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| SPARKNET PRESENTATION - COMPLETE SPEAKER NOTES | |
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| SLIDE 1 | |
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| OPENING REMARKS (2 minutes): | |
| Good [morning/afternoon]. Thank you for this opportunity to present SPARKNET, an AI-powered system for academic research valorization. | |
| KEY MESSAGE: We are at the BEGINNING of a 3-year research journey. Today's demonstration represents approximately 5-10% of the planned work - a proof-of-concept prototype that validates technical feasibility while revealing the extensive research and development ahead. | |
| POSITIONING: | |
| - This is NOT a finished product - it's an early-stage research prototype | |
| - We're seeking stakeholder buy-in for a comprehensive 3-year development program | |
| - The prototype demonstrates technical viability but requires significant investment in all areas | |
| AGENDA OVERVIEW: | |
| 1. Research context and VISTA alignment | |
| 2. Current prototype capabilities (10% complete) | |
| 3. Detailed breakdown of work remaining (90% ahead) | |
| 4. 3-year research roadmap by VISTA work packages | |
| 5. Resource requirements and expected outcomes | |
| Let's begin with the research context... | |
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| SLIDE 2 | |
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| PROJECT STAGE TRANSPARENCY (3 minutes): | |
| CRITICAL FRAMING: Set realistic expectations immediately. We must be completely transparent about our current stage to build trust and justify the 3-year timeline. | |
| WHAT THE PROTOTYPE IS: | |
| - A working demonstration that proves the core concept is technically viable | |
| - Sufficient to show stakeholders what the final system COULD become | |
| - Evidence that our multi-agent architecture can handle patent valorization workflows | |
| - A foundation upon which extensive research and development will be built | |
| WHAT THE PROTOTYPE IS NOT: | |
| - Not production-ready - lacks robustness, scalability, security | |
| - Not research-complete - many algorithms, methods, and frameworks are placeholder or simplified | |
| - Not feature-complete - critical capabilities are missing or stubbed | |
| - Not validated - no user studies, no real-world testing, no performance benchmarks | |
| THE 5-10% ESTIMATE BREAKDOWN: | |
| - Architecture & Infrastructure: 15% complete (basic workflow established) | |
| - AI/ML Capabilities: 5% complete (simple LLM chains, no sophisticated reasoning) | |
| - Data & Knowledge Bases: 2% complete (tiny mock databases) | |
| - User Experience: 8% complete (basic interface, no usability testing) | |
| - VISTA Compliance: 10% complete (awareness of standards, minimal implementation) | |
| - Integration & Deployment: 5% complete (local dev environment only) | |
| WHY THIS IS GOOD NEWS FOR STAKEHOLDERS: | |
| - We've de-risked the technical approach - we know it CAN work | |
| - The 90% remaining gives us clear scope for innovation and IP generation | |
| - Three-year timeline is realistic and defensible | |
| - Significant opportunities for stakeholder input to shape development | |
| TRANSITION: "Let's examine our research context and how SPARKNET aligns with VISTA objectives..." | |
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| SLIDE 3 | |
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| VISTA ALIGNMENT & WORK PACKAGE DECOMPOSITION (4 minutes): | |
| PURPOSE: Show stakeholders how SPARKNET maps directly to VISTA's structure and where the bulk of work remains. | |
| WP1 - PROJECT MANAGEMENT (Current: 5%): | |
| What we have: | |
| - Basic Git version control | |
| - Simple documentation in Markdown | |
| - Informal development process | |
| What we need (36 months): | |
| - Formal project governance structure | |
| - Stakeholder advisory board and regular consultations | |
| - Deliverable and milestone tracking system | |
| - Risk management framework | |
| - Quality assurance processes | |
| - Budget management and reporting | |
| - IP management and exploitation planning | |
| - Dissemination and communication strategy | |
| WP2 - VALORIZATION PATHWAYS (Current: 15%): | |
| What we have: | |
| - Scenario 1 (Patent Wake-Up) basic workflow | |
| - Simple TRL assessment (rule-based) | |
| - Basic technology domain identification | |
| - Simplified market opportunity analysis | |
| What we need (36 months): | |
| Research challenges: | |
| - Sophisticated TRL assessment methodology (ML-based, context-aware) | |
| - Multi-criteria decision support for valorization pathway selection | |
| - Comparative analysis across multiple patents (portfolio management) | |
| - Technology maturity prediction models | |
| - Market readiness assessment frameworks | |
| - Batch processing and workflow optimization | |
| Implementation challenges: | |
| - Scenario 2 (Agreement Safety): Legal document analysis, risk assessment, compliance checking | |
| - Scenario 3 (Partner Matching): Profile analysis, collaboration history, complementarity scoring | |
| - Integration with real technology transfer workflows | |
| - Performance optimization for large patent portfolios | |
| - User interface for pathway exploration and what-if analysis | |
| WP3 - QUALITY STANDARDS (Current: 8%): | |
| What we have: | |
| - Simple quality threshold (0.8 cutoff) | |
| - Basic Critic agent validation | |
| - Rudimentary output checking | |
| What we need (36 months): | |
| Research challenges: | |
| - Operationalize VISTA's 12-dimension quality framework: | |
| 1. Completeness: Are all required sections present? | |
| 2. Accuracy: Is information factually correct? | |
| 3. Relevance: Does analysis match patent scope? | |
| 4. Timeliness: Are market insights current? | |
| 5. Consistency: Is terminology uniform? | |
| 6. Objectivity: Are assessments unbiased? | |
| 7. Clarity: Is language accessible? | |
| 8. Actionability: Are recommendations concrete? | |
| 9. Evidence-based: Are claims supported? | |
| 10. Stakeholder-aligned: Does it meet needs? | |
| 11. Reproducibility: Can results be replicated? | |
| 12. Ethical compliance: Does it meet standards? | |
| - Develop computational metrics for each dimension | |
| - Create weighted scoring models | |
| - Build automated compliance checking | |
| - Establish benchmarking methodologies | |
| Implementation challenges: | |
| - Quality dashboard and reporting | |
| - Real-time quality monitoring | |
| - Historical quality tracking and improvement analysis | |
| - Integration with VISTA quality certification process | |
| WP4 - STAKEHOLDER NETWORKS (Current: 3%): | |
| What we have: | |
| - Mock database (50 fabricated entries) | |
| - Basic vector similarity search | |
| - Simple scoring (single-dimension) | |
| What we need (36 months): | |
| Data challenges: | |
| - Build comprehensive stakeholder database (10,000+ real entities) | |
| * Universities: 2,000+ institutions (EU + Canada) | |
| * Research centers: 1,500+ organizations | |
| * Technology transfer offices: 500+ TTOs | |
| * Industry partners: 4,000+ companies | |
| * Government agencies: 1,000+ entities | |
| - Data collection strategy (web scraping, partnerships, public databases) | |
| - Data quality and maintenance (update frequency, verification) | |
| - Privacy and consent management (GDPR, Canadian privacy law) | |
| Research challenges: | |
| - Multi-dimensional stakeholder profiling: | |
| * Research expertise and focus areas | |
| * Historical collaboration patterns | |
| * Technology absorption capacity | |
| * Geographic reach and networks | |
| * Funding availability | |
| * Strategic priorities | |
| - Advanced matching algorithms: | |
| * Semantic similarity (embeddings) | |
| * Graph-based network analysis | |
| * Temporal dynamics (changing interests) | |
| * Success prediction models | |
| - Complementarity assessment (who works well together?) | |
| - Network effect analysis (introducing multiple parties) | |
| Implementation challenges: | |
| - CRM integration (Salesforce, Microsoft Dynamics) | |
| - Real-time stakeholder data updates | |
| - Stakeholder portal (self-service profile management) | |
| - Privacy-preserving search (anonymization, secure computation) | |
| WP5 - DIGITAL TOOLS & PLATFORMS (Current: 10%): | |
| What we have: | |
| - Basic Next.js web interface (demo quality) | |
| - Simple FastAPI backend | |
| - Local deployment only | |
| - No user management or security | |
| What we need (36 months): | |
| Platform development: | |
| - Production-ready web application | |
| * Enterprise-grade UI/UX (user testing, accessibility) | |
| * Multi-tenant architecture (institution-specific instances) | |
| * Role-based access control (researcher, TTO, admin) | |
| * Mobile-responsive design (tablet, smartphone) | |
| - API ecosystem | |
| * RESTful API for third-party integration | |
| * Webhook support for event notifications | |
| * API rate limiting and monitoring | |
| * Developer documentation and sandbox | |
| Infrastructure & deployment: | |
| - Cloud infrastructure (AWS/Azure/GCP) | |
| - Containerization (Docker, Kubernetes) | |
| - CI/CD pipelines | |
| - Monitoring and logging (Prometheus, Grafana, ELK stack) | |
| - Backup and disaster recovery | |
| - Scalability (handle 1000+ concurrent users) | |
| - Security hardening (penetration testing, OWASP compliance) | |
| Integration requirements: | |
| - Single Sign-On (SSO) / SAML / OAuth | |
| - Integration with university systems (CRIS, RIS) | |
| - Document management systems | |
| - Email and notification services | |
| - Payment gateways (for premium features) | |
| - Analytics and business intelligence | |
| TRANSITION: "Now let's examine the specific research and implementation challenges ahead..." | |
| ================================================================================ | |
| SLIDE 4 | |
| ================================================================================ | |
| CURRENT CAPABILITIES - HONEST ASSESSMENT (3 minutes): | |
| PURPOSE: Show what works while being transparent about limitations. Build credibility through honesty. | |
| MULTI-AGENT ARCHITECTURE (Functional Prototype): | |
| What's working: | |
| - 4 agents successfully communicate and coordinate | |
| - LangGraph manages workflow state correctly | |
| - Planner-Critic loop demonstrates iterative improvement | |
| - Memory stores persist and retrieve data | |
| Technical limitations: | |
| - Agents use simple prompt chains (no sophisticated reasoning) | |
| - No agent learning or improvement over time | |
| - Memory is not properly structured or indexed | |
| - No conflict resolution when agents disagree | |
| - Workflow is rigid (cannot adapt to different patent types) | |
| Research needed: | |
| - Advanced agent reasoning (chain-of-thought, tree-of-thought) | |
| - Multi-agent coordination strategies | |
| - Memory architecture optimization | |
| - Dynamic workflow adaptation | |
| - Agent performance evaluation metrics | |
| DOCUMENT ANALYSIS (Basic Text Processing): | |
| What's working: | |
| - Extracts text from text-based PDFs | |
| - Parses independent and dependent claims | |
| - Assigns TRL levels (though simplistic) | |
| - Identifies basic innovation themes | |
| Technical limitations: | |
| - Fails on scanned PDFs (image-based) | |
| - Cannot analyze diagrams or figures | |
| - Misses important information in tables | |
| - English-only (no multi-language) | |
| - No context understanding (treats all patents the same) | |
| Research needed: | |
| - Robust OCR pipeline (PDF→image→text→structure) | |
| - Diagram and figure analysis (computer vision) | |
| - Table extraction and interpretation | |
| - Multi-language NLP (French, German, etc.) | |
| - Patent type classification and adapted processing | |
| - Technical domain-specific analysis | |
| OCR FOUNDATION (Just Implemented - Nov 2025): | |
| What's working: | |
| - llava:7b vision model operational on GPU | |
| - VisionOCRAgent class created with 5 methods | |
| - Successfully integrated with DocumentAnalysisAgent | |
| - Basic text extraction from images demonstrated | |
| Technical limitations: | |
| - NO PDF-to-image conversion (critical missing piece) | |
| - No batch processing (one image at a time) | |
| - No quality assessment (how good is the OCR?) | |
| - No error recovery (what if OCR fails?) | |
| - Not optimized (slow, high GPU memory) | |
| - No production deployment strategy | |
| Research needed (Major Work Ahead): | |
| Phase 2 (Months 4-6): PDF→Image Pipeline | |
| - Implement pdf2image conversion | |
| - Handle multi-page documents | |
| - Detect diagrams vs text regions | |
| - Optimize image quality for OCR | |
| Phase 3 (Months 7-12): Production OCR System | |
| - Batch processing and queuing | |
| - Quality assessment and confidence scoring | |
| - Error detection and human review workflow | |
| - OCR output post-processing (spelling correction, formatting) | |
| - Performance optimization (reduce GPU usage, speed) | |
| - Fallback strategies (when OCR fails) | |
| Phase 4 (Months 13-18): Advanced Vision Analysis | |
| - Diagram type classification (flowchart, circuit, etc.) | |
| - Figure-caption association | |
| - Table structure understanding | |
| - Handwritten annotation detection | |
| - Multi-language OCR (not just English) | |
| STAKEHOLDER MATCHING (Mock Data Proof): | |
| What's working: | |
| - Vector search returns similar entities | |
| - Basic similarity scoring | |
| - Simple recommendation list | |
| Technical limitations: | |
| - Mock database (50 fabricated entries - NOT REAL DATA) | |
| - Single-dimension matching (text similarity only) | |
| - No validation (are matches actually good?) | |
| - No user feedback or learning | |
| - No network effects (doesn't consider who knows whom) | |
| Research needed: | |
| - Real data collection (massive undertaking, see WP4) | |
| - Multi-dimensional matching algorithms | |
| - Success prediction models (will this collaboration work?) | |
| - User feedback integration and learning | |
| - Network analysis and graph algorithms | |
| - Privacy-preserving matching techniques | |
| KEY TAKEAWAY: We have a working demo that proves the concept, but every component needs significant research and development to be production-ready. | |
| TRANSITION: "Now let's break down the extensive work ahead across our 3-year timeline..." | |
| ================================================================================ | |
| SLIDE 5 | |
| ================================================================================ | |
| 3-YEAR ROADMAP - DETAILED TIMELINE (5 minutes): | |
| PURPOSE: Give stakeholders a realistic, structured view of the work ahead and resource requirements. | |
| YEAR 1: FOUNDATION & CORE RESEARCH (Months 1-12) | |
| ======================================== | |
| Quarter 1 (Months 1-3): OCR Pipeline Development | |
| - Task: Build production-ready PDF→Image→Text→Structure pipeline | |
| - Challenges: | |
| * PDF parsing (various formats, encryption, damage) | |
| * Image quality optimization (resolution, contrast, noise) | |
| * OCR engine selection and tuning (llava vs alternatives) | |
| * Structure reconstruction (maintain layout, reading order) | |
| - Deliverables: | |
| * Working OCR pipeline handling 95%+ of patent PDFs | |
| * Quality assessment module (confidence scoring) | |
| * Performance benchmarks (speed, accuracy) | |
| - Resources needed: | |
| * 2 research engineers (computer vision + NLP) | |
| * GPU infrastructure (8 GPUs for parallel processing) | |
| * Test dataset (1,000+ diverse patents) | |
| * 3 months × 2 FTEs = 6 person-months | |
| Quarter 2 (Months 4-6): Database & Quality Framework Start | |
| - Parallel Track A: Stakeholder Database | |
| * Task: Begin constructing real stakeholder database | |
| * Target: 2,000 initial entries (universities + major research centers) | |
| * Challenges: Data collection, verification, schema design, privacy compliance | |
| * Resources: 1 data engineer + partnerships with university networks | |
| - Parallel Track B: Quality Framework | |
| * Task: Implement VISTA's 12-dimension quality framework | |
| * Operationalize each dimension into computable metrics | |
| * Build quality dashboard and reporting | |
| * Resources: 1 research scientist + VISTA quality team consultation | |
| Quarter 3 (Months 7-9): Quality Framework Completion & User Studies | |
| - Task A: Complete quality framework implementation | |
| * Validation studies (does it match human assessment?) | |
| * Refinement based on stakeholder feedback | |
| * Integration with workflow | |
| - Task B: User studies & requirement gathering | |
| * Recruit 20-30 TTO professionals for studies | |
| * Usability testing of prototype | |
| * Requirement elicitation for Scenarios 2 & 3 | |
| * Resources: UX researcher, travel budget, participant compensation | |
| Quarter 4 (Months 10-12): Scenario 2 Design & Database Expansion | |
| - Task A: Scenario 2 (Agreement Safety) design | |
| * Literature review on legal document analysis | |
| * Requirement gathering from legal experts | |
| * Architecture design and initial implementation | |
| * Resources: Legal informatics expert (consultant) | |
| - Task B: Stakeholder database expansion | |
| * Grow from 2,000 to 5,000 entries | |
| * Add industry partners and government agencies | |
| * Improve data quality and coverage | |
| Year 1 Milestones: | |
| - M6: OCR pipeline operational, 2,000 stakeholders in database | |
| - M9: Quality framework validated, user study results | |
| - M12: Scenario 2 design complete, 5,000 stakeholders | |
| YEAR 2: SCALE & INTELLIGENCE (Months 13-24) | |
| ======================================== | |
| Quarter 1 (Months 13-15): Advanced AI/ML Models | |
| - Task: Move beyond simple LLM chains to sophisticated reasoning | |
| - Research challenges: | |
| * Chain-of-thought and tree-of-thought reasoning for complex analysis | |
| * Few-shot and zero-shot learning for rare patent types | |
| * Multi-modal models (text + images + tables together) | |
| * Agent learning and improvement over time | |
| - Implementation: | |
| * Fine-tune specialized models for patent analysis | |
| * Implement advanced prompting techniques | |
| * Build agent memory and learning mechanisms | |
| - Resources: 2 AI/ML researchers, GPU cluster, training data | |
| Quarter 2 (Months 16-18): Prediction & Stakeholder Expansion | |
| - Task A: Success prediction models | |
| * Predict likelihood of successful technology transfer | |
| * Estimate time-to-market for different pathways | |
| * Assess collaboration compatibility between partners | |
| * Resources: Data scientist, historical collaboration data | |
| - Task B: Stakeholder database to 10,000+ | |
| * Automated data collection pipelines (web scraping) | |
| * Partnership with stakeholder networks for data sharing | |
| * Comprehensive coverage across EU and Canada | |
| Quarter 3 (Months 19-21): Scenarios 2 & 3 Development | |
| - Parallel development of both scenarios | |
| * Scenario 2: Agreement Safety (legal analysis, risk assessment) | |
| * Scenario 3: Partner Matching (deep profile analysis, network effects) | |
| - Resources: 3 research engineers (1 per scenario + 1 for integration) | |
| - Challenge: Ensure all scenarios share common infrastructure | |
| Quarter 4 (Months 22-24): Multi-language & Integration | |
| - Task A: Multi-language support | |
| * French, German, Spanish (minimum for EU context) | |
| * Multi-language NLP models | |
| * Language detection and routing | |
| * Resources: NLP specialists, native speakers for validation | |
| - Task B: Platform integration | |
| * CRM integration (Salesforce, Dynamics) | |
| * University system integration (CRIS, RIS) | |
| * SSO and authentication (SAML, OAuth) | |
| * Resources: 2 integration engineers | |
| Year 2 Milestones: | |
| - M18: Advanced AI models operational, 10,000+ stakeholders | |
| - M21: Scenarios 2 & 3 functional | |
| - M24: Multi-language support, major integrations complete | |
| YEAR 3: PRODUCTION, VALIDATION & DEPLOYMENT (Months 25-36) | |
| ========================================================== | |
| Quarter 1 (Months 25-27): Production Infrastructure | |
| - Task: Deploy to production cloud environment | |
| - Activities: | |
| * Cloud architecture (AWS/Azure multi-region) | |
| * Containerization (Docker, Kubernetes) | |
| * Security hardening (penetration testing, OWASP) | |
| * Monitoring and alerting (Prometheus, Grafana) | |
| * Backup and disaster recovery | |
| * Load testing and performance optimization | |
| - Resources: 2 DevOps engineers, cloud infrastructure budget | |
| Quarter 2 (Months 28-30): Pilot Deployments | |
| - Task: Real-world validation with pilot institutions | |
| - Target: 10-15 institutions (5 EU universities, 5 Canadian, 5 TTOs) | |
| - Activities: | |
| * Onboarding and training | |
| * Customization for each institution | |
| * Data migration and integration | |
| * Support and monitoring | |
| - Resources: Implementation team (4 people), travel, support infrastructure | |
| - Metrics: User satisfaction, adoption rates, success stories | |
| Quarter 3 (Months 31-33): Refinement & Knowledge Transfer | |
| - Task A: Refinement based on pilot feedback | |
| * Bug fixes and performance improvements | |
| * Feature additions based on real usage | |
| * UI/UX improvements | |
| - Task B: Documentation & training | |
| * User documentation (guides, videos, tutorials) | |
| * API documentation for developers | |
| * Training materials for TTOs | |
| * System administration documentation | |
| - Resources: Technical writer, video producer, trainers | |
| Quarter 4 (Months 34-36): Final Evaluation & Dissemination | |
| - Task A: Comprehensive evaluation | |
| * Quantitative analysis (usage statistics, success rates) | |
| * Qualitative research (interviews, case studies) | |
| * Impact assessment (technology transfers facilitated) | |
| * Publication of research findings | |
| - Task B: Dissemination & transition | |
| * Academic publications (3-5 papers) | |
| * Conference presentations | |
| * Stakeholder workshops | |
| * Transition to operational team (handover from research to operations) | |
| * Sustainability planning (funding model for maintenance) | |
| Year 3 Milestones: | |
| - M30: Pilot deployments complete, validation data collected | |
| - M33: Documentation complete, training program launched | |
| - M36: SPARKNET production system operational, research complete | |
| CRITICAL SUCCESS FACTORS: | |
| 1. Consistent funding (no gaps - momentum is crucial) | |
| 2. Access to real stakeholders and data | |
| 3. Strong partnerships with VISTA network institutions | |
| 4. Iterative feedback from end-users throughout | |
| 5. Flexibility to adapt to emerging needs | |
| TRANSITION: "Let's now examine the specific research challenges and innovations required..." | |
| ================================================================================ | |
| SLIDE 6 | |
| ================================================================================ | |
| YEAR 1 RESEARCH CHALLENGES - TECHNICAL DEEP DIVE (5 minutes): | |
| PURPOSE: Show stakeholders the research depth required. This isn't just engineering - it's novel R&D. | |
| OCR PRODUCTION PIPELINE - MULTI-FACETED CHALLENGE | |
| ================================================== | |
| Challenge 1: Robust PDF Parsing (Month 1-2) | |
| Problem: Patents come in many formats | |
| - Digitally-born PDFs (text embedded - easy case) | |
| - Scanned PDFs (images only - need OCR - hard case) | |
| - Mixed PDFs (some pages text, some scanned - very hard) | |
| - Encrypted or password-protected PDFs (legal barriers) | |
| - Damaged PDFs (corrupted files, missing pages) | |
| - Non-standard formats (old patents, custom layouts) | |
| Research questions: | |
| - How to automatically detect PDF type? | |
| - When should we use OCR vs text extraction? | |
| - How to handle malformed documents gracefully? | |
| Proposed approach: | |
| - Implement multi-strategy PDF processing pipeline | |
| - Try text extraction first (fast), fall back to OCR if needed | |
| - Use metadata to guide processing decisions | |
| - Build quality checker (did extraction work?) | |
| Novel contribution: | |
| - Adaptive PDF processing based on document characteristics | |
| - Quality assessment without ground truth | |
| - Hybrid text extraction + OCR strategy | |
| Challenge 2: Intelligent Image Processing (Month 2-3) | |
| Problem: OCR quality depends heavily on image quality | |
| - Patents have varying scan quality (resolution, contrast, noise) | |
| - Text regions vs diagram regions need different processing | |
| - Tables need specialized handling | |
| - Handwritten annotations must be detected and handled separately | |
| Research questions: | |
| - How to optimize image quality for OCR automatically? | |
| - How to segment document into regions (text, diagram, table, handwriting)? | |
| - What preprocessing works best for patent-specific layouts? | |
| Proposed approach: | |
| - Implement computer vision pipeline for page segmentation | |
| * YOLOv8 or similar for region detection | |
| * Classify regions: title, body text, claims, diagrams, tables | |
| * Route each region to specialized processing | |
| - Adaptive image enhancement | |
| * Detect image quality issues (blur, noise, low contrast) | |
| * Apply targeted enhancements (sharpening, denoising, contrast) | |
| * Validate improvement (quality went up?) | |
| Novel contribution: | |
| - Patent-specific page layout analysis model | |
| - Adaptive preprocessing based on detected issues | |
| - Region-specific OCR strategies | |
| Challenge 3: Multi-Model OCR Strategy (Month 3) | |
| Problem: No single OCR model works best for everything | |
| - llava:7b great for understanding context and diagrams | |
| - Tesseract excellent for clean printed text | |
| - Specialized models for tables and formulas | |
| - Each has different speed/accuracy/cost tradeoffs | |
| Research questions: | |
| - How to select best model for each region? | |
| - How to ensemble multiple models for higher accuracy? | |
| - How to balance speed vs accuracy for production? | |
| Proposed approach: | |
| - Build model router (which model for which region?) | |
| * Text regions → Tesseract (fast, accurate for clean text) | |
| * Diagrams → llava:7b (contextual understanding) | |
| * Tables → specialized table extraction models | |
| * Complex layouts → ensemble approach (combine multiple models) | |
| - Implement confidence scoring | |
| * Each model returns confidence in its extraction | |
| * Flag low-confidence results for human review | |
| * Learn which model is most reliable for different content types | |
| Novel contribution: | |
| - Intelligent OCR model routing based on content type | |
| - Ensemble strategies for higher accuracy | |
| - Confidence-based quality control | |
| Integration Challenge (Month 3): | |
| Problem: Putting it all together into production pipeline | |
| - Must handle 1000s of patents efficiently | |
| - Need queuing, batch processing, error recovery | |
| - Performance: <5 minutes per patent average | |
| - Reliability: 95%+ success rate | |
| Research questions: | |
| - How to parallelize processing across multiple GPUs? | |
| - How to recover from errors gracefully? | |
| - How to balance batch processing vs real-time requests? | |
| VISTA QUALITY FRAMEWORK - METHODOLOGICAL CHALLENGE | |
| =================================================== | |
| The Operationalization Problem (Months 4-9): | |
| VISTA defines 12 dimensions of quality, but they're qualitative: | |
| 1. Completeness: "Are all required sections present and thorough?" | |
| 2. Accuracy: "Is information factually correct and verifiable?" | |
| 3. Relevance: "Does analysis match patent scope and stakeholder needs?" | |
| 4. Timeliness: "Are market insights and data current?" | |
| 5. Consistency: "Is terminology and format uniform throughout?" | |
| 6. Objectivity: "Are assessments unbiased and balanced?" | |
| 7. Clarity: "Is language clear and accessible to target audience?" | |
| 8. Actionability: "Are recommendations concrete and implementable?" | |
| 9. Evidence-based: "Are claims supported by data and references?" | |
| 10. Stakeholder-aligned: "Does output meet stakeholder requirements?" | |
| 11. Reproducibility: "Can results be replicated independently?" | |
| 12. Ethical compliance: "Does it meet ethical standards and regulations?" | |
| Challenge: How do you compute these? | |
| Research approach: | |
| Phase 1: Expert labeling (Months 4-5) | |
| - Recruit 10-15 VISTA network experts | |
| - Have them assess 500 SPARKNET outputs on all 12 dimensions | |
| - Each output gets scored 1-5 on each dimension | |
| - This gives us ground truth training data | |
| - Cost: ~€20,000 for expert time | |
| Phase 2: Feature engineering (Month 6) | |
| For each dimension, identify computable features: | |
| Completeness features: | |
| - Section presence (boolean for each expected section) | |
| - Word count per section | |
| - Key information coverage (TRL, domains, stakeholders mentioned?) | |
| Accuracy features: | |
| - Consistency checks (do numbers add up? dates make sense?) | |
| - External validation (cross-reference with databases) | |
| - Confidence scores from underlying models | |
| Relevance features: | |
| - Keyword overlap (patent keywords vs analysis keywords) | |
| - Topic coherence (LDA, semantic similarity) | |
| - Stakeholder alignment (do recommendations match stakeholder profiles?) | |
| [Continue for all 12 dimensions...] | |
| Phase 3: Model training (Months 7-8) | |
| - Train ML models (Random Forest, XGBoost) to predict each dimension | |
| - Input: Extracted features | |
| - Output: Score 1-5 for each dimension | |
| - Validate: Hold out 20% of expert-labeled data for testing | |
| - Target: >0.7 correlation with expert scores | |
| Phase 4: Integration & dashboard (Month 9) | |
| - Integrate quality models into workflow | |
| - Build quality dashboard (visualize scores, trends over time) | |
| - Implement alerts (quality drops below threshold) | |
| - Create quality reports for stakeholders | |
| Novel contribution: | |
| - First computational operationalization of VISTA quality framework | |
| - Machine learning approach to quality assessment | |
| - Automated quality monitoring and reporting | |
| STAKEHOLDER DATABASE - DATA ENGINEERING AT SCALE | |
| ================================================= | |
| Challenge: Build comprehensive, high-quality database of 5,000+ entities | |
| Sub-challenge 1: Data collection (Months 4-8) | |
| Where does data come from? | |
| - Public university websites (scraping) | |
| - Research information systems (APIs where available) | |
| - LinkedIn and professional networks | |
| - Government databases (CORDIS for EU, NSERC for Canada) | |
| - Publication databases (Scopus, Web of Science - research profiles) | |
| - Patent databases (inventor and assignee information) | |
| Research questions: | |
| - How to scrape ethically and legally? | |
| - How to structure unstructured web data? | |
| - How to keep data current (websites change)? | |
| Approach: | |
| - Build web scraping infrastructure (Scrapy, Beautiful Soup) | |
| - Implement change detection (monitor for updates) | |
| - Data extraction models (NER for extracting structured info from text) | |
| Sub-challenge 2: Data quality (Months 6-10) | |
| Problems: | |
| - Duplicates (same entity, different names/spellings) | |
| - Incomplete (missing critical fields) | |
| - Outdated (people change positions, interests evolve) | |
| - Inconsistent (different formats, units, schemas) | |
| Research questions: | |
| - How to deduplicate entities (fuzzy matching, ML)? | |
| - How to assess completeness (what's essential vs nice-to-have)? | |
| - How to detect and flag outdated information? | |
| Approach: | |
| - Entity resolution pipeline (identify duplicates) | |
| - Completeness scoring (% of key fields populated) | |
| - Freshness tracking (last verified date) | |
| - Enrichment strategies (fill in missing data from multiple sources) | |
| Sub-challenge 3: Privacy compliance (Months 8-12) | |
| Legal requirements: | |
| - GDPR (EU): Consent, right to access, right to be forgotten | |
| - Canadian privacy laws: Similar requirements | |
| - Institutional policies: Universities may have restrictions | |
| Research questions: | |
| - How to obtain consent at scale? | |
| - How to implement data minimization? | |
| - How to handle data deletion requests? | |
| Approach: | |
| - Build consent management system | |
| - Implement data minimization (only store what's needed) | |
| - Create data deletion workflows | |
| - Regular privacy audits | |
| Novel contribution: | |
| - Scalable stakeholder database construction methodology | |
| - Privacy-preserving approaches for research network databases | |
| - Quality assessment framework for stakeholder data | |
| RESOURCES NEEDED FOR YEAR 1: | |
| Personnel: | |
| - 2 Computer vision/NLP researchers (OCR pipeline): €120k | |
| - 1 Data engineer (stakeholder database): €60k | |
| - 1 Research scientist (quality framework): €70k | |
| - 1 UX researcher (user studies): €65k | |
| - 1 Project manager: €50k | |
| Total: €365k | |
| Infrastructure: | |
| - GPU cluster (8x NVIDIA A100): €50k | |
| - Cloud services (storage, compute): €20k | |
| - Software licenses: €10k | |
| Total: €80k | |
| Other: | |
| - Expert quality assessments: €20k | |
| - User study participant compensation: €10k | |
| - Travel and workshops: €15k | |
| - Contingency: €10k | |
| Total: €55k | |
| YEAR 1 TOTAL: ~€500k | |
| TRANSITION: "Let's look at Years 2 and 3 challenges..." | |
| ================================================================================ | |
| SLIDE 7 | |
| ================================================================================ | |
| YEARS 2-3 RESEARCH CHALLENGES - ADVANCED DEVELOPMENT (4 minutes): | |
| YEAR 2: INTELLIGENCE & SCALE (Months 13-24) | |
| ============================================ | |
| Advanced AI/ML Development (Months 13-18) - CUTTING-EDGE RESEARCH | |
| Challenge 1: Chain-of-Thought Reasoning | |
| Current state: Our LLMs generate outputs directly (no intermediate reasoning visible) | |
| Problem: Complex patent analysis requires multi-step reasoning | |
| - First understand the technology | |
| - Then assess maturity | |
| - Consider market context | |
| - Identify potential applications | |
| - Synthesize into recommendations | |
| Research goal: Implement chain-of-thought prompting | |
| Approach: | |
| - Prompt models to "think out loud" - show reasoning steps | |
| - Example: "Let's analyze this patent step by step: | |
| Step 1: The core innovation is... [analysis] | |
| Step 2: The technical maturity is... [reasoning] | |
| Step 3: Therefore, the TRL level is... [conclusion]" | |
| - Advantages: Better reasoning, explainable decisions, easier debugging | |
| Research questions: | |
| - How to structure prompts for optimal reasoning? | |
| - How to balance reasoning quality vs computational cost? | |
| - How to present reasoning to users (show all steps or just conclusion)? | |
| Novel contribution: | |
| - Patent-specific chain-of-thought templates | |
| - Evaluation of reasoning quality | |
| - User study on explainability value | |
| Challenge 2: Few-Shot Learning for Rare Patents | |
| Current state: Models trained on common patent types | |
| Problem: Some patent domains are rare (emerging technologies, niche fields) | |
| - Limited training data available | |
| - Models perform poorly on unfamiliar types | |
| Research goal: Enable models to handle rare patents with just a few examples | |
| Approach: | |
| - Few-shot prompting: "Here are 2-3 examples of patents in quantum computing... now analyze this new quantum patent" | |
| - Meta-learning: Train models to learn from limited examples | |
| - Transfer learning: Leverage knowledge from common patents | |
| Research questions: | |
| - How few examples are sufficient? | |
| - Which learning strategies work best for patents? | |
| - How to detect when a patent is "rare" and needs few-shot approach? | |
| Novel contribution: | |
| - Few-shot learning framework for patent analysis | |
| - Benchmarking on rare patent types | |
| - Adaptive approach selection | |
| Challenge 3: Multi-Modal Understanding | |
| Current state: Text analysis separate from image/diagram analysis | |
| Problem: Patents are inherently multi-modal | |
| - Figures illustrate concepts in text | |
| - Tables provide supporting data | |
| - Diagrams show technical architecture | |
| - Understanding requires integrating ALL modalities | |
| Research goal: Joint text-image-table understanding | |
| Approach: | |
| - Use multi-modal models (CLIP, Flamingo, GPT-4V-like) | |
| - Link textual descriptions to referenced figures | |
| - Extract information from tables and correlate with text | |
| - Build unified representation | |
| Research questions: | |
| - How to represent multi-modal patent content? | |
| - How to train/fine-tune multi-modal models for patents? | |
| - How to evaluate multi-modal understanding? | |
| Novel contribution: | |
| - Multi-modal patent representation | |
| - Cross-modal reasoning for patent analysis | |
| - Benchmark dataset for multi-modal patent understanding | |
| Challenge 4: Agent Learning & Improvement | |
| Current state: Agents don't learn from experience | |
| Problem: Static agents don't improve over time | |
| - Every patent analyzed from scratch | |
| - Don't learn from mistakes or successes | |
| - No personalization to stakeholder preferences | |
| Research goal: Agents that learn and improve | |
| Approach: | |
| - Reinforcement learning from human feedback (RLHF) | |
| * Users rate agent outputs | |
| * Agent learns to produce higher-rated outputs | |
| - Experience replay: Store successful analyses, use as examples | |
| - Personalization: Adapt to individual stakeholder preferences | |
| Research questions: | |
| - What feedback signals are most useful? | |
| - How to prevent overfitting to specific users? | |
| - How to balance exploration (try new approaches) vs exploitation (use what works)? | |
| Novel contribution: | |
| - RLHF framework for patent valorization agents | |
| - Personalization strategies for stakeholder-specific needs | |
| - Long-term learning and performance tracking | |
| Challenge 5: Success Prediction Models (Months 16-18) | |
| Current state: System recommends technology transfer pathways, but doesn't predict success | |
| Problem: Not all recommendations lead to successful outcomes | |
| - Some collaborations don't work out | |
| - Some markets aren't actually ready | |
| - Some technologies take longer than predicted | |
| Research goal: Predict likelihood of successful technology transfer | |
| Approach: | |
| - Collect historical data on technology transfer outcomes | |
| * Successful transfers: Which factors led to success? | |
| * Failed transfers: What went wrong? | |
| - Train predictive models | |
| * Input: Patent characteristics, stakeholder profiles, market conditions | |
| * Output: Probability of success, estimated time to transfer | |
| - Feature engineering | |
| * Technology maturity (TRL) | |
| * Market readiness (demand indicators, competition) | |
| * Stakeholder capability (track record, resources) | |
| * Relationship strength (previous collaborations, network distance) | |
| Research questions: | |
| - What historical data is available and accessible? | |
| - Which features are most predictive? | |
| - How to handle rare events (most tech transfers don't happen)? | |
| Novel contribution: | |
| - Technology transfer success prediction model | |
| - Feature importance analysis (what matters most for success?) | |
| - Decision support tool (should we pursue this pathway?) | |
| Scenarios 2 & 3 Development (Months 19-24) - NEW DOMAINS | |
| Scenario 2: Agreement Safety (Months 19-21) | |
| Domain: Legal document analysis | |
| Goal: Analyze agreements (NDAs, licensing agreements, collaboration contracts) for risks | |
| Challenges: | |
| - Legal language is specialized and complex | |
| - Need legal domain expertise (hire consultant?) | |
| - Risk assessment requires understanding implications | |
| - Compliance checking with different jurisdictions | |
| Research approach: | |
| - Legal NLP: Named entity recognition for legal concepts | |
| - Risk taxonomy: Classify risks (IP, liability, termination, etc.) | |
| - Compliance database: Rules and regulations across jurisdictions | |
| - Extraction: Key terms, obligations, deadlines | |
| Novel contribution: | |
| - AI-powered agreement safety analysis for research collaborations | |
| - Risk visualization and explanation | |
| Scenario 3: Partner Matching (Months 22-24) | |
| Domain: Deep stakeholder profiling and network analysis | |
| Goal: Go beyond simple matching to sophisticated compatibility assessment | |
| Challenges: | |
| - Requires rich stakeholder profiles (research interests, capabilities, culture) | |
| - Network effects: Who knows whom? Warm introductions are more successful | |
| - Temporal dynamics: Interests and capabilities change over time | |
| - Success prediction: Will this collaboration work? | |
| Research approach: | |
| - Deep profiling: | |
| * Research interests (from publications, grants, patents) | |
| * Capabilities (equipment, expertise, resources) | |
| * Cultural fit (collaboration style, communication preferences) | |
| * Strategic priorities (what are they trying to achieve?) | |
| - Network analysis: | |
| * Build collaboration network (who has worked with whom?) | |
| * Identify bridges (connectors between communities) | |
| * Compute network distance (degrees of separation) | |
| - Compatibility scoring: | |
| * Research complementarity (do skills complement?) | |
| * Cultural alignment (will they work well together?) | |
| * Strategic fit (do priorities align?) | |
| * Track record (have similar collaborations succeeded?) | |
| Novel contribution: | |
| - Multi-dimensional partner compatibility framework | |
| - Network-aware matching (leveraging social connections) | |
| - Success prediction for collaborations | |
| YEAR 3: PRODUCTION & VALIDATION (Months 25-36) | |
| =============================================== | |
| Production Deployment (Months 25-27) - ENGINEERING CHALLENGE | |
| Challenge: Transform research prototype into production system | |
| Requirements: | |
| - Scalability: Handle 1000+ concurrent users | |
| - Reliability: 99.9% uptime (< 9 hours downtime per year) | |
| - Performance: <2s average response time | |
| - Security: Protect sensitive data, prevent attacks | |
| - Maintainability: Easy to update, monitor, debug | |
| Architecture decisions: | |
| - Cloud platform: AWS, Azure, or GCP? | |
| * Multi-region deployment (EU + Canada) | |
| * Auto-scaling (handle traffic spikes) | |
| * Managed services (reduce operational burden) | |
| - Containerization: Docker + Kubernetes | |
| * Microservices architecture (each agent is a service) | |
| * Easy deployment and scaling | |
| * Fault isolation (one service failure doesn't crash everything) | |
| - Database strategy: | |
| * PostgreSQL for structured data (stakeholders, users, sessions) | |
| * ChromaDB/Pinecone for vector search (embeddings) | |
| * Redis for caching (speed up repeat queries) | |
| * S3/Blob Storage for files (PDFs, outputs) | |
| - Security hardening: | |
| * Penetration testing (hire security firm) | |
| * OWASP Top 10 compliance | |
| * Data encryption (at rest and in transit) | |
| * SOC 2 certification (for enterprise customers) | |
| * Regular security audits | |
| Resources needed: | |
| - 2 DevOps engineers: €120k | |
| - Cloud infrastructure: €50k/year | |
| - Security audit & penetration testing: €30k | |
| - Monitoring tools (Datadog, New Relic): €10k/year | |
| Real-World Validation (Months 28-33) - RESEARCH EVALUATION | |
| Challenge: Prove SPARKNET works in practice, not just in lab | |
| Approach: Multi-site pilot study | |
| Pilot sites (10-15 institutions): | |
| - 5 EU universities (diverse sizes, countries) | |
| - 5 Canadian universities | |
| - 3-5 Technology Transfer Offices | |
| - 2 research funding agencies (stretch goal) | |
| Pilot process for each site: | |
| 1. Onboarding (Month 1) | |
| - Install/configure system | |
| - Train users (TTO staff, researchers) | |
| - Import their data (stakeholders, patents) | |
| 2. Active use (Months 2-4) | |
| - Process 20-50 real patents per site | |
| - Monitor usage, collect metrics | |
| - Provide support (help desk, bug fixes) | |
| 3. Evaluation (Month 5) | |
| - Quantitative data: Usage stats, success rates, time savings | |
| - Qualitative data: Interviews, surveys, case studies | |
| - Impact assessment: Did tech transfers happen? | |
| Research questions: | |
| - Does SPARKNET improve technology transfer outcomes? | |
| - How much time does it save TTOs? | |
| - What's the return on investment? | |
| - What are the barriers to adoption? | |
| - How can we improve the system? | |
| Metrics to track: | |
| Quantitative: | |
| - Number of patents analyzed | |
| - Number of stakeholder matches made | |
| - Number of introductions/connections facilitated | |
| - Number of agreements reached | |
| - Time saved per patent (compare to manual process) | |
| - User satisfaction scores (NPS, CSAT) | |
| Qualitative: | |
| - User testimonials and case studies | |
| - Pain points and feature requests | |
| - Organizational impact (process changes, new capabilities) | |
| - Unexpected uses and benefits | |
| Novel contribution: | |
| - Rigorous evaluation of AI-powered technology transfer system | |
| - Multi-site validation study | |
| - Best practices for deployment and adoption | |
| Documentation & Knowledge Transfer (Months 31-33) | |
| Challenge: Enable others to use and maintain SPARKNET | |
| Deliverables: | |
| - User documentation | |
| * Getting started guides | |
| * Feature tutorials (video + text) | |
| * FAQ and troubleshooting | |
| * Best practices | |
| - Technical documentation | |
| * System architecture | |
| * API reference | |
| * Database schemas | |
| * Deployment guides | |
| * Monitoring and maintenance | |
| - Training materials | |
| * TTO staff training program (2-day workshop) | |
| * System administrator training | |
| * Developer training (for customization) | |
| - Knowledge transfer | |
| * Handover to operational team | |
| * Sustainability planning (who maintains this long-term?) | |
| * Funding model (subscriptions, licensing, grants?) | |
| Resources needed: | |
| - Technical writer: €40k | |
| - Video producer: €20k | |
| - Training program development: €30k | |
| YEARS 2-3 TOTAL RESOURCES: | |
| Year 2: ~€600k (personnel + infrastructure + R&D) | |
| Year 3: ~€400k (deployment + validation + knowledge transfer) | |
| 3-YEAR TOTAL: ~€1.5M | |
| TRANSITION: "Now let's examine the expected research outcomes and impact..." | |
| ================================================================================ | |
| SLIDE 8 | |
| ================================================================================ | |
| RESEARCH QUESTIONS & SCIENTIFIC CONTRIBUTIONS (4 minutes): | |
| PURPOSE: Position SPARKNET as serious research, not just software development. Show intellectual contributions beyond the system itself. | |
| FRAMING THE RESEARCH CONTRIBUTION: | |
| SPARKNET is not just building a tool - it's advancing the state of knowledge in multiple areas: | |
| 1. Multi-agent systems | |
| 2. Quality assessment of AI outputs | |
| 3. Knowledge transfer and technology commercialization | |
| 4. Multi-modal document understanding | |
| 5. Semantic matching and recommendation systems | |
| RQ1: MULTI-AGENT COORDINATION FOR COMPLEX WORKFLOWS | |
| ==================================================== | |
| Background: | |
| Multi-agent systems (MAS) have been studied for decades, but mostly in controlled environments (robotics, games, simulations). Applying MAS to open-ended knowledge work like patent valorization is less explored. | |
| Research gap: | |
| - How should agents divide complex tasks? | |
| - How to handle conflicts when agents disagree? | |
| - What communication protocols maximize efficiency? | |
| - How to ensure quality when multiple agents contribute? | |
| SPARKNET's contribution: | |
| We're building a real-world MAS for a complex domain, giving us opportunity to study: | |
| Sub-question 1.1: Task decomposition strategies | |
| - We have 4 agents (Document, Market, Matchmaking, Outreach) | |
| - Is this the right granularity? Should we have more agents? Fewer? | |
| - How to decide which agent handles which sub-tasks? | |
| Experiment: | |
| - Try different agent configurations (3, 4, 5, 6 agents) | |
| - Measure quality and efficiency for each | |
| - Identify patterns (when are more agents better? when do they add overhead?) | |
| Sub-question 1.2: Communication overhead | |
| - Agents need to share information (DocumentAnalysisAgent results go to MarketAnalysisAgent) | |
| - Too much communication slows things down | |
| - Too little communication loses important context | |
| Experiment: | |
| - Measure communication patterns (what info is actually used?) | |
| - Test different communication strategies (full sharing vs selective sharing) | |
| - Find optimal balance | |
| Sub-question 1.3: Quality assurance in MAS | |
| - When 4 agents contribute to one output, who's responsible for quality? | |
| - How does CriticAgent effectively evaluate multi-agent outputs? | |
| Experiment: | |
| - Compare quality with vs without CriticAgent | |
| - Study what makes criticism effective | |
| - Identify failure modes (when does quality slip through?) | |
| Expected publications: | |
| Paper 1: "Multi-Agent Workflow Patterns for Knowledge-Intensive Tasks: Lessons from Patent Valorization" (Target: AAMAS - Autonomous Agents and Multi-Agent Systems conference) | |
| Paper 2: "Quality Assurance in Multi-Agent Systems: A Case Study in Automated Research Analysis" (Target: JAAMAS - Journal of Autonomous Agents and Multi-Agent Systems) | |
| RQ2: QUALITY ASSESSMENT OF AI-GENERATED OUTPUTS | |
| ================================================ | |
| Background: | |
| As AI generates more content (reports, analyses, recommendations), assessing quality becomes critical. Current approaches are limited: | |
| - Manual review (doesn't scale) | |
| - Simple metrics (word count, readability - miss deeper quality aspects) | |
| - Model-based (using another AI to judge - but how do we trust it?) | |
| Research gap: | |
| - What makes an AI-generated valorization analysis "high quality"? | |
| - Can we predict expert quality ratings from computable features? | |
| - How to operationalize qualitative standards (like VISTA's framework)? | |
| SPARKNET's contribution: | |
| We're implementing VISTA's 12-dimension quality framework computationally, creating: | |
| Sub-question 2.1: Feature engineering for quality | |
| - For each dimension (completeness, accuracy, relevance...), what features predict it? | |
| - Example for completeness: section presence, word counts, coverage of key concepts | |
| Experiment: | |
| - Collect 500+ expert quality assessments | |
| - Extract 100+ features from each output | |
| - Train models to predict expert scores | |
| - Analyze feature importance (what matters most?) | |
| Sub-question 2.2: Quality prediction models | |
| - Which ML models work best for quality assessment? | |
| - How much training data is needed? | |
| - Can models generalize across different patent types? | |
| Experiment: | |
| - Compare models: Linear regression, Random Forest, XGBoost, Neural Networks | |
| - Learning curves: How many examples needed for good performance? | |
| - Cross-domain testing: Train on some domains, test on others | |
| Sub-question 2.3: Explaining quality scores | |
| - Quality scores alone aren't enough - users need to understand WHY | |
| - How to provide actionable feedback? | |
| Experiment: | |
| - Implement explainable AI techniques (SHAP values, attention visualization) | |
| - User study: Do explanations help users improve outputs? | |
| Expected publications: | |
| Paper 3: "Computational Operationalization of Multi-Dimensional Quality Frameworks: A Case Study in Knowledge Transfer" (Target: Journal of the Association for Information Science and Technology - JASIST) | |
| Paper 4: "Predicting Expert Quality Assessments of AI-Generated Research Analyses" (Target: ACM Conference on AI, Ethics, and Society) | |
| RQ3: SEMANTIC MATCHING FOR COLLABORATION | |
| ========================================= | |
| Background: | |
| Stakeholder matching is crucial for technology transfer, but current approaches are limited: | |
| - Keyword matching (too simplistic) | |
| - Citation networks (miss non-publishing partners) | |
| - Manual curation (doesn't scale) | |
| Research gap: | |
| - How to match stakeholders across multiple dimensions? | |
| - How to predict collaboration success? | |
| - How to leverage network effects (social connections)? | |
| SPARKNET's contribution: | |
| We're building a comprehensive matching system, enabling research on: | |
| Sub-question 3.1: Multi-dimensional profile representation | |
| - How to represent stakeholder profiles richly? | |
| - What information predicts good matches? | |
| Experiment: | |
| - Extract profiles from multiple sources (websites, publications, patents) | |
| - Build vector representations (embeddings) | |
| - Test different embedding models (word2vec, BERT, specialized models) | |
| - Evaluate: Do better embeddings lead to better matches? | |
| Sub-question 3.2: Matching algorithms | |
| - Beyond similarity: How to find complementary partners? | |
| - How to incorporate constraints (geography, size, resources)? | |
| Experiment: | |
| - Compare algorithms: | |
| * Cosine similarity (baseline) | |
| * Learning-to-rank models | |
| * Graph-based approaches (network analysis) | |
| * Hybrid methods | |
| - Evaluate against ground truth (successful collaborations) | |
| Sub-question 3.3: Network effects | |
| - Warm introductions more successful than cold contacts | |
| - How to leverage social networks for matching? | |
| Experiment: | |
| - Build collaboration network from historical data | |
| - Compute network-aware matching scores | |
| - Test hypothesis: Network-aware matching leads to more successful introductions | |
| Sub-question 3.4: Temporal dynamics | |
| - Stakeholder interests and capabilities change over time | |
| - How to keep profiles current? | |
| - How to predict future interests? | |
| Experiment: | |
| - Analyze temporal evolution of research interests | |
| - Build predictive models (what will they be interested in next year?) | |
| - Test: Do temporally-aware matches improve success? | |
| Expected publications: | |
| Paper 5: "Multi-Dimensional Semantic Matching for Academic-Industry Collaboration" (Target: ACM Conference on Recommender Systems - RecSys) | |
| Paper 6: "Network-Aware Partner Recommendations in Research Collaboration Networks" (Target: Social Network Analysis and Mining journal) | |
| RQ4: MULTI-MODAL PATENT UNDERSTANDING | |
| ====================================== | |
| Background: | |
| Patents are inherently multi-modal: | |
| - Text (abstract, claims, description) | |
| - Figures (diagrams, flowcharts, technical drawings) | |
| - Tables (data, comparisons, specifications) | |
| - Mathematical formulas | |
| Current AI approaches analyze these separately, missing connections. | |
| Research gap: | |
| - How to jointly understand text and visual elements? | |
| - How to link textual descriptions to referenced figures? | |
| - What representations enable cross-modal reasoning? | |
| SPARKNET's contribution: | |
| Our OCR pipeline and multi-modal analysis provide opportunities to study: | |
| Sub-question 4.1: Cross-modal reference resolution | |
| - Text often references figures: "as shown in Figure 3" | |
| - How to automatically link text to corresponding figures? | |
| Experiment: | |
| - Build dataset of text-figure pairs | |
| - Train models to detect references | |
| - Extract referred visual elements | |
| - Evaluate quality of linking | |
| Sub-question 4.2: Joint text-image reasoning | |
| - Understanding requires integrating both modalities | |
| - Example: "The system consists of three components [see Figure 2]" | |
| * Text describes components | |
| * Figure shows their relationships | |
| * Full understanding needs both | |
| Experiment: | |
| - Test multi-modal models (CLIP, Flamingo-style architectures) | |
| - Compare uni-modal (text-only) vs multi-modal understanding | |
| - Measure: Does adding visual information improve analysis? | |
| Sub-question 4.3: Diagram classification and understanding | |
| - Different diagram types need different processing | |
| - Flowcharts vs circuit diagrams vs organizational charts | |
| Experiment: | |
| - Build diagram type classifier | |
| - Develop type-specific analysis methods | |
| - Evaluate diagram understanding across types | |
| Expected publications: | |
| Paper 7: "Multi-Modal Understanding of Technical Patents: Integrating Text, Diagrams, and Tables" (Target: Association for Computational Linguistics - ACL) | |
| Paper 8: "Automated Diagram Analysis in Patent Documents: A Deep Learning Approach" (Target: International Conference on Document Analysis and Recognition - ICDAR) | |
| ADDITIONAL RESEARCH OUTPUTS | |
| ============================ | |
| Beyond publications, SPARKNET will generate: | |
| 1. Datasets for research community: | |
| - Annotated patent corpus (text + quality labels) | |
| - Stakeholder profiles with collaboration histories | |
| - Multi-modal patent dataset (text + figures + annotations) | |
| - These enable other researchers to build on our work | |
| 2. Open-source tools: | |
| - OCR pipeline (PDF→text→structure) | |
| - Quality assessment framework | |
| - Stakeholder matching library | |
| - Benefit: Accelerate research, establish standards | |
| 3. Methodological contributions: | |
| - VISTA quality framework operationalization (becomes standard) | |
| - Best practices for AI in knowledge transfer | |
| - Evaluation protocols for research support systems | |
| 4. Training materials: | |
| - Workshops for TTO professionals | |
| - Online courses for researchers | |
| - Dissemination of SPARKNET methodology | |
| DOCTORAL/MASTER'S RESEARCH OPPORTUNITIES: | |
| SPARKNET is large enough to support multiple theses: | |
| Potential PhD topics: | |
| - "Multi-Agent Coordination for Complex Knowledge Work" (3 years, CS/AI) | |
| - "Quality Assessment of AI-Generated Research Analyses" (3 years, Information Science) | |
| - "Network-Aware Semantic Matching for Research Collaboration" (3 years, CS/Social Computing) | |
| Potential Master's topics: | |
| - "Diagram Classification in Patent Documents" (1 year, CS) | |
| - "Stakeholder Profile Construction from Web Sources" (1 year, Data Science) | |
| - "User Experience Design for AI-Powered Technology Transfer Tools" (1 year, HCI) | |
| IMPACT ON VISTA PROJECT: | |
| - Demonstrates feasibility of AI for knowledge transfer | |
| - Provides tools for other VISTA partners | |
| - Generates insights on technology transfer processes | |
| - Establishes methodological standards | |
| - Contributes to VISTA's intellectual output | |
| TRANSITION: "Let's discuss resource requirements and timeline..." | |
| ================================================================================ | |
| SLIDE 9 | |
| ================================================================================ | |
| RESOURCE REQUIREMENTS & RISK MANAGEMENT (4 minutes): | |
| PURPOSE: Be transparent about what's needed for success and show we've thought through risks. | |
| BUDGET BREAKDOWN (3-Year Total: ~€1.65M) | |
| ======================================== | |
| PERSONNEL COSTS (€1.2M - 73% of budget) | |
| This is the largest cost because we need top talent for 3 years. | |
| Year 1 (5-6 FTEs): | |
| - 2 AI/ML Researchers @ €60k each = €120k | |
| * Computer vision + NLP expertise for OCR pipeline | |
| * PhD required, 2-5 years post-doc experience | |
| - 1 Data Engineer @ €60k = €60k | |
| * Stakeholder database construction | |
| * Web scraping, data quality, ETL | |
| - 1 Research Scientist (Quality Framework) @ €70k = €70k | |
| * PhD in information science or related field | |
| * Expertise in quality assessment methodologies | |
| - 1 UX Researcher @ €65k = €65k | |
| * User studies, requirements gathering | |
| * Interface design | |
| - 1 Project Manager @ €50k = €50k | |
| * Coordinate across team and stakeholders | |
| * Budget management, reporting | |
| Year 1 Total: €425k | |
| Year 2 (7-8 FTEs - peak staffing): | |
| - Same as Year 1 (€365k) + | |
| - 3 Research Engineers @ €65k each = €195k | |
| * Scenarios 2 & 3 development | |
| * Platform development | |
| * Integration work | |
| - 1 DevOps Engineer @ €60k = €60k | |
| * Infrastructure setup | |
| * CI/CD, monitoring | |
| Year 2 Total: €620k | |
| Year 3 (4-5 FTEs - wind-down phase): | |
| - 2 Research Engineers @ €65k each = €130k | |
| * Refinement, bug fixes | |
| * Support for pilot sites | |
| - 1 Technical Writer/Trainer @ €40k = €40k | |
| * Documentation | |
| * Training material development | |
| - 0.5 Project Manager @ €25k = €25k | |
| * Part-time for final deliverables | |
| Year 3 Total: €195k | |
| 3-Year Personnel Total: €1,240k | |
| Notes on personnel: | |
| - Rates are European academic institution rates (may differ in Canada) | |
| - Includes social charges (~30% overhead on salaries) | |
| - Assumes institutional infrastructure (office, basic IT) provided | |
| - Does NOT include PI/faculty time (in-kind contribution) | |
| INFRASTRUCTURE COSTS (€200k - 12% of budget) | |
| Hardware (Year 1 investment: €80k) | |
| - 8x NVIDIA A100 GPUs @ €10k each = €80k | |
| * For OCR processing, model training | |
| * Hosted at institutional HPC center (no hosting cost) | |
| * Amortized over 3 years | |
| Cloud Services (€40k/year × 3 = €120k) | |
| Year 1 (Development): | |
| - AWS/Azure compute (staging environment): €10k | |
| - Storage (S3/Blob - datasets, outputs): €5k | |
| - Database services (RDS, managed PostgreSQL): €5k | |
| Year 1: €20k | |
| Year 2 (Pilot deployment): | |
| - Production environment (multi-region): €20k | |
| - Increased storage (more data): €10k | |
| - CDN & other services: €5k | |
| Year 2: €35k | |
| Year 3 (Full pilot): | |
| - Production at scale: €40k | |
| - Backup & disaster recovery: €10k | |
| - Monitoring & analytics: €5k | |
| Year 3: €55k | |
| Software Licenses (€10k/year × 3 = €30k) | |
| - IDEs & development tools (JetBrains, etc.): €2k/year | |
| - Design tools (Figma, Adobe): €1k/year | |
| - Project management (Jira, Confluence): €2k/year | |
| - Monitoring (Datadog, New Relic): €3k/year | |
| - Security scanning tools: €2k/year | |
| 3-Year Infrastructure Total: €230k | |
| RESEARCH ACTIVITIES (€150k - 9% of budget) | |
| User Studies & Requirements Gathering (€50k) | |
| - Participant compensation: €30k | |
| * Year 1: 20 TTO professionals @ €500 each = €10k | |
| * Year 2: 30 end-users for usability testing @ €300 each = €9k | |
| * Year 3: 50 pilot participants @ €200 each = €10k | |
| - Travel to user sites (interviews, workshops): €15k | |
| - Transcription & analysis services: €5k | |
| Expert Quality Assessments (€30k) | |
| - 10-15 VISTA experts @ €2k each for labeling 50 outputs = €30k | |
| - This is for ground truth data for quality framework ML models | |
| Data Collection & Licensing (€40k) | |
| - Web scraping infrastructure & services: €10k | |
| - Data enrichment services (company data, contact info): €15k | |
| - Database licenses (Scopus, Web of Science access): €10k | |
| - Legal review (privacy compliance): €5k | |
| Validation Studies (€30k) | |
| - Pilot site support (travel, on-site assistance): €15k | |
| - Survey & interview services: €5k | |
| - Case study development (writing, production): €10k | |
| 3-Year Research Activities Total: €150k | |
| KNOWLEDGE TRANSFER & DISSEMINATION (€100k - 6% of budget) | |
| Publications (€20k) | |
| - Open access fees (€2k per paper × 8 papers): €16k | |
| - Professional editing services: €4k | |
| Conferences (€30k) | |
| - Conference attendance (registration, travel): €20k | |
| * 3 conferences/year × 3 years × €2k = €18k | |
| - Poster printing, presentation materials: €2k | |
| Documentation & Training (€40k) | |
| - Technical writer (Year 3): Already in personnel budget | |
| - Video production (tutorials, demos): €15k | |
| - Interactive training platform (development): €10k | |
| - Training workshops (materials, venue, catering): €15k | |
| Dissemination Events (€10k) | |
| - Stakeholder workshops (3 over 3 years): €9k | |
| - Press & communications: €1k | |
| 3-Year Knowledge Transfer Total: €100k | |
| GRAND TOTAL: €1,720k (~€1.7M) | |
| Let's round to €1.65M with €50k contingency. | |
| TEAM COMPOSITION | |
| ================ | |
| Core team (permanent throughout): | |
| 1. Project Manager (100%): Day-to-day coordination, stakeholder liaison | |
| 2. Lead AI Researcher (100%): Technical leadership, architecture decisions | |
| 3. Senior Engineer (100%): Implementation lead, code quality | |
| Phase-specific additions: | |
| Year 1 Add: | |
| - Computer Vision Researcher: OCR pipeline | |
| - NLP Researcher: Text analysis, quality models | |
| - Data Engineer: Stakeholder database | |
| - UX Researcher: User studies | |
| Year 2 Add: | |
| - 3 Research Engineers: Scenarios 2 & 3, platform development | |
| - DevOps Engineer: Infrastructure & deployment | |
| Year 3 Shift: | |
| - Wind down research team | |
| - Add technical writer/trainer | |
| - Maintain small support team for pilots | |
| Consultants & External Expertise: | |
| - Legal informatics expert (Year 2 - Scenario 2): €20k | |
| - Security audit firm (Year 3): €30k | |
| - Privacy/GDPR consultant: €10k | |
| - Domain experts (patent law, technology transfer): In-kind from VISTA partners | |
| Student Assistance: | |
| - 2-3 Master's students each year | |
| - Tasks: Data collection, testing, documentation | |
| - Compensation: €15k/year × 3 = €45k (included in personnel) | |
| RISK MANAGEMENT | |
| =============== | |
| Risk 1: Stakeholder Data Access | |
| Probability: Medium-High | |
| Impact: High (no data = no matching) | |
| Description: We need access to detailed stakeholder data (contact info, research profiles, etc.). Universities and TTOs may be reluctant to share due to privacy concerns or competitive reasons. | |
| Mitigation strategies: | |
| - EARLY ENGAGEMENT: Start conversations with potential partners NOW (Year 0) | |
| * Explain benefits (better matching for them too) | |
| * Address privacy concerns (anonymization, access controls) | |
| * Offer reciprocity (they get access to full database) | |
| - LEGAL FRAMEWORK: Work with VISTA legal team to create data sharing agreement template | |
| * Clear terms on data use, retention, deletion | |
| * GDPR compliant | |
| * Opt-in for sensitive data | |
| - FALLBACK: If real data not available, can use synthetic data for development | |
| * But limits validation and value | |
| * Need real data by Year 2 at latest | |
| Risk 2: OCR Quality Insufficient | |
| Probability: Medium | |
| Impact: Medium (affects data quality for image-based patents) | |
| Description: OCR technology may not accurately extract text from complex patent documents, especially old/scanned patents with poor quality. | |
| Mitigation strategies: | |
| - MULTI-MODEL APPROACH: Don't rely on single OCR engine | |
| * Combine multiple models (llava, Tesseract, commercial APIs) | |
| * Ensemble predictions for higher accuracy | |
| - QUALITY ASSESSMENT: Implement confidence scoring | |
| * Flag low-confidence extractions for human review | |
| * Learn which models work best for which document types | |
| - HUMAN-IN-THE-LOOP: For critical documents, have human verification | |
| * Not scalable, but ensures quality for high-value patents | |
| - CONTINUOUS IMPROVEMENT: Collect feedback, retrain models | |
| * Build dataset of corrections | |
| * Fine-tune models on patent-specific data | |
| Risk 3: User Adoption Barriers | |
| Probability: Medium-High | |
| Impact: High (system unused = project failure) | |
| Description: TTOs may not adopt SPARKNET due to: | |
| - Change resistance (prefer existing workflows) | |
| - Lack of trust in AI recommendations | |
| - Perceived complexity | |
| - Integration difficulties with existing systems | |
| Mitigation strategies: | |
| - CO-DESIGN FROM START: Involve TTOs in design process (Year 1) | |
| * Understand their workflows deeply | |
| * Design to fit existing processes, not replace entirely | |
| * Regular feedback sessions | |
| - EXPLAINABILITY: Ensure AI recommendations are understandable and trustworthy | |
| * Show reasoning, not just conclusions | |
| * Provide confidence scores | |
| * Allow human override | |
| - TRAINING & SUPPORT: Comprehensive onboarding and ongoing assistance | |
| * Hands-on workshops | |
| * Video tutorials | |
| * Responsive help desk | |
| - INTEGRATION: Make it easy to integrate with existing tools | |
| * APIs for connecting to CRM, RIS, etc. | |
| * Export to familiar formats | |
| * SSO for easy access | |
| - PILOT STRATEGY: Start small, build momentum | |
| * Identify champions in each organization | |
| * Quick wins (show value fast) | |
| * Case studies and testimonials | |
| Risk 4: Technical Complexity Underestimated | |
| Probability: Medium | |
| Impact: Medium (delays, budget overruns) | |
| Description: AI systems are notoriously difficult to build. We may encounter unexpected technical challenges that delay progress or increase costs. | |
| Mitigation strategies: | |
| - AGILE DEVELOPMENT: Iterative approach with frequent deliverables | |
| * 2-week sprints | |
| * Regular demos to stakeholders | |
| * Fail fast, pivot quickly | |
| - PROTOTYPING: Build quick proofs-of-concept before committing to full implementation | |
| * Validate technical approach early | |
| * Discover issues sooner | |
| - MODULAR ARCHITECTURE: Keep components independent | |
| * If one component fails, doesn't derail everything | |
| * Can swap out components if needed | |
| - CONTINGENCY BUFFER: 10% time/budget buffer for unknowns | |
| * In €1.65M budget, €150k is contingency | |
| - TECHNICAL ADVISORY BOARD: Engage external experts for review | |
| * Quarterly reviews of architecture and progress | |
| * Early warning of potential issues | |
| Risk 5: Key Personnel Turnover | |
| Probability: Low-Medium | |
| Impact: High (loss of knowledge, delays) | |
| Description: Researchers or engineers may leave during project (new job, relocation, personal reasons). | |
| Mitigation strategies: | |
| - COMPETITIVE COMPENSATION: Pay at or above market rates to retain talent | |
| - CAREER DEVELOPMENT: Offer learning opportunities, publication support | |
| * People stay if they're growing | |
| - KNOWLEDGE MANAGEMENT: Document everything | |
| * Code well-commented | |
| * Architecture decisions recorded | |
| * Onboarding materials ready | |
| - OVERLAP PERIODS: When someone leaves, have replacement overlap if possible | |
| * Knowledge transfer | |
| * Relationship continuity | |
| - CROSS-TRAINING: Multiple people understand each component | |
| * Not single points of failure | |
| Risk 6: VISTA Project Changes | |
| Probability: Low | |
| Impact: Medium (scope changes, realignment needed) | |
| Description: VISTA project priorities or structure may evolve, affecting SPARKNET's alignment and requirements. | |
| Mitigation strategies: | |
| - REGULAR ALIGNMENT: Quarterly meetings with VISTA leadership | |
| * Ensure continued alignment | |
| * Adapt to evolving priorities | |
| - MODULAR DESIGN: Flexible architecture that can adapt to new requirements | |
| - COMMUNICATION: Maintain strong relationships with VISTA work package leaders | |
| * Early warning of changes | |
| * Influence direction | |
| TRANSITION: "Let's conclude with expected impact and next steps..." | |
| ================================================================================ | |
| SLIDE 10 | |
| ================================================================================ | |
| EXPECTED IMPACT & SUCCESS METRICS (3 minutes): | |
| PURPOSE: Show stakeholders what success looks like and how we'll measure it. Make commitments we can meet. | |
| QUANTITATIVE SUCCESS METRICS | |
| ============================= | |
| Academic Impact (Research Contribution) | |
| ---------------------------------------- | |
| Publications (Target: 6-10 papers in 3 years) | |
| Breakdown by venue type: | |
| - AI/ML Conferences (3-4 papers): | |
| * AAMAS, JAAMAS: Multi-agent systems papers (RQ1) | |
| * ACL, EMNLP: NLP and multi-modal papers (RQ4) | |
| * RecSys: Matching algorithms paper (RQ3) | |
| * Target: Top-tier (A/A* conferences) | |
| - Information Science Journals (2-3 papers): | |
| * JASIST: Quality framework paper (RQ2) | |
| * Journal of Documentation: Knowledge transfer methodology | |
| * Target: High impact factor (IF > 3) | |
| - Domain-Specific Venues (1-2 papers): | |
| * Technology Transfer journals | |
| * Innovation management conferences | |
| * Target: Practitioner reach | |
| Success criteria: | |
| - At least 6 papers accepted by Month 36 | |
| - Average citation count > 20 by Year 5 (post-publication) | |
| - At least 2 papers in top-tier venues (A/A*) | |
| Why publications matter: | |
| - Validates research quality (peer review) | |
| - Disseminates findings to academic community | |
| - Establishes SPARKNET as research contribution, not just software | |
| - Builds reputation for future funding | |
| Theses (Target: 2-3 completed by Month 36) | |
| - 1 PhD thesis (Computer Science): Multi-agent systems or quality assessment | |
| * Student would be embedded in SPARKNET team | |
| * Thesis: 3 papers + synthesis chapter | |
| * Timeline: Month 6 (recruitment) to Month 36 (defense) | |
| - 1-2 Master's theses (CS, Data Science, HCI) | |
| * Students do 6-12 month projects within SPARKNET | |
| * Topics: Diagram analysis, stakeholder profiling, UX evaluation | |
| * Multiple students over 3 years | |
| Why theses matter: | |
| - Cost-effective research capacity (students are cheaper than postdocs) | |
| - Training next generation of researchers | |
| - Produces detailed technical documentation | |
| - Often leads to high-quality publications | |
| Citations (Target: 500+ by Year 5 post-publication) | |
| - Average good paper gets 50-100 citations over 5 years | |
| - 10 papers × 50 citations each = 500 citations | |
| - This indicates real impact (others building on our work) | |
| System Performance (Technical Quality) | |
| --------------------------------------- | |
| OCR Accuracy (Target: 95%+ character-level accuracy) | |
| Measurement: | |
| - Benchmark dataset: 100 diverse patents (old, new, different languages) | |
| - Ground truth: Manual transcription | |
| - Metric: Character Error Rate (CER), Word Error Rate (WER) | |
| - Target: CER < 5%, WER < 5% | |
| Why 95%? | |
| - Industry standard for production OCR | |
| - Good enough for downstream analysis (small errors don't derail understanding) | |
| - Achievable with multi-model ensemble approach | |
| User Satisfaction (Target: 90%+ satisfaction, NPS > 50) | |
| Measurement: | |
| - Quarterly surveys of pilot users | |
| - Questions on: | |
| * Ease of use (1-5 scale) | |
| * Quality of results (1-5 scale) | |
| * Time savings (% compared to manual) | |
| * Would you recommend to colleague? (NPS: promoters - detractors) | |
| - Target: Average satisfaction > 4.5/5, NPS > 50 | |
| Why these targets? | |
| - 90% satisfaction is excellent (few tools achieve this) | |
| - NPS > 50 is "excellent" zone (indicates strong word-of-mouth) | |
| - Shows system is genuinely useful, not just technically impressive | |
| Time Savings (Target: 70% reduction in analysis time) | |
| Measurement: | |
| - Time study comparing manual vs SPARKNET-assisted patent analysis | |
| - Manual baseline: ~8-16 hours per patent (TTO professional) | |
| - With SPARKNET: Target 2-4 hours (30% of manual time = 70% reduction) | |
| - Caveat: Includes human review time (not fully automated) | |
| Why 70%? | |
| - Significant impact (can analyze 3x more patents with same effort) | |
| - Realistic (not claiming 100% automation, acknowledging human-in-loop) | |
| - Based on early prototype timing | |
| Deployment & Adoption (Real-World Usage) | |
| ----------------------------------------- | |
| Active Institutions (Target: 10-15 by Month 36) | |
| - Year 1: 2-3 early adopters (close partners) | |
| - Year 2: 5-7 additional (pilot expansion) | |
| - Year 3: 10-15 total (full pilot network) | |
| Distribution: | |
| - 5 EU universities | |
| - 5 Canadian universities | |
| - 3-5 TTOs | |
| - Diverse sizes and contexts | |
| Patents Analyzed (Target: 1000+ by Month 36) | |
| - Year 1: 100 patents (system development, testing) | |
| - Year 2: 300 patents (pilot sites starting) | |
| - Year 3: 600 patents (full operation) | |
| - Total: 1000+ patents | |
| Why 1000? | |
| - Sufficient for meaningful validation | |
| - Shows scalability (can handle volume) | |
| - Diverse patent portfolio (multiple domains, institutions) | |
| Successful Introductions (Target: 100+ by Month 36) | |
| - Definition: Stakeholder connections facilitated by SPARKNET that led to: | |
| * Meeting or correspondence | |
| * Information exchange | |
| * Collaboration discussion | |
| * (Success beyond this: actual agreements, but that's longer timeframe) | |
| Measurement: | |
| - Track introductions made through system | |
| - Follow-up surveys (what happened after introduction?) | |
| - Case studies of successful collaborations | |
| Why 100? | |
| - 10% success rate (1000 patents → ~500 recommendations → 100 connections) | |
| - Realistic for 3-year timeframe (full collaborations take 2-5 years) | |
| - Demonstrates value (system producing real connections) | |
| QUALITATIVE IMPACT | |
| ================== | |
| Research Community Impact | |
| ------------------------- | |
| Expected contributions: | |
| 1. Benchmarks & Datasets | |
| - Annotated patent corpus for training/evaluation | |
| - Stakeholder network dataset (anonymized) | |
| - Quality assessment dataset (expert-labeled outputs) | |
| - These become community resources (like ImageNet for computer vision) | |
| 2. Open-Source Tools | |
| - OCR pipeline (PDF→text→structure) | |
| - Quality assessment framework | |
| - Stakeholder matching library | |
| - Benefits: Accelerate research, enable comparisons | |
| 3. Methodologies | |
| - How to operationalize quality frameworks | |
| - Best practices for AI in knowledge work | |
| - Evaluation protocols for research support systems | |
| Impact: SPARKNET becomes standard reference for patent analysis AI | |
| VISTA Network Impact | |
| -------------------- | |
| Direct benefits to VISTA: | |
| - Demonstrates feasibility of AI for knowledge transfer | |
| - Provides operational tool for VISTA institutions | |
| - Generates insights on technology transfer processes | |
| - Establishes standards and best practices | |
| - Contributes to VISTA's goals and deliverables | |
| Specific to VISTA Work Packages: | |
| - WP2: Automated valorization pathway analysis | |
| - WP3: Operational quality framework | |
| - WP4: Expanded stakeholder network | |
| - WP5: Production-ready digital tool | |
| Broader impact: | |
| - Strengthens EU-Canada research connections | |
| - Increases capacity for knowledge transfer | |
| - Demonstrates value of international collaboration | |
| Technology Transfer Office Impact | |
| ---------------------------------- | |
| Expected improvements for TTOs: | |
| 1. Efficiency | |
| - 70% time savings per patent | |
| - Can analyze 3x more patents with same staff | |
| - Faster response to researcher inquiries | |
| 2. Quality | |
| - More thorough analysis (AI catches details humans miss) | |
| - Consistent methodology (reduces variability) | |
| - Evidence-based recommendations (data-driven) | |
| 3. Effectiveness | |
| - Better stakeholder matches (beyond personal networks) | |
| - More successful introductions (data shows complementarity) | |
| - Broader reach (access to international partners) | |
| 4. Capability Building | |
| - Training for TTO staff (AI literacy) | |
| - Best practices from multiple institutions | |
| - Professional development | |
| Case Study Example (Hypothetical): | |
| University X TTO before SPARKNET: | |
| - 10 patents analyzed per year | |
| - 2-3 successful technology transfers | |
| - Mostly local/regional partnerships | |
| - 200 hours per patent analysis | |
| University X TTO with SPARKNET (Year 3): | |
| - 30 patents analyzed per year (3x increase) | |
| - 5-6 successful technology transfers (2x increase) | |
| - National and international partnerships | |
| - 60 hours per patent analysis (70% reduction, includes review time) | |
| Economic Impact (Longer-Term) | |
| ------------------------------ | |
| While difficult to measure directly in 3 years, expected trajectory: | |
| - More patents commercialized (SPARKNET lowers barriers) | |
| - Faster time-to-market (efficient pathway identification) | |
| - Better matches (higher success rate) | |
| - Economic benefits materialize 5-10 years out | |
| Hypothetical (if SPARKNET used by 50 institutions over 10 years): | |
| - 5000+ patents analyzed | |
| - 500+ additional technology transfers | |
| - €50M+ in commercialization value | |
| - 1000+ jobs created (startups, licensing deals) | |
| Note: These are projections, not guarantees. Actual impact depends on many factors. | |
| EVALUATION FRAMEWORK | |
| ==================== | |
| Continuous Monitoring (Not Just End-of-Project) | |
| ------------------------------------------------ | |
| Quarterly assessments: | |
| - Usage statistics (patents analyzed, users active) | |
| - Performance metrics (OCR accuracy, response time) | |
| - User satisfaction surveys | |
| - Bug tracking and resolution rates | |
| Annual reviews: | |
| - External evaluation by VISTA team | |
| - Academic publications progress | |
| - Budget and timeline status | |
| - Strategic adjustments based on findings | |
| Mixed Methods Evaluation | |
| ------------------------- | |
| Quantitative: | |
| - Usage logs and analytics | |
| - Performance benchmarks | |
| - Survey responses (Likert scales, NPS) | |
| Qualitative: | |
| - User interviews (in-depth, 1-hour) | |
| - Case studies (successful collaborations) | |
| - Focus groups (collective insights) | |
| - Ethnographic observation (watch people use system) | |
| Why mixed methods? | |
| - Numbers alone don't tell full story | |
| - Qualitative explains WHY metrics are what they are | |
| - Stories and case studies convince stakeholders | |
| External Evaluation | |
| ------------------- | |
| Independence ensures credibility: | |
| - VISTA evaluation team (not SPARKNET team) | |
| - External academic reviewers (peer review) | |
| - User feedback (pilot institutions provide assessment) | |
| Final evaluation report (Month 36): | |
| - Comprehensive assessment against all metrics | |
| - Lessons learned | |
| - Recommendations for future development | |
| - Sustainability plan | |
| SUCCESS DEFINITION (Summary) | |
| ============================= | |
| SPARKNET will be considered successful if by Month 36: | |
| 1. It produces high-quality research (6+ publications, theses) | |
| 2. It works technically (95% OCR, 90% satisfaction, 70% time savings) | |
| 3. It's adopted (10-15 institutions, 1000+ patents) | |
| 4. It makes impact (100+ connections, case studies of successful transfers) | |
| 5. It's sustainable (transition plan for ongoing operation) | |
| PARTIAL SUCCESS: | |
| Even if not all metrics met, valuable outcomes: | |
| - Research contributions stand alone (publications, datasets, methodologies) | |
| - Lessons learned valuable for future AI in knowledge transfer | |
| - Prototype demonstrates feasibility, even if not fully production-ready | |
| TRANSITION: "Let's wrap up with next steps and how stakeholders can engage..." | |
| ================================================================================ | |
| SLIDE 11 | |
| ================================================================================ | |
| NEXT STEPS & STAKEHOLDER ENGAGEMENT (3 minutes): | |
| PURPOSE: Make clear what happens next and how stakeholders can get involved. Create urgency and excitement. | |
| IMMEDIATE NEXT STEPS (Months 0-6) | |
| ================================== | |
| Month 0-1: Proposal Finalization & Approval | |
| -------------------------------------------- | |
| Activities: | |
| 1. Stakeholder Feedback Session (THIS MEETING) | |
| - Present proposal | |
| - Collect feedback and questions | |
| - Identify concerns and address them | |
| 2. Proposal Revision (Week 1-2 after this meeting) | |
| - Incorporate feedback | |
| - Refine timeline, budget, deliverables | |
| - Strengthen weak areas identified | |
| - Add missing details | |
| 3. Formal Approval Process (Week 3-4) | |
| - Submit to VISTA steering committee | |
| - Present to institutional leadership | |
| - Obtain signed funding commitments | |
| - Set up project accounts and legal structures | |
| Stakeholder role: | |
| - Provide honest, constructive feedback TODAY | |
| - Champion proposal within your organizations | |
| - Expedite approval processes where possible | |
| Target: Signed agreements by end of Month 1 | |
| Month 1-2: Team Recruitment & Kick-off | |
| --------------------------------------- | |
| Activities: | |
| 1. Core Team Recruitment (Month 1-2) | |
| - Post positions internationally | |
| - Target: 5-6 positions initially | |
| - Priority: Lead AI Researcher, Project Manager (start immediately) | |
| - Others: Data Engineer, UX Researcher, Research Engineers | |
| Recruitment channels: | |
| - University job boards | |
| - Professional networks (LinkedIn, research conferences) | |
| - Direct recruitment (reach out to strong candidates) | |
| Timeline: | |
| - Post positions: Week 1 | |
| - Applications due: Week 4 | |
| - Interviews: Week 5-6 | |
| - Offers: Week 7 | |
| - Start dates: Month 2-3 (allow time for notice period) | |
| 2. Infrastructure Setup (Month 1-2) | |
| - Order GPU hardware (8x NVIDIA A100s) | |
| - Set up cloud accounts (AWS/Azure) | |
| - Configure development environment (Git, CI/CD) | |
| - Establish communication channels (Slack, email lists, project management) | |
| 3. Project Kick-off Meeting (Month 2) | |
| - In-person if possible (build team cohesion) | |
| - Agenda: | |
| * Welcome and introductions | |
| * Project vision and goals | |
| * Roles and responsibilities | |
| * Work plan and milestones | |
| * Communication protocols | |
| * Risk management | |
| * Team building activities | |
| - Duration: 2-3 days | |
| - Location: Lead institution (or rotate among partners) | |
| Stakeholder role: | |
| - Help recruit (share job postings, recommend candidates) | |
| - Attend kick-off meeting (steering committee members) | |
| - Provide institutional support (access, resources) | |
| Target: Team in place, infrastructure ready by end of Month 2 | |
| Month 2-6: Foundation Phase Begins | |
| ----------------------------------- | |
| This is where real work starts. Three parallel tracks: | |
| Track 1: OCR Pipeline Development (Months 2-5) | |
| Led by: 2 AI/ML Researchers | |
| Activities: | |
| - Literature review (state-of-the-art OCR methods) | |
| - Test various OCR engines (llava, Tesseract, commercial APIs) | |
| - Implement PDF→image conversion | |
| - Build quality assessment module | |
| - Benchmark on diverse patents | |
| Deliverable (Month 6): Working OCR pipeline, accuracy report | |
| Track 2: Stakeholder Data Collection (Months 2-6) | |
| Led by: Data Engineer | |
| Activities: | |
| - Negotiate data sharing agreements with 5-10 partner institutions | |
| - Build web scraping infrastructure | |
| - Extract data from public sources | |
| - Data quality assessment and cleaning | |
| - Begin constructing database (target: 500 entries by Month 6) | |
| Deliverable (Month 6): Initial stakeholder database, data collection report | |
| Track 3: User Studies & Requirements (Months 3-6) | |
| Led by: UX Researcher | |
| Activities: | |
| - Recruit TTO professionals for studies (target: 20 participants) | |
| - Conduct contextual inquiry (observe current workflows) | |
| - Requirements workshops (what do they need?) | |
| - Prototype testing (validate design directions) | |
| - Synthesize findings | |
| Deliverable (Month 6): User requirements document, prototype feedback | |
| Governance: | |
| - Monthly all-hands meetings (whole team) | |
| - Bi-weekly work package meetings (each track) | |
| - Quarterly steering committee review (Month 3, Month 6) | |
| Stakeholder role: | |
| - Steering committee: Attend quarterly reviews, provide guidance | |
| - Partner institutions: Facilitate user study participation | |
| - Data partners: Expedite data sharing agreements | |
| Target: Solid foundation by Month 6 (ready for Year 1 Q3 work) | |
| STAKEHOLDER ENGAGEMENT OPPORTUNITIES | |
| ==================================== | |
| For VISTA Partners (Universities, TTOs, Research Centers) | |
| ---------------------------------------------------------- | |
| Opportunity 1: Steering Committee Membership | |
| Commitment: 4 meetings per year (quarterly), 2 hours each + preparation | |
| Role: | |
| - Strategic oversight (ensure alignment with VISTA goals) | |
| - Risk management (identify and address issues early) | |
| - Resource allocation (advise on priorities) | |
| - Quality assurance (review deliverables, provide feedback) | |
| - Stakeholder liaison (represent interests of broader community) | |
| Benefits: | |
| - Shape project direction | |
| - Early visibility into findings and outputs | |
| - Networking with other VISTA leaders | |
| - Recognition in project materials and publications | |
| Target: 8-10 steering committee members representing VISTA Work Packages | |
| Opportunity 2: User Study Participation | |
| Commitment: Various (interviews, workshops, testing sessions) | |
| Year 1: 2-4 hours (interviews, requirements gathering) | |
| Year 2: 4-6 hours (usability testing, feedback sessions) | |
| Year 3: 2-3 hours (evaluation interviews, case studies) | |
| Role: | |
| - Share expertise (how do you currently do patent analysis?) | |
| - Test prototypes (is this useful? usable?) | |
| - Provide feedback (what works, what doesn't?) | |
| - Suggest improvements | |
| Benefits: | |
| - Ensure system meets real needs (you shape it) | |
| - Early access to prototypes and findings | |
| - Training on AI for knowledge transfer | |
| - Co-authorship on user study papers | |
| Target: 50+ TTO professionals participating over 3 years | |
| Opportunity 3: Pilot Site Participation (Year 2-3) | |
| Commitment: Year 2-3 (Months 13-36), active use of system | |
| Requirements: | |
| - Designate 2-3 staff as primary SPARKNET users | |
| - Analyze 20-50 patents through system | |
| - Provide regular feedback (monthly surveys, quarterly interviews) | |
| - Participate in case study development | |
| - Allow site visits for evaluation | |
| Benefits: | |
| - Free access to SPARKNET (€10k+ value) | |
| - Enhanced technology transfer capabilities | |
| - Staff training and professional development | |
| - Co-authorship on pilot study publications | |
| - Recognition as innovation leader | |
| Target: 10-15 pilot sites (5 EU, 5 Canada, 3-5 TTOs) | |
| Selection criteria: | |
| - Commitment to active use | |
| - Diversity (size, type, geography) | |
| - Data sharing willingness | |
| - Technical capacity | |
| Application process (Year 1, Month 9): | |
| - Open call for pilot sites | |
| - Application form (motivation, capacity, commitment) | |
| - Selection by steering committee | |
| - Onboarding (Months 10-12) | |
| Opportunity 4: Data Sharing Partnerships | |
| Commitment: One-time or ongoing data contribution | |
| Options: | |
| - Share stakeholder profiles (researchers, companies in your network) | |
| - Provide access to institutional databases (CRIS, RIS) | |
| - Contribute historical technology transfer data (successful collaborations) | |
| Benefits: | |
| - Better matching for your institution (more data = better results) | |
| - Access to broader VISTA network database | |
| - Co-authorship on database methodology papers | |
| - Recognition as data contributor | |
| Concerns (we'll address): | |
| - Privacy: Anonymization, access controls, GDPR compliance | |
| - Competition: Selective sharing (mark sensitive data as private) | |
| - Effort: We do the data extraction, you provide access | |
| - Control: You can review and approve what's included | |
| Target: 15-20 data partners contributing over 3 years | |
| For Funding Agencies (VISTA, National Agencies, EU Programs) | |
| ------------------------------------------------------------ | |
| Opportunity 1: Co-Funding | |
| Rationale: | |
| - SPARKNET budget (€1.65M) is substantial for one source | |
| - Co-funding reduces risk, increases buy-in | |
| - Aligns with multiple funding priorities (AI, innovation, EU-Canada collaboration) | |
| Potential models: | |
| - VISTA core contribution: €800k (50%) | |
| - Institutional co-funding: €500k (30%) - from partner universities | |
| - National agencies: €300k (20%) - from NSERC (Canada), EU programs (Innovation Actions) | |
| Benefits of co-funding: | |
| - Shared risk and ownership | |
| - Broader support base (politically valuable) | |
| - Potential for larger scope or extended timeline | |
| - Sustainability beyond initial 3 years | |
| Process: | |
| - VISTA provides seed funding (€200k Year 1) | |
| - Use early results to secure additional funding (Month 6-12) | |
| - Full budget secured by Year 2 | |
| Opportunity 2: Strategic Alignment | |
| How SPARKNET aligns with funding priorities: | |
| For VISTA: | |
| - Directly supports VISTA mission (knowledge transfer enhancement) | |
| - Contributes to all 5 work packages | |
| - Showcases EU-Canada collaboration success | |
| For EU programs (Horizon Europe, Digital Europe): | |
| - AI for public good | |
| - Digital transformation of research | |
| - European innovation ecosystem | |
| - Aligns with Key Digital Technologies (KDT) priority | |
| For Canadian agencies (NSERC, NRC): | |
| - AI and machine learning research | |
| - University-industry collaboration | |
| - Technology commercialization | |
| - Aligns with Innovation, Science and Economic Development (ISED) priorities | |
| Benefits of explicit alignment: | |
| - Higher chance of approval (fits strategic priorities) | |
| - Access to funding streams | |
| - Policy impact (SPARKNET as model for other initiatives) | |
| Opportunity 3: Access to Intellectual Property and Outputs | |
| What funding agencies get: | |
| - Publications (open access where possible) | |
| - Datasets and benchmarks (community resources) | |
| - Software (open-source components) | |
| - Methodologies (replicable by others) | |
| - Lessons learned (what works, what doesn't) | |
| Potential for: | |
| - Licensing revenue (if SPARKNET becomes commercial product) | |
| - Economic impact (job creation, startup formation) | |
| - Policy influence (inform AI policy, research policy) | |
| Terms: | |
| - Open science principles (FAIR data, reproducibility) | |
| - No exclusive licenses (benefits go to community) | |
| - Attribution and acknowledgment | |
| For Academic Institutions (Universities, Research Centers) | |
| ---------------------------------------------------------- | |
| Opportunity 1: Embed Students in Project | |
| PhD students (3-year commitment): | |
| - 1 PhD position available | |
| - Fully funded (salary, tuition, research budget) | |
| - Co-supervision by SPARKNET PI and institutional supervisor | |
| - Topic negotiable (within SPARKNET scope) | |
| Benefits for institution: | |
| - No cost PhD student (fully funded by project) | |
| - High-quality research (embedded in large project) | |
| - Publications (student + SPARKNET team) | |
| - Training in AI, multi-agent systems, knowledge transfer | |
| Benefits for student: | |
| - Interesting, impactful research topic | |
| - Interdisciplinary experience | |
| - Large team collaboration | |
| - Real-world validation of research | |
| - Strong publication record | |
| Application process: | |
| - Open call (Month 3) | |
| - Interview candidates (Month 4) | |
| - Selection (Month 5) | |
| - Start (Month 6) | |
| Master's students (6-12 month projects): | |
| - 2-3 positions per year | |
| - Partially funded (stipend for full-time students) | |
| - Topics: Diagram analysis, stakeholder profiling, UX, specific engineering tasks | |
| Benefits for institution: | |
| - Supervised projects for Master's program | |
| - Research output | |
| - Potential for publication | |
| Opportunity 2: Research Collaboration | |
| Joint research on topics of mutual interest: | |
| - Multi-agent systems (if you have MAS research group) | |
| - Natural language processing (if you have NLP group) | |
| - Knowledge management (if you have KM researchers) | |
| - Human-computer interaction (if you have HCI group) | |
| Collaboration models: | |
| - Co-authorship on papers (SPARKNET provides data/platform, you provide expertise) | |
| - Joint proposals (use SPARKNET as foundation for new projects) | |
| - Shared students (your student works on SPARKNET problem) | |
| - Visiting researchers (your faculty spend sabbatical with SPARKNET team) | |
| Benefits: | |
| - Access to unique platform and data | |
| - New publication venues and opportunities | |
| - Grant proposals (SPARKNET as preliminary work) | |
| - Network expansion | |
| Opportunity 3: Institutional Use of SPARKNET | |
| Once operational (Year 3+), your institution can: | |
| - Use SPARKNET for your own technology transfer | |
| - Customize for your specific needs | |
| - Integrate with your systems (CRIS, RIS, CRM) | |
| - Train your staff | |
| Pricing model (post-project): | |
| - VISTA partners: Free for duration of VISTA project | |
| - Other institutions: Subscription model (€5-10k/year) | |
| - Open-source core: Always free (but no support) | |
| MAKING IT HAPPEN | |
| ================ | |
| What we need from you today: | |
| 1. Feedback on proposal | |
| - What's missing? | |
| - What concerns do you have? | |
| - What would make this better? | |
| 2. Indication of interest | |
| - Would you support this project? | |
| - Would you participate (steering committee, pilot site, data partner)? | |
| - Would you co-fund? | |
| 3. Next steps | |
| - Who should we follow up with? | |
| - What approvals are needed in your organization? | |
| - What's your timeline? | |
| What happens after today: | |
| - Week 1: Incorporate feedback, revise proposal | |
| - Week 2: Individual follow-ups with interested stakeholders | |
| - Week 3-4: Finalize proposal, submit for approval | |
| - Month 2: Kick-off (if approved) | |
| Contact: | |
| Mohamed Hamdan | |
| [email@institution.edu] | |
| [phone] | |
| SPARKNET Project Website: | |
| [URL] (will be set up once project approved) | |
| TRANSITION: "Let's open the floor for questions and discussion..." | |
| ================================================================================ | |
| SLIDE 12 | |
| ================================================================================ | |
| CLOSING REMARKS (2 minutes): | |
| SUMMARY: | |
| Today, I've presented SPARKNET - an ambitious 3-year research program to transform patent valorization through AI. | |
| KEY TAKEAWAYS: | |
| 1. We have a working prototype (5-10% complete) that proves the concept | |
| 2. 90-95% of the work lies ahead - significant research and development needed | |
| 3. Clear 3-year roadmap with milestones, deliverables, and success metrics | |
| 4. Budget of ~€1.65M is realistic for the scope of work | |
| 5. Multiple opportunities for stakeholder engagement | |
| WHY THIS MATTERS: | |
| - Knowledge transfer is crucial for innovation and economic growth | |
| - Current manual processes don't scale - AI can help | |
| - VISTA provides perfect context for this research | |
| - We have the expertise and commitment to deliver | |
| WHAT WE'RE ASKING: | |
| - Support for the 3-year program | |
| - Active engagement from stakeholders (steering committee, pilot sites, data partners) | |
| - Funding commitment (from VISTA and potentially other sources) | |
| - Permission to proceed with team recruitment and kickoff | |
| WHAT YOU GET: | |
| - Cutting-edge research outputs (publications, datasets, tools) | |
| - Production-ready SPARKNET platform (by Year 3) | |
| - Enhanced knowledge transfer capabilities for your institution | |
| - Leadership role in EU-Canada research collaboration | |
| THE JOURNEY AHEAD: | |
| - This is a marathon, not a sprint | |
| - We'll encounter challenges and setbacks - that's research | |
| - We need your support, patience, and active participation | |
| - Together, we can build something transformative | |
| IMMEDIATE NEXT STEPS: | |
| 1. Your feedback (TODAY) | |
| 2. Proposal revision (NEXT WEEK) | |
| 3. Approval process (MONTH 1) | |
| 4. Team recruitment (MONTH 1-2) | |
| 5. Kickoff (MONTH 2) | |
| FINAL THOUGHT: | |
| We're not just building software. We're advancing the state of knowledge in multi-agent AI, quality assessment, and knowledge transfer. We're creating tools that will help researchers bring their innovations to the world. We're strengthening the EU-Canada research ecosystem. | |
| This is important work. Let's do it right. | |
| Thank you for your time and attention. I'm excited to answer your questions and discuss how we can move forward together. | |
| QUESTIONS & DISCUSSION: | |
| [Open floor for Q&A - be prepared for:] | |
| Expected questions: | |
| Q: "Why 3 years? Can it be done faster?" | |
| A: We considered 2 years but that's too rushed for quality research. Need time for publications, student theses, real-world validation. Could do in 4 years if more comprehensive, but 3 is sweet spot. | |
| Q: "What if you can't get access to stakeholder data?" | |
| A: Risk we've identified. Mitigation: Start partnerships early, use synthetic data for dev, have fallback approaches. But we're confident with VISTA network support. | |
| Q: "How do you ensure AI quality/avoid hallucinations?" | |
| A: Multi-layered approach: CriticAgent review, quality framework with 12 dimensions, human-in-the-loop for critical decisions, confidence scoring to flag uncertain outputs. | |
| Q: "What happens after 3 years? Is this sustainable?" | |
| A: Plan for transition to operational team. Potential models: Subscription for institutions, licensing, continued grant funding, VISTA operational budget. Details TBD but sustainability is core consideration. | |
| Q: "Can we see a demo?" | |
| A: Yes! We have working prototype. Can show: Patent upload, analysis workflow, stakeholder matching, valorization brief output. [Be ready to demo or schedule follow-up] | |
| Q: "How do you manage IP? Who owns SPARKNET?" | |
| A: Intellectual property generated will be owned by lead institution but licensed openly to VISTA partners. Publications open access. Software has open-source core + proprietary extensions. Details in formal project agreement. | |
| Be confident, honest, and enthusiastic. Show expertise but also humility (acknowledge challenges). Build trust through transparency. | |
| Thank you! | |
| ================================================================================ | |
| END OF SPEAKER NOTES | |
| ================================================================================ | |