sub-set designations include an outstanding designation including an assigned numerical score exceeding a sub-set designation specific threshold. 6. A method according to claim 5 wherein said specified category database of items includes genre sub-sets. 7. A method according to claim 6 wherein said genre sub-sets are formed by establishing variable threshold levels in response to user input, and including thresholds relating to music selection genre. 8. A method according to claim 1 wherein said step of comparing said discrete stored criteria data with said specified category data further comprises the steps of:
a. searching said specified category database genre sub-sets of said primary data files and matching said sub-sets with data items having the same qualitative criteria sub-set designations defined as outstanding and
b. comparing said primary and secondary emotive features of said primary data file to the primary and secondary emotive features of those items in said specified category database genre sub-set of said primary data file to locate items in said specified category database genre sub-set that match emotive feature criteria sub-set designations of said primary data file. 9. A method according to claim 1 wherein local time of day is included as a data element of said primary and said subsequent related data files. 10. A method according to claim 1 wherein local time of year is included as a data element of said primary and said subsequent related data files. 11. A method according to claim 1 wherein local geographic location is included as a data element of said primary and said subsequent related data files. 12. A method according to claim 1 wherein proximity of a device to other devices or persons is included as a data element of said primary and said subsequent related data files. 13. A method according to claim 1 wherein time spent using a device and/or listening to a specific musical selection is included as a data element of said primary and said subsequent related data files. 14. A method according to claim 1 wherein velocity or acceleration of a device is included as a data element of said primary and said subsequent related data files. 15. A method according to claim 1 wherein local lighting or illumination levels is included as a data element of said primary and said subsequent related data files. 16. A method according to claim 1 wherein local weather is included as a data element of said primary and said subsequent related data files. 17. A method according to claim 1 wherein said specified category data is further defined as videos. 18. A method for recommending a second musical selection in relation to a first musical selection comprising the steps of:
a. requesting a recommendation for a second musical selection in relation to a first musical selection; b. recalling discrete stored criteria and genre information in relation to the first musical selection; c. comparing said first musical selection discrete criteria and genre information to a database of musical selections with related discrete criteria; d. recalling information regarding said second musical selection in said database whose discrete criteria and genre information most closely match said first musical selection discrete criteria and genre information, and e. presenting said information regarding said second musical selection. 19. A method according to claim 18 wherein of said discrete criteria is further defined as qualitative criteria and emotive criteria specific to music. 20. A method for creating a searchable database of musical selections comprising the steps of:
a. categorizing each musical selection according to criteria, and b. aggregating results of said categorization relating to discrete criteria corresponding to each of said musical selections, wherein said discrete criteria being further defined as comprising qualitative criteria and emotive features corresponding specifically to said musical selections, said qualitative criteria being further defined as comprising one or more of the categories of: 1. song-writing, arrangement and instrumentation; 2. vocals; 3. originality; 4. lyrics; 5. musicianship, or 6. production; and said emotive features being further defined as comprising one or more of the categories of: 1. romantic, emotional, sentimental, heart-felt or dramatic; 2. sad, morose, melancholic or dark; 3. message driven, story-telling or poetic; 4. trippy or dreamy; 5. energizing or upbeat; 6. mellow or laid-back; 7. danceable; 8. groove; 9. mixed tempo when referring to albums; 10. mid tempo when referring to tracks; 11. gritty or raw; 12. subversive or rebellious; 13. quirky, or 14. uplifting or joyful; wherein said aggregated results being operable to be further refined by preferences regarding said qualitative criteria, said emotive features, or a combination of said qualitative criteria and said emotive features. | 2,100 |
6,491 | 6,491 | 15,844,425 | 2,154 | Described herein are technologies that provide an element of security related to file system operations. Individual nodes in a file system, such as a directory or a file, can be associated with information that describes how to handle letter case when a file name included in a file system operation request is used to locate a file in the file system. For example, a case sensitive designation associated with a directory can require a case sensitive match between a file name included in a request and a file name included in the directory, in order to perform the requested file system operation. In another example, a case preferring designation associated with a directory first checks for a case sensitive match between file names. If a case sensitive match does not exist, then a case insensitive match between the file names can be used to perform the requested file system operation. | 1. A system comprising:
one or more processors; and one or more storage units comprising a file system that includes one or more directories each containing one or more files and/or one or more directories, the file system executable by the one or more processors to:
receive a request to perform a file system operation, the request including a path name with a first component that identifies a directory and a second component that identifies a file name;
determine whether the directory is associated with a case sensitive designation or a case insensitive designation;
based on a determination that the directory is associated with the case sensitive designation:
determine whether a case sensitive match exists between the file name identified by the second component of the path name and a file name associated with a file contained in the directory; and
perform the file system operation in association with the file contained in the directory based on a determination that the case sensitive match exists between the file name identified by the second component of the path name and the file name associated with the file contained in the directory; or
refrain from performing the file system operation in association with the file contained in the directory based on a determination that the case sensitive match does not exist between the file name identified by the second component of the path name and the file name associated with a file contained in the directory; or
based on a determination that the directory is associated with the case insensitive designation:
determine that a case insensitive match exists between the file name identified by the second component of the path name and a file name associated with a file contained in the directory; and
perform the file system operation in association with the file contained in the directory based on a determination that the case insensitive match exists between the file name identified by the second component of the path name and the file name associated with the file contained in the directory. 2. The system of claim 1, wherein the determining whether the directory is associated with the case sensitive designation or the case insensitive designation comprises checking an attribute assigned to the directory. 3. The system of claim 2, wherein the file system is further executable by the one or more processors to receive an instruction to assign the attribute to the directory, the instruction generated based on user input. 4. The system of claim 2, wherein the file system is further executable by the one or more processors to publish the attribute assigned to the directory so that a developer of an operating system or an application is aware that the directory is associated with the case sensitive designation or the case insensitive designation. 5. The system of claim 2, wherein the attribute is referred to by an access control list defining user accounts or group accounts to be governed by the case sensitive designation or the case insensitive designation. 6. The system of claim 1, wherein each of the file name identified by the second component and the file name associated with the file contained in the directory comprises a file type. 7. The system of claim 1, wherein the case sensitive designation or the case insensitive designation associated with the directory comprises a default designation that is part of an initial configuration of the file system. 8. The system of claim 1, wherein the file system operation comprises one of:
open a file, delete a file, write to a file, read from a file, replace a file, copy a file, move a file, or search for a file as part of a query given a pattern. 9. The system of claim 1, wherein the file system is further executable by the one or more processors to:
create, in a cache associated with the file system, a tree structure of an actual path name for accessing the file contained in the directory, the tree structure including cache nodes corresponding to one or more directory components and a file component of the actual path name; and generate, for an individual cache node, a hash; and associate a label with the hash, the label indicating that the hash is to be matched in a case sensitive mariner or in a case insensitive manner. 10. The system of claim 9, wherein the file system is further executable by the one or more processors to use the hash of the individual cache node in the case insensitive manner or in the case insensitive mariner to determine a cache hit in the cache. 11. A system comprising:
one or more processors; and one or more storage units comprising a file system that includes one or more directories each containing one or more files and/or one or more directories, the file system executable by the one or more processors to:
receive a request to perform a file system operation, the request including a path name with a first component that identifies a directory and a second component that identifies a file name;
determine that a case sensitive match does not exist in the directory for the file name identified by the second component of the path name;
determine that a case insensitive match exists between the file name identified by the second component of the path name and a file name associated with a file contained in the directory; and
based on a determination that that the case insensitive match exists between the file name identified by the second component of the path name and the file name associated with the file contained in the directory, perform the file system operation in association with the file contained in the directory. 12. The system of claim 11, wherein the file name associated with the file contained in the directory is designated as a preferred case insensitive match. 13. The system of claim 12, wherein the file system is further executable by the one or more processors to receive an instruction to designate the file name associated with the file contained in the directory as the preferred case insensitive match, the instruction generated based on user input. 14. The system of claim 13, wherein the user input is provided via a user account or a group account listed in an access control list associated with the file contained in the directory, the access control list defining user accounts or group accounts that have permission to designate the file contained in the directory as the preferred case insensitive match. 15. The system of claim 11, wherein the file system operation comprises one of:
open a file, delete a file, write to a file, read from a file, replace a file, copy a file, move a file, or search for a file as part of a query given a pattern. 16. A system comprising:
one or more processors; and one or more storage units comprising a file system that includes one or more directories each containing one or more files and/or one or more directories, the file system executable by the one or more processors to:
associate a designation with a directory in the file system, the designation indicating that the directory is prohibited from containing two or more files with case varying duplicate file names;
receive a request to create a file with a file name in the directory, the file name being a case varying duplicate file name of an existing file name in the directory; and
prevent, based at least in part on the designation, creation of the file in the directory. 17. The system of claim 16, wherein the file system is further executable by the one or more processors to receive an instruction to associate the designation with the directory, the instruction generated based on user input. 18. The system of claim 17, wherein the user input is provided via a user account or a group account listed in an access control list associated with the directory, the access control list defining user accounts or group accounts that have permission to designate the directory as a directory that is prohibited from containing the two or more files with the case varying duplicate file names. 19. The system of claim 16, wherein the designation associated with the directory comprises a default designation. 20. The system of claim 16, wherein the designation associated with the directory is different from a default configuration in the file system which allows the two or more files to have the case varying duplicate file names. | Described herein are technologies that provide an element of security related to file system operations. Individual nodes in a file system, such as a directory or a file, can be associated with information that describes how to handle letter case when a file name included in a file system operation request is used to locate a file in the file system. For example, a case sensitive designation associated with a directory can require a case sensitive match between a file name included in a request and a file name included in the directory, in order to perform the requested file system operation. In another example, a case preferring designation associated with a directory first checks for a case sensitive match between file names. If a case sensitive match does not exist, then a case insensitive match between the file names can be used to perform the requested file system operation.1. A system comprising:
one or more processors; and one or more storage units comprising a file system that includes one or more directories each containing one or more files and/or one or more directories, the file system executable by the one or more processors to:
receive a request to perform a file system operation, the request including a path name with a first component that identifies a directory and a second component that identifies a file name;
determine whether the directory is associated with a case sensitive designation or a case insensitive designation;
based on a determination that the directory is associated with the case sensitive designation:
determine whether a case sensitive match exists between the file name identified by the second component of the path name and a file name associated with a file contained in the directory; and
perform the file system operation in association with the file contained in the directory based on a determination that the case sensitive match exists between the file name identified by the second component of the path name and the file name associated with the file contained in the directory; or
refrain from performing the file system operation in association with the file contained in the directory based on a determination that the case sensitive match does not exist between the file name identified by the second component of the path name and the file name associated with a file contained in the directory; or
based on a determination that the directory is associated with the case insensitive designation:
determine that a case insensitive match exists between the file name identified by the second component of the path name and a file name associated with a file contained in the directory; and
perform the file system operation in association with the file contained in the directory based on a determination that the case insensitive match exists between the file name identified by the second component of the path name and the file name associated with the file contained in the directory. 2. The system of claim 1, wherein the determining whether the directory is associated with the case sensitive designation or the case insensitive designation comprises checking an attribute assigned to the directory. 3. The system of claim 2, wherein the file system is further executable by the one or more processors to receive an instruction to assign the attribute to the directory, the instruction generated based on user input. 4. The system of claim 2, wherein the file system is further executable by the one or more processors to publish the attribute assigned to the directory so that a developer of an operating system or an application is aware that the directory is associated with the case sensitive designation or the case insensitive designation. 5. The system of claim 2, wherein the attribute is referred to by an access control list defining user accounts or group accounts to be governed by the case sensitive designation or the case insensitive designation. 6. The system of claim 1, wherein each of the file name identified by the second component and the file name associated with the file contained in the directory comprises a file type. 7. The system of claim 1, wherein the case sensitive designation or the case insensitive designation associated with the directory comprises a default designation that is part of an initial configuration of the file system. 8. The system of claim 1, wherein the file system operation comprises one of:
open a file, delete a file, write to a file, read from a file, replace a file, copy a file, move a file, or search for a file as part of a query given a pattern. 9. The system of claim 1, wherein the file system is further executable by the one or more processors to:
create, in a cache associated with the file system, a tree structure of an actual path name for accessing the file contained in the directory, the tree structure including cache nodes corresponding to one or more directory components and a file component of the actual path name; and generate, for an individual cache node, a hash; and associate a label with the hash, the label indicating that the hash is to be matched in a case sensitive mariner or in a case insensitive manner. 10. The system of claim 9, wherein the file system is further executable by the one or more processors to use the hash of the individual cache node in the case insensitive manner or in the case insensitive mariner to determine a cache hit in the cache. 11. A system comprising:
one or more processors; and one or more storage units comprising a file system that includes one or more directories each containing one or more files and/or one or more directories, the file system executable by the one or more processors to:
receive a request to perform a file system operation, the request including a path name with a first component that identifies a directory and a second component that identifies a file name;
determine that a case sensitive match does not exist in the directory for the file name identified by the second component of the path name;
determine that a case insensitive match exists between the file name identified by the second component of the path name and a file name associated with a file contained in the directory; and
based on a determination that that the case insensitive match exists between the file name identified by the second component of the path name and the file name associated with the file contained in the directory, perform the file system operation in association with the file contained in the directory. 12. The system of claim 11, wherein the file name associated with the file contained in the directory is designated as a preferred case insensitive match. 13. The system of claim 12, wherein the file system is further executable by the one or more processors to receive an instruction to designate the file name associated with the file contained in the directory as the preferred case insensitive match, the instruction generated based on user input. 14. The system of claim 13, wherein the user input is provided via a user account or a group account listed in an access control list associated with the file contained in the directory, the access control list defining user accounts or group accounts that have permission to designate the file contained in the directory as the preferred case insensitive match. 15. The system of claim 11, wherein the file system operation comprises one of:
open a file, delete a file, write to a file, read from a file, replace a file, copy a file, move a file, or search for a file as part of a query given a pattern. 16. A system comprising:
one or more processors; and one or more storage units comprising a file system that includes one or more directories each containing one or more files and/or one or more directories, the file system executable by the one or more processors to:
associate a designation with a directory in the file system, the designation indicating that the directory is prohibited from containing two or more files with case varying duplicate file names;
receive a request to create a file with a file name in the directory, the file name being a case varying duplicate file name of an existing file name in the directory; and
prevent, based at least in part on the designation, creation of the file in the directory. 17. The system of claim 16, wherein the file system is further executable by the one or more processors to receive an instruction to associate the designation with the directory, the instruction generated based on user input. 18. The system of claim 17, wherein the user input is provided via a user account or a group account listed in an access control list associated with the directory, the access control list defining user accounts or group accounts that have permission to designate the directory as a directory that is prohibited from containing the two or more files with the case varying duplicate file names. 19. The system of claim 16, wherein the designation associated with the directory comprises a default designation. 20. The system of claim 16, wherein the designation associated with the directory is different from a default configuration in the file system which allows the two or more files to have the case varying duplicate file names. | 2,100 |
6,492 | 6,492 | 15,386,570 | 2,199 | In various embodiments, hardware resources of a processing circuit may be allocated to a plurality of processes based on priorities of the processes. A hardware resource utilization sensor may detect a current utilization of the hardware resources by a process. A utilization accumulation circuit may determine a utilization of the hardware resources by the process over a particular amount of time. A target utilization of the hardware resources for the process may be determined based on the utilization of the hardware resources over the particular amount of time. A comparator circuit may compare the current utilization to the target utilization. A process priority adjustment circuit may adjust a priority of the process based on the comparison. Based on the adjusted priority, a different amount of hardware resources may be allocated to the processes. | 1. A system, comprising:
a utilization accumulation circuit configured to determine a utilization of a plurality of hardware resources by a process over a particular amount of time; a comparator circuit configured to compare a current utilization of the plurality of hardware resources with a target utilization of the plurality of hardware resources, wherein the target utilization is determined based on the utilization of the plurality of hardware resources by the process over the particular amount of time; and a process priority adjustment circuit configured, based on an output of the comparator circuit, to adjust a priority of the process. 2. The system of claim 1, further comprising a hardware resource utilization sensor configured to detect the current utilization of a plurality of hardware resources by the process. 3. The system of claim 1, further comprising a hardware resource arbitration circuit configured to allocate the plurality of hardware resources to the process based on the priority of the process. 4. The system of claim 1, wherein the comparator circuit is configured to indicate a difference between the current utilization and the target utilization. 5. The system of claim 4, wherein the process priority adjustment circuit is configured to track a total difference for the process based on a plurality of outputs of the comparator circuit. 6. The system of claim 5, wherein the process priority adjustment circuit is configured to:
adjust the priority of the process by a first amount in response to the total difference having a value between a first value and a second value; and adjust the priority of the process by a second amount in response to the total difference having a value greater than the second value. 7. The system of claim 5, wherein the process priority adjustment circuit is configured to initiate a context switch of the process in response to the total difference exceeding a particular amount. 8. The system of claim 1, wherein the target utilization is determined based on a utilization of the plurality of hardware resources by a second process over the particular amount of time. 9. The system of claim 8 wherein data of the process is received from first process queue, and wherein data of the second process is received from a second process queue. 10. The system of claim 9, wherein the process priority adjustment circuit is configured to adjust priorities such that processes received from the first process queue are within a first range of priorities and processes received from the second process queue are within a second range of priorities. 11. A method, comprising,
receiving current utilizations of a plurality of hardware resources by a respective plurality of processes; determining respective utilizations of the plurality of hardware resources by the plurality of processes over a particular amount of time; determining target utilizations of the plurality of hardware resources by the plurality of processes; adjusting, for a particular process of the plurality of processes, a priority based on the current utilization of the particular process and the target utilization of the particular process. 12. The method of claim 11, further comprising adjusting the current utilizations of the plurality of hardware resources by the plurality of processes based on adjusting the priority of the particular process. 13. The method of claim 11, wherein adjusting the priority of the particular process causes a resource utilization of the plurality of hardware resources by the particular process to be within a specified range over a specified amount of time. 14. The method of claim 11, wherein target utilizations are determined based on an amount of the plurality of hardware resources requested by the plurality of processes. 15. The method of claim 11, wherein adjusting the priority of the particular process comprises determining that the adjusted priority is within a specified range of priorities. 16. The method of claim 11, wherein the priority of the process is determined based on a process type, a priority requested by the process, and a queue from which the process is received. 17. The method of claim 11, further comprising:
performing, for a second process of the plurality of processes, a second comparison between the current utilization of the second process and the target utilization of the second process; and in response to a difference between the current utilization of the second process and the target utilization of the second process exceeding a specified threshold, terminating the second process. 18. A non-transitory computer readable storage medium having stored thereon design information that specifies a circuit design in a format recognized by a fabrication system that is configured to use the design information to fabricate a hardware integrated circuit that includes circuitry configured to operate according to the circuit design, wherein the circuitry includes:
a hardware resource utilization sensor configured to detect a current utilization of a plurality of hardware resources by a process; a utilization accumulation circuit configured to determine a utilization of the plurality of hardware resources by the process over a particular amount of time; a comparator circuit configured to compare the current utilization of the plurality of hardware resources with a target utilization of the plurality of hardware resources, wherein the target utilization is determined based on the utilization of the plurality of hardware resources by the process over the particular amount of time; and a process priority adjustment circuit configured, based on an output of the comparator circuit, to adjust a priority of the process. 19. The non-transitory computer readable storage medium of claim 18, wherein the circuitry further includes a hardware resource arbitration circuit configured to allocate the plurality of hardware resources to the process based on the priority of the process. 20. The non-transitory computer readable storage medium of claim 18, wherein the plurality of hardware resources comprise an execution unit and a plurality of registers. | In various embodiments, hardware resources of a processing circuit may be allocated to a plurality of processes based on priorities of the processes. A hardware resource utilization sensor may detect a current utilization of the hardware resources by a process. A utilization accumulation circuit may determine a utilization of the hardware resources by the process over a particular amount of time. A target utilization of the hardware resources for the process may be determined based on the utilization of the hardware resources over the particular amount of time. A comparator circuit may compare the current utilization to the target utilization. A process priority adjustment circuit may adjust a priority of the process based on the comparison. Based on the adjusted priority, a different amount of hardware resources may be allocated to the processes.1. A system, comprising:
a utilization accumulation circuit configured to determine a utilization of a plurality of hardware resources by a process over a particular amount of time; a comparator circuit configured to compare a current utilization of the plurality of hardware resources with a target utilization of the plurality of hardware resources, wherein the target utilization is determined based on the utilization of the plurality of hardware resources by the process over the particular amount of time; and a process priority adjustment circuit configured, based on an output of the comparator circuit, to adjust a priority of the process. 2. The system of claim 1, further comprising a hardware resource utilization sensor configured to detect the current utilization of a plurality of hardware resources by the process. 3. The system of claim 1, further comprising a hardware resource arbitration circuit configured to allocate the plurality of hardware resources to the process based on the priority of the process. 4. The system of claim 1, wherein the comparator circuit is configured to indicate a difference between the current utilization and the target utilization. 5. The system of claim 4, wherein the process priority adjustment circuit is configured to track a total difference for the process based on a plurality of outputs of the comparator circuit. 6. The system of claim 5, wherein the process priority adjustment circuit is configured to:
adjust the priority of the process by a first amount in response to the total difference having a value between a first value and a second value; and adjust the priority of the process by a second amount in response to the total difference having a value greater than the second value. 7. The system of claim 5, wherein the process priority adjustment circuit is configured to initiate a context switch of the process in response to the total difference exceeding a particular amount. 8. The system of claim 1, wherein the target utilization is determined based on a utilization of the plurality of hardware resources by a second process over the particular amount of time. 9. The system of claim 8 wherein data of the process is received from first process queue, and wherein data of the second process is received from a second process queue. 10. The system of claim 9, wherein the process priority adjustment circuit is configured to adjust priorities such that processes received from the first process queue are within a first range of priorities and processes received from the second process queue are within a second range of priorities. 11. A method, comprising,
receiving current utilizations of a plurality of hardware resources by a respective plurality of processes; determining respective utilizations of the plurality of hardware resources by the plurality of processes over a particular amount of time; determining target utilizations of the plurality of hardware resources by the plurality of processes; adjusting, for a particular process of the plurality of processes, a priority based on the current utilization of the particular process and the target utilization of the particular process. 12. The method of claim 11, further comprising adjusting the current utilizations of the plurality of hardware resources by the plurality of processes based on adjusting the priority of the particular process. 13. The method of claim 11, wherein adjusting the priority of the particular process causes a resource utilization of the plurality of hardware resources by the particular process to be within a specified range over a specified amount of time. 14. The method of claim 11, wherein target utilizations are determined based on an amount of the plurality of hardware resources requested by the plurality of processes. 15. The method of claim 11, wherein adjusting the priority of the particular process comprises determining that the adjusted priority is within a specified range of priorities. 16. The method of claim 11, wherein the priority of the process is determined based on a process type, a priority requested by the process, and a queue from which the process is received. 17. The method of claim 11, further comprising:
performing, for a second process of the plurality of processes, a second comparison between the current utilization of the second process and the target utilization of the second process; and in response to a difference between the current utilization of the second process and the target utilization of the second process exceeding a specified threshold, terminating the second process. 18. A non-transitory computer readable storage medium having stored thereon design information that specifies a circuit design in a format recognized by a fabrication system that is configured to use the design information to fabricate a hardware integrated circuit that includes circuitry configured to operate according to the circuit design, wherein the circuitry includes:
a hardware resource utilization sensor configured to detect a current utilization of a plurality of hardware resources by a process; a utilization accumulation circuit configured to determine a utilization of the plurality of hardware resources by the process over a particular amount of time; a comparator circuit configured to compare the current utilization of the plurality of hardware resources with a target utilization of the plurality of hardware resources, wherein the target utilization is determined based on the utilization of the plurality of hardware resources by the process over the particular amount of time; and a process priority adjustment circuit configured, based on an output of the comparator circuit, to adjust a priority of the process. 19. The non-transitory computer readable storage medium of claim 18, wherein the circuitry further includes a hardware resource arbitration circuit configured to allocate the plurality of hardware resources to the process based on the priority of the process. 20. The non-transitory computer readable storage medium of claim 18, wherein the plurality of hardware resources comprise an execution unit and a plurality of registers. | 2,100 |
6,493 | 6,493 | 15,450,915 | 2,186 | A PoE powered device and method of operation are provided. The device includes a first port unit configured to negotiate receipt of a level of PoE power from a power sourcing equipment. The power is received on a first pair of taps on a first communication port. A detection unit is configured to detect a presence of a first optional circuit load and to detect a presence of a second optional power load. A control circuit is configured to establish connectivity between a second pair of taps on the first communication port and a second powered device port unit in response to the detection unit detecting the first optional load, and further configured to establish connectivity between the second pair of taps and a third pair of taps on a pass-through communication port in response to the detection unit failing to detect the first load and detecting the second load. | 1. A telecommunications device comprising:
a power receipt circuitry configured to negotiate and receive power at a power level from power sourcing equipment on a plurality of pairs of taps; a base load coupled with the power receipt circuitry, wherein the base load is configured to receive power from the power receipt circuitry using a first pair of taps of the plurality of pairs of taps; a detection circuitry configured to detect when an optional load is coupled with the telecommunications device in addition to the base load; wherein the telecommunications device is configured to establish connectivity between a second pair of taps and the optional load when the detection circuitry detects that the optional load is coupled with the telecommunications device. 2. The telecommunications device of claim 1, further comprising:
the optional load; wherein the power receipt circuitry is configured to negotiate receipt of a first power level for the base load from the first pair of taps; wherein the power receipt circuitry is configured to negotiate receipt of a second power level for the optional load from the second pair of taps. 3. The telecommunications device of claim 1, wherein the optional load is remotely located from the telecommunications device and communicatively coupled to the telecommunications device through a port; and
wherein the telecommunications device is further configured to establish connectivity between the second pair of taps and third taps associated with the port. 4. The telecommunications device of claim 3, wherein the optional load is within an external device, wherein the external device includes:
a second power receipt circuitry configured to negotiate and receive power at a second power level for the optional load from power sourcing equipment through the second pair of taps. 5. The telecommunications device of claim 1, further comprising:
a control circuit configured to establish connectivity between the second pair of taps and the optional load in response to detection of the optional load. 6. The telecommunications device of claim 5, wherein the detection circuitry is configured to detect when multiple optional loads are coupled with the telecommunications device in addition to the base load; and
wherein the control circuit is configured to establish connectivity between the second pair of taps and each of the multiple optional loads in a particular order. 7. The telecommunications device of claim 6, wherein the control circuit is configured to establish connectively with one of the multiple optional loads in response to detecting the one of the multiple optional loads and not detecting another of the multiple optional loads. 8. The telecommunications device of claim 1, wherein the power from the power sourcing equipment is provided using Power over Ethernet. 9. The telecommunications device of claim 1, wherein at least one of the multiple pair of taps are Power over Ethernet taps. 10. The telecommunications device of claim 1, wherein the base load and the optional load are connected with and receive power from the power receipt circuitry. 11. The telecommunications device of claim 1, wherein the telecommunications device is further configured to establish connectivity between the second pair of taps and a third pair of taps associated with a pass-through communication port. 12. The telecommunications device of claim 1, wherein the power receipt circuitry includes:
a first circuit configured to negotiate a first level of power with the power sourcing equipment; a second circuit configured to negotiate a second level of power with the power sourcing equipment; a third circuit configured to convert the first level of power to an intermediate voltage level; a fourth circuit configured to convert the second level of power to the intermediate voltage level; a fifth circuit configured to combine power at the intermediate voltage level received from the third circuit and the second circuit; and a sixth circuit configured to convert power at the intermediate voltage received from the fifth circuit to a source voltage level prior to distributing power to a load. 13. The telecommunications device of claim 1, wherein the power receipt circuitry includes:
a first circuit configured to negotiate a first level of power with the power sourcing equipment; a second circuit configured to negotiate a second level of power with the power sourcing equipment; a third circuit configured to combine power received from the first circuit and the second circuit; a fourth circuit configured to convert the combined power received from the third circuit to an intermediate voltage level; and a fifth circuit configured to convert power at the intermediate voltage received from the fourth circuit to a source voltage level prior to distributing power to a load. 14. The telecommunications device of claim 1, wherein the telecommunication device is a remote antenna unit of a distributed antenna system. 15. The telecommunications device of claim 1, wherein the optional load is a radio frequency signal processing board for a remote antenna unit of a distributed antenna system. 16. The telecommunications device of claim 1, wherein the optional load includes at least one of: a standard access point, a WiFi access point, a WiMax access point, a maintenance terminal, and an IP camera. 17. A method of distributing power using a telecommunication device, the method comprising:
negotiating and receiving power at a power level from power sourcing equipment on a plurality of taps at power receipt circuitry; receiving power from the power receipt circuitry using a first pair of taps of the plurality of pairs of taps at a base load; detecting when an optional load is coupled with the telecommunications device in addition to the base load; and establishing connectivity between a second pair of taps and the optional load when the detection circuitry detects that the optional load is coupled with the telecommunications device. 18. The method of claim 17, wherein the optional load is part of the telecommunications device along with the base load, the method further comprising:
using the power receipt circuitry to negotiate receipt of a level of power for the at least one pair of taps for the base load and for the second pair of taps for the optional load. 19. The method of claim 17, wherein the optional load is remotely located from the telecommunications device and coupled to the telecommunications device through a port, the method further comprising:
establishing connectivity between the second pair of taps and third taps that are associated with the port. 20. The method of claim 17, further comprising:
detecting when the multiple optional loads are coupled with the telecommunications device in addition to the base load; and establishing connectivity between the second pair of taps and each of the multiple optional loads in a particular order. | A PoE powered device and method of operation are provided. The device includes a first port unit configured to negotiate receipt of a level of PoE power from a power sourcing equipment. The power is received on a first pair of taps on a first communication port. A detection unit is configured to detect a presence of a first optional circuit load and to detect a presence of a second optional power load. A control circuit is configured to establish connectivity between a second pair of taps on the first communication port and a second powered device port unit in response to the detection unit detecting the first optional load, and further configured to establish connectivity between the second pair of taps and a third pair of taps on a pass-through communication port in response to the detection unit failing to detect the first load and detecting the second load.1. A telecommunications device comprising:
a power receipt circuitry configured to negotiate and receive power at a power level from power sourcing equipment on a plurality of pairs of taps; a base load coupled with the power receipt circuitry, wherein the base load is configured to receive power from the power receipt circuitry using a first pair of taps of the plurality of pairs of taps; a detection circuitry configured to detect when an optional load is coupled with the telecommunications device in addition to the base load; wherein the telecommunications device is configured to establish connectivity between a second pair of taps and the optional load when the detection circuitry detects that the optional load is coupled with the telecommunications device. 2. The telecommunications device of claim 1, further comprising:
the optional load; wherein the power receipt circuitry is configured to negotiate receipt of a first power level for the base load from the first pair of taps; wherein the power receipt circuitry is configured to negotiate receipt of a second power level for the optional load from the second pair of taps. 3. The telecommunications device of claim 1, wherein the optional load is remotely located from the telecommunications device and communicatively coupled to the telecommunications device through a port; and
wherein the telecommunications device is further configured to establish connectivity between the second pair of taps and third taps associated with the port. 4. The telecommunications device of claim 3, wherein the optional load is within an external device, wherein the external device includes:
a second power receipt circuitry configured to negotiate and receive power at a second power level for the optional load from power sourcing equipment through the second pair of taps. 5. The telecommunications device of claim 1, further comprising:
a control circuit configured to establish connectivity between the second pair of taps and the optional load in response to detection of the optional load. 6. The telecommunications device of claim 5, wherein the detection circuitry is configured to detect when multiple optional loads are coupled with the telecommunications device in addition to the base load; and
wherein the control circuit is configured to establish connectivity between the second pair of taps and each of the multiple optional loads in a particular order. 7. The telecommunications device of claim 6, wherein the control circuit is configured to establish connectively with one of the multiple optional loads in response to detecting the one of the multiple optional loads and not detecting another of the multiple optional loads. 8. The telecommunications device of claim 1, wherein the power from the power sourcing equipment is provided using Power over Ethernet. 9. The telecommunications device of claim 1, wherein at least one of the multiple pair of taps are Power over Ethernet taps. 10. The telecommunications device of claim 1, wherein the base load and the optional load are connected with and receive power from the power receipt circuitry. 11. The telecommunications device of claim 1, wherein the telecommunications device is further configured to establish connectivity between the second pair of taps and a third pair of taps associated with a pass-through communication port. 12. The telecommunications device of claim 1, wherein the power receipt circuitry includes:
a first circuit configured to negotiate a first level of power with the power sourcing equipment; a second circuit configured to negotiate a second level of power with the power sourcing equipment; a third circuit configured to convert the first level of power to an intermediate voltage level; a fourth circuit configured to convert the second level of power to the intermediate voltage level; a fifth circuit configured to combine power at the intermediate voltage level received from the third circuit and the second circuit; and a sixth circuit configured to convert power at the intermediate voltage received from the fifth circuit to a source voltage level prior to distributing power to a load. 13. The telecommunications device of claim 1, wherein the power receipt circuitry includes:
a first circuit configured to negotiate a first level of power with the power sourcing equipment; a second circuit configured to negotiate a second level of power with the power sourcing equipment; a third circuit configured to combine power received from the first circuit and the second circuit; a fourth circuit configured to convert the combined power received from the third circuit to an intermediate voltage level; and a fifth circuit configured to convert power at the intermediate voltage received from the fourth circuit to a source voltage level prior to distributing power to a load. 14. The telecommunications device of claim 1, wherein the telecommunication device is a remote antenna unit of a distributed antenna system. 15. The telecommunications device of claim 1, wherein the optional load is a radio frequency signal processing board for a remote antenna unit of a distributed antenna system. 16. The telecommunications device of claim 1, wherein the optional load includes at least one of: a standard access point, a WiFi access point, a WiMax access point, a maintenance terminal, and an IP camera. 17. A method of distributing power using a telecommunication device, the method comprising:
negotiating and receiving power at a power level from power sourcing equipment on a plurality of taps at power receipt circuitry; receiving power from the power receipt circuitry using a first pair of taps of the plurality of pairs of taps at a base load; detecting when an optional load is coupled with the telecommunications device in addition to the base load; and establishing connectivity between a second pair of taps and the optional load when the detection circuitry detects that the optional load is coupled with the telecommunications device. 18. The method of claim 17, wherein the optional load is part of the telecommunications device along with the base load, the method further comprising:
using the power receipt circuitry to negotiate receipt of a level of power for the at least one pair of taps for the base load and for the second pair of taps for the optional load. 19. The method of claim 17, wherein the optional load is remotely located from the telecommunications device and coupled to the telecommunications device through a port, the method further comprising:
establishing connectivity between the second pair of taps and third taps that are associated with the port. 20. The method of claim 17, further comprising:
detecting when the multiple optional loads are coupled with the telecommunications device in addition to the base load; and establishing connectivity between the second pair of taps and each of the multiple optional loads in a particular order. | 2,100 |
6,494 | 6,494 | 15,628,859 | 2,175 | To facilitate various functionality related to interactions between a portable device and a vehicle head unit, systems and methods (i) efficiently provide audio navigation instructions to a vehicle head unit; (ii) enable data exchange between a portable device which is not in direct communication with a vehicle head unit and the vehicle head unit; and (iii) provide visual output in response to user gestures in an automotive environment. | 1. A method for providing structured sets of items via an automotive user interface (UI) configured to receive gesture-based user input, the method comprising:
receiving, by one or more processors, an ordered plurality of items; causing, by the one or more processors, a first subset of the plurality of items to be displayed via the automotive UI along a certain axis; detecting, by the one or more processors, a gesture having a motion component directed along the axis applied to the automotive UI; in response to the gesture, causing, by the one or more processors, a second subset of the plurality of items to be displayed via the automotive UI independently of a velocity of the motion component of the gesture, wherein each of the first subset and the second subset includes multiple items, and wherein the second subset includes items that immediately follow the items in the first subset. 2. The method of claim 1, wherein the ordered plurality of items is an ordered list of search results, and wherein causing the first subset and the second subset to be displayed via the automotive UI includes generating equal-sized informational cards for each item. 3. The method of claim 1, wherein each of the ordered plurality of items is one of a column or a row in a two-dimensional array of equal-sized map tiles that make up a digital map, wherein each map tile is a respective digital image. 4. The method of claim 3, wherein causing the second subset of the plurality of items to be displayed includes selecting the second subset that includes a plurality of rows or columns not included in the first subset and at least one row or column also included in the first subset, wherein each of the first subset and the second subset includes a same number of rows or columns. 5. The method of claim 1, further including determining, by the one or more processors, a size of each subset based on an amount of space available for display in the automotive UI. 6. The method of claim 1, wherein the automotive UI includes a touchscreen installed in a head unit of a vehicle. 7. The method of claim 6, wherein the one or more processors operate in a portable device coupled to the head unit via a short-range communication link; the method further comprising:
causing, by the one or more processors, a description of the gesture to be provided to the portable device; and causing, by the one or more processors, the first subset and the second subset to be provided to the head unit for display on the touchscreen, at respective times. 8. A portable computing device comprising:
one or more processors; a short-range communication interface to couple the portable computing device to a head unit of a vehicle to receive input from, and provide output to, automotive user interface (UI) implemented in a head unit of a vehicle; a non-transitory computer-readable memory storing thereon instructions configured to execute on the one or more processors to:
receive an ordered plurality of items I1, I2, . . . IM,
provide an initial subset of N successive items I1, I2, . . . IN to the head unit for display via the automotive UI,
receive an indication of a flick gesture detected via automotive UI, and
in response to the received indication, provide to the head unit a new subset of N successive items I1+O, I2+O, . . . IN+O which are offset from the initial subset by a certain fixed number O independently of a velocity of the flick gesture. 9. The portable computing device of claim 8, wherein the instructions are further configured to:
receive, via the short-range communication interface, parameters describing dimensions of available screen space in the automotive UI, and determine the fixed number O based on the received parameters. 10. The portable computing device of claim 8, further comprising a long-range communication network to receive the ordered plurality of items from a network server. 11. The portable computing device of claim 8, wherein the ordered plurality of items is an ordered list of search results, each provided via the automotive UI in an informational card of a fixed size. 12. The portable computing device of claim 8, wherein each of the ordered plurality of items is one of a column or a row in a two-dimensional array of equal-sized map tiles that make up a digital map, wherein each map tile is a respective digital image. 13. The portable computing device of claim 8, wherein the short-range communication interface is configured to receive the indication of the flick gesture including (i) an indication of a direction of at least one motion and (ii) and indication of the velocity of the at least one motion. 14. A system for providing output in response to user gestures in an automotive environment, the system comprising:
one or more processors; a user interface (UI) communicatively coupled to the one or more processors and configured to display content to a driver of a vehicle and receive gesture-based input from the driver; and a non-transitory computer-readable memory storing thereon instructions that, when executed on the one or more processors, cause the one or more processors to:
display, via the user interface, a first subset of an ordered plurality of items along an axis,
detect, via the user interface, a gesture having a motion component directed along the axis,
in response to the gesture, select a second subset of the ordered plurality of items for display via the user interface independently of a velocity of the motion component, wherein each of the first subset and the second subset includes multiple items, and wherein the second subset includes items that immediately follow the items in the first subset, and
display the subset via the user interface. 15. The system of claim 14, wherein the user interface includes a touchscreen embedded in a head unit of a vehicle. 16. The system of claim 15, wherein the one or more processors and the computer-readable memory are embedded in the head unit. 17. The system of claim 15, wherein the one or more processors and the computer-readable memory are implemented in a portable device,
the system further comprising:
a short-range communication interface to couple the portable computing device to the head unit. 18. The system of claim 14, the system further comprising:
a long-range communication network to receive the ordered plurality of items from a network server 19. The system of claim 14, wherein the ordered plurality of items is an ordered list of search results, each provided via the UI in an informational card of a fixed size. 20. The system of claim 14, wherein each of the ordered plurality of items is one of a column or a row in a two-dimensional array of equal-sized map tiles that make up a digital map, wherein each map tile is a respective digital image. | To facilitate various functionality related to interactions between a portable device and a vehicle head unit, systems and methods (i) efficiently provide audio navigation instructions to a vehicle head unit; (ii) enable data exchange between a portable device which is not in direct communication with a vehicle head unit and the vehicle head unit; and (iii) provide visual output in response to user gestures in an automotive environment.1. A method for providing structured sets of items via an automotive user interface (UI) configured to receive gesture-based user input, the method comprising:
receiving, by one or more processors, an ordered plurality of items; causing, by the one or more processors, a first subset of the plurality of items to be displayed via the automotive UI along a certain axis; detecting, by the one or more processors, a gesture having a motion component directed along the axis applied to the automotive UI; in response to the gesture, causing, by the one or more processors, a second subset of the plurality of items to be displayed via the automotive UI independently of a velocity of the motion component of the gesture, wherein each of the first subset and the second subset includes multiple items, and wherein the second subset includes items that immediately follow the items in the first subset. 2. The method of claim 1, wherein the ordered plurality of items is an ordered list of search results, and wherein causing the first subset and the second subset to be displayed via the automotive UI includes generating equal-sized informational cards for each item. 3. The method of claim 1, wherein each of the ordered plurality of items is one of a column or a row in a two-dimensional array of equal-sized map tiles that make up a digital map, wherein each map tile is a respective digital image. 4. The method of claim 3, wherein causing the second subset of the plurality of items to be displayed includes selecting the second subset that includes a plurality of rows or columns not included in the first subset and at least one row or column also included in the first subset, wherein each of the first subset and the second subset includes a same number of rows or columns. 5. The method of claim 1, further including determining, by the one or more processors, a size of each subset based on an amount of space available for display in the automotive UI. 6. The method of claim 1, wherein the automotive UI includes a touchscreen installed in a head unit of a vehicle. 7. The method of claim 6, wherein the one or more processors operate in a portable device coupled to the head unit via a short-range communication link; the method further comprising:
causing, by the one or more processors, a description of the gesture to be provided to the portable device; and causing, by the one or more processors, the first subset and the second subset to be provided to the head unit for display on the touchscreen, at respective times. 8. A portable computing device comprising:
one or more processors; a short-range communication interface to couple the portable computing device to a head unit of a vehicle to receive input from, and provide output to, automotive user interface (UI) implemented in a head unit of a vehicle; a non-transitory computer-readable memory storing thereon instructions configured to execute on the one or more processors to:
receive an ordered plurality of items I1, I2, . . . IM,
provide an initial subset of N successive items I1, I2, . . . IN to the head unit for display via the automotive UI,
receive an indication of a flick gesture detected via automotive UI, and
in response to the received indication, provide to the head unit a new subset of N successive items I1+O, I2+O, . . . IN+O which are offset from the initial subset by a certain fixed number O independently of a velocity of the flick gesture. 9. The portable computing device of claim 8, wherein the instructions are further configured to:
receive, via the short-range communication interface, parameters describing dimensions of available screen space in the automotive UI, and determine the fixed number O based on the received parameters. 10. The portable computing device of claim 8, further comprising a long-range communication network to receive the ordered plurality of items from a network server. 11. The portable computing device of claim 8, wherein the ordered plurality of items is an ordered list of search results, each provided via the automotive UI in an informational card of a fixed size. 12. The portable computing device of claim 8, wherein each of the ordered plurality of items is one of a column or a row in a two-dimensional array of equal-sized map tiles that make up a digital map, wherein each map tile is a respective digital image. 13. The portable computing device of claim 8, wherein the short-range communication interface is configured to receive the indication of the flick gesture including (i) an indication of a direction of at least one motion and (ii) and indication of the velocity of the at least one motion. 14. A system for providing output in response to user gestures in an automotive environment, the system comprising:
one or more processors; a user interface (UI) communicatively coupled to the one or more processors and configured to display content to a driver of a vehicle and receive gesture-based input from the driver; and a non-transitory computer-readable memory storing thereon instructions that, when executed on the one or more processors, cause the one or more processors to:
display, via the user interface, a first subset of an ordered plurality of items along an axis,
detect, via the user interface, a gesture having a motion component directed along the axis,
in response to the gesture, select a second subset of the ordered plurality of items for display via the user interface independently of a velocity of the motion component, wherein each of the first subset and the second subset includes multiple items, and wherein the second subset includes items that immediately follow the items in the first subset, and
display the subset via the user interface. 15. The system of claim 14, wherein the user interface includes a touchscreen embedded in a head unit of a vehicle. 16. The system of claim 15, wherein the one or more processors and the computer-readable memory are embedded in the head unit. 17. The system of claim 15, wherein the one or more processors and the computer-readable memory are implemented in a portable device,
the system further comprising:
a short-range communication interface to couple the portable computing device to the head unit. 18. The system of claim 14, the system further comprising:
a long-range communication network to receive the ordered plurality of items from a network server 19. The system of claim 14, wherein the ordered plurality of items is an ordered list of search results, each provided via the UI in an informational card of a fixed size. 20. The system of claim 14, wherein each of the ordered plurality of items is one of a column or a row in a two-dimensional array of equal-sized map tiles that make up a digital map, wherein each map tile is a respective digital image. | 2,100 |
6,495 | 6,495 | 13,613,607 | 2,147 | Product data management (PDM) systems, methods, and computer-readable media. A method includes receiving a solid body in a PDM data processing system. The method includes determining an operation to perform on the solid body and a target region of the solid body. The method includes moving the target region into a secondary solid body and removing the geometries from the secondary solid body. The method includes generating new geometries corresponding to the operation and the secondary solid body, and applying the new geometries to the topological entities of the secondary solid body. The method includes transforming the adjoining regions to a new position according to the operation. The method includes knitting the transformed adjoining regions to the modified secondary solid body to produce a processed solid body. | 1. A method for performing a modification operation on a solid model, comprising:
receiving a solid body in a product data management (PDM) data processing system; determining an operation to perform on the solid body and a target region of the solid body, the solid body including at least one adjoining region connected to the target region and the target region having a plurality of geometries and topological entities; moving the target region into a secondary solid body; removing the geometries from the secondary solid body; generating new geometries corresponding to the operation and the secondary solid body, and applying the new geometries to the topological entities of the secondary solid body, to produce a modified secondary solid body; transforming the adjoining regions to a new position according to the operation; and knitting the transformed adjoining regions to the modified secondary solid body to produce a processed solid body, and storing the processed solid body in the PDM data processing system. 2. The method of claim 1, wherein the operation is an unbend operation, and the target region is the region of the solid body to be unbent. 3. The method of claim 1, wherein the operation is a bend operation, and the target region is the region of the solid body to be bent. 4. The method of claim 1, wherein the PDM data processing system also forms heal cap faces on the adjoining regions where the adjoining regions joined the target region. 5. The method of claim 1, wherein removing the geometries includes removing geometries from all vertices, fins, edges, and faces on the secondary solid body. 6. The method of claim 1, wherein the geometries include points, curves, and surfaces. 7. The method of claim 1, wherein the topological entities include vertices, edges, and faces. 8. A product data management (PDM) data processing system, comprising:
at least one processor; and an accessible memory, the PDM data processing system configured to receive a solid body; determine an operation to perform on the solid body and a target region of the solid body, the solid body including at least one adjoining region connected to the target region and the target region having a plurality of geometries and topological entities; move the target region into a secondary solid body; remove the geometries from the secondary solid body; generate new geometries corresponding to the operation and the secondary solid body, and applying the new geometries to the topological entities of the secondary solid body, to produce a modified secondary solid body; transform the adjoining regions to a new position according to the operation; and knit the transformed adjoining regions to the modified secondary solid body to produce a processed solid body, and storing the processed solid body in the PDM data processing system. 9. The PDM data processing system of claim 8, wherein the operation is an unbend operation, and the target region is the region of the solid body to be unbent. 10. The PDM data processing system of claim 8, wherein the operation is a bend operation, and the target region is the region of the solid body to be bent. 11. The PDM data processing system of claim 8, wherein the PDM data processing system also forms heal cap faces on the adjoining regions where the adjoining regions joined the target region. 12. The PDM data processing system of claim 8, wherein removing the geometries includes removing geometries from all vertices, fins, edges, and faces on the secondary solid body. 13. The PDM data processing system of claim 8, wherein the geometries include points, curves, and surfaces. 14. The PDM data processing system of claim 8, wherein the topological entities include vertices, edges, and faces. 15. A non-transitory computer-readable medium encoded with computer-executable instructions that, when executed, cause a product data management (PDM) data processing system to:
receive a solid body; determine an operation to perform on the solid body and a target region of the solid body, the solid body including at least one adjoining region connected to the target region and the target region having a plurality of geometries and topological entities; move the target region into a secondary solid body; remove the geometries from the secondary solid body; generate new geometries corresponding to the operation and the secondary solid body, and applying the new geometries to the topological entities of the secondary solid body, to produce a modified secondary solid body; transform the adjoining regions to a new position according to the operation; and knit the transformed adjoining regions to the modified secondary solid body to produce a processed solid body, and storing the processed solid body in the PDM data processing system. 16. The computer-readable medium of claim 15, wherein the operation is an unbend operation, and the target region is the region of the solid body to be unbent. 17. The computer-readable medium of claim 15, wherein the operation is a bend operation, and the target region is the region of the solid body to be bent. 18. The computer-readable medium of claim 15, wherein the PDM data processing system also forms heal cap faces on the adjoining regions where the adjoining regions joined the target region. 19. The computer-readable medium of claim 15, wherein removing the geometries includes removing geometries from all vertices, fins, edges, and faces on the secondary solid body. 20. The computer-readable medium of claim 15, wherein the geometries include points, curves, and surfaces and the topological entities include vertices, edges, and faces. | Product data management (PDM) systems, methods, and computer-readable media. A method includes receiving a solid body in a PDM data processing system. The method includes determining an operation to perform on the solid body and a target region of the solid body. The method includes moving the target region into a secondary solid body and removing the geometries from the secondary solid body. The method includes generating new geometries corresponding to the operation and the secondary solid body, and applying the new geometries to the topological entities of the secondary solid body. The method includes transforming the adjoining regions to a new position according to the operation. The method includes knitting the transformed adjoining regions to the modified secondary solid body to produce a processed solid body.1. A method for performing a modification operation on a solid model, comprising:
receiving a solid body in a product data management (PDM) data processing system; determining an operation to perform on the solid body and a target region of the solid body, the solid body including at least one adjoining region connected to the target region and the target region having a plurality of geometries and topological entities; moving the target region into a secondary solid body; removing the geometries from the secondary solid body; generating new geometries corresponding to the operation and the secondary solid body, and applying the new geometries to the topological entities of the secondary solid body, to produce a modified secondary solid body; transforming the adjoining regions to a new position according to the operation; and knitting the transformed adjoining regions to the modified secondary solid body to produce a processed solid body, and storing the processed solid body in the PDM data processing system. 2. The method of claim 1, wherein the operation is an unbend operation, and the target region is the region of the solid body to be unbent. 3. The method of claim 1, wherein the operation is a bend operation, and the target region is the region of the solid body to be bent. 4. The method of claim 1, wherein the PDM data processing system also forms heal cap faces on the adjoining regions where the adjoining regions joined the target region. 5. The method of claim 1, wherein removing the geometries includes removing geometries from all vertices, fins, edges, and faces on the secondary solid body. 6. The method of claim 1, wherein the geometries include points, curves, and surfaces. 7. The method of claim 1, wherein the topological entities include vertices, edges, and faces. 8. A product data management (PDM) data processing system, comprising:
at least one processor; and an accessible memory, the PDM data processing system configured to receive a solid body; determine an operation to perform on the solid body and a target region of the solid body, the solid body including at least one adjoining region connected to the target region and the target region having a plurality of geometries and topological entities; move the target region into a secondary solid body; remove the geometries from the secondary solid body; generate new geometries corresponding to the operation and the secondary solid body, and applying the new geometries to the topological entities of the secondary solid body, to produce a modified secondary solid body; transform the adjoining regions to a new position according to the operation; and knit the transformed adjoining regions to the modified secondary solid body to produce a processed solid body, and storing the processed solid body in the PDM data processing system. 9. The PDM data processing system of claim 8, wherein the operation is an unbend operation, and the target region is the region of the solid body to be unbent. 10. The PDM data processing system of claim 8, wherein the operation is a bend operation, and the target region is the region of the solid body to be bent. 11. The PDM data processing system of claim 8, wherein the PDM data processing system also forms heal cap faces on the adjoining regions where the adjoining regions joined the target region. 12. The PDM data processing system of claim 8, wherein removing the geometries includes removing geometries from all vertices, fins, edges, and faces on the secondary solid body. 13. The PDM data processing system of claim 8, wherein the geometries include points, curves, and surfaces. 14. The PDM data processing system of claim 8, wherein the topological entities include vertices, edges, and faces. 15. A non-transitory computer-readable medium encoded with computer-executable instructions that, when executed, cause a product data management (PDM) data processing system to:
receive a solid body; determine an operation to perform on the solid body and a target region of the solid body, the solid body including at least one adjoining region connected to the target region and the target region having a plurality of geometries and topological entities; move the target region into a secondary solid body; remove the geometries from the secondary solid body; generate new geometries corresponding to the operation and the secondary solid body, and applying the new geometries to the topological entities of the secondary solid body, to produce a modified secondary solid body; transform the adjoining regions to a new position according to the operation; and knit the transformed adjoining regions to the modified secondary solid body to produce a processed solid body, and storing the processed solid body in the PDM data processing system. 16. The computer-readable medium of claim 15, wherein the operation is an unbend operation, and the target region is the region of the solid body to be unbent. 17. The computer-readable medium of claim 15, wherein the operation is a bend operation, and the target region is the region of the solid body to be bent. 18. The computer-readable medium of claim 15, wherein the PDM data processing system also forms heal cap faces on the adjoining regions where the adjoining regions joined the target region. 19. The computer-readable medium of claim 15, wherein removing the geometries includes removing geometries from all vertices, fins, edges, and faces on the secondary solid body. 20. The computer-readable medium of claim 15, wherein the geometries include points, curves, and surfaces and the topological entities include vertices, edges, and faces. | 2,100 |
6,496 | 6,496 | 15,612,242 | 2,165 | Described herein are technologies relating to generating search results responsive to receipt of a query. More specifically, the query is mapped to a topic in response to receipt of a query, and social media accounts that have been labeled as being knowledgeable on the topic are identified. Messages in a message feed of the social media account that are germane to the topic are retrieved, and documents referenced (linked) in the retrieved messages are identified. These documents are positioned in a ranked list based upon the documents being referenced in the messages. | 1. A computing system comprising:
at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform acts comprising:
receiving a query from a client computing device that is in network communication with the computing system, wherein the query comprises a keyword, and further wherein search results are to be returned based upon the query;
responsive to receiving the query, mapping the query to a topic in a predefined list of topics;
responsive to mapping the query to the topic, identifying a social media account that is mapped to the topic in a database;
responsive to identifying the social media account, identifying a web page that is referenced in a message of a message feed of the social media account;
ranking a plurality of web pages to form an ordered list of web pages, wherein the web page is positioned in the ordered list of web pages based upon the web page being referenced in the message; and
transmitting search results to the client computing device for presentment on a display of the client computing device, wherein the search results include selectable links to the plurality of web pages, the links are ordered in the search results in accordance with the ordered list of web pages. 2. The computing system of claim 1, wherein mapping the query to the topic comprises identifying a named entity in the query, wherein the named entity is the topic. 3. The computing system of claim 1, wherein several social media accounts are mapped to the topic in the database, the social media accounts are ranked for the topic, and further wherein the social media account is ranked most highly for the topic from amongst the social media accounts. 4. The computing system of claim 1, the acts further comprising:
mapping the social media account to the topic in the database, wherein mapping the social media account to the topic comprises:
identifying a second message in the message feed;
determining that the second message references the topic; and
mapping the social media account to the topic based upon the determination that the message in the message feed references the topic. 5. The computing system of claim 4, wherein mapping the social media account to the topic further comprises:
counting a number of messages in the message feed that are labeled as referencing the topic; and mapping the social media account to the topic based upon the number of messages in the message feed that are labeled as referencing the topic. 6. The computing system of claim 1, wherein identifying the web page comprises determining that the social media account has shared the web page in the message feed. 7. The computing system of claim 1, wherein the message in the message feed is directed to the social media account from another social media account. 8. The computing system of claim 1, wherein a search result in the search results that represents the web page is labeled as being shared by the social media account, and further wherein the search result includes a selectable link for the social media account, the acts further comprising:
receiving an indication that a user of the client computing device has selected the selectable link for the social media account; responsive to receiving the indication, identifying a second plurality of web pages, wherein the web pages in the second plurality of web pages are referenced in messages in the social media feed of the social media account; ranking the second plurality of web pages to form a second ordered list of web pages, wherein the second plurality of web pages are ranked based upon a feature of the second plurality of web pages; and transmitting second search results to the client computing device responsive to ranking the second plurality of web pages, wherein the second search results include second selectable links to the second plurality of web pages, the second selectable links ordered in the second search results in accordance with the second ordered list of web pages. 9. The computing system of claim 8, wherein the second search results further include graphics that indicate that the selectable search results correspond to web pages referenced in messages of the message feed of the social media account. 10. The computing system of claim 8, wherein the feature is a timestamp assigned to each message in the message feed that referenced one of the web pages in the second plurality of web pages such that a most highly positioned link in the second search results is referenced in a most recent message in the message feed from amongst messages that include references to the web pages in the second plurality of web pages. 11. A method executed at a server computing device, the method comprising:
identifying a plurality of documents from a document collection as being relevant to a query received from a client computing device; mapping the query to a topic from amongst a plurality of potential topics; responsive to mapping the query to the topic, identifying a social media account that is labeled as being authoritative on the topic, wherein the social media account has a message feed that includes messages; responsive to identifying the social media account, identifying a reference to a document in the plurality of documents in a message of the message feed; ordering the plurality of documents in a ranked list, wherein a position of the document in the ranked list is based upon the message in the message teed being identified as comprising the reference to the document; and transmitting search results to the client computing device responsive to ordering the plurality of documents, wherein the search results include links to the documents and are ordered in accordance with the ranked list. 12. The method of claim 11, wherein the documents are web pages, and wherein the reference to the document in the message is a uniform resource locator for the document. 13. The method of claim 11, wherein the social media account represents an entity. 14. The method of claim 13, wherein the position of the document in the ranked list is further based upon the social media account representing an entity rather than an individual. 15. The method of claim 11, further comprising:
receiving the messages of the message feed; identifying at least one of:
a number of messages in the message feed that reference the topic; or
a number of times that messages that reference the topic in the message feed have been rebroadcast by other social media accounts; and
labeling the social media account as being authoritative on the topic based upon at least one of the number of messages in message feed that reference the topic or the number of times that messages in the message feed have been rebroadcast by other social media accounts. 16. The method of claim 15, wherein identifying the reference to the document in the message of the message feed comprises:
determining that content of the message in the message feed is germane to the topic based upon content of the message; and identifying the reference to the document only after determining that the content of the message is germane to the topic. 17. The method of claim 11. wherein a search result in the search results comprises:
a link to the document; and a selectable graphic, wherein the selectable graphic identifies the social media account. 18. The method of claim 17, further comprising:
receiving an indication that a user of the client computing device has selected the selectable graphic; and responsive to receiving the indication, generating second search results, wherein the second search results consist of links to documents referenced in a subset of the messages of the message feed. 19. The method of claim 18, wherein the links to the documents in the second search results are ordered based upon timestamps assigned to the subset of the messages in the message feed. 20. A computer-readable storage medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform acts comprising:
receiving a query from a client computing device; generating search results responsive to receipt of the query, wherein the search results include an ordered list of links to web pages; and transmitting the search results to the client computing device, wherein generating the search results comprises:
identifying a topic referenced in the query,
identifying a social media account that is mapped to the topic in a database, wherein the social media account is mapped to the topic based upon the social media account being labeled as being authoritative on the topic;
determining that a message in a message feed of the social media account is relevant to the query;
identifying a reference to a web page in the message; and
positioning the web page in a ranked list of web pages responsive to identifying the reference to the web page in the message, wherein the web page is positioned in the ranked list of web pages based upon the reference to the web page being included in the message of the message feed of the social media account, wherein the ordered list of links of the search results corresponds to the ranked list of web pages. | Described herein are technologies relating to generating search results responsive to receipt of a query. More specifically, the query is mapped to a topic in response to receipt of a query, and social media accounts that have been labeled as being knowledgeable on the topic are identified. Messages in a message feed of the social media account that are germane to the topic are retrieved, and documents referenced (linked) in the retrieved messages are identified. These documents are positioned in a ranked list based upon the documents being referenced in the messages.1. A computing system comprising:
at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform acts comprising:
receiving a query from a client computing device that is in network communication with the computing system, wherein the query comprises a keyword, and further wherein search results are to be returned based upon the query;
responsive to receiving the query, mapping the query to a topic in a predefined list of topics;
responsive to mapping the query to the topic, identifying a social media account that is mapped to the topic in a database;
responsive to identifying the social media account, identifying a web page that is referenced in a message of a message feed of the social media account;
ranking a plurality of web pages to form an ordered list of web pages, wherein the web page is positioned in the ordered list of web pages based upon the web page being referenced in the message; and
transmitting search results to the client computing device for presentment on a display of the client computing device, wherein the search results include selectable links to the plurality of web pages, the links are ordered in the search results in accordance with the ordered list of web pages. 2. The computing system of claim 1, wherein mapping the query to the topic comprises identifying a named entity in the query, wherein the named entity is the topic. 3. The computing system of claim 1, wherein several social media accounts are mapped to the topic in the database, the social media accounts are ranked for the topic, and further wherein the social media account is ranked most highly for the topic from amongst the social media accounts. 4. The computing system of claim 1, the acts further comprising:
mapping the social media account to the topic in the database, wherein mapping the social media account to the topic comprises:
identifying a second message in the message feed;
determining that the second message references the topic; and
mapping the social media account to the topic based upon the determination that the message in the message feed references the topic. 5. The computing system of claim 4, wherein mapping the social media account to the topic further comprises:
counting a number of messages in the message feed that are labeled as referencing the topic; and mapping the social media account to the topic based upon the number of messages in the message feed that are labeled as referencing the topic. 6. The computing system of claim 1, wherein identifying the web page comprises determining that the social media account has shared the web page in the message feed. 7. The computing system of claim 1, wherein the message in the message feed is directed to the social media account from another social media account. 8. The computing system of claim 1, wherein a search result in the search results that represents the web page is labeled as being shared by the social media account, and further wherein the search result includes a selectable link for the social media account, the acts further comprising:
receiving an indication that a user of the client computing device has selected the selectable link for the social media account; responsive to receiving the indication, identifying a second plurality of web pages, wherein the web pages in the second plurality of web pages are referenced in messages in the social media feed of the social media account; ranking the second plurality of web pages to form a second ordered list of web pages, wherein the second plurality of web pages are ranked based upon a feature of the second plurality of web pages; and transmitting second search results to the client computing device responsive to ranking the second plurality of web pages, wherein the second search results include second selectable links to the second plurality of web pages, the second selectable links ordered in the second search results in accordance with the second ordered list of web pages. 9. The computing system of claim 8, wherein the second search results further include graphics that indicate that the selectable search results correspond to web pages referenced in messages of the message feed of the social media account. 10. The computing system of claim 8, wherein the feature is a timestamp assigned to each message in the message feed that referenced one of the web pages in the second plurality of web pages such that a most highly positioned link in the second search results is referenced in a most recent message in the message feed from amongst messages that include references to the web pages in the second plurality of web pages. 11. A method executed at a server computing device, the method comprising:
identifying a plurality of documents from a document collection as being relevant to a query received from a client computing device; mapping the query to a topic from amongst a plurality of potential topics; responsive to mapping the query to the topic, identifying a social media account that is labeled as being authoritative on the topic, wherein the social media account has a message feed that includes messages; responsive to identifying the social media account, identifying a reference to a document in the plurality of documents in a message of the message feed; ordering the plurality of documents in a ranked list, wherein a position of the document in the ranked list is based upon the message in the message teed being identified as comprising the reference to the document; and transmitting search results to the client computing device responsive to ordering the plurality of documents, wherein the search results include links to the documents and are ordered in accordance with the ranked list. 12. The method of claim 11, wherein the documents are web pages, and wherein the reference to the document in the message is a uniform resource locator for the document. 13. The method of claim 11, wherein the social media account represents an entity. 14. The method of claim 13, wherein the position of the document in the ranked list is further based upon the social media account representing an entity rather than an individual. 15. The method of claim 11, further comprising:
receiving the messages of the message feed; identifying at least one of:
a number of messages in the message feed that reference the topic; or
a number of times that messages that reference the topic in the message feed have been rebroadcast by other social media accounts; and
labeling the social media account as being authoritative on the topic based upon at least one of the number of messages in message feed that reference the topic or the number of times that messages in the message feed have been rebroadcast by other social media accounts. 16. The method of claim 15, wherein identifying the reference to the document in the message of the message feed comprises:
determining that content of the message in the message feed is germane to the topic based upon content of the message; and identifying the reference to the document only after determining that the content of the message is germane to the topic. 17. The method of claim 11. wherein a search result in the search results comprises:
a link to the document; and a selectable graphic, wherein the selectable graphic identifies the social media account. 18. The method of claim 17, further comprising:
receiving an indication that a user of the client computing device has selected the selectable graphic; and responsive to receiving the indication, generating second search results, wherein the second search results consist of links to documents referenced in a subset of the messages of the message feed. 19. The method of claim 18, wherein the links to the documents in the second search results are ordered based upon timestamps assigned to the subset of the messages in the message feed. 20. A computer-readable storage medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform acts comprising:
receiving a query from a client computing device; generating search results responsive to receipt of the query, wherein the search results include an ordered list of links to web pages; and transmitting the search results to the client computing device, wherein generating the search results comprises:
identifying a topic referenced in the query,
identifying a social media account that is mapped to the topic in a database, wherein the social media account is mapped to the topic based upon the social media account being labeled as being authoritative on the topic;
determining that a message in a message feed of the social media account is relevant to the query;
identifying a reference to a web page in the message; and
positioning the web page in a ranked list of web pages responsive to identifying the reference to the web page in the message, wherein the web page is positioned in the ranked list of web pages based upon the reference to the web page being included in the message of the message feed of the social media account, wherein the ordered list of links of the search results corresponds to the ranked list of web pages. | 2,100 |
6,497 | 6,497 | 15,336,699 | 2,179 | Improving content descriptions and interaction efficiency by the dynamic enrichment of communication items is disclosed herein. A communication enrichment system receives a communication item, such as an email, for display within an application user interface. The system extracts enrichment details from the communication items. The communication enrichment system utilizes the enrichment details for querying one or more data sources and obtains any enrichment items relating to the enrichment details. Further, the communications items are modified to include the enrichment items to provide additional information, actions, functionality, or visual identifies to provide an enriched user interface. Accordingly, the communication enrichment system improves the efficiency of the communication client to visually identify the substance of the communication item and execute/perform any functionality associated with a communication item without opening the communication item. | 1. A method for providing dynamic enrichment of communication items, comprising:
receiving a communication item to display within an application user interface; extracting enrichment details from the communication item; querying one or more data sources for enrichment items relating to the enrichment details; obtaining an enrichment item relating to the enrichment details, the enrichment item including enriched textual information relating to the communication item; and modifying the communication item with the enrichment item. 2. The method of claim 1, wherein obtaining the enrichment item including enriched textual information relating to the communication item further comprises using machine learning models to generate the enriched textual information. 3. The method of claim 2, wherein the enriched textual information includes an enriched preview of the communication item. 4. The method of claim 2, wherein the enriched textual information includes an enriched subject of the communication item. 5. The method of claim 1, wherein extracting enrichment details from the communication item further includes parsing the communication item for one or more of: keywords, phrases, links, subject, message body, and contact information. 6. The method of claim 1, wherein extracting enrichment details from the communication item further includes extracting entity information that identifies an intent, communication type, or organization. 7. The method of claim 1, further comprising monitoring the enrichment item for changes. 8. The method of claim 7, wherein monitoring the enrichment item for changes further comprises monitoring the enrichment details, communication items, or the enrichment items to determine whether a change has occurred. 9. The method of claim 7, wherein a change occurs when the context relating to the communication item changed. 10. The method of claim 7, further comprising providing real-time status updates relating to the communication item and enrichment items. 11. A computing device for providing automatic enrichment of content with contextually relevant information, comprising:
a processing unit; and a memory, including computer readable instructions, which when executed by the processing unit is operable to:
receive a communication item to display within an application user interface;
extract enrichment details from the communication item;
query one or more data sources for enrichment items relating to the enrichment details;
obtain enriched textual information relating to the communication item and responsive to the enrichment details;
modify the communication item with an enriched textual information; and
monitor the enriched textual information for changes. 12. The computing device of claim 11, wherein the enriched textual information is generated using machine learning models. 13. The computing device of claim 12, wherein the enriched textual information includes an enriched preview of the communication item. 14. The computing device of claim 12, wherein the enriched textual information includes an enriched subject of the communication item. 15. The computing device of claim 11, wherein the processing unit is further operable to extract enrichment details from the communication item further includes parsing the communication item for one or more of: keywords, phrases, links, subject, message body, and contact information. 16. The computing device of claim 11, wherein the processing unit is further operable to extract enrichment details from the communication item further includes extracting entity information that identifies an intent, communication type, or organization. 17. The computing device of claim 11, wherein the processing unit is further operable to monitor the enriched textual information for changes further comprises monitoring the enrichment details, communication items, or the enriched textual information to determine whether a change has occurred. 18. The computing device of claim 11, wherein a change occurred when the context relating to the communication item changes. 19. The computing device of claim 11, wherein the processing unit is further operable to provide real-time status updates relating to the communication item and enrichment items. 20. A computer readable storage device including computer readable instructions, which when executed by a processing unit is operable to:
receiving a communication item to display within an application user interface; extracting enrichment details from the communication item; querying one or more data sources for enrichment items relating to the enrichment details; obtaining an enrichment item relating to the enrichment details, the enrichment item including enriched textual information relating to the communication item; modifying the communication item with the enrichment item; monitoring the enrichment item for changes; and providing real-time status updates to the enrichment items relating to the communication item. | Improving content descriptions and interaction efficiency by the dynamic enrichment of communication items is disclosed herein. A communication enrichment system receives a communication item, such as an email, for display within an application user interface. The system extracts enrichment details from the communication items. The communication enrichment system utilizes the enrichment details for querying one or more data sources and obtains any enrichment items relating to the enrichment details. Further, the communications items are modified to include the enrichment items to provide additional information, actions, functionality, or visual identifies to provide an enriched user interface. Accordingly, the communication enrichment system improves the efficiency of the communication client to visually identify the substance of the communication item and execute/perform any functionality associated with a communication item without opening the communication item.1. A method for providing dynamic enrichment of communication items, comprising:
receiving a communication item to display within an application user interface; extracting enrichment details from the communication item; querying one or more data sources for enrichment items relating to the enrichment details; obtaining an enrichment item relating to the enrichment details, the enrichment item including enriched textual information relating to the communication item; and modifying the communication item with the enrichment item. 2. The method of claim 1, wherein obtaining the enrichment item including enriched textual information relating to the communication item further comprises using machine learning models to generate the enriched textual information. 3. The method of claim 2, wherein the enriched textual information includes an enriched preview of the communication item. 4. The method of claim 2, wherein the enriched textual information includes an enriched subject of the communication item. 5. The method of claim 1, wherein extracting enrichment details from the communication item further includes parsing the communication item for one or more of: keywords, phrases, links, subject, message body, and contact information. 6. The method of claim 1, wherein extracting enrichment details from the communication item further includes extracting entity information that identifies an intent, communication type, or organization. 7. The method of claim 1, further comprising monitoring the enrichment item for changes. 8. The method of claim 7, wherein monitoring the enrichment item for changes further comprises monitoring the enrichment details, communication items, or the enrichment items to determine whether a change has occurred. 9. The method of claim 7, wherein a change occurs when the context relating to the communication item changed. 10. The method of claim 7, further comprising providing real-time status updates relating to the communication item and enrichment items. 11. A computing device for providing automatic enrichment of content with contextually relevant information, comprising:
a processing unit; and a memory, including computer readable instructions, which when executed by the processing unit is operable to:
receive a communication item to display within an application user interface;
extract enrichment details from the communication item;
query one or more data sources for enrichment items relating to the enrichment details;
obtain enriched textual information relating to the communication item and responsive to the enrichment details;
modify the communication item with an enriched textual information; and
monitor the enriched textual information for changes. 12. The computing device of claim 11, wherein the enriched textual information is generated using machine learning models. 13. The computing device of claim 12, wherein the enriched textual information includes an enriched preview of the communication item. 14. The computing device of claim 12, wherein the enriched textual information includes an enriched subject of the communication item. 15. The computing device of claim 11, wherein the processing unit is further operable to extract enrichment details from the communication item further includes parsing the communication item for one or more of: keywords, phrases, links, subject, message body, and contact information. 16. The computing device of claim 11, wherein the processing unit is further operable to extract enrichment details from the communication item further includes extracting entity information that identifies an intent, communication type, or organization. 17. The computing device of claim 11, wherein the processing unit is further operable to monitor the enriched textual information for changes further comprises monitoring the enrichment details, communication items, or the enriched textual information to determine whether a change has occurred. 18. The computing device of claim 11, wherein a change occurred when the context relating to the communication item changes. 19. The computing device of claim 11, wherein the processing unit is further operable to provide real-time status updates relating to the communication item and enrichment items. 20. A computer readable storage device including computer readable instructions, which when executed by a processing unit is operable to:
receiving a communication item to display within an application user interface; extracting enrichment details from the communication item; querying one or more data sources for enrichment items relating to the enrichment details; obtaining an enrichment item relating to the enrichment details, the enrichment item including enriched textual information relating to the communication item; modifying the communication item with the enrichment item; monitoring the enrichment item for changes; and providing real-time status updates to the enrichment items relating to the communication item. | 2,100 |
6,498 | 6,498 | 16,166,006 | 2,113 | Dynamic checkpointing can include determining, using a processor, a process criticality corresponding to a process configured to execute on computer hardware. The process criticality indicates a priority of the process relative to at least one other process configured to execute on the computer hardware. A checkpoint schedule can be generated, using the processor and based on the process criticality, for checkpointing the process when it executes on the computer hardware. | 1. A method, comprising:
determining, using a processor, a process criticality corresponding to a first process, the process criticality indicating a priority for the first process relative to at least a second process configured to execute on computer hardware; and generating a checkpointing schedule, using the processor and based on the process criticality, for checkpointing the first process when the first process executes on the computer hardware. 2. The method of claim 1, wherein the checkpointing schedule generated comprises at least a first and a second checkpointing frequency, the first checkpointing frequency corresponding to an operation executed when executing the first process and the second checkpointing frequency corresponding to a different operation executed when executing the first process. 3. The method of claim 1, wherein the generating the checkpointing schedule comprises generating a first checkpointing schedule in response to a first predefined external event and generating a second checkpointing schedule in response to a second predefined external event. 4. The method of claim 3, further comprising detecting the first external event. 5. The method of claim 1, wherein the checkpointing schedule generated corresponds to a user-selected checkpointing class of service. 6. The method of claim 5, wherein the selected checkpointing class of service is selected from a plurality of checkpoint classes of service. 7. The method of claim 6, further comprising automatically changing the checkpointing class of service responsive to a predesignated external event occurring. 8. A system, comprising:
a processor programmed to initiate executable operations, the operations including:
determining a process criticality corresponding to a first process configured to execute on computer hardware, the process criticality indicating a priority for the first process relative to at least a second process configured to execute on the computer hardware; and
generating a checkpointing schedule based on the process criticality for checkpointing the first process when the first process executes on the computer hardware. 9. The system of claim 8, wherein the schedule generated comprises at least a first and a second checkpointing frequency, the first checkpointing frequency corresponding to an operation executed when processing the first process and the second checkpointing frequency corresponding to a different operation executed when processing the first process. 10. The system of claim 8, wherein generating the checkpointing schedule comprises generating a first checkpointing schedule if a first predefined external event occurs and generating a second checkpointing schedule if a second predefined external event occurs. 11. The system of claim 10, further comprising automatically determining when the first external event occurs. 12. The system of claim 8, wherein the checkpointing schedule generated corresponds to a user-selected checkpointing class of service. 13. The system of claim 12, wherein the selected checkpointing class of service is selected from a plurality of checkpointing classes of service. 14. The system of claim 13, further comprising automatically changing the checkpointing class of service responsive to a predesignated external event occurring. 15. A computer program product, comprising a computer readable storage medium having program code stored thereon, the program code executable by a processor to initiate operations comprising:
determining a process criticality corresponding to a first process configured to execute on computer hardware, the process criticality indicating a priority for the first process relative to at least a second process configured to execute on the computer hardware; and generating a checkpointing schedule, based on the process criticality, for checkpointing the first process when the first process executes on the computer hardware. 16. The computer program product of claim 15, wherein the schedule generated comprises at least a first and a second checkpointing frequency, the first checkpointing frequency corresponding to an operation executed when processing the first process and the second checkpointing frequency corresponding to a different operation executed when processing the first process. 17. The computer program product of claim 15, wherein generating the checkpointing schedule comprises generating a first checkpointing schedule if a first predefined external event occurs and generating a second checkpointing schedule if a second predefined external event occurs. 18. The computer program product of claim 17, further comprising automatically determining when the first external event occurs. 19. The computer program product of claim 15, wherein the checkpointing schedule generated corresponds to a user-selected checkpointing class of service. 20. The computer program product of claim 19, wherein the selected checkpointing class of service is selected from a plurality of checkpointing classes of service. | Dynamic checkpointing can include determining, using a processor, a process criticality corresponding to a process configured to execute on computer hardware. The process criticality indicates a priority of the process relative to at least one other process configured to execute on the computer hardware. A checkpoint schedule can be generated, using the processor and based on the process criticality, for checkpointing the process when it executes on the computer hardware.1. A method, comprising:
determining, using a processor, a process criticality corresponding to a first process, the process criticality indicating a priority for the first process relative to at least a second process configured to execute on computer hardware; and generating a checkpointing schedule, using the processor and based on the process criticality, for checkpointing the first process when the first process executes on the computer hardware. 2. The method of claim 1, wherein the checkpointing schedule generated comprises at least a first and a second checkpointing frequency, the first checkpointing frequency corresponding to an operation executed when executing the first process and the second checkpointing frequency corresponding to a different operation executed when executing the first process. 3. The method of claim 1, wherein the generating the checkpointing schedule comprises generating a first checkpointing schedule in response to a first predefined external event and generating a second checkpointing schedule in response to a second predefined external event. 4. The method of claim 3, further comprising detecting the first external event. 5. The method of claim 1, wherein the checkpointing schedule generated corresponds to a user-selected checkpointing class of service. 6. The method of claim 5, wherein the selected checkpointing class of service is selected from a plurality of checkpoint classes of service. 7. The method of claim 6, further comprising automatically changing the checkpointing class of service responsive to a predesignated external event occurring. 8. A system, comprising:
a processor programmed to initiate executable operations, the operations including:
determining a process criticality corresponding to a first process configured to execute on computer hardware, the process criticality indicating a priority for the first process relative to at least a second process configured to execute on the computer hardware; and
generating a checkpointing schedule based on the process criticality for checkpointing the first process when the first process executes on the computer hardware. 9. The system of claim 8, wherein the schedule generated comprises at least a first and a second checkpointing frequency, the first checkpointing frequency corresponding to an operation executed when processing the first process and the second checkpointing frequency corresponding to a different operation executed when processing the first process. 10. The system of claim 8, wherein generating the checkpointing schedule comprises generating a first checkpointing schedule if a first predefined external event occurs and generating a second checkpointing schedule if a second predefined external event occurs. 11. The system of claim 10, further comprising automatically determining when the first external event occurs. 12. The system of claim 8, wherein the checkpointing schedule generated corresponds to a user-selected checkpointing class of service. 13. The system of claim 12, wherein the selected checkpointing class of service is selected from a plurality of checkpointing classes of service. 14. The system of claim 13, further comprising automatically changing the checkpointing class of service responsive to a predesignated external event occurring. 15. A computer program product, comprising a computer readable storage medium having program code stored thereon, the program code executable by a processor to initiate operations comprising:
determining a process criticality corresponding to a first process configured to execute on computer hardware, the process criticality indicating a priority for the first process relative to at least a second process configured to execute on the computer hardware; and generating a checkpointing schedule, based on the process criticality, for checkpointing the first process when the first process executes on the computer hardware. 16. The computer program product of claim 15, wherein the schedule generated comprises at least a first and a second checkpointing frequency, the first checkpointing frequency corresponding to an operation executed when processing the first process and the second checkpointing frequency corresponding to a different operation executed when processing the first process. 17. The computer program product of claim 15, wherein generating the checkpointing schedule comprises generating a first checkpointing schedule if a first predefined external event occurs and generating a second checkpointing schedule if a second predefined external event occurs. 18. The computer program product of claim 17, further comprising automatically determining when the first external event occurs. 19. The computer program product of claim 15, wherein the checkpointing schedule generated corresponds to a user-selected checkpointing class of service. 20. The computer program product of claim 19, wherein the selected checkpointing class of service is selected from a plurality of checkpointing classes of service. | 2,100 |
6,499 | 6,499 | 15,609,978 | 2,175 | In one general aspect, a method can include receiving, in a user interface of a first page of an application executing on a computing device, a selection of an animation option, receiving, in a user interface of a second page of the application executing on the computing device, a selection of an icon. In response to receiving the selection of the icon, the method can further include launching a third page of the application, and performing an animation of a visual presentation of the launching of the third page of the application from the second page of the application. The animation can be based on the received animation option selection. | 1. A method comprising:
receiving, in a user interface of a first page of an application executing on a computing device, a selection of an animation option; receiving, in a user interface of a second page of the application executing on the computing device, a selection of an icon; in response to receiving the selection of the icon, launching a third page of the application; and performing an animation of a visual presentation of the launching of the third page of the application from the second page of the application, the animation being based on the received animation option selection. 2. The method of claim 1, wherein the animation option is one of a full option, a basic option, or a minimal option. 3. The method of claim 1, further including identifying an animation mode of operation based on the received animation option selection. 4. The method of claim 3, wherein an animation action is associated with the identified animation mode of operation. 5. The method of claim 4, wherein the animation action determines the performing of the animation of the visual presentation of the launching of the third page of the application from the second page of the application by the computing device. 6. The method of claim 1, wherein the animation is further based on a type of computing device. 7. The method of claim 1, wherein the visual presentation includes a busy indicator. 8. A non-transitory, machine-readable medium having instructions stored thereon, the instructions, when executed by a processor, cause a computing device to:
receive, in a user interface of a first page of an application executing on a computing device, a selection of an animation option; receive, in a user interface of a second page of the application executing on the computing device, a selection of an icon; in response to receiving the selection of the icon, launch a third page of the application; and perform an animation of a visual presentation of the launching of the third page of the application from the second page of the application, the animation being based on the received animation option selection. 9. The medium of claim 8, wherein the animation option is one of a full option, a basic option, or a minimal option. 10. The medium of claim 8, wherein the instructions, when executed by the processor, further cause the computing device to identify an animation mode of operation based on the received animation option selection. 11. The medium of claim 10, wherein an animation action is associated with the identified animation mode of operation. 12. The medium of claim 11, wherein the animation action determines the performing of the animation of the visual presentation of the launching of the third page of the application from the second page of the application by the computing device. 13. The medium of claim 8, wherein the animation is further based on a type of computing device. 14. The medium of claim 8, wherein the visual presentation includes a busy indicator. 15. A system comprising:
a computer system including an animation control module; and a computing device including a display device, the computing device configured to:
receive, in a user interface of a first page of an application executing on a computing device, a selection of an animation option;
provide the selected animation option to the computer system for use by the animation control module;
receive, in a user interface of a second page of the application executing on the computing device, a selection of an icon;
in response to receiving the selection of the icon, launch a third page of the application; and
perform an animation of a visual presentation of the launching of the third page of the application from the second page of the application, the animation being based on the received animation option selection. 16. The system of claim 15, wherein the selected animation option is stored in a database accessible by the computer system. 17. The system of claim 15, wherein the animation option is one of a full option, a basic option, or a minimal option. 18. The system of claim 15, wherein the animation control module is configured to identify an animation mode of operation based on the selected animation option. 19. The system of claim 18,
wherein the animation control module is further configured to associate an animation action with the identified animation mode of operation, and wherein the animation action determines how the animating of the visual presentation of the launching of the third page of the application from the second page of the application is performed by the computing device. 20. The system of claim 15, wherein the animation is further based on a type of computing device. | In one general aspect, a method can include receiving, in a user interface of a first page of an application executing on a computing device, a selection of an animation option, receiving, in a user interface of a second page of the application executing on the computing device, a selection of an icon. In response to receiving the selection of the icon, the method can further include launching a third page of the application, and performing an animation of a visual presentation of the launching of the third page of the application from the second page of the application. The animation can be based on the received animation option selection.1. A method comprising:
receiving, in a user interface of a first page of an application executing on a computing device, a selection of an animation option; receiving, in a user interface of a second page of the application executing on the computing device, a selection of an icon; in response to receiving the selection of the icon, launching a third page of the application; and performing an animation of a visual presentation of the launching of the third page of the application from the second page of the application, the animation being based on the received animation option selection. 2. The method of claim 1, wherein the animation option is one of a full option, a basic option, or a minimal option. 3. The method of claim 1, further including identifying an animation mode of operation based on the received animation option selection. 4. The method of claim 3, wherein an animation action is associated with the identified animation mode of operation. 5. The method of claim 4, wherein the animation action determines the performing of the animation of the visual presentation of the launching of the third page of the application from the second page of the application by the computing device. 6. The method of claim 1, wherein the animation is further based on a type of computing device. 7. The method of claim 1, wherein the visual presentation includes a busy indicator. 8. A non-transitory, machine-readable medium having instructions stored thereon, the instructions, when executed by a processor, cause a computing device to:
receive, in a user interface of a first page of an application executing on a computing device, a selection of an animation option; receive, in a user interface of a second page of the application executing on the computing device, a selection of an icon; in response to receiving the selection of the icon, launch a third page of the application; and perform an animation of a visual presentation of the launching of the third page of the application from the second page of the application, the animation being based on the received animation option selection. 9. The medium of claim 8, wherein the animation option is one of a full option, a basic option, or a minimal option. 10. The medium of claim 8, wherein the instructions, when executed by the processor, further cause the computing device to identify an animation mode of operation based on the received animation option selection. 11. The medium of claim 10, wherein an animation action is associated with the identified animation mode of operation. 12. The medium of claim 11, wherein the animation action determines the performing of the animation of the visual presentation of the launching of the third page of the application from the second page of the application by the computing device. 13. The medium of claim 8, wherein the animation is further based on a type of computing device. 14. The medium of claim 8, wherein the visual presentation includes a busy indicator. 15. A system comprising:
a computer system including an animation control module; and a computing device including a display device, the computing device configured to:
receive, in a user interface of a first page of an application executing on a computing device, a selection of an animation option;
provide the selected animation option to the computer system for use by the animation control module;
receive, in a user interface of a second page of the application executing on the computing device, a selection of an icon;
in response to receiving the selection of the icon, launch a third page of the application; and
perform an animation of a visual presentation of the launching of the third page of the application from the second page of the application, the animation being based on the received animation option selection. 16. The system of claim 15, wherein the selected animation option is stored in a database accessible by the computer system. 17. The system of claim 15, wherein the animation option is one of a full option, a basic option, or a minimal option. 18. The system of claim 15, wherein the animation control module is configured to identify an animation mode of operation based on the selected animation option. 19. The system of claim 18,
wherein the animation control module is further configured to associate an animation action with the identified animation mode of operation, and wherein the animation action determines how the animating of the visual presentation of the launching of the third page of the application from the second page of the application is performed by the computing device. 20. The system of claim 15, wherein the animation is further based on a type of computing device. | 2,100 |
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