# SPARKNET ## AI-Powered Research Valorization Platform **A Multi-Agent System for Patent Wake-Up and Technology Transfer** --- ## What is SPARKNET? SPARKNET is an intelligent platform that analyzes patent documents and research to: - **Assess commercialization potential** - **Identify technology applications** - **Match with industry partners** - **Accelerate technology transfer** Built on modern AI agent architecture with LangGraph workflow orchestration. --- ## System Architecture ``` ┌─────────────────────────────────────────────────┐ │ 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 │ │ │ └───────┘ └──────────┘ └────────┘ │ │ │ └─────────────────────────────────────────────────┘ ``` --- ## User Workflow ### Simple 4-Step Process: 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 ``` 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 ``` --- ## Core Components ### 1. **Frontend (Next.js + React)** - Modern, responsive UI - Drag-and-drop file upload - Real-time workflow visualization - Interactive results dashboard ### 2. **Backend (FastAPI)** - RESTful API architecture - Async processing pipeline - CORS-enabled for frontend integration - Comprehensive logging ### 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 ### 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) ### 5. **Workflow Engine (LangGraph)** - State machine-based execution - Parallel agent coordination - Error handling & recovery - Checkpointing for long-running tasks --- ## Key Features ✓ **Intelligent Document Analysis** - Automatic title & abstract extraction - Patent claims identification - Technical domain classification ✓ **Technology Assessment** - TRL (Technology Readiness Level) scoring - Innovation identification - Novelty assessment ✓ **Commercialization Analysis** - Market potential evaluation - Application domain suggestions - Partner matching recommendations ✓ **Multi-Format Support** - Standard patent PDFs - Press releases & technical docs - Fallback extraction for non-standard formats --- ## Technology Stack | 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 | --- ## Current Status ### ✅ Operational - Multi-agent system fully initialized - All 4 LLM models loaded - Workflow engine running - Frontend & backend connected ### 📊 Capabilities Demonstrated - Patent PDF processing - Document extraction (with fallback) - TRL assessment - Technical domain classification - Commercialization potential scoring --- ## Use Cases ### 1. **Patent Wake-Up (Primary)** University tech transfer offices can: - Rapidly assess dormant patent portfolios - Identify commercialization opportunities - Match technologies with industry needs ### 2. **Technology Transfer** - Evaluate research outputs - Prioritize licensing opportunities - Generate technology briefs ### 3. **Partner Matching** (Future) - Connect inventors with industry - Identify potential licensees - Facilitate collaboration --- ## Sample Analysis Output ```yaml Patent: Toyota Hydrogen Fuel Cell Initiative ───────────────────────────────────────────── 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 TRL Level: 8 (System Complete & Qualified) Commercialization Potential: HIGH Key Innovations: • High-pressure hydrogen storage • Fuel cell stack optimization • System control software Applications: • Hydrogen vehicles • Stationary power systems • Industrial fuel cells ``` --- ## Why SPARKNET? ### **Problem**: - Manual patent analysis is slow and expensive - Technology transfer offices overwhelmed - Valuable IP sits dormant in university portfolios ### **Solution**: - **Automated**: AI agents handle complex analysis - **Fast**: Minutes instead of days - **Scalable**: Batch processing capability - **Intelligent**: Multi-model approach ensures accuracy --- ## Next Steps ### Immediate (v1.0) - [ ] Enhance patent structure extraction - [ ] Add batch processing for multiple patents - [ ] Improve TRL assessment accuracy ### Short-term (v1.5) - [ ] Industry partner database integration - [ ] Automated technology brief generation - [ ] Export to PDF reports ### Future (v2.0) - [ ] Real-time collaboration features - [ ] Market trend analysis integration - [ ] Automated prior art search --- ## Demo Access - **Frontend**: http://localhost:3000 - **Backend API**: http://localhost:8000 - **API Docs**: http://localhost:8000/docs - **Health Check**: http://localhost:8000/api/health --- ## Team & Contact **Project**: SPARKNET - Research Valorization Platform **Architecture**: Multi-Agent AI System **Framework**: LangGraph + LangChain **Deployment**: Local (Ollama) / Cloud-ready **For more information**: See documentation in `/home/mhamdan/SPARKNET/` --- ## Summary SPARKNET is a **production-ready AI platform** that automates patent analysis and technology assessment using: - **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) **Result**: Accelerated technology transfer from lab to market. --- **Questions?** *This is a preliminary overview for initial searching and evaluation.*