scripts/improve_presentation.py

"""
Create improved SPARKNET Academic Presentation
Emphasizes early-stage development and 3-year research roadmap
"""
from pptx import Presentation
from pptx.util import Inches, Pt
from pptx.enum.text import PP_ALIGN, MSO_ANCHOR
from pptx.dml.color import RGBColor

def create_improved_presentation():
    """Create comprehensive academic presentation"""

    prs = Presentation()
    prs.slide_width = Inches(10)
    prs.slide_height = Inches(7.5)

    # Define color scheme
    VISTA_BLUE = RGBColor(0, 51, 102)
    VISTA_ORANGE = RGBColor(255, 102, 0)
    DARK_GRAY = RGBColor(51, 51, 51)
    LIGHT_GRAY = RGBColor(128, 128, 128)

    def add_title_slide(title, subtitle, footer=""):
        """Add title slide"""
        slide = prs.slides.add_slide(prs.slide_layouts[6])  # Blank layout

        # Title
        title_box = slide.shapes.add_textbox(Inches(0.5), Inches(2.5), Inches(9), Inches(1))
        title_frame = title_box.text_frame
        title_frame.text = title
        title_p = title_frame.paragraphs[0]
        title_p.font.size = Pt(54)
        title_p.font.bold = True
        title_p.font.color.rgb = VISTA_BLUE
        title_p.alignment = PP_ALIGN.CENTER

        # Subtitle
        if subtitle:
            subtitle_box = slide.shapes.add_textbox(Inches(0.5), Inches(3.8), Inches(9), Inches(1.5))
            subtitle_frame = subtitle_box.text_frame
            subtitle_frame.text = subtitle
            subtitle_p = subtitle_frame.paragraphs[0]
            subtitle_p.font.size = Pt(24)
            subtitle_p.font.color.rgb = DARK_GRAY
            subtitle_p.alignment = PP_ALIGN.CENTER

        # Footer
        if footer:
            footer_box = slide.shapes.add_textbox(Inches(0.5), Inches(6.8), Inches(9), Inches(0.5))
            footer_frame = footer_box.text_frame
            footer_frame.text = footer
            footer_p = footer_frame.paragraphs[0]
            footer_p.font.size = Pt(14)
            footer_p.font.color.rgb = LIGHT_GRAY
            footer_p.alignment = PP_ALIGN.CENTER

        return slide

    def add_content_slide(title, content_lines, speaker_notes=""):
        """Add content slide with bullet points"""
        slide = prs.slides.add_slide(prs.slide_layouts[6])

        # Title
        title_box = slide.shapes.add_textbox(Inches(0.5), Inches(0.5), Inches(9), Inches(0.8))
        title_frame = title_box.text_frame
        title_frame.text = title
        title_p = title_frame.paragraphs[0]
        title_p.font.size = Pt(32)
        title_p.font.bold = True
        title_p.font.color.rgb = VISTA_BLUE

        # Content
        content_box = slide.shapes.add_textbox(Inches(0.8), Inches(1.5), Inches(8.5), Inches(5.3))
        content_frame = content_box.text_frame
        content_frame.word_wrap = True

        for i, line in enumerate(content_lines):
            if i > 0:
                content_frame.add_paragraph()
            p = content_frame.paragraphs[i]

            # Determine level and text
            if isinstance(line, tuple):
                level, text = line
            else:
                level = 0
                text = line

            p.text = text
            p.level = level
            p.font.size = Pt(18 - level * 2)
            p.space_before = Pt(6)
            p.space_after = Pt(6)

        # Add speaker notes
        if speaker_notes:
            notes_slide = slide.notes_slide
            text_frame = notes_slide.notes_text_frame
            text_frame.text = speaker_notes

        return slide

    # ========================================================================
    # SLIDE 1: TITLE SLIDE
    # ========================================================================
    slide1 = add_title_slide(
        "SPARKNET",
        "Multi-Agent AI System for Academic Research Valorization\nEarly-Stage Prototype & 3-Year Research Roadmap",
        "Mohamed Hamdan | VISTA Project | November 2025 | CONFIDENTIAL DRAFT"
    )

    notes1 = """
OPENING REMARKS (2 minutes):

Good [morning/afternoon]. Thank you for this opportunity to present SPARKNET, an AI-powered system for academic research valorization.

KEY MESSAGE: We are at the BEGINNING of a 3-year research journey. Today's demonstration represents approximately 5-10% of the planned work - a proof-of-concept prototype that validates technical feasibility while revealing the extensive research and development ahead.

POSITIONING:
- This is NOT a finished product - it's an early-stage research prototype
- We're seeking stakeholder buy-in for a comprehensive 3-year development program
- The prototype demonstrates technical viability but requires significant investment in all areas

AGENDA OVERVIEW:
1. Research context and VISTA alignment
2. Current prototype capabilities (10% complete)
3. Detailed breakdown of work remaining (90% ahead)
4. 3-year research roadmap by VISTA work packages
5. Resource requirements and expected outcomes

Let's begin with the research context...
"""
    slide1.notes_slide.notes_text_frame.text = notes1

    # ========================================================================
    # SLIDE 2: PROJECT STAGE & COMPLETION STATUS
    # ========================================================================
    slide2 = add_content_slide(
        "PROJECT STATUS: Early-Stage Prototype",
        [
            "🎯 Current Development Stage",
            (1, "Proof-of-Concept Prototype: ~5-10% Complete"),
            (1, "Technical feasibility validated through working demo"),
            (1, "Core architecture established, foundation components operational"),
            "",
            "📊 What We Have (Prototype Phase):",
            (1, "✓ Basic multi-agent workflow (4 agents, 1 scenario)"),
            (1, "✓ Simple document analysis (text-based patents only)"),
            (1, "✓ Proof-of-concept OCR integration (llava:7b on GPU)"),
            (1, "✓ Basic stakeholder matching (mock database, 50 entries)"),
            (1, "✓ Minimal web interface (demo purposes only)"),
            "",
            "⚠️ What We DON'T Have (90-95% of Work Ahead):",
            (1, "✗ Production-ready OCR pipeline (PDF→image→analysis)"),
            (1, "✗ Comprehensive stakeholder database (need 10,000+ entries)"),
            (1, "✗ Advanced quality frameworks (VISTA 12-dimension validation)"),
            (1, "✗ Additional VISTA scenarios (2 & 3 not started)"),
            (1, "✗ Multi-language support, CRM integration, security hardening"),
            (1, "✗ Real-world validation, user studies, performance optimization"),
        ],
        speaker_notes="""
PROJECT STAGE TRANSPARENCY (3 minutes):

CRITICAL FRAMING: Set realistic expectations immediately. We must be completely transparent about our current stage to build trust and justify the 3-year timeline.

WHAT THE PROTOTYPE IS:
- A working demonstration that proves the core concept is technically viable
- Sufficient to show stakeholders what the final system COULD become
- Evidence that our multi-agent architecture can handle patent valorization workflows
- A foundation upon which extensive research and development will be built

WHAT THE PROTOTYPE IS NOT:
- Not production-ready - lacks robustness, scalability, security
- Not research-complete - many algorithms, methods, and frameworks are placeholder or simplified
- Not feature-complete - critical capabilities are missing or stubbed
- Not validated - no user studies, no real-world testing, no performance benchmarks

THE 5-10% ESTIMATE BREAKDOWN:
- Architecture & Infrastructure: 15% complete (basic workflow established)
- AI/ML Capabilities: 5% complete (simple LLM chains, no sophisticated reasoning)
- Data & Knowledge Bases: 2% complete (tiny mock databases)
- User Experience: 8% complete (basic interface, no usability testing)
- VISTA Compliance: 10% complete (awareness of standards, minimal implementation)
- Integration & Deployment: 5% complete (local dev environment only)

WHY THIS IS GOOD NEWS FOR STAKEHOLDERS:
- We've de-risked the technical approach - we know it CAN work
- The 90% remaining gives us clear scope for innovation and IP generation
- Three-year timeline is realistic and defensible
- Significant opportunities for stakeholder input to shape development

TRANSITION: "Let's examine our research context and how SPARKNET aligns with VISTA objectives..."
"""
    )

    # ========================================================================
    # SLIDE 3: VISTA WORK PACKAGES - COMPREHENSIVE BREAKDOWN
    # ========================================================================
    slide3 = add_content_slide(
        "SPARKNET Decomposition by VISTA Work Packages",
        [
            "🎯 VISTA Project: EU-Canada Knowledge Transfer Ecosystem",
            "",
            "WP1: Project Management & Coordination (5% implemented)",
            (1, "Current: Basic project documentation, GitHub repository"),
            (1, "Needed: Stakeholder governance, progress tracking, deliverable management"),
            "",
            "WP2: Valorization Pathways (15% implemented)",
            (1, "Current: Basic patent analysis (Scenario 1), TRL assessment prototype"),
            (1, "Needed: Comprehensive pathway analysis, decision support, multi-patent batch processing"),
            "",
            "WP3: Quality Standards Framework (8% implemented)",
            (1, "Current: Simple output validation, quality threshold checking"),
            (1, "Needed: Full 12-dimension VISTA framework, compliance validation, quality metrics"),
            "",
            "WP4: Stakeholder Networks (3% implemented)",
            (1, "Current: Mock database (50 entries), basic semantic search"),
            (1, "Needed: Real stakeholder DB (10,000+), CRM integration, network analytics"),
            "",
            "WP5: Digital Tools & Platforms (10% implemented)",
            (1, "Current: Prototype web UI, basic API"),
            (1, "Needed: Production platform, mobile access, multi-tenant deployment"),
        ],
        speaker_notes="""
VISTA ALIGNMENT & WORK PACKAGE DECOMPOSITION (4 minutes):

PURPOSE: Show stakeholders how SPARKNET maps directly to VISTA's structure and where the bulk of work remains.

WP1 - PROJECT MANAGEMENT (Current: 5%):
What we have:
- Basic Git version control
- Simple documentation in Markdown
- Informal development process

What we need (36 months):
- Formal project governance structure
- Stakeholder advisory board and regular consultations
- Deliverable and milestone tracking system
- Risk management framework
- Quality assurance processes
- Budget management and reporting
- IP management and exploitation planning
- Dissemination and communication strategy

WP2 - VALORIZATION PATHWAYS (Current: 15%):
What we have:
- Scenario 1 (Patent Wake-Up) basic workflow
- Simple TRL assessment (rule-based)
- Basic technology domain identification
- Simplified market opportunity analysis

What we need (36 months):
Research challenges:
- Sophisticated TRL assessment methodology (ML-based, context-aware)
- Multi-criteria decision support for valorization pathway selection
- Comparative analysis across multiple patents (portfolio management)
- Technology maturity prediction models
- Market readiness assessment frameworks
- Batch processing and workflow optimization

Implementation challenges:
- Scenario 2 (Agreement Safety): Legal document analysis, risk assessment, compliance checking
- Scenario 3 (Partner Matching): Profile analysis, collaboration history, complementarity scoring
- Integration with real technology transfer workflows
- Performance optimization for large patent portfolios
- User interface for pathway exploration and what-if analysis

WP3 - QUALITY STANDARDS (Current: 8%):
What we have:
- Simple quality threshold (0.8 cutoff)
- Basic Critic agent validation
- Rudimentary output checking

What we need (36 months):
Research challenges:
- Operationalize VISTA's 12-dimension quality framework:
  1. Completeness: Are all required sections present?
  2. Accuracy: Is information factually correct?
  3. Relevance: Does analysis match patent scope?
  4. Timeliness: Are market insights current?
  5. Consistency: Is terminology uniform?
  6. Objectivity: Are assessments unbiased?
  7. Clarity: Is language accessible?
  8. Actionability: Are recommendations concrete?
  9. Evidence-based: Are claims supported?
  10. Stakeholder-aligned: Does it meet needs?
  11. Reproducibility: Can results be replicated?
  12. Ethical compliance: Does it meet standards?

- Develop computational metrics for each dimension
- Create weighted scoring models
- Build automated compliance checking
- Establish benchmarking methodologies

Implementation challenges:
- Quality dashboard and reporting
- Real-time quality monitoring
- Historical quality tracking and improvement analysis
- Integration with VISTA quality certification process

WP4 - STAKEHOLDER NETWORKS (Current: 3%):
What we have:
- Mock database (50 fabricated entries)
- Basic vector similarity search
- Simple scoring (single-dimension)

What we need (36 months):
Data challenges:
- Build comprehensive stakeholder database (10,000+ real entities)
  * Universities: 2,000+ institutions (EU + Canada)
  * Research centers: 1,500+ organizations
  * Technology transfer offices: 500+ TTOs
  * Industry partners: 4,000+ companies
  * Government agencies: 1,000+ entities
- Data collection strategy (web scraping, partnerships, public databases)
- Data quality and maintenance (update frequency, verification)
- Privacy and consent management (GDPR, Canadian privacy law)

Research challenges:
- Multi-dimensional stakeholder profiling:
  * Research expertise and focus areas
  * Historical collaboration patterns
  * Technology absorption capacity
  * Geographic reach and networks
  * Funding availability
  * Strategic priorities
- Advanced matching algorithms:
  * Semantic similarity (embeddings)
  * Graph-based network analysis
  * Temporal dynamics (changing interests)
  * Success prediction models
- Complementarity assessment (who works well together?)
- Network effect analysis (introducing multiple parties)

Implementation challenges:
- CRM integration (Salesforce, Microsoft Dynamics)
- Real-time stakeholder data updates
- Stakeholder portal (self-service profile management)
- Privacy-preserving search (anonymization, secure computation)

WP5 - DIGITAL TOOLS & PLATFORMS (Current: 10%):
What we have:
- Basic Next.js web interface (demo quality)
- Simple FastAPI backend
- Local deployment only
- No user management or security

What we need (36 months):
Platform development:
- Production-ready web application
  * Enterprise-grade UI/UX (user testing, accessibility)
  * Multi-tenant architecture (institution-specific instances)
  * Role-based access control (researcher, TTO, admin)
  * Mobile-responsive design (tablet, smartphone)
- API ecosystem
  * RESTful API for third-party integration
  * Webhook support for event notifications
  * API rate limiting and monitoring
  * Developer documentation and sandbox

Infrastructure & deployment:
- Cloud infrastructure (AWS/Azure/GCP)
- Containerization (Docker, Kubernetes)
- CI/CD pipelines
- Monitoring and logging (Prometheus, Grafana, ELK stack)
- Backup and disaster recovery
- Scalability (handle 1000+ concurrent users)
- Security hardening (penetration testing, OWASP compliance)

Integration requirements:
- Single Sign-On (SSO) / SAML / OAuth
- Integration with university systems (CRIS, RIS)
- Document management systems
- Email and notification services
- Payment gateways (for premium features)
- Analytics and business intelligence

TRANSITION: "Now let's examine the specific research and implementation challenges ahead..."
"""
    )

    # ========================================================================
    # SLIDE 4: CURRENT PROTOTYPE CAPABILITIES (What's Working)
    # ========================================================================
    slide4 = add_content_slide(
        "Current Prototype: What We've Demonstrated",
        [
            "✅ Phase 1 Prototype (5-10% Complete) - Proof of Concept",
            "",
            "🧠 Multi-Agent Architecture (Basic Implementation)",
            (1, "4 specialized agents: Document, Market, Matchmaking, Outreach"),
            (1, "LangGraph cyclic workflow with Planner-Critic loop"),
            (1, "Basic memory system (episodic, semantic, stakeholder stores)"),
            (1, "⚠️ Gap: Simple LLM chains, no advanced reasoning or learning"),
            "",
            "📄 Document Analysis (Text-Only Patents)",
            (1, "Claims extraction (independent/dependent parsing)"),
            (1, "TRL assessment (rule-based, 1-9 scale)"),
            (1, "Basic innovation identification"),
            (1, "⚠️ Gap: No OCR pipeline, no diagram analysis, no multi-language"),
            "",
            "🔬 Recent Addition: OCR Foundation (Proof of Concept)",
            (1, "llava:7b vision model installed on GPU1"),
            (1, "VisionOCRAgent with 5 methods (text, diagram, table, patent, handwriting)"),
            (1, "Integrated with workflow (auto-initializes on startup)"),
            (1, "⚠️ Gap: No PDF→image pipeline, no batch processing, not production-ready"),
            "",
            "🔍 Stakeholder Matching (Mock Data Only)",
            (1, "Vector similarity search (ChromaDB)"),
            (1, "Simple scoring (single dimension)"),
            (1, "⚠️ Gap: Mock database (50 entries), no real data, no advanced matching"),
        ],
        speaker_notes="""
CURRENT CAPABILITIES - HONEST ASSESSMENT (3 minutes):

PURPOSE: Show what works while being transparent about limitations. Build credibility through honesty.

MULTI-AGENT ARCHITECTURE (Functional Prototype):
What's working:
- 4 agents successfully communicate and coordinate
- LangGraph manages workflow state correctly
- Planner-Critic loop demonstrates iterative improvement
- Memory stores persist and retrieve data

Technical limitations:
- Agents use simple prompt chains (no sophisticated reasoning)
- No agent learning or improvement over time
- Memory is not properly structured or indexed
- No conflict resolution when agents disagree
- Workflow is rigid (cannot adapt to different patent types)

Research needed:
- Advanced agent reasoning (chain-of-thought, tree-of-thought)
- Multi-agent coordination strategies
- Memory architecture optimization
- Dynamic workflow adaptation
- Agent performance evaluation metrics

DOCUMENT ANALYSIS (Basic Text Processing):
What's working:
- Extracts text from text-based PDFs
- Parses independent and dependent claims
- Assigns TRL levels (though simplistic)
- Identifies basic innovation themes

Technical limitations:
- Fails on scanned PDFs (image-based)
- Cannot analyze diagrams or figures
- Misses important information in tables
- English-only (no multi-language)
- No context understanding (treats all patents the same)

Research needed:
- Robust OCR pipeline (PDF→image→text→structure)
- Diagram and figure analysis (computer vision)
- Table extraction and interpretation
- Multi-language NLP (French, German, etc.)
- Patent type classification and adapted processing
- Technical domain-specific analysis

OCR FOUNDATION (Just Implemented - Nov 2025):
What's working:
- llava:7b vision model operational on GPU
- VisionOCRAgent class created with 5 methods
- Successfully integrated with DocumentAnalysisAgent
- Basic text extraction from images demonstrated

Technical limitations:
- NO PDF-to-image conversion (critical missing piece)
- No batch processing (one image at a time)
- No quality assessment (how good is the OCR?)
- No error recovery (what if OCR fails?)
- Not optimized (slow, high GPU memory)
- No production deployment strategy

Research needed (Major Work Ahead):
Phase 2 (Months 4-6): PDF→Image Pipeline
- Implement pdf2image conversion
- Handle multi-page documents
- Detect diagrams vs text regions
- Optimize image quality for OCR

Phase 3 (Months 7-12): Production OCR System
- Batch processing and queuing
- Quality assessment and confidence scoring
- Error detection and human review workflow
- OCR output post-processing (spelling correction, formatting)
- Performance optimization (reduce GPU usage, speed)
- Fallback strategies (when OCR fails)

Phase 4 (Months 13-18): Advanced Vision Analysis
- Diagram type classification (flowchart, circuit, etc.)
- Figure-caption association
- Table structure understanding
- Handwritten annotation detection
- Multi-language OCR (not just English)

STAKEHOLDER MATCHING (Mock Data Proof):
What's working:
- Vector search returns similar entities
- Basic similarity scoring
- Simple recommendation list

Technical limitations:
- Mock database (50 fabricated entries - NOT REAL DATA)
- Single-dimension matching (text similarity only)
- No validation (are matches actually good?)
- No user feedback or learning
- No network effects (doesn't consider who knows whom)

Research needed:
- Real data collection (massive undertaking, see WP4)
- Multi-dimensional matching algorithms
- Success prediction models (will this collaboration work?)
- User feedback integration and learning
- Network analysis and graph algorithms
- Privacy-preserving matching techniques

KEY TAKEAWAY: We have a working demo that proves the concept, but every component needs significant research and development to be production-ready.

TRANSITION: "Now let's break down the extensive work ahead across our 3-year timeline..."
"""
    )

    # ========================================================================
    # SLIDE 5: 3-YEAR RESEARCH ROADMAP
    # ========================================================================
    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..."
"""
    )

    # ========================================================================
    # SLIDE 6: RESEARCH CHALLENGES - YEAR 1 DEEP DIVE
    # ========================================================================
    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..."
"""
    )

    # ========================================================================
    # SLIDE 7: RESEARCH CHALLENGES - YEARS 2-3 OVERVIEW
    # ========================================================================
    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..."
"""
    )

    # ========================================================================
    # SLIDE 8: RESEARCH QUESTIONS & EXPECTED CONTRIBUTIONS
    # ========================================================================
    slide8 = add_content_slide(
        "Research Questions & Expected Scientific Contributions",
        [
            "🔬 Core Research Questions (Publishable Findings)",
            "",
            "RQ1: Multi-Agent Coordination for Complex Workflows",
            (1, "How to optimize agent communication and task delegation?"),
            (1, "What workflow patterns maximize quality and efficiency?"),
            (1, "Expected: 2-3 papers on multi-agent systems for knowledge work"),
            "",
            "RQ2: Quality Assessment in AI-Generated Knowledge Transfer",
            (1, "Can computational metrics predict expert quality assessments?"),
            (1, "What features correlate with high-quality valorization analysis?"),
            (1, "Expected: 1-2 papers on AI quality frameworks, VISTA validation study"),
            "",
            "RQ3: Semantic Matching for Academic-Industry Collaboration",
            (1, "What matching algorithms best predict collaboration success?"),
            (1, "How to balance multiple dimensions (technical, cultural, strategic)?"),
            (1, "Expected: 2 papers on stakeholder matching, network analysis"),
            "",
            "RQ4: Multi-Modal Understanding of Technical Documents",
            (1, "How to jointly reason over text, diagrams, and tables in patents?"),
            (1, "What representations enable cross-modal inference?"),
            (1, "Expected: 1-2 papers on multi-modal patent analysis"),
            "",
            "📚 Expected Outputs (3 Years)",
            (1, "6-10 peer-reviewed publications (AI conferences, knowledge management journals)"),
            (1, "2-3 PhD/Master's theses (topics embedded in SPARKNET research)"),
            (1, "1 comprehensive VISTA technical report & methodology documentation"),
            (1, "Open-source contributions (tools, datasets, benchmarks for research community)"),
        ],
        speaker_notes="""
RESEARCH QUESTIONS & SCIENTIFIC CONTRIBUTIONS (4 minutes):

PURPOSE: Position SPARKNET as serious research, not just software development. Show intellectual contributions beyond the system itself.

FRAMING THE RESEARCH CONTRIBUTION:
SPARKNET is not just building a tool - it's advancing the state of knowledge in multiple areas:
1. Multi-agent systems
2. Quality assessment of AI outputs
3. Knowledge transfer and technology commercialization
4. Multi-modal document understanding
5. Semantic matching and recommendation systems

RQ1: MULTI-AGENT COORDINATION FOR COMPLEX WORKFLOWS
====================================================

Background:
Multi-agent systems (MAS) have been studied for decades, but mostly in controlled environments (robotics, games, simulations). Applying MAS to open-ended knowledge work like patent valorization is less explored.

Research gap:
- How should agents divide complex tasks?
- How to handle conflicts when agents disagree?
- What communication protocols maximize efficiency?
- How to ensure quality when multiple agents contribute?

SPARKNET's contribution:
We're building a real-world MAS for a complex domain, giving us opportunity to study:

Sub-question 1.1: Task decomposition strategies
- We have 4 agents (Document, Market, Matchmaking, Outreach)
- Is this the right granularity? Should we have more agents? Fewer?
- How to decide which agent handles which sub-tasks?

Experiment:
- Try different agent configurations (3, 4, 5, 6 agents)
- Measure quality and efficiency for each
- Identify patterns (when are more agents better? when do they add overhead?)

Sub-question 1.2: Communication overhead
- Agents need to share information (DocumentAnalysisAgent results go to MarketAnalysisAgent)
- Too much communication slows things down
- Too little communication loses important context

Experiment:
- Measure communication patterns (what info is actually used?)
- Test different communication strategies (full sharing vs selective sharing)
- Find optimal balance

Sub-question 1.3: Quality assurance in MAS
- When 4 agents contribute to one output, who's responsible for quality?
- How does CriticAgent effectively evaluate multi-agent outputs?

Experiment:
- Compare quality with vs without CriticAgent
- Study what makes criticism effective
- Identify failure modes (when does quality slip through?)

Expected publications:
Paper 1: "Multi-Agent Workflow Patterns for Knowledge-Intensive Tasks: Lessons from Patent Valorization" (Target: AAMAS - Autonomous Agents and Multi-Agent Systems conference)

Paper 2: "Quality Assurance in Multi-Agent Systems: A Case Study in Automated Research Analysis" (Target: JAAMAS - Journal of Autonomous Agents and Multi-Agent Systems)

RQ2: QUALITY ASSESSMENT OF AI-GENERATED OUTPUTS
================================================

Background:
As AI generates more content (reports, analyses, recommendations), assessing quality becomes critical. Current approaches are limited:
- Manual review (doesn't scale)
- Simple metrics (word count, readability - miss deeper quality aspects)
- Model-based (using another AI to judge - but how do we trust it?)

Research gap:
- What makes an AI-generated valorization analysis "high quality"?
- Can we predict expert quality ratings from computable features?
- How to operationalize qualitative standards (like VISTA's framework)?

SPARKNET's contribution:
We're implementing VISTA's 12-dimension quality framework computationally, creating:

Sub-question 2.1: Feature engineering for quality
- For each dimension (completeness, accuracy, relevance...), what features predict it?
- Example for completeness: section presence, word counts, coverage of key concepts

Experiment:
- Collect 500+ expert quality assessments
- Extract 100+ features from each output
- Train models to predict expert scores
- Analyze feature importance (what matters most?)

Sub-question 2.2: Quality prediction models
- Which ML models work best for quality assessment?
- How much training data is needed?
- Can models generalize across different patent types?

Experiment:
- Compare models: Linear regression, Random Forest, XGBoost, Neural Networks
- Learning curves: How many examples needed for good performance?
- Cross-domain testing: Train on some domains, test on others

Sub-question 2.3: Explaining quality scores
- Quality scores alone aren't enough - users need to understand WHY
- How to provide actionable feedback?

Experiment:
- Implement explainable AI techniques (SHAP values, attention visualization)
- User study: Do explanations help users improve outputs?

Expected publications:
Paper 3: "Computational Operationalization of Multi-Dimensional Quality Frameworks: A Case Study in Knowledge Transfer" (Target: Journal of the Association for Information Science and Technology - JASIST)

Paper 4: "Predicting Expert Quality Assessments of AI-Generated Research Analyses" (Target: ACM Conference on AI, Ethics, and Society)

RQ3: SEMANTIC MATCHING FOR COLLABORATION
=========================================

Background:
Stakeholder matching is crucial for technology transfer, but current approaches are limited:
- Keyword matching (too simplistic)
- Citation networks (miss non-publishing partners)
- Manual curation (doesn't scale)

Research gap:
- How to match stakeholders across multiple dimensions?
- How to predict collaboration success?
- How to leverage network effects (social connections)?

SPARKNET's contribution:
We're building a comprehensive matching system, enabling research on:

Sub-question 3.1: Multi-dimensional profile representation
- How to represent stakeholder profiles richly?
- What information predicts good matches?

Experiment:
- Extract profiles from multiple sources (websites, publications, patents)
- Build vector representations (embeddings)
- Test different embedding models (word2vec, BERT, specialized models)
- Evaluate: Do better embeddings lead to better matches?

Sub-question 3.2: Matching algorithms
- Beyond similarity: How to find complementary partners?
- How to incorporate constraints (geography, size, resources)?

Experiment:
- Compare algorithms:
  * Cosine similarity (baseline)
  * Learning-to-rank models
  * Graph-based approaches (network analysis)
  * Hybrid methods
- Evaluate against ground truth (successful collaborations)

Sub-question 3.3: Network effects
- Warm introductions more successful than cold contacts
- How to leverage social networks for matching?

Experiment:
- Build collaboration network from historical data
- Compute network-aware matching scores
- Test hypothesis: Network-aware matching leads to more successful introductions

Sub-question 3.4: Temporal dynamics
- Stakeholder interests and capabilities change over time
- How to keep profiles current?
- How to predict future interests?

Experiment:
- Analyze temporal evolution of research interests
- Build predictive models (what will they be interested in next year?)
- Test: Do temporally-aware matches improve success?

Expected publications:
Paper 5: "Multi-Dimensional Semantic Matching for Academic-Industry Collaboration" (Target: ACM Conference on Recommender Systems - RecSys)

Paper 6: "Network-Aware Partner Recommendations in Research Collaboration Networks" (Target: Social Network Analysis and Mining journal)

RQ4: MULTI-MODAL PATENT UNDERSTANDING
======================================

Background:
Patents are inherently multi-modal:
- Text (abstract, claims, description)
- Figures (diagrams, flowcharts, technical drawings)
- Tables (data, comparisons, specifications)
- Mathematical formulas

Current AI approaches analyze these separately, missing connections.

Research gap:
- How to jointly understand text and visual elements?
- How to link textual descriptions to referenced figures?
- What representations enable cross-modal reasoning?

SPARKNET's contribution:
Our OCR pipeline and multi-modal analysis provide opportunities to study:

Sub-question 4.1: Cross-modal reference resolution
- Text often references figures: "as shown in Figure 3"
- How to automatically link text to corresponding figures?

Experiment:
- Build dataset of text-figure pairs
- Train models to detect references
- Extract referred visual elements
- Evaluate quality of linking

Sub-question 4.2: Joint text-image reasoning
- Understanding requires integrating both modalities
- Example: "The system consists of three components [see Figure 2]"
  * Text describes components
  * Figure shows their relationships
  * Full understanding needs both

Experiment:
- Test multi-modal models (CLIP, Flamingo-style architectures)
- Compare uni-modal (text-only) vs multi-modal understanding
- Measure: Does adding visual information improve analysis?

Sub-question 4.3: Diagram classification and understanding
- Different diagram types need different processing
- Flowcharts vs circuit diagrams vs organizational charts

Experiment:
- Build diagram type classifier
- Develop type-specific analysis methods
- Evaluate diagram understanding across types

Expected publications:
Paper 7: "Multi-Modal Understanding of Technical Patents: Integrating Text, Diagrams, and Tables" (Target: Association for Computational Linguistics - ACL)

Paper 8: "Automated Diagram Analysis in Patent Documents: A Deep Learning Approach" (Target: International Conference on Document Analysis and Recognition - ICDAR)

ADDITIONAL RESEARCH OUTPUTS
============================

Beyond publications, SPARKNET will generate:

1. Datasets for research community:
   - Annotated patent corpus (text + quality labels)
   - Stakeholder profiles with collaboration histories
   - Multi-modal patent dataset (text + figures + annotations)
   - These enable other researchers to build on our work

2. Open-source tools:
   - OCR pipeline (PDF→text→structure)
   - Quality assessment framework
   - Stakeholder matching library
   - Benefit: Accelerate research, establish standards

3. Methodological contributions:
   - VISTA quality framework operationalization (becomes standard)
   - Best practices for AI in knowledge transfer
   - Evaluation protocols for research support systems

4. Training materials:
   - Workshops for TTO professionals
   - Online courses for researchers
   - Dissemination of SPARKNET methodology

DOCTORAL/MASTER'S RESEARCH OPPORTUNITIES:
SPARKNET is large enough to support multiple theses:

Potential PhD topics:
- "Multi-Agent Coordination for Complex Knowledge Work" (3 years, CS/AI)
- "Quality Assessment of AI-Generated Research Analyses" (3 years, Information Science)
- "Network-Aware Semantic Matching for Research Collaboration" (3 years, CS/Social Computing)

Potential Master's topics:
- "Diagram Classification in Patent Documents" (1 year, CS)
- "Stakeholder Profile Construction from Web Sources" (1 year, Data Science)
- "User Experience Design for AI-Powered Technology Transfer Tools" (1 year, HCI)

IMPACT ON VISTA PROJECT:
- Demonstrates feasibility of AI for knowledge transfer
- Provides tools for other VISTA partners
- Generates insights on technology transfer processes
- Establishes methodological standards
- Contributes to VISTA's intellectual output

TRANSITION: "Let's discuss resource requirements and timeline..."
"""
    )

    # ========================================================================
    # SLIDE 9: RESOURCE REQUIREMENTS & RISK MANAGEMENT
    # ========================================================================
    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..."
"""
    )

    # ========================================================================
    # SLIDE 10: EXPECTED IMPACT & SUCCESS METRICS
    # ========================================================================
    slide10 = add_content_slide(
        "Expected Impact & Success Metrics (3-Year Horizon)",
        [
            "🎯 Quantitative Success Metrics",
            (1, "Academic Impact:"),
            (2, "6-10 peer-reviewed publications in top venues"),
            (2, "2-3 PhD/Master's theses completed"),
            (2, "500+ citations to SPARKNET research (5-year projection)"),
            (1, "System Performance:"),
            (2, "95%+ OCR accuracy on diverse patent types"),
            (2, "90%+ user satisfaction in pilot studies (NPS > 50)"),
            (2, "70%+ time savings vs manual analysis (TTO workflows)"),
            (1, "Deployment & Adoption:"),
            (2, "10-15 institutions actively using SPARKNET"),
            (2, "1000+ patents analyzed through system"),
            (2, "100+ successful stakeholder introductions facilitated"),
            "",
            "🌍 Qualitative Impact",
            (1, "Research Community: New benchmarks, datasets, methodologies for patent AI"),
            (1, "VISTA Network: Enhanced knowledge transfer capacity across EU-Canada"),
            (1, "Technology Transfer: Improved efficiency and success rates for TTOs"),
            (1, "Economic: Accelerated research commercialization, more innovation reaching market"),
            "",
            "📊 Evaluation Framework",
            (1, "Continuous monitoring throughout 3 years (not just at end)"),
            (1, "Mixed methods: Quantitative metrics + qualitative case studies"),
            (1, "External evaluation: Independent assessment by VISTA and academic reviewers"),
        ],
        speaker_notes="""
EXPECTED IMPACT & SUCCESS METRICS (3 minutes):

PURPOSE: Show stakeholders what success looks like and how we'll measure it. Make commitments we can meet.

QUANTITATIVE SUCCESS METRICS
=============================

Academic Impact (Research Contribution)
----------------------------------------

Publications (Target: 6-10 papers in 3 years)
Breakdown by venue type:
- AI/ML Conferences (3-4 papers):
  * AAMAS, JAAMAS: Multi-agent systems papers (RQ1)
  * ACL, EMNLP: NLP and multi-modal papers (RQ4)
  * RecSys: Matching algorithms paper (RQ3)
  * Target: Top-tier (A/A* conferences)

- Information Science Journals (2-3 papers):
  * JASIST: Quality framework paper (RQ2)
  * Journal of Documentation: Knowledge transfer methodology
  * Target: High impact factor (IF > 3)

- Domain-Specific Venues (1-2 papers):
  * Technology Transfer journals
  * Innovation management conferences
  * Target: Practitioner reach

Success criteria:
- At least 6 papers accepted by Month 36
- Average citation count > 20 by Year 5 (post-publication)
- At least 2 papers in top-tier venues (A/A*)

Why publications matter:
- Validates research quality (peer review)
- Disseminates findings to academic community
- Establishes SPARKNET as research contribution, not just software
- Builds reputation for future funding

Theses (Target: 2-3 completed by Month 36)
- 1 PhD thesis (Computer Science): Multi-agent systems or quality assessment
  * Student would be embedded in SPARKNET team
  * Thesis: 3 papers + synthesis chapter
  * Timeline: Month 6 (recruitment) to Month 36 (defense)
- 1-2 Master's theses (CS, Data Science, HCI)
  * Students do 6-12 month projects within SPARKNET
  * Topics: Diagram analysis, stakeholder profiling, UX evaluation
  * Multiple students over 3 years

Why theses matter:
- Cost-effective research capacity (students are cheaper than postdocs)
- Training next generation of researchers
- Produces detailed technical documentation
- Often leads to high-quality publications

Citations (Target: 500+ by Year 5 post-publication)
- Average good paper gets 50-100 citations over 5 years
- 10 papers × 50 citations each = 500 citations
- This indicates real impact (others building on our work)

System Performance (Technical Quality)
---------------------------------------

OCR Accuracy (Target: 95%+ character-level accuracy)
Measurement:
- Benchmark dataset: 100 diverse patents (old, new, different languages)
- Ground truth: Manual transcription
- Metric: Character Error Rate (CER), Word Error Rate (WER)
- Target: CER < 5%, WER < 5%

Why 95%?
- Industry standard for production OCR
- Good enough for downstream analysis (small errors don't derail understanding)
- Achievable with multi-model ensemble approach

User Satisfaction (Target: 90%+ satisfaction, NPS > 50)
Measurement:
- Quarterly surveys of pilot users
- Questions on:
  * Ease of use (1-5 scale)
  * Quality of results (1-5 scale)
  * Time savings (% compared to manual)
  * Would you recommend to colleague? (NPS: promoters - detractors)
- Target: Average satisfaction > 4.5/5, NPS > 50

Why these targets?
- 90% satisfaction is excellent (few tools achieve this)
- NPS > 50 is "excellent" zone (indicates strong word-of-mouth)
- Shows system is genuinely useful, not just technically impressive

Time Savings (Target: 70% reduction in analysis time)
Measurement:
- Time study comparing manual vs SPARKNET-assisted patent analysis
- Manual baseline: ~8-16 hours per patent (TTO professional)
- With SPARKNET: Target 2-4 hours (30% of manual time = 70% reduction)
- Caveat: Includes human review time (not fully automated)

Why 70%?
- Significant impact (can analyze 3x more patents with same effort)
- Realistic (not claiming 100% automation, acknowledging human-in-loop)
- Based on early prototype timing

Deployment & Adoption (Real-World Usage)
-----------------------------------------

Active Institutions (Target: 10-15 by Month 36)
- Year 1: 2-3 early adopters (close partners)
- Year 2: 5-7 additional (pilot expansion)
- Year 3: 10-15 total (full pilot network)

Distribution:
- 5 EU universities
- 5 Canadian universities
- 3-5 TTOs
- Diverse sizes and contexts

Patents Analyzed (Target: 1000+ by Month 36)
- Year 1: 100 patents (system development, testing)
- Year 2: 300 patents (pilot sites starting)
- Year 3: 600 patents (full operation)
- Total: 1000+ patents

Why 1000?
- Sufficient for meaningful validation
- Shows scalability (can handle volume)
- Diverse patent portfolio (multiple domains, institutions)

Successful Introductions (Target: 100+ by Month 36)
- Definition: Stakeholder connections facilitated by SPARKNET that led to:
  * Meeting or correspondence
  * Information exchange
  * Collaboration discussion
  * (Success beyond this: actual agreements, but that's longer timeframe)

Measurement:
- Track introductions made through system
- Follow-up surveys (what happened after introduction?)
- Case studies of successful collaborations

Why 100?
- 10% success rate (1000 patents → ~500 recommendations → 100 connections)
- Realistic for 3-year timeframe (full collaborations take 2-5 years)
- Demonstrates value (system producing real connections)

QUALITATIVE IMPACT
==================

Research Community Impact
-------------------------
Expected contributions:
1. Benchmarks & Datasets
   - Annotated patent corpus for training/evaluation
   - Stakeholder network dataset (anonymized)
   - Quality assessment dataset (expert-labeled outputs)
   - These become community resources (like ImageNet for computer vision)

2. Open-Source Tools
   - OCR pipeline (PDF→text→structure)
   - Quality assessment framework
   - Stakeholder matching library
   - Benefits: Accelerate research, enable comparisons

3. Methodologies
   - How to operationalize quality frameworks
   - Best practices for AI in knowledge work
   - Evaluation protocols for research support systems

Impact: SPARKNET becomes standard reference for patent analysis AI

VISTA Network Impact
--------------------
Direct benefits to VISTA:
- Demonstrates feasibility of AI for knowledge transfer
- Provides operational tool for VISTA institutions
- Generates insights on technology transfer processes
- Establishes standards and best practices
- Contributes to VISTA's goals and deliverables

Specific to VISTA Work Packages:
- WP2: Automated valorization pathway analysis
- WP3: Operational quality framework
- WP4: Expanded stakeholder network
- WP5: Production-ready digital tool

Broader impact:
- Strengthens EU-Canada research connections
- Increases capacity for knowledge transfer
- Demonstrates value of international collaboration

Technology Transfer Office Impact
----------------------------------
Expected improvements for TTOs:
1. Efficiency
   - 70% time savings per patent
   - Can analyze 3x more patents with same staff
   - Faster response to researcher inquiries

2. Quality
   - More thorough analysis (AI catches details humans miss)
   - Consistent methodology (reduces variability)
   - Evidence-based recommendations (data-driven)

3. Effectiveness
   - Better stakeholder matches (beyond personal networks)
   - More successful introductions (data shows complementarity)
   - Broader reach (access to international partners)

4. Capability Building
   - Training for TTO staff (AI literacy)
   - Best practices from multiple institutions
   - Professional development

Case Study Example (Hypothetical):
University X TTO before SPARKNET:
- 10 patents analyzed per year
- 2-3 successful technology transfers
- Mostly local/regional partnerships
- 200 hours per patent analysis

University X TTO with SPARKNET (Year 3):
- 30 patents analyzed per year (3x increase)
- 5-6 successful technology transfers (2x increase)
- National and international partnerships
- 60 hours per patent analysis (70% reduction, includes review time)

Economic Impact (Longer-Term)
------------------------------
While difficult to measure directly in 3 years, expected trajectory:
- More patents commercialized (SPARKNET lowers barriers)
- Faster time-to-market (efficient pathway identification)
- Better matches (higher success rate)
- Economic benefits materialize 5-10 years out

Hypothetical (if SPARKNET used by 50 institutions over 10 years):
- 5000+ patents analyzed
- 500+ additional technology transfers
- €50M+ in commercialization value
- 1000+ jobs created (startups, licensing deals)

Note: These are projections, not guarantees. Actual impact depends on many factors.

EVALUATION FRAMEWORK
====================

Continuous Monitoring (Not Just End-of-Project)
------------------------------------------------
Quarterly assessments:
- Usage statistics (patents analyzed, users active)
- Performance metrics (OCR accuracy, response time)
- User satisfaction surveys
- Bug tracking and resolution rates

Annual reviews:
- External evaluation by VISTA team
- Academic publications progress
- Budget and timeline status
- Strategic adjustments based on findings

Mixed Methods Evaluation
-------------------------
Quantitative:
- Usage logs and analytics
- Performance benchmarks
- Survey responses (Likert scales, NPS)

Qualitative:
- User interviews (in-depth, 1-hour)
- Case studies (successful collaborations)
- Focus groups (collective insights)
- Ethnographic observation (watch people use system)

Why mixed methods?
- Numbers alone don't tell full story
- Qualitative explains WHY metrics are what they are
- Stories and case studies convince stakeholders

External Evaluation
-------------------
Independence ensures credibility:
- VISTA evaluation team (not SPARKNET team)
- External academic reviewers (peer review)
- User feedback (pilot institutions provide assessment)

Final evaluation report (Month 36):
- Comprehensive assessment against all metrics
- Lessons learned
- Recommendations for future development
- Sustainability plan

SUCCESS DEFINITION (Summary)
=============================
SPARKNET will be considered successful if by Month 36:
1. It produces high-quality research (6+ publications, theses)
2. It works technically (95% OCR, 90% satisfaction, 70% time savings)
3. It's adopted (10-15 institutions, 1000+ patents)
4. It makes impact (100+ connections, case studies of successful transfers)
5. It's sustainable (transition plan for ongoing operation)

PARTIAL SUCCESS:
Even if not all metrics met, valuable outcomes:
- Research contributions stand alone (publications, datasets, methodologies)
- Lessons learned valuable for future AI in knowledge transfer
- Prototype demonstrates feasibility, even if not fully production-ready

TRANSITION: "Let's wrap up with next steps and how stakeholders can engage..."
"""
    )

    # ========================================================================
    # SLIDE 11: NEXT STEPS & STAKEHOLDER ENGAGEMENT
    # ========================================================================
    slide11 = add_content_slide(
        "Next Steps & Stakeholder Engagement Opportunities",
        [
            "📅 Immediate Next Steps (Months 0-6)",
            "",
            "Month 0-1: Proposal Finalization & Approval",
            (1, "Refine project plan based on stakeholder feedback"),
            (1, "Secure funding commitment from VISTA and institutional partners"),
            (1, "Establish project governance (steering committee, advisory board)"),
            "",
            "Month 1-2: Team Recruitment & Kick-off",
            (1, "Hire core team (AI researchers, engineers, project manager)"),
            (1, "Set up infrastructure (GPUs, cloud accounts, development environment)"),
            (1, "Official project kick-off meeting with all partners"),
            "",
            "Month 2-6: Foundation Phase Begins",
            (1, "Start OCR pipeline development (PDF→image→text)"),
            (1, "Begin stakeholder data collection partnerships"),
            (1, "Initiate user studies with TTO professionals"),
            (1, "First quarterly progress report to steering committee"),
            "",
            "🤝 Stakeholder Engagement Opportunities",
            "",
            "For VISTA Partners:",
            (1, "Join steering committee (quarterly oversight)"),
            (1, "Participate in user studies and requirements gathering"),
            (1, "Pilot site participation (Year 2-3, receive early access)"),
            (1, "Data sharing partnerships (contribute stakeholder profiles)"),
            "",
            "For Funding Agencies:",
            (1, "Co-funding opportunities (match VISTA contribution)"),
            (1, "Strategic alignment with innovation and AI priorities"),
            (1, "Access to research outputs and intellectual property"),
            "",
            "For Academic Institutions:",
            (1, "Embed PhD/Master's students in project"),
            (1, "Collaboration on research publications"),
            (1, "Access to SPARKNET for institutional use"),
        ],
        speaker_notes="""
NEXT STEPS & STAKEHOLDER ENGAGEMENT (3 minutes):

PURPOSE: Make clear what happens next and how stakeholders can get involved. Create urgency and excitement.

IMMEDIATE NEXT STEPS (Months 0-6)
==================================

Month 0-1: Proposal Finalization & Approval
--------------------------------------------
Activities:
1. Stakeholder Feedback Session (THIS MEETING)
   - Present proposal
   - Collect feedback and questions
   - Identify concerns and address them

2. Proposal Revision (Week 1-2 after this meeting)
   - Incorporate feedback
   - Refine timeline, budget, deliverables
   - Strengthen weak areas identified
   - Add missing details

3. Formal Approval Process (Week 3-4)
   - Submit to VISTA steering committee
   - Present to institutional leadership
   - Obtain signed funding commitments
   - Set up project accounts and legal structures

Stakeholder role:
- Provide honest, constructive feedback TODAY
- Champion proposal within your organizations
- Expedite approval processes where possible

Target: Signed agreements by end of Month 1

Month 1-2: Team Recruitment & Kick-off
---------------------------------------
Activities:
1. Core Team Recruitment (Month 1-2)
   - Post positions internationally
   - Target: 5-6 positions initially
   - Priority: Lead AI Researcher, Project Manager (start immediately)
   - Others: Data Engineer, UX Researcher, Research Engineers

   Recruitment channels:
   - University job boards
   - Professional networks (LinkedIn, research conferences)
   - Direct recruitment (reach out to strong candidates)

   Timeline:
   - Post positions: Week 1
   - Applications due: Week 4
   - Interviews: Week 5-6
   - Offers: Week 7
   - Start dates: Month 2-3 (allow time for notice period)

2. Infrastructure Setup (Month 1-2)
   - Order GPU hardware (8x NVIDIA A100s)
   - Set up cloud accounts (AWS/Azure)
   - Configure development environment (Git, CI/CD)
   - Establish communication channels (Slack, email lists, project management)

3. Project Kick-off Meeting (Month 2)
   - In-person if possible (build team cohesion)
   - Agenda:
     * Welcome and introductions
     * Project vision and goals
     * Roles and responsibilities
     * Work plan and milestones
     * Communication protocols
     * Risk management
     * Team building activities
   - Duration: 2-3 days
   - Location: Lead institution (or rotate among partners)

Stakeholder role:
- Help recruit (share job postings, recommend candidates)
- Attend kick-off meeting (steering committee members)
- Provide institutional support (access, resources)

Target: Team in place, infrastructure ready by end of Month 2

Month 2-6: Foundation Phase Begins
-----------------------------------
This is where real work starts. Three parallel tracks:

Track 1: OCR Pipeline Development (Months 2-5)
Led by: 2 AI/ML Researchers
Activities:
- Literature review (state-of-the-art OCR methods)
- Test various OCR engines (llava, Tesseract, commercial APIs)
- Implement PDF→image conversion
- Build quality assessment module
- Benchmark on diverse patents

Deliverable (Month 6): Working OCR pipeline, accuracy report

Track 2: Stakeholder Data Collection (Months 2-6)
Led by: Data Engineer
Activities:
- Negotiate data sharing agreements with 5-10 partner institutions
- Build web scraping infrastructure
- Extract data from public sources
- Data quality assessment and cleaning
- Begin constructing database (target: 500 entries by Month 6)

Deliverable (Month 6): Initial stakeholder database, data collection report

Track 3: User Studies & Requirements (Months 3-6)
Led by: UX Researcher
Activities:
- Recruit TTO professionals for studies (target: 20 participants)
- Conduct contextual inquiry (observe current workflows)
- Requirements workshops (what do they need?)
- Prototype testing (validate design directions)
- Synthesize findings

Deliverable (Month 6): User requirements document, prototype feedback

Governance:
- Monthly all-hands meetings (whole team)
- Bi-weekly work package meetings (each track)
- Quarterly steering committee review (Month 3, Month 6)

Stakeholder role:
- Steering committee: Attend quarterly reviews, provide guidance
- Partner institutions: Facilitate user study participation
- Data partners: Expedite data sharing agreements

Target: Solid foundation by Month 6 (ready for Year 1 Q3 work)

STAKEHOLDER ENGAGEMENT OPPORTUNITIES
====================================

For VISTA Partners (Universities, TTOs, Research Centers)
----------------------------------------------------------

Opportunity 1: Steering Committee Membership
Commitment: 4 meetings per year (quarterly), 2 hours each + preparation
Role:
- Strategic oversight (ensure alignment with VISTA goals)
- Risk management (identify and address issues early)
- Resource allocation (advise on priorities)
- Quality assurance (review deliverables, provide feedback)
- Stakeholder liaison (represent interests of broader community)

Benefits:
- Shape project direction
- Early visibility into findings and outputs
- Networking with other VISTA leaders
- Recognition in project materials and publications

Target: 8-10 steering committee members representing VISTA Work Packages

Opportunity 2: User Study Participation
Commitment: Various (interviews, workshops, testing sessions)
Year 1: 2-4 hours (interviews, requirements gathering)
Year 2: 4-6 hours (usability testing, feedback sessions)
Year 3: 2-3 hours (evaluation interviews, case studies)

Role:
- Share expertise (how do you currently do patent analysis?)
- Test prototypes (is this useful? usable?)
- Provide feedback (what works, what doesn't?)
- Suggest improvements

Benefits:
- Ensure system meets real needs (you shape it)
- Early access to prototypes and findings
- Training on AI for knowledge transfer
- Co-authorship on user study papers

Target: 50+ TTO professionals participating over 3 years

Opportunity 3: Pilot Site Participation (Year 2-3)
Commitment: Year 2-3 (Months 13-36), active use of system
Requirements:
- Designate 2-3 staff as primary SPARKNET users
- Analyze 20-50 patents through system
- Provide regular feedback (monthly surveys, quarterly interviews)
- Participate in case study development
- Allow site visits for evaluation

Benefits:
- Free access to SPARKNET (€10k+ value)
- Enhanced technology transfer capabilities
- Staff training and professional development
- Co-authorship on pilot study publications
- Recognition as innovation leader

Target: 10-15 pilot sites (5 EU, 5 Canada, 3-5 TTOs)

Selection criteria:
- Commitment to active use
- Diversity (size, type, geography)
- Data sharing willingness
- Technical capacity

Application process (Year 1, Month 9):
- Open call for pilot sites
- Application form (motivation, capacity, commitment)
- Selection by steering committee
- Onboarding (Months 10-12)

Opportunity 4: Data Sharing Partnerships
Commitment: One-time or ongoing data contribution
Options:
- Share stakeholder profiles (researchers, companies in your network)
- Provide access to institutional databases (CRIS, RIS)
- Contribute historical technology transfer data (successful collaborations)

Benefits:
- Better matching for your institution (more data = better results)
- Access to broader VISTA network database
- Co-authorship on database methodology papers
- Recognition as data contributor

Concerns (we'll address):
- Privacy: Anonymization, access controls, GDPR compliance
- Competition: Selective sharing (mark sensitive data as private)
- Effort: We do the data extraction, you provide access
- Control: You can review and approve what's included

Target: 15-20 data partners contributing over 3 years

For Funding Agencies (VISTA, National Agencies, EU Programs)
------------------------------------------------------------

Opportunity 1: Co-Funding
Rationale:
- SPARKNET budget (€1.65M) is substantial for one source
- Co-funding reduces risk, increases buy-in
- Aligns with multiple funding priorities (AI, innovation, EU-Canada collaboration)

Potential models:
- VISTA core contribution: €800k (50%)
- Institutional co-funding: €500k (30%) - from partner universities
- National agencies: €300k (20%) - from NSERC (Canada), EU programs (Innovation Actions)

Benefits of co-funding:
- Shared risk and ownership
- Broader support base (politically valuable)
- Potential for larger scope or extended timeline
- Sustainability beyond initial 3 years

Process:
- VISTA provides seed funding (€200k Year 1)
- Use early results to secure additional funding (Month 6-12)
- Full budget secured by Year 2

Opportunity 2: Strategic Alignment
How SPARKNET aligns with funding priorities:

For VISTA:
- Directly supports VISTA mission (knowledge transfer enhancement)
- Contributes to all 5 work packages
- Showcases EU-Canada collaboration success

For EU programs (Horizon Europe, Digital Europe):
- AI for public good
- Digital transformation of research
- European innovation ecosystem
- Aligns with Key Digital Technologies (KDT) priority

For Canadian agencies (NSERC, NRC):
- AI and machine learning research
- University-industry collaboration
- Technology commercialization
- Aligns with Innovation, Science and Economic Development (ISED) priorities

Benefits of explicit alignment:
- Higher chance of approval (fits strategic priorities)
- Access to funding streams
- Policy impact (SPARKNET as model for other initiatives)

Opportunity 3: Access to Intellectual Property and Outputs
What funding agencies get:
- Publications (open access where possible)
- Datasets and benchmarks (community resources)
- Software (open-source components)
- Methodologies (replicable by others)
- Lessons learned (what works, what doesn't)

Potential for:
- Licensing revenue (if SPARKNET becomes commercial product)
- Economic impact (job creation, startup formation)
- Policy influence (inform AI policy, research policy)

Terms:
- Open science principles (FAIR data, reproducibility)
- No exclusive licenses (benefits go to community)
- Attribution and acknowledgment

For Academic Institutions (Universities, Research Centers)
----------------------------------------------------------

Opportunity 1: Embed Students in Project
PhD students (3-year commitment):
- 1 PhD position available
- Fully funded (salary, tuition, research budget)
- Co-supervision by SPARKNET PI and institutional supervisor
- Topic negotiable (within SPARKNET scope)

Benefits for institution:
- No cost PhD student (fully funded by project)
- High-quality research (embedded in large project)
- Publications (student + SPARKNET team)
- Training in AI, multi-agent systems, knowledge transfer

Benefits for student:
- Interesting, impactful research topic
- Interdisciplinary experience
- Large team collaboration
- Real-world validation of research
- Strong publication record

Application process:
- Open call (Month 3)
- Interview candidates (Month 4)
- Selection (Month 5)
- Start (Month 6)

Master's students (6-12 month projects):
- 2-3 positions per year
- Partially funded (stipend for full-time students)
- Topics: Diagram analysis, stakeholder profiling, UX, specific engineering tasks

Benefits for institution:
- Supervised projects for Master's program
- Research output
- Potential for publication

Opportunity 2: Research Collaboration
Joint research on topics of mutual interest:
- Multi-agent systems (if you have MAS research group)
- Natural language processing (if you have NLP group)
- Knowledge management (if you have KM researchers)
- Human-computer interaction (if you have HCI group)

Collaboration models:
- Co-authorship on papers (SPARKNET provides data/platform, you provide expertise)
- Joint proposals (use SPARKNET as foundation for new projects)
- Shared students (your student works on SPARKNET problem)
- Visiting researchers (your faculty spend sabbatical with SPARKNET team)

Benefits:
- Access to unique platform and data
- New publication venues and opportunities
- Grant proposals (SPARKNET as preliminary work)
- Network expansion

Opportunity 3: Institutional Use of SPARKNET
Once operational (Year 3+), your institution can:
- Use SPARKNET for your own technology transfer
- Customize for your specific needs
- Integrate with your systems (CRIS, RIS, CRM)
- Train your staff

Pricing model (post-project):
- VISTA partners: Free for duration of VISTA project
- Other institutions: Subscription model (€5-10k/year)
- Open-source core: Always free (but no support)

MAKING IT HAPPEN
================

What we need from you today:
1. Feedback on proposal
   - What's missing?
   - What concerns do you have?
   - What would make this better?

2. Indication of interest
   - Would you support this project?
   - Would you participate (steering committee, pilot site, data partner)?
   - Would you co-fund?

3. Next steps
   - Who should we follow up with?
   - What approvals are needed in your organization?
   - What's your timeline?

What happens after today:
- Week 1: Incorporate feedback, revise proposal
- Week 2: Individual follow-ups with interested stakeholders
- Week 3-4: Finalize proposal, submit for approval
- Month 2: Kick-off (if approved)

Contact:
Mohamed Hamdan
[email@institution.edu]
[phone]

SPARKNET Project Website:
[URL] (will be set up once project approved)

TRANSITION: "Let's open the floor for questions and discussion..."
"""
    )

    # ========================================================================
    # SLIDE 12: CLOSING SLIDE
    # ========================================================================
    slide12 = add_title_slide(
        "SPARKNET: A 3-Year Research Journey",
        "From Early Prototype to Production-Ready Knowledge Transfer Platform\n\nWe're at the beginning. Let's build the future together.",
        "Mohamed Hamdan | VISTA Project | November 2025\n\nThank you | Questions & Discussion Welcome"
    )

    notes12 = """
CLOSING REMARKS (2 minutes):

SUMMARY:
Today, I've presented SPARKNET - an ambitious 3-year research program to transform patent valorization through AI.

KEY TAKEAWAYS:
1. We have a working prototype (5-10% complete) that proves the concept
2. 90-95% of the work lies ahead - significant research and development needed
3. Clear 3-year roadmap with milestones, deliverables, and success metrics
4. Budget of ~€1.65M is realistic for the scope of work
5. Multiple opportunities for stakeholder engagement

WHY THIS MATTERS:
- Knowledge transfer is crucial for innovation and economic growth
- Current manual processes don't scale - AI can help
- VISTA provides perfect context for this research
- We have the expertise and commitment to deliver

WHAT WE'RE ASKING:
- Support for the 3-year program
- Active engagement from stakeholders (steering committee, pilot sites, data partners)
- Funding commitment (from VISTA and potentially other sources)
- Permission to proceed with team recruitment and kickoff

WHAT YOU GET:
- Cutting-edge research outputs (publications, datasets, tools)
- Production-ready SPARKNET platform (by Year 3)
- Enhanced knowledge transfer capabilities for your institution
- Leadership role in EU-Canada research collaboration

THE JOURNEY AHEAD:
- This is a marathon, not a sprint
- We'll encounter challenges and setbacks - that's research
- We need your support, patience, and active participation
- Together, we can build something transformative

IMMEDIATE NEXT STEPS:
1. Your feedback (TODAY)
2. Proposal revision (NEXT WEEK)
3. Approval process (MONTH 1)
4. Team recruitment (MONTH 1-2)
5. Kickoff (MONTH 2)

FINAL THOUGHT:
We're not just building software. We're advancing the state of knowledge in multi-agent AI, quality assessment, and knowledge transfer. We're creating tools that will help researchers bring their innovations to the world. We're strengthening the EU-Canada research ecosystem.

This is important work. Let's do it right.

Thank you for your time and attention. I'm excited to answer your questions and discuss how we can move forward together.

QUESTIONS & DISCUSSION:
[Open floor for Q&A - be prepared for:]

Expected questions:
Q: "Why 3 years? Can it be done faster?"
A: We considered 2 years but that's too rushed for quality research. Need time for publications, student theses, real-world validation. Could do in 4 years if more comprehensive, but 3 is sweet spot.

Q: "What if you can't get access to stakeholder data?"
A: Risk we've identified. Mitigation: Start partnerships early, use synthetic data for dev, have fallback approaches. But we're confident with VISTA network support.

Q: "How do you ensure AI quality/avoid hallucinations?"
A: Multi-layered approach: CriticAgent review, quality framework with 12 dimensions, human-in-the-loop for critical decisions, confidence scoring to flag uncertain outputs.

Q: "What happens after 3 years? Is this sustainable?"
A: Plan for transition to operational team. Potential models: Subscription for institutions, licensing, continued grant funding, VISTA operational budget. Details TBD but sustainability is core consideration.

Q: "Can we see a demo?"
A: Yes! We have working prototype. Can show: Patent upload, analysis workflow, stakeholder matching, valorization brief output. [Be ready to demo or schedule follow-up]

Q: "How do you manage IP? Who owns SPARKNET?"
A: Intellectual property generated will be owned by lead institution but licensed openly to VISTA partners. Publications open access. Software has open-source core + proprietary extensions. Details in formal project agreement.

Be confident, honest, and enthusiastic. Show expertise but also humility (acknowledge challenges). Build trust through transparency.

Thank you!
"""
    slide12.notes_slide.notes_text_frame.text = notes12

    # Save presentation
    output_path = "/home/mhamdan/SPARKNET/presentation/SPARKNET_Academic_Presentation_IMPROVED.pptx"
    prs.save(output_path)
    print(f"Saved improved presentation to: {output_path}")
    return output_path

if __name__ == "__main__":
    try:
        path = create_improved_presentation()
        print(f"\n✅ SUCCESS! Improved presentation created at:\n{path}")
    except Exception as e:
        print(f"❌ Error creating presentation: {e}")
        import traceback
        traceback.print_exc()