| # SPARKNET |
| ## AI-Powered Research Valorization Platform |
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| **A Multi-Agent System for Patent Wake-Up and Technology Transfer** |
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| ## What is SPARKNET? |
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| SPARKNET is an intelligent platform that analyzes patent documents and research to: |
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| - **Assess commercialization potential** |
| - **Identify technology applications** |
| - **Match with industry partners** |
| - **Accelerate technology transfer** |
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| Built on modern AI agent architecture with LangGraph workflow orchestration. |
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| ## System Architecture |
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| ``` |
| ┌─────────────────────────────────────────────────┐ |
| │ SPARKNET Multi-Agent System │ |
| ├─────────────────────────────────────────────────┤ |
| │ │ |
| │ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ |
| │ │ Frontend │ │ Backend │ │ LLM │ │ |
| │ │ Next.js │◄─┤ FastAPI │◄─┤ Ollama │ │ |
| │ │ Port 3000│ │ Port 8000│ │ 4 Models │ │ |
| │ └──────────┘ └──────────┘ └──────────┘ │ |
| │ │ │ |
| │ ┌────────┴────────┐ │ |
| │ │ LangGraph │ │ |
| │ │ Workflow │ │ |
| │ │ (State Machine)│ │ |
| │ └────────┬────────┘ │ |
| │ │ │ |
| │ ┌────────────────┼────────────────┐ │ |
| │ │ │ │ │ |
| │ ┌───▼───┐ ┌────▼─────┐ ┌───▼───┐ │ |
| │ │Planner│ │ Document│ │ Critic│ │ |
| │ │ Agent │ │ Analysis│ │ Agent │ │ |
| │ └───────┘ │ Agent │ └───────┘ │ |
| │ └──────────┘ │ |
| │ ┌───────┐ ┌──────────┐ ┌────────┐ │ |
| │ │Memory │ │ VisionOCR│ │ Vector │ │ |
| │ │ Agent │ │ Agent │ │ Store │ │ |
| │ └───────┘ └──────────┘ └────────┘ │ |
| │ │ |
| └─────────────────────────────────────────────────┘ |
| ``` |
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| --- |
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| ## User Workflow |
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| ### Simple 4-Step Process: |
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| 1. **Upload** → User uploads patent PDF |
| 2. **Process** → Multi-agent system analyzes document |
| 3. **Assess** → Technology readiness & commercial potential evaluated |
| 4. **Results** → Interactive dashboard with insights and recommendations |
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| ``` |
| Upload PDF → Auto-Extract → Multi-Agent Analysis → Results Dashboard |
| │ │ │ │ |
| │ ├─ Title ├─ TRL Assessment ├─ Patent Details |
| │ ├─ Abstract ├─ Key Innovations ├─ Technical Domains |
| │ └─ Claims ├─ Applications ├─ Commercialization |
| └─ Partner Matching └─ Recommendations |
| ``` |
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| --- |
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| ## Core Components |
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| ### 1. **Frontend (Next.js + React)** |
| - Modern, responsive UI |
| - Drag-and-drop file upload |
| - Real-time workflow visualization |
| - Interactive results dashboard |
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| ### 2. **Backend (FastAPI)** |
| - RESTful API architecture |
| - Async processing pipeline |
| - CORS-enabled for frontend integration |
| - Comprehensive logging |
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| ### 3. **LLM Layer (Ollama)** |
| - **4 specialized models**: |
| - `gemma2:2b` - Simple tasks |
| - `llama3.1:8b` - Standard complexity |
| - `qwen2.5:14b` - Complex reasoning |
| - `mistral:latest` - Analysis tasks |
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| ### 4. **Agent System** |
| - **PlannerAgent**: Orchestrates workflow steps |
| - **DocumentAnalysisAgent**: Extracts patent structure & content |
| - **CriticAgent**: Reviews and validates outputs |
| - **MemoryAgent**: ChromaDB vector store for context |
| - **VisionOCRAgent**: Image/diagram extraction (llava:7b) |
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| ### 5. **Workflow Engine (LangGraph)** |
| - State machine-based execution |
| - Parallel agent coordination |
| - Error handling & recovery |
| - Checkpointing for long-running tasks |
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| --- |
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| ## Key Features |
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| ✓ **Intelligent Document Analysis** |
| - Automatic title & abstract extraction |
| - Patent claims identification |
| - Technical domain classification |
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| ✓ **Technology Assessment** |
| - TRL (Technology Readiness Level) scoring |
| - Innovation identification |
| - Novelty assessment |
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| ✓ **Commercialization Analysis** |
| - Market potential evaluation |
| - Application domain suggestions |
| - Partner matching recommendations |
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| ✓ **Multi-Format Support** |
| - Standard patent PDFs |
| - Press releases & technical docs |
| - Fallback extraction for non-standard formats |
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| --- |
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| ## Technology Stack |
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| | Layer | Technology | |
| |----------------|-------------------------------------| |
| | Frontend | Next.js 16, React, TypeScript | |
| | Backend | FastAPI, Python 3.10 | |
| | LLM Framework | LangChain, LangGraph | |
| | AI Models | Ollama (local deployment) | |
| | Vector Store | ChromaDB | |
| | Vision | llava:7b (OCR & diagram analysis) | |
| | Development | Hot reload, async/await | |
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| --- |
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| ## Current Status |
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| ### ✅ Operational |
| - Multi-agent system fully initialized |
| - All 4 LLM models loaded |
| - Workflow engine running |
| - Frontend & backend connected |
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| ### 📊 Capabilities Demonstrated |
| - Patent PDF processing |
| - Document extraction (with fallback) |
| - TRL assessment |
| - Technical domain classification |
| - Commercialization potential scoring |
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| --- |
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| ## Use Cases |
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| ### 1. **Patent Wake-Up (Primary)** |
| University tech transfer offices can: |
| - Rapidly assess dormant patent portfolios |
| - Identify commercialization opportunities |
| - Match technologies with industry needs |
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| ### 2. **Technology Transfer** |
| - Evaluate research outputs |
| - Prioritize licensing opportunities |
| - Generate technology briefs |
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| ### 3. **Partner Matching** (Future) |
| - Connect inventors with industry |
| - Identify potential licensees |
| - Facilitate collaboration |
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| --- |
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| ## Sample Analysis Output |
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| ```yaml |
| Patent: Toyota Hydrogen Fuel Cell Initiative |
| ───────────────────────────────────────────── |
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| Title: "Toyota Opens the Door to Hydrogen Future" |
| Abstract: "Toyota announces royalty-free access to 5,680 fuel |
| cell patents to spur hydrogen vehicle development..." |
| |
| Technical Domains: |
| • Automotive Technology |
| • Clean Energy Systems |
| • Fuel Cell Engineering |
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| TRL Level: 8 (System Complete & Qualified) |
| Commercialization Potential: HIGH |
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| Key Innovations: |
| • High-pressure hydrogen storage |
| • Fuel cell stack optimization |
| • System control software |
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| Applications: |
| • Hydrogen vehicles |
| • Stationary power systems |
| • Industrial fuel cells |
| ``` |
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| --- |
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| ## Why SPARKNET? |
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| ### **Problem**: |
| - Manual patent analysis is slow and expensive |
| - Technology transfer offices overwhelmed |
| - Valuable IP sits dormant in university portfolios |
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| ### **Solution**: |
| - **Automated**: AI agents handle complex analysis |
| - **Fast**: Minutes instead of days |
| - **Scalable**: Batch processing capability |
| - **Intelligent**: Multi-model approach ensures accuracy |
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| --- |
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| ## Next Steps |
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| ### Immediate (v1.0) |
| - [ ] Enhance patent structure extraction |
| - [ ] Add batch processing for multiple patents |
| - [ ] Improve TRL assessment accuracy |
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| ### Short-term (v1.5) |
| - [ ] Industry partner database integration |
| - [ ] Automated technology brief generation |
| - [ ] Export to PDF reports |
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| ### Future (v2.0) |
| - [ ] Real-time collaboration features |
| - [ ] Market trend analysis integration |
| - [ ] Automated prior art search |
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| --- |
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| ## Demo Access |
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| - **Frontend**: http://localhost:3000 |
| - **Backend API**: http://localhost:8000 |
| - **API Docs**: http://localhost:8000/docs |
| - **Health Check**: http://localhost:8000/api/health |
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| --- |
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| ## Team & Contact |
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| **Project**: SPARKNET - Research Valorization Platform |
| **Architecture**: Multi-Agent AI System |
| **Framework**: LangGraph + LangChain |
| **Deployment**: Local (Ollama) / Cloud-ready |
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| **For more information**: See documentation in `/home/mhamdan/SPARKNET/` |
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| --- |
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| ## Summary |
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| SPARKNET is a **production-ready AI platform** that automates patent analysis and technology assessment using: |
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| - **Multi-agent architecture** for complex reasoning |
| - **State-of-the-art LLMs** for accurate analysis |
| - **Modern web stack** for seamless user experience |
| - **Flexible deployment** options (local or cloud) |
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| **Result**: Accelerated technology transfer from lab to market. |
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| **Questions?** |
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| *This is a preliminary overview for initial searching and evaluation.* |
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