Datasets:

midah
/

patent-wireframes

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
figure_id: string
patent_id: string
patent_title: string
caption: string
drawing_description: string
detailed_description: string
brief_summary: string
claims: string
viewpoint: string
figure_number: int32
n_figures_in_patent: int32
year: int32
svg: string
sibling_figure_ids: list<element: string>
  child 0, element: string
locarno_class: string
patent_date: string
image: struct<bytes: binary, path: string>
  child 0, bytes: binary
  child 1, path: string
-- schema metadata --
huggingface: '{"info": {"features": {"figure_id": {"dtype": "string", "_t' + 894
to
{'figure_id': Value('string'), 'patent_id': Value('string'), 'image': Image(mode=None, decode=True), 'patent_title': Value('string'), 'caption': Value('string'), 'drawing_description': Value('string'), 'detailed_description': Value('string'), 'brief_summary': Value('string'), 'claims': Value('string'), 'viewpoint': Value('string'), 'figure_number': Value('int32'), 'n_figures_in_patent': Value('int32'), 'sibling_figure_ids': List(Value('string')), 'reference_numerals': List({'numeral': Value('string'), 'label': Value('string')}), 'locarno_class': Value('string'), 'patent_date': Value('string'), 'year': Value('int32')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2567, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2102, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2125, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 479, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 380, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 209, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 147, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              figure_id: string
              patent_id: string
              patent_title: string
              caption: string
              drawing_description: string
              detailed_description: string
              brief_summary: string
              claims: string
              viewpoint: string
              figure_number: int32
              n_figures_in_patent: int32
              year: int32
              svg: string
              sibling_figure_ids: list<element: string>
                child 0, element: string
              locarno_class: string
              patent_date: string
              image: struct<bytes: binary, path: string>
                child 0, bytes: binary
                child 1, path: string
              -- schema metadata --
              huggingface: '{"info": {"features": {"figure_id": {"dtype": "string", "_t' + 894
              to
              {'figure_id': Value('string'), 'patent_id': Value('string'), 'image': Image(mode=None, decode=True), 'patent_title': Value('string'), 'caption': Value('string'), 'drawing_description': Value('string'), 'detailed_description': Value('string'), 'brief_summary': Value('string'), 'claims': Value('string'), 'viewpoint': Value('string'), 'figure_number': Value('int32'), 'n_figures_in_patent': Value('int32'), 'sibling_figure_ids': List(Value('string')), 'reference_numerals': List({'numeral': Value('string'), 'label': Value('string')}), 'locarno_class': Value('string'), 'patent_date': Value('string'), 'year': Value('int32')}
              because column names don't match

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Patent Wireframes

Structured patent figure dataset for multimodal understanding — generation, reconstruction, retrieval, and cross-modal association.

Overview

Each record is a single patent figure paired with:

Full patent text (drawing description, detailed description, brief summary, claims)
IMPACT-generated figure caption
Extracted reference numeral mappings (numeral → component label)
Sibling figure links (other views of the same object)
Classification metadata (Locarno codes, dates)

Sources

Images + captions: IMPACT (CC-BY-SA-4.0)
Patent text: PatentsView (CC-BY-4.0)
Scope: US design patents, initially 2022

Schema

Field	Type	Description
`figure_id`	string	Unique: `{patent_id}_{figure_number}`
`patent_id`	string	USPTO design patent number
`image`	image	Figure raster
`patent_title`	string	Patent title
`caption`	string	IMPACT-generated caption
`drawing_description`	string	Drawing description from patent text
`detailed_description`	string	Full detailed description
`brief_summary`	string	Brief summary
`claims`	string	Patent claims
`viewpoint`	string	front / side / perspective / etc.
`figure_number`	int32	Figure index within patent
`n_figures_in_patent`	int32	Total figures in patent
`sibling_figure_ids`	list[string]	Other figure IDs from same patent
`reference_numerals`	list[{numeral, label}]	Extracted component mappings
`locarno_class`	string	Locarno classification code
`patent_date`	string	Grant date
`year`	int32	Grant year

Intended uses

Reconstruction: predict figure from text description (or vice versa)
Retrieval: text-to-figure, figure-to-figure, component-to-figure
Generation: produce patent figures from structured descriptions
Multi-view reasoning: predict unseen views given other views
Component understanding: reference numeral grounding

Citation

If you use this dataset, please cite:

@dataset{patent_wireframes,
  title={Patent Wireframes: A Structured Dataset for Multimodal Patent Figure Understanding},
  year={2026},
  url={https://huggingface.co/datasets/midah/patent-wireframes},
}

Built on IMPACT (NeurIPS 2024) and PatentsView.

Downloads last month: 6

Size of downloaded dataset files:

137 kB

Size of the auto-converted Parquet files:

137 kB

Number of rows:

603