# SPARC

**Semiconductor Patent & Analytics Report Core**

A patent analysis system that estimates company performance by analyzing their patent portfolios using LLM-powered insights.

## Overview

SPARC automatically collects, parses, and analyzes patents from companies to provide performance estimations. It uses Claude AI to evaluate innovation quality, strategic direction, and competitive positioning based on patent content.

## Features

- **Patent Retrieval**: Automated collection via SerpAPI's Google Patents engine
- **Intelligent Parsing**: Extracts key sections (abstract, claims, summary) from patent PDFs
- **Content Minimization**: Removes verbose descriptions to reduce LLM token usage
- **AI Analysis**: Uses Claude 3.5 Sonnet via OpenRouter to analyze innovation quality and market potential
- **Portfolio Analysis**: Evaluates multiple patents holistically for comprehensive insights
- **Robust Testing**: 26 tests covering all major functionality

## Architecture

```
SPARC/
├── serp_api.py       # Patent retrieval and PDF parsing
├── llm.py            # Claude AI integration via OpenRouter
├── analyzer.py       # High-level orchestration
├── types.py          # Data models
└── config.py         # Environment configuration
```

## Installation

### NixOS (Recommended)

```bash
nix develop
```

This automatically creates a virtual environment and installs all dependencies.

### Manual Installation

```bash
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```

## Configuration

Create a `.env` file in the project root:

```bash
# SerpAPI key for patent search
API_KEY=your_serpapi_key_here

# OpenRouter API key for Claude AI analysis
OPENROUTER_API_KEY=your_openrouter_key_here
```

Get your API keys:
- SerpAPI: https://serpapi.com/
- OpenRouter: https://openrouter.ai/

## Usage

### Basic Usage

```python
from SPARC.analyzer import CompanyAnalyzer

# Initialize the analyzer
analyzer = CompanyAnalyzer()

# Analyze a company's patent portfolio
analysis = analyzer.analyze_company("nvidia")
print(analysis)
```

### Run the Example

```bash
python main.py
```

This will:
1. Retrieve recent NVIDIA patents
2. Parse and minimize content
3. Analyze with Claude AI
4. Print comprehensive performance assessment

### Single Patent Analysis

```python
# Analyze a specific patent
result = analyzer.analyze_single_patent(
    patent_id="US11322171B1",
    company_name="nvidia"
)
```

## Running Tests

```bash
# Run all tests
pytest tests/ -v

# Run specific test modules
pytest tests/test_analyzer.py -v
pytest tests/test_llm.py -v
pytest tests/test_serp_api.py -v

# Run with coverage
pytest tests/ --cov=SPARC --cov-report=term-missing
```

## How It Works

1. **Patent Collection**: Queries SerpAPI for company patents
2. **PDF Download**: Retrieves patent PDF files
3. **Section Extraction**: Parses abstract, claims, summary, and description
4. **Content Minimization**: Keeps essential sections, removes bloated descriptions
5. **LLM Analysis**: Sends minimized content to Claude for analysis
6. **Performance Estimation**: Returns insights on innovation quality and outlook

## Roadmap

- [X] Retrieve `publicationID` from SERP API
- [X] Parse patents from PDFs (no need for Google Patent API)
- [X] Extract and minimize patent content
- [X] LLM integration for analysis
- [X] Company performance estimation
- [ ] Multi-company batch processing
- [ ] FastAPI web service wrapper
- [ ] Docker containerization
- [ ] Results persistence (database)
- [ ] Visualization dashboard

## Development

### Code Style

- Type hints throughout
- Comprehensive docstrings
- Small, testable functions
- Conventional commits

### Testing Philosophy

- Unit tests for core logic
- Integration tests for orchestration
- Mock external APIs
- Aim for high coverage

### Making Changes

1. Write tests first
2. Implement feature
3. Verify all tests pass
4. Commit with conventional format: `type: description`

Types: `feat`, `fix`, `docs`, `test`, `refactor`, `chore`

## License

For open source projects, say how it is licensed.

## Project Status

Core functionality complete. Ready for production use with API keys configured.

Next steps: API wrapper, containerization, and multi-company support.