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SPARKNET

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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 │  │
│  └───────┘      └──────────┘     └────────┘  │
│                                                 │
└─────────────────────────────────────────────────┘

User Workflow

Simple 4-Step Process:

Upload → User uploads patent PDF
Process → Multi-agent system analyzes document
Assess → Technology readiness & commercial potential evaluated
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

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.