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# 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.*
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