| | """ |
| | Create improved SPARKNET Academic Presentation |
| | Emphasizes early-stage development and 3-year research roadmap |
| | """ |
| | from pptx import Presentation |
| | from pptx.util import Inches, Pt |
| | from pptx.enum.text import PP_ALIGN, MSO_ANCHOR |
| | from pptx.dml.color import RGBColor |
| |
|
| | def create_improved_presentation(): |
| | """Create comprehensive academic presentation""" |
| |
|
| | prs = Presentation() |
| | prs.slide_width = Inches(10) |
| | prs.slide_height = Inches(7.5) |
| |
|
| | |
| | VISTA_BLUE = RGBColor(0, 51, 102) |
| | VISTA_ORANGE = RGBColor(255, 102, 0) |
| | DARK_GRAY = RGBColor(51, 51, 51) |
| | LIGHT_GRAY = RGBColor(128, 128, 128) |
| |
|
| | def add_title_slide(title, subtitle, footer=""): |
| | """Add title slide""" |
| | slide = prs.slides.add_slide(prs.slide_layouts[6]) |
| |
|
| | |
| | title_box = slide.shapes.add_textbox(Inches(0.5), Inches(2.5), Inches(9), Inches(1)) |
| | title_frame = title_box.text_frame |
| | title_frame.text = title |
| | title_p = title_frame.paragraphs[0] |
| | title_p.font.size = Pt(54) |
| | title_p.font.bold = True |
| | title_p.font.color.rgb = VISTA_BLUE |
| | title_p.alignment = PP_ALIGN.CENTER |
| |
|
| | |
| | if subtitle: |
| | subtitle_box = slide.shapes.add_textbox(Inches(0.5), Inches(3.8), Inches(9), Inches(1.5)) |
| | subtitle_frame = subtitle_box.text_frame |
| | subtitle_frame.text = subtitle |
| | subtitle_p = subtitle_frame.paragraphs[0] |
| | subtitle_p.font.size = Pt(24) |
| | subtitle_p.font.color.rgb = DARK_GRAY |
| | subtitle_p.alignment = PP_ALIGN.CENTER |
| |
|
| | |
| | if footer: |
| | footer_box = slide.shapes.add_textbox(Inches(0.5), Inches(6.8), Inches(9), Inches(0.5)) |
| | footer_frame = footer_box.text_frame |
| | footer_frame.text = footer |
| | footer_p = footer_frame.paragraphs[0] |
| | footer_p.font.size = Pt(14) |
| | footer_p.font.color.rgb = LIGHT_GRAY |
| | footer_p.alignment = PP_ALIGN.CENTER |
| |
|
| | return slide |
| |
|
| | def add_content_slide(title, content_lines, speaker_notes=""): |
| | """Add content slide with bullet points""" |
| | slide = prs.slides.add_slide(prs.slide_layouts[6]) |
| |
|
| | |
| | title_box = slide.shapes.add_textbox(Inches(0.5), Inches(0.5), Inches(9), Inches(0.8)) |
| | title_frame = title_box.text_frame |
| | title_frame.text = title |
| | title_p = title_frame.paragraphs[0] |
| | title_p.font.size = Pt(32) |
| | title_p.font.bold = True |
| | title_p.font.color.rgb = VISTA_BLUE |
| |
|
| | |
| | content_box = slide.shapes.add_textbox(Inches(0.8), Inches(1.5), Inches(8.5), Inches(5.3)) |
| | content_frame = content_box.text_frame |
| | content_frame.word_wrap = True |
| |
|
| | for i, line in enumerate(content_lines): |
| | if i > 0: |
| | content_frame.add_paragraph() |
| | p = content_frame.paragraphs[i] |
| |
|
| | |
| | if isinstance(line, tuple): |
| | level, text = line |
| | else: |
| | level = 0 |
| | text = line |
| |
|
| | p.text = text |
| | p.level = level |
| | p.font.size = Pt(18 - level * 2) |
| | p.space_before = Pt(6) |
| | p.space_after = Pt(6) |
| |
|
| | |
| | if speaker_notes: |
| | notes_slide = slide.notes_slide |
| | text_frame = notes_slide.notes_text_frame |
| | text_frame.text = speaker_notes |
| |
|
| | return slide |
| |
|
| | |
| | |
| | |
| | slide1 = add_title_slide( |
| | "SPARKNET", |
| | "Multi-Agent AI System for Academic Research Valorization\nEarly-Stage Prototype & 3-Year Research Roadmap", |
| | "Mohamed Hamdan | VISTA Project | November 2025 | CONFIDENTIAL DRAFT" |
| | ) |
| |
|
| | notes1 = """ |
| | 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... |
| | """ |
| | slide1.notes_slide.notes_text_frame.text = notes1 |
| |
|
| | |
| | |
| | |
| | slide2 = add_content_slide( |
| | "PROJECT STATUS: Early-Stage Prototype", |
| | [ |
| | "🎯 Current Development Stage", |
| | (1, "Proof-of-Concept Prototype: ~5-10% Complete"), |
| | (1, "Technical feasibility validated through working demo"), |
| | (1, "Core architecture established, foundation components operational"), |
| | "", |
| | "📊 What We Have (Prototype Phase):", |
| | (1, "✓ Basic multi-agent workflow (4 agents, 1 scenario)"), |
| | (1, "✓ Simple document analysis (text-based patents only)"), |
| | (1, "✓ Proof-of-concept OCR integration (llava:7b on GPU)"), |
| | (1, "✓ Basic stakeholder matching (mock database, 50 entries)"), |
| | (1, "✓ Minimal web interface (demo purposes only)"), |
| | "", |
| | "⚠️ What We DON'T Have (90-95% of Work Ahead):", |
| | (1, "✗ Production-ready OCR pipeline (PDF→image→analysis)"), |
| | (1, "✗ Comprehensive stakeholder database (need 10,000+ entries)"), |
| | (1, "✗ Advanced quality frameworks (VISTA 12-dimension validation)"), |
| | (1, "✗ Additional VISTA scenarios (2 & 3 not started)"), |
| | (1, "✗ Multi-language support, CRM integration, security hardening"), |
| | (1, "✗ Real-world validation, user studies, performance optimization"), |
| | ], |
| | speaker_notes=""" |
| | 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..." |
| | """ |
| | ) |
| |
|
| | |
| | |
| | |
| | slide3 = add_content_slide( |
| | "SPARKNET Decomposition by VISTA Work Packages", |
| | [ |
| | "🎯 VISTA Project: EU-Canada Knowledge Transfer Ecosystem", |
| | "", |
| | "WP1: Project Management & Coordination (5% implemented)", |
| | (1, "Current: Basic project documentation, GitHub repository"), |
| | (1, "Needed: Stakeholder governance, progress tracking, deliverable management"), |
| | "", |
| | "WP2: Valorization Pathways (15% implemented)", |
| | (1, "Current: Basic patent analysis (Scenario 1), TRL assessment prototype"), |
| | (1, "Needed: Comprehensive pathway analysis, decision support, multi-patent batch processing"), |
| | "", |
| | "WP3: Quality Standards Framework (8% implemented)", |
| | (1, "Current: Simple output validation, quality threshold checking"), |
| | (1, "Needed: Full 12-dimension VISTA framework, compliance validation, quality metrics"), |
| | "", |
| | "WP4: Stakeholder Networks (3% implemented)", |
| | (1, "Current: Mock database (50 entries), basic semantic search"), |
| | (1, "Needed: Real stakeholder DB (10,000+), CRM integration, network analytics"), |
| | "", |
| | "WP5: Digital Tools & Platforms (10% implemented)", |
| | (1, "Current: Prototype web UI, basic API"), |
| | (1, "Needed: Production platform, mobile access, multi-tenant deployment"), |
| | ], |
| | speaker_notes=""" |
| | 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..." |
| | """ |
| | ) |
| |
|
| | |
| | |
| | |
| | slide4 = add_content_slide( |
| | "Current Prototype: What We've Demonstrated", |
| | [ |
| | "✅ Phase 1 Prototype (5-10% Complete) - Proof of Concept", |
| | "", |
| | "🧠 Multi-Agent Architecture (Basic Implementation)", |
| | (1, "4 specialized agents: Document, Market, Matchmaking, Outreach"), |
| | (1, "LangGraph cyclic workflow with Planner-Critic loop"), |
| | (1, "Basic memory system (episodic, semantic, stakeholder stores)"), |
| | (1, "⚠️ Gap: Simple LLM chains, no advanced reasoning or learning"), |
| | "", |
| | "📄 Document Analysis (Text-Only Patents)", |
| | (1, "Claims extraction (independent/dependent parsing)"), |
| | (1, "TRL assessment (rule-based, 1-9 scale)"), |
| | (1, "Basic innovation identification"), |
| | (1, "⚠️ Gap: No OCR pipeline, no diagram analysis, no multi-language"), |
| | "", |
| | "🔬 Recent Addition: OCR Foundation (Proof of Concept)", |
| | (1, "llava:7b vision model installed on GPU1"), |
| | (1, "VisionOCRAgent with 5 methods (text, diagram, table, patent, handwriting)"), |
| | (1, "Integrated with workflow (auto-initializes on startup)"), |
| | (1, "⚠️ Gap: No PDF→image pipeline, no batch processing, not production-ready"), |
| | "", |
| | "🔍 Stakeholder Matching (Mock Data Only)", |
| | (1, "Vector similarity search (ChromaDB)"), |
| | (1, "Simple scoring (single dimension)"), |
| | (1, "⚠️ Gap: Mock database (50 entries), no real data, no advanced matching"), |
| | ], |
| | speaker_notes=""" |
| | 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..." |
| | """ |
| | ) |
| |
|
| | |
| | |
| | |
| | slide5 = add_content_slide( |
| | "3-Year Research Roadmap: From Prototype to Production", |
| | [ |
| | "Year 1 (Months 1-12): Foundation & Core Research", |
| | (1, "Q1-Q2: OCR Production Pipeline (PDF→Image→Text→Structure)"), |
| | (1, "Q2-Q3: Stakeholder Database Construction (initial 2,000 entries)"), |
| | (1, "Q3-Q4: VISTA Quality Framework Implementation (12 dimensions)"), |
| | (1, "Q4: Scenario 2 Design & Initial Development (Agreement Safety)"), |
| | (1, "Ongoing: User studies, requirement gathering, design iterations"), |
| | "", |
| | "Year 2 (Months 13-24): Scale & Intelligence", |
| | (1, "Q1-Q2: Advanced AI/ML Models (reasoning, prediction, learning)"), |
| | (1, "Q2-Q3: Stakeholder Database Expansion (to 10,000+ entries)"), |
| | (1, "Q3-Q4: Scenario 2 Completion + Scenario 3 Development"), |
| | (1, "Q4: Multi-language Support (French, German, Spanish)"), |
| | (1, "Ongoing: Platform development, integration, performance optimization"), |
| | "", |
| | "Year 3 (Months 25-36): Production, Validation & Deployment", |
| | (1, "Q1-Q2: Production Deployment (cloud infrastructure, security)"), |
| | (1, "Q2-Q3: Large-Scale Validation (real-world pilots, 10+ institutions)"), |
| | (1, "Q3-Q4: Documentation, Training Materials, Knowledge Transfer"), |
| | (1, "Q4: Final Evaluation, Publication, Dissemination"), |
| | (1, "Deliverable: Production-ready SPARKNET platform for VISTA network"), |
| | ], |
| | speaker_notes=""" |
| | 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..." |
| | """ |
| | ) |
| |
|
| | |
| | |
| | |
| | slide6 = add_content_slide( |
| | "Year 1 Research Challenges: Core Technical Innovations", |
| | [ |
| | "🔬 OCR Production Pipeline (Months 1-3) - MAJOR RESEARCH EFFORT", |
| | (1, "Challenge 1: Robust PDF Parsing"), |
| | (2, "Handle encrypted, damaged, non-standard PDFs"), |
| | (2, "Maintain document structure across conversion"), |
| | (1, "Challenge 2: Intelligent Image Processing"), |
| | (2, "Adaptive resolution and quality optimization"), |
| | (2, "Text region vs diagram detection (computer vision)"), |
| | (1, "Challenge 3: Multi-Model OCR Strategy"), |
| | (2, "llava:7b for diagrams, Tesseract for text, specialized for tables"), |
| | (2, "Confidence scoring and quality assessment"), |
| | "", |
| | "📊 VISTA Quality Framework (Months 4-9) - METHODOLOGICAL INNOVATION", |
| | (1, "Challenge: Operationalize 12 qualitative dimensions"), |
| | (2, "Completeness, Accuracy, Relevance, Timeliness, Consistency..."), |
| | (2, "Convert human assessments into computational metrics"), |
| | (1, "Approach: Machine learning from expert-labeled examples"), |
| | (2, "Collect 500+ expert quality assessments"), |
| | (2, "Train models to predict each dimension"), |
| | "", |
| | "🗄️ Stakeholder Database (Months 4-12) - DATA ENGINEERING CHALLENGE", |
| | (1, "Target: 5,000 real entities by end of Year 1"), |
| | (1, "Data sources: Web scraping, partnerships, public databases"), |
| | (1, "Quality assurance: Verification, deduplication, enrichment"), |
| | (1, "Privacy compliance: GDPR, Canadian privacy laws"), |
| | ], |
| | speaker_notes=""" |
| | 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..." |
| | """ |
| | ) |
| |
|
| | |
| | |
| | |
| | slide7 = add_content_slide( |
| | "Years 2-3 Research Challenges: Advanced Capabilities", |
| | [ |
| | "🧠 Year 2 (Months 13-24): Intelligence & Scale", |
| | "", |
| | "Advanced AI/ML (Q1-Q2):", |
| | (1, "Chain-of-thought reasoning for complex patent analysis"), |
| | (1, "Few-shot learning for rare patent types (no training data)"), |
| | (1, "Multi-modal models (text + images + tables simultaneously)"), |
| | (1, "Agent learning and improvement from experience"), |
| | (1, "Success prediction models (likelihood of tech transfer)"), |
| | "", |
| | "Scenarios 2 & 3 (Q3-Q4):", |
| | (1, "Scenario 2 - Agreement Safety: Legal NLP, risk assessment, compliance"), |
| | (1, "Scenario 3 - Partner Matching: Network analysis, compatibility prediction"), |
| | (1, "Challenge: Reuse infrastructure while handling domain-specific needs"), |
| | "", |
| | "🚀 Year 3 (Months 25-36): Production & Validation", |
| | "", |
| | "Production Deployment (Q1):", |
| | (1, "Cloud architecture: Multi-region, high-availability, auto-scaling"), |
| | (1, "Security: Penetration testing, OWASP compliance, SOC 2"), |
| | (1, "Performance: <2s response time, 1000+ concurrent users"), |
| | "", |
| | "Real-World Validation (Q2-Q3):", |
| | (1, "Pilot with 10-15 institutions (EU + Canada)"), |
| | (1, "Quantitative: Usage metrics, success rates, time savings"), |
| | (1, "Qualitative: User interviews, case studies, testimonials"), |
| | ], |
| | speaker_notes=""" |
| | 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..." |
| | """ |
| | ) |
| |
|
| | |
| | |
| | |
| | slide8 = add_content_slide( |
| | "Research Questions & Expected Scientific Contributions", |
| | [ |
| | "🔬 Core Research Questions (Publishable Findings)", |
| | "", |
| | "RQ1: Multi-Agent Coordination for Complex Workflows", |
| | (1, "How to optimize agent communication and task delegation?"), |
| | (1, "What workflow patterns maximize quality and efficiency?"), |
| | (1, "Expected: 2-3 papers on multi-agent systems for knowledge work"), |
| | "", |
| | "RQ2: Quality Assessment in AI-Generated Knowledge Transfer", |
| | (1, "Can computational metrics predict expert quality assessments?"), |
| | (1, "What features correlate with high-quality valorization analysis?"), |
| | (1, "Expected: 1-2 papers on AI quality frameworks, VISTA validation study"), |
| | "", |
| | "RQ3: Semantic Matching for Academic-Industry Collaboration", |
| | (1, "What matching algorithms best predict collaboration success?"), |
| | (1, "How to balance multiple dimensions (technical, cultural, strategic)?"), |
| | (1, "Expected: 2 papers on stakeholder matching, network analysis"), |
| | "", |
| | "RQ4: Multi-Modal Understanding of Technical Documents", |
| | (1, "How to jointly reason over text, diagrams, and tables in patents?"), |
| | (1, "What representations enable cross-modal inference?"), |
| | (1, "Expected: 1-2 papers on multi-modal patent analysis"), |
| | "", |
| | "📚 Expected Outputs (3 Years)", |
| | (1, "6-10 peer-reviewed publications (AI conferences, knowledge management journals)"), |
| | (1, "2-3 PhD/Master's theses (topics embedded in SPARKNET research)"), |
| | (1, "1 comprehensive VISTA technical report & methodology documentation"), |
| | (1, "Open-source contributions (tools, datasets, benchmarks for research community)"), |
| | ], |
| | speaker_notes=""" |
| | 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..." |
| | """ |
| | ) |
| |
|
| | |
| | |
| | |
| | slide9 = add_content_slide( |
| | "Resource Requirements & Risk Management", |
| | [ |
| | "💰 Budget Estimate (3 Years)", |
| | (1, "Personnel: €1.2M (researchers, engineers, project manager)"), |
| | (1, "Infrastructure: €200k (GPUs, cloud services, software licenses)"), |
| | (1, "Research activities: €150k (user studies, data collection, validation)"), |
| | (1, "Knowledge transfer: €100k (documentation, training, dissemination)"), |
| | (1, "Total: ~€1.65M over 36 months"), |
| | "", |
| | "👥 Team Composition (Peak staffing: Year 2)", |
| | (1, "2 AI/ML Researchers (PhDs or senior)"), |
| | (1, "3 Research Engineers (software development)"), |
| | (1, "1 Data Engineer (stakeholder database)"), |
| | (1, "1 UX Researcher / Designer"), |
| | (1, "1 DevOps Engineer (deployment, infrastructure)"), |
| | (1, "1 Project Manager"), |
| | (1, "Plus: Consultants (legal, domain experts), Student assistants"), |
| | "", |
| | "⚠️ Key Risks & Mitigation Strategies", |
| | (1, "Risk: Stakeholder data access → Mitigation: Partner early with institutions"), |
| | (1, "Risk: OCR quality insufficient → Mitigation: Multi-model approach, human review"), |
| | (1, "Risk: User adoption barriers → Mitigation: Co-design with TTOs from start"), |
| | (1, "Risk: Technical complexity underestimated → Mitigation: Agile, iterative development"), |
| | ], |
| | speaker_notes=""" |
| | 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..." |
| | """ |
| | ) |
| |
|
| | |
| | |
| | |
| | slide10 = add_content_slide( |
| | "Expected Impact & Success Metrics (3-Year Horizon)", |
| | [ |
| | "🎯 Quantitative Success Metrics", |
| | (1, "Academic Impact:"), |
| | (2, "6-10 peer-reviewed publications in top venues"), |
| | (2, "2-3 PhD/Master's theses completed"), |
| | (2, "500+ citations to SPARKNET research (5-year projection)"), |
| | (1, "System Performance:"), |
| | (2, "95%+ OCR accuracy on diverse patent types"), |
| | (2, "90%+ user satisfaction in pilot studies (NPS > 50)"), |
| | (2, "70%+ time savings vs manual analysis (TTO workflows)"), |
| | (1, "Deployment & Adoption:"), |
| | (2, "10-15 institutions actively using SPARKNET"), |
| | (2, "1000+ patents analyzed through system"), |
| | (2, "100+ successful stakeholder introductions facilitated"), |
| | "", |
| | "🌍 Qualitative Impact", |
| | (1, "Research Community: New benchmarks, datasets, methodologies for patent AI"), |
| | (1, "VISTA Network: Enhanced knowledge transfer capacity across EU-Canada"), |
| | (1, "Technology Transfer: Improved efficiency and success rates for TTOs"), |
| | (1, "Economic: Accelerated research commercialization, more innovation reaching market"), |
| | "", |
| | "📊 Evaluation Framework", |
| | (1, "Continuous monitoring throughout 3 years (not just at end)"), |
| | (1, "Mixed methods: Quantitative metrics + qualitative case studies"), |
| | (1, "External evaluation: Independent assessment by VISTA and academic reviewers"), |
| | ], |
| | speaker_notes=""" |
| | 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..." |
| | """ |
| | ) |
| |
|
| | |
| | |
| | |
| | slide11 = add_content_slide( |
| | "Next Steps & Stakeholder Engagement Opportunities", |
| | [ |
| | "📅 Immediate Next Steps (Months 0-6)", |
| | "", |
| | "Month 0-1: Proposal Finalization & Approval", |
| | (1, "Refine project plan based on stakeholder feedback"), |
| | (1, "Secure funding commitment from VISTA and institutional partners"), |
| | (1, "Establish project governance (steering committee, advisory board)"), |
| | "", |
| | "Month 1-2: Team Recruitment & Kick-off", |
| | (1, "Hire core team (AI researchers, engineers, project manager)"), |
| | (1, "Set up infrastructure (GPUs, cloud accounts, development environment)"), |
| | (1, "Official project kick-off meeting with all partners"), |
| | "", |
| | "Month 2-6: Foundation Phase Begins", |
| | (1, "Start OCR pipeline development (PDF→image→text)"), |
| | (1, "Begin stakeholder data collection partnerships"), |
| | (1, "Initiate user studies with TTO professionals"), |
| | (1, "First quarterly progress report to steering committee"), |
| | "", |
| | "🤝 Stakeholder Engagement Opportunities", |
| | "", |
| | "For VISTA Partners:", |
| | (1, "Join steering committee (quarterly oversight)"), |
| | (1, "Participate in user studies and requirements gathering"), |
| | (1, "Pilot site participation (Year 2-3, receive early access)"), |
| | (1, "Data sharing partnerships (contribute stakeholder profiles)"), |
| | "", |
| | "For Funding Agencies:", |
| | (1, "Co-funding opportunities (match VISTA contribution)"), |
| | (1, "Strategic alignment with innovation and AI priorities"), |
| | (1, "Access to research outputs and intellectual property"), |
| | "", |
| | "For Academic Institutions:", |
| | (1, "Embed PhD/Master's students in project"), |
| | (1, "Collaboration on research publications"), |
| | (1, "Access to SPARKNET for institutional use"), |
| | ], |
| | speaker_notes=""" |
| | 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..." |
| | """ |
| | ) |
| |
|
| | |
| | |
| | |
| | slide12 = add_title_slide( |
| | "SPARKNET: A 3-Year Research Journey", |
| | "From Early Prototype to Production-Ready Knowledge Transfer Platform\n\nWe're at the beginning. Let's build the future together.", |
| | "Mohamed Hamdan | VISTA Project | November 2025\n\nThank you | Questions & Discussion Welcome" |
| | ) |
| |
|
| | notes12 = """ |
| | 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! |
| | """ |
| | slide12.notes_slide.notes_text_frame.text = notes12 |
| |
|
| | |
| | output_path = "/home/mhamdan/SPARKNET/presentation/SPARKNET_Academic_Presentation_IMPROVED.pptx" |
| | prs.save(output_path) |
| | print(f"Saved improved presentation to: {output_path}") |
| | return output_path |
| |
|
| | if __name__ == "__main__": |
| | try: |
| | path = create_improved_presentation() |
| | print(f"\n✅ SUCCESS! Improved presentation created at:\n{path}") |
| | except Exception as e: |
| | print(f"❌ Error creating presentation: {e}") |
| | import traceback |
| | traceback.print_exc() |
| |
|
