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""" |
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Create improved SPARKNET Academic Presentation |
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Emphasizes early-stage development and 3-year research roadmap |
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""" |
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from pptx import Presentation |
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from pptx.util import Inches, Pt |
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from pptx.enum.text import PP_ALIGN, MSO_ANCHOR |
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from pptx.dml.color import RGBColor |
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def create_improved_presentation(): |
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"""Create comprehensive academic presentation""" |
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prs = Presentation() |
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prs.slide_width = Inches(10) |
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prs.slide_height = Inches(7.5) |
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VISTA_BLUE = RGBColor(0, 51, 102) |
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VISTA_ORANGE = RGBColor(255, 102, 0) |
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DARK_GRAY = RGBColor(51, 51, 51) |
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LIGHT_GRAY = RGBColor(128, 128, 128) |
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def add_title_slide(title, subtitle, footer=""): |
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"""Add title slide""" |
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slide = prs.slides.add_slide(prs.slide_layouts[6]) |
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title_box = slide.shapes.add_textbox(Inches(0.5), Inches(2.5), Inches(9), Inches(1)) |
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title_frame = title_box.text_frame |
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title_frame.text = title |
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title_p = title_frame.paragraphs[0] |
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title_p.font.size = Pt(54) |
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title_p.font.bold = True |
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title_p.font.color.rgb = VISTA_BLUE |
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title_p.alignment = PP_ALIGN.CENTER |
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if subtitle: |
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subtitle_box = slide.shapes.add_textbox(Inches(0.5), Inches(3.8), Inches(9), Inches(1.5)) |
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subtitle_frame = subtitle_box.text_frame |
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subtitle_frame.text = subtitle |
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subtitle_p = subtitle_frame.paragraphs[0] |
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subtitle_p.font.size = Pt(24) |
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subtitle_p.font.color.rgb = DARK_GRAY |
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subtitle_p.alignment = PP_ALIGN.CENTER |
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if footer: |
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footer_box = slide.shapes.add_textbox(Inches(0.5), Inches(6.8), Inches(9), Inches(0.5)) |
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footer_frame = footer_box.text_frame |
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footer_frame.text = footer |
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footer_p = footer_frame.paragraphs[0] |
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footer_p.font.size = Pt(14) |
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footer_p.font.color.rgb = LIGHT_GRAY |
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footer_p.alignment = PP_ALIGN.CENTER |
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return slide |
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def add_content_slide(title, content_lines, speaker_notes=""): |
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"""Add content slide with bullet points""" |
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slide = prs.slides.add_slide(prs.slide_layouts[6]) |
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title_box = slide.shapes.add_textbox(Inches(0.5), Inches(0.5), Inches(9), Inches(0.8)) |
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title_frame = title_box.text_frame |
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title_frame.text = title |
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title_p = title_frame.paragraphs[0] |
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title_p.font.size = Pt(32) |
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title_p.font.bold = True |
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title_p.font.color.rgb = VISTA_BLUE |
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content_box = slide.shapes.add_textbox(Inches(0.8), Inches(1.5), Inches(8.5), Inches(5.3)) |
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content_frame = content_box.text_frame |
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content_frame.word_wrap = True |
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for i, line in enumerate(content_lines): |
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if i > 0: |
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content_frame.add_paragraph() |
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p = content_frame.paragraphs[i] |
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if isinstance(line, tuple): |
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level, text = line |
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else: |
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level = 0 |
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text = line |
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p.text = text |
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p.level = level |
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p.font.size = Pt(18 - level * 2) |
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p.space_before = Pt(6) |
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p.space_after = Pt(6) |
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if speaker_notes: |
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notes_slide = slide.notes_slide |
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text_frame = notes_slide.notes_text_frame |
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text_frame.text = speaker_notes |
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return slide |
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slide1 = add_title_slide( |
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"SPARKNET", |
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"Multi-Agent AI System for Academic Research Valorization\nEarly-Stage Prototype & 3-Year Research Roadmap", |
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"Mohamed Hamdan | VISTA Project | November 2025 | CONFIDENTIAL DRAFT" |
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) |
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notes1 = """ |
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OPENING REMARKS (2 minutes): |
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Good [morning/afternoon]. Thank you for this opportunity to present SPARKNET, an AI-powered system for academic research valorization. |
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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. |
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POSITIONING: |
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- This is NOT a finished product - it's an early-stage research prototype |
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- We're seeking stakeholder buy-in for a comprehensive 3-year development program |
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- The prototype demonstrates technical viability but requires significant investment in all areas |
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AGENDA OVERVIEW: |
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1. Research context and VISTA alignment |
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2. Current prototype capabilities (10% complete) |
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3. Detailed breakdown of work remaining (90% ahead) |
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4. 3-year research roadmap by VISTA work packages |
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5. Resource requirements and expected outcomes |
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Let's begin with the research context... |
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""" |
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slide1.notes_slide.notes_text_frame.text = notes1 |
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slide2 = add_content_slide( |
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"PROJECT STATUS: Early-Stage Prototype", |
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[ |
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"🎯 Current Development Stage", |
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(1, "Proof-of-Concept Prototype: ~5-10% Complete"), |
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(1, "Technical feasibility validated through working demo"), |
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(1, "Core architecture established, foundation components operational"), |
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"", |
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"📊 What We Have (Prototype Phase):", |
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(1, "✓ Basic multi-agent workflow (4 agents, 1 scenario)"), |
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(1, "✓ Simple document analysis (text-based patents only)"), |
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(1, "✓ Proof-of-concept OCR integration (llava:7b on GPU)"), |
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(1, "✓ Basic stakeholder matching (mock database, 50 entries)"), |
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(1, "✓ Minimal web interface (demo purposes only)"), |
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"", |
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"⚠️ What We DON'T Have (90-95% of Work Ahead):", |
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(1, "✗ Production-ready OCR pipeline (PDF→image→analysis)"), |
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(1, "✗ Comprehensive stakeholder database (need 10,000+ entries)"), |
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(1, "✗ Advanced quality frameworks (VISTA 12-dimension validation)"), |
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(1, "✗ Additional VISTA scenarios (2 & 3 not started)"), |
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(1, "✗ Multi-language support, CRM integration, security hardening"), |
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(1, "✗ Real-world validation, user studies, performance optimization"), |
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], |
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speaker_notes=""" |
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PROJECT STAGE TRANSPARENCY (3 minutes): |
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CRITICAL FRAMING: Set realistic expectations immediately. We must be completely transparent about our current stage to build trust and justify the 3-year timeline. |
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WHAT THE PROTOTYPE IS: |
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- A working demonstration that proves the core concept is technically viable |
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- Sufficient to show stakeholders what the final system COULD become |
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- Evidence that our multi-agent architecture can handle patent valorization workflows |
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- A foundation upon which extensive research and development will be built |
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WHAT THE PROTOTYPE IS NOT: |
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- Not production-ready - lacks robustness, scalability, security |
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- Not research-complete - many algorithms, methods, and frameworks are placeholder or simplified |
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- Not feature-complete - critical capabilities are missing or stubbed |
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- Not validated - no user studies, no real-world testing, no performance benchmarks |
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THE 5-10% ESTIMATE BREAKDOWN: |
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- Architecture & Infrastructure: 15% complete (basic workflow established) |
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- AI/ML Capabilities: 5% complete (simple LLM chains, no sophisticated reasoning) |
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- Data & Knowledge Bases: 2% complete (tiny mock databases) |
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- User Experience: 8% complete (basic interface, no usability testing) |
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- VISTA Compliance: 10% complete (awareness of standards, minimal implementation) |
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- Integration & Deployment: 5% complete (local dev environment only) |
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WHY THIS IS GOOD NEWS FOR STAKEHOLDERS: |
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- We've de-risked the technical approach - we know it CAN work |
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- The 90% remaining gives us clear scope for innovation and IP generation |
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- Three-year timeline is realistic and defensible |
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- Significant opportunities for stakeholder input to shape development |
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TRANSITION: "Let's examine our research context and how SPARKNET aligns with VISTA objectives..." |
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""" |
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) |
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slide3 = add_content_slide( |
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"SPARKNET Decomposition by VISTA Work Packages", |
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[ |
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"🎯 VISTA Project: EU-Canada Knowledge Transfer Ecosystem", |
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"", |
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"WP1: Project Management & Coordination (5% implemented)", |
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(1, "Current: Basic project documentation, GitHub repository"), |
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(1, "Needed: Stakeholder governance, progress tracking, deliverable management"), |
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"", |
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"WP2: Valorization Pathways (15% implemented)", |
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(1, "Current: Basic patent analysis (Scenario 1), TRL assessment prototype"), |
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(1, "Needed: Comprehensive pathway analysis, decision support, multi-patent batch processing"), |
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"", |
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"WP3: Quality Standards Framework (8% implemented)", |
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(1, "Current: Simple output validation, quality threshold checking"), |
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(1, "Needed: Full 12-dimension VISTA framework, compliance validation, quality metrics"), |
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"", |
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"WP4: Stakeholder Networks (3% implemented)", |
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(1, "Current: Mock database (50 entries), basic semantic search"), |
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(1, "Needed: Real stakeholder DB (10,000+), CRM integration, network analytics"), |
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"", |
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"WP5: Digital Tools & Platforms (10% implemented)", |
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(1, "Current: Prototype web UI, basic API"), |
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(1, "Needed: Production platform, mobile access, multi-tenant deployment"), |
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], |
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speaker_notes=""" |
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VISTA ALIGNMENT & WORK PACKAGE DECOMPOSITION (4 minutes): |
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PURPOSE: Show stakeholders how SPARKNET maps directly to VISTA's structure and where the bulk of work remains. |
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WP1 - PROJECT MANAGEMENT (Current: 5%): |
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What we have: |
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- Basic Git version control |
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- Simple documentation in Markdown |
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- Informal development process |
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What we need (36 months): |
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- Formal project governance structure |
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- Stakeholder advisory board and regular consultations |
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- Deliverable and milestone tracking system |
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- Risk management framework |
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- Quality assurance processes |
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- Budget management and reporting |
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- IP management and exploitation planning |
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- Dissemination and communication strategy |
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WP2 - VALORIZATION PATHWAYS (Current: 15%): |
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|
What we have: |
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|
- Scenario 1 (Patent Wake-Up) basic workflow |
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- Simple TRL assessment (rule-based) |
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- Basic technology domain identification |
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- Simplified market opportunity analysis |
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What we need (36 months): |
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Research challenges: |
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- Sophisticated TRL assessment methodology (ML-based, context-aware) |
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- Multi-criteria decision support for valorization pathway selection |
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- Comparative analysis across multiple patents (portfolio management) |
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- Technology maturity prediction models |
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- Market readiness assessment frameworks |
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- Batch processing and workflow optimization |
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Implementation challenges: |
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- Scenario 2 (Agreement Safety): Legal document analysis, risk assessment, compliance checking |
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- Scenario 3 (Partner Matching): Profile analysis, collaboration history, complementarity scoring |
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- Integration with real technology transfer workflows |
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- Performance optimization for large patent portfolios |
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- User interface for pathway exploration and what-if analysis |
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WP3 - QUALITY STANDARDS (Current: 8%): |
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|
What we have: |
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|
- Simple quality threshold (0.8 cutoff) |
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- Basic Critic agent validation |
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- Rudimentary output checking |
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What we need (36 months): |
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Research challenges: |
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- Operationalize VISTA's 12-dimension quality framework: |
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|
1. Completeness: Are all required sections present? |
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|
2. Accuracy: Is information factually correct? |
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3. Relevance: Does analysis match patent scope? |
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4. Timeliness: Are market insights current? |
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5. Consistency: Is terminology uniform? |
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6. Objectivity: Are assessments unbiased? |
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7. Clarity: Is language accessible? |
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8. Actionability: Are recommendations concrete? |
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9. Evidence-based: Are claims supported? |
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10. Stakeholder-aligned: Does it meet needs? |
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|
11. Reproducibility: Can results be replicated? |
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|
12. Ethical compliance: Does it meet standards? |
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- Develop computational metrics for each dimension |
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|
- Create weighted scoring models |
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|
- Build automated compliance checking |
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- Establish benchmarking methodologies |
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Implementation challenges: |
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- Quality dashboard and reporting |
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- Real-time quality monitoring |
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|
- Historical quality tracking and improvement analysis |
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|
- Integration with VISTA quality certification process |
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|
WP4 - STAKEHOLDER NETWORKS (Current: 3%): |
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|
What we have: |
|
|
- Mock database (50 fabricated entries) |
|
|
- Basic vector similarity search |
|
|
- Simple scoring (single-dimension) |
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|
|
|
What we need (36 months): |
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|
Data challenges: |
|
|
- Build comprehensive stakeholder database (10,000+ real entities) |
|
|
* Universities: 2,000+ institutions (EU + Canada) |
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|
* Research centers: 1,500+ organizations |
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|
* Technology transfer offices: 500+ TTOs |
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|
* Industry partners: 4,000+ companies |
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|
* Government agencies: 1,000+ entities |
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|
- Data collection strategy (web scraping, partnerships, public databases) |
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|
- Data quality and maintenance (update frequency, verification) |
|
|
- Privacy and consent management (GDPR, Canadian privacy law) |
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Research challenges: |
|
|
- Multi-dimensional stakeholder profiling: |
|
|
* Research expertise and focus areas |
|
|
* Historical collaboration patterns |
|
|
* Technology absorption capacity |
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|
* Geographic reach and networks |
|
|
* Funding availability |
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|
* 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) |
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|
|
Implementation challenges: |
|
|
- CRM integration (Salesforce, Microsoft Dynamics) |
|
|
- Real-time stakeholder data updates |
|
|
- Stakeholder portal (self-service profile management) |
|
|
- Privacy-preserving search (anonymization, secure computation) |
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|
|
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 |
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|
|
|
What we need (36 months): |
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|
Platform development: |
|
|
- Production-ready web application |
|
|
* Enterprise-grade UI/UX (user testing, accessibility) |
|
|
* Multi-tenant architecture (institution-specific instances) |
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|
* Role-based access control (researcher, TTO, admin) |
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|
* 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 |
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|
|
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) |
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|
|
|
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 |
|
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|
|
|
TRANSITION: "Now let's examine the specific research and implementation challenges ahead..." |
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|
""" |
|
|
) |
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|
|
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): |
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|
|
|
PURPOSE: Show what works while being transparent about limitations. Build credibility through honesty. |
|
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|
|
|
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..." |
|
|
""" |
|
|
) |
|
|
|
|
|
|
|
|
|
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|
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): |
|
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|
|
|
PURPOSE: Position SPARKNET as serious research, not just software development. Show intellectual contributions beyond the system itself. |
|
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|
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 |
|
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|
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|
RQ1: MULTI-AGENT COORDINATION FOR COMPLEX WORKFLOWS |
|
|
==================================================== |
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|
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|
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. |
|
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|
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|
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: |
|
|
|
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|
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?) |
|
|
|
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|
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) |
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|
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|
Why mixed methods? |
|
|
- Numbers alone don't tell full story |
|
|
- Qualitative explains WHY metrics are what they are |
|
|
- Stories and case studies convince stakeholders |
|
|
|
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|
External Evaluation |
|
|
------------------- |
|
|
Independence ensures credibility: |
|
|
- VISTA evaluation team (not SPARKNET team) |
|
|
- External academic reviewers (peer review) |
|
|
- User feedback (pilot institutions provide assessment) |
|
|
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|
Final evaluation report (Month 36): |
|
|
- Comprehensive assessment against all metrics |
|
|
- Lessons learned |
|
|
- Recommendations for future development |
|
|
- Sustainability plan |
|
|
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|
SUCCESS DEFINITION (Summary) |
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|
============================= |
|
|
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) |
|
|
|
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|
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 |
|
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TRANSITION: "Let's wrap up with next steps and how stakeholders can engage..." |
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|
""" |
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) |
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slide11 = add_content_slide( |
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"Next Steps & Stakeholder Engagement Opportunities", |
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[ |
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"📅 Immediate Next Steps (Months 0-6)", |
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|
"", |
|
|
"Month 0-1: Proposal Finalization & Approval", |
|
|
(1, "Refine project plan based on stakeholder feedback"), |
|
|
(1, "Secure funding commitment from VISTA and institutional partners"), |
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|
(1, "Establish project governance (steering committee, advisory board)"), |
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|
"", |
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|
"Month 1-2: Team Recruitment & Kick-off", |
|
|
(1, "Hire core team (AI researchers, engineers, project manager)"), |
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|
(1, "Set up infrastructure (GPUs, cloud accounts, development environment)"), |
|
|
(1, "Official project kick-off meeting with all partners"), |
|
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"", |
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|
"Month 2-6: Foundation Phase Begins", |
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|
(1, "Start OCR pipeline development (PDF→image→text)"), |
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|
(1, "Begin stakeholder data collection partnerships"), |
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|
(1, "Initiate user studies with TTO professionals"), |
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(1, "First quarterly progress report to steering committee"), |
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"", |
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"🤝 Stakeholder Engagement Opportunities", |
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"", |
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|
"For VISTA Partners:", |
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|
(1, "Join steering committee (quarterly oversight)"), |
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|
(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:", |
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|
(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:", |
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|
(1, "Embed PhD/Master's students in project"), |
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|
(1, "Collaboration on research publications"), |
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|
(1, "Access to SPARKNET for institutional use"), |
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], |
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speaker_notes=""" |
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|
NEXT STEPS & STAKEHOLDER ENGAGEMENT (3 minutes): |
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PURPOSE: Make clear what happens next and how stakeholders can get involved. Create urgency and excitement. |
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|
IMMEDIATE NEXT STEPS (Months 0-6) |
|
|
================================== |
|
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Month 0-1: Proposal Finalization & Approval |
|
|
-------------------------------------------- |
|
|
Activities: |
|
|
1. Stakeholder Feedback Session (THIS MEETING) |
|
|
- Present proposal |
|
|
- Collect feedback and questions |
|
|
- Identify concerns and address them |
|
|
|
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|
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 |
|
|
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|
|
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: |
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Today, I've presented SPARKNET - an ambitious 3-year research program to transform patent valorization through AI. |
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KEY TAKEAWAYS: |
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1. We have a working prototype (5-10% complete) that proves the concept |
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2. 90-95% of the work lies ahead - significant research and development needed |
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3. Clear 3-year roadmap with milestones, deliverables, and success metrics |
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4. Budget of ~€1.65M is realistic for the scope of work |
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5. Multiple opportunities for stakeholder engagement |
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WHY THIS MATTERS: |
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- Knowledge transfer is crucial for innovation and economic growth |
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- Current manual processes don't scale - AI can help |
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- VISTA provides perfect context for this research |
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- We have the expertise and commitment to deliver |
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WHAT WE'RE ASKING: |
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- Support for the 3-year program |
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- Active engagement from stakeholders (steering committee, pilot sites, data partners) |
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- Funding commitment (from VISTA and potentially other sources) |
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- Permission to proceed with team recruitment and kickoff |
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WHAT YOU GET: |
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- Cutting-edge research outputs (publications, datasets, tools) |
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- Production-ready SPARKNET platform (by Year 3) |
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- Enhanced knowledge transfer capabilities for your institution |
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- Leadership role in EU-Canada research collaboration |
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THE JOURNEY AHEAD: |
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- This is a marathon, not a sprint |
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- We'll encounter challenges and setbacks - that's research |
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- We need your support, patience, and active participation |
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- Together, we can build something transformative |
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IMMEDIATE NEXT STEPS: |
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1. Your feedback (TODAY) |
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2. Proposal revision (NEXT WEEK) |
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3. Approval process (MONTH 1) |
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4. Team recruitment (MONTH 1-2) |
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5. Kickoff (MONTH 2) |
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FINAL THOUGHT: |
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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. |
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This is important work. Let's do it right. |
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Thank you for your time and attention. I'm excited to answer your questions and discuss how we can move forward together. |
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QUESTIONS & DISCUSSION: |
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[Open floor for Q&A - be prepared for:] |
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Expected questions: |
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Q: "Why 3 years? Can it be done faster?" |
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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. |
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Q: "What if you can't get access to stakeholder data?" |
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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. |
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Q: "How do you ensure AI quality/avoid hallucinations?" |
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A: Multi-layered approach: CriticAgent review, quality framework with 12 dimensions, human-in-the-loop for critical decisions, confidence scoring to flag uncertain outputs. |
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Q: "What happens after 3 years? Is this sustainable?" |
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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. |
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Q: "Can we see a demo?" |
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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] |
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Q: "How do you manage IP? Who owns SPARKNET?" |
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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. |
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Be confident, honest, and enthusiastic. Show expertise but also humility (acknowledge challenges). Build trust through transparency. |
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Thank you! |
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""" |
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slide12.notes_slide.notes_text_frame.text = notes12 |
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output_path = "/home/mhamdan/SPARKNET/presentation/SPARKNET_Academic_Presentation_IMPROVED.pptx" |
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prs.save(output_path) |
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print(f"Saved improved presentation to: {output_path}") |
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return output_path |
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if __name__ == "__main__": |
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try: |
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path = create_improved_presentation() |
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print(f"\n✅ SUCCESS! Improved presentation created at:\n{path}") |
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except Exception as e: |
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print(f"❌ Error creating presentation: {e}") |
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import traceback |
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traceback.print_exc() |
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