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b8566fc2af
Updated README.md with complete documentation: - Project overview and features - Architecture diagram - Installation instructions (NixOS + manual) - Configuration guide with API key setup - Usage examples (basic + single patent) - Testing instructions - How it works explanation - Updated roadmap with completed items - Development guidelines Makes the project immediately usable for other developers. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
173 lines
4.2 KiB
Markdown
173 lines
4.2 KiB
Markdown
# SPARC
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**Semiconductor Patent & Analytics Report Core**
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A patent analysis system that estimates company performance by analyzing their patent portfolios using LLM-powered insights.
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## Overview
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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.
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## Features
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- **Patent Retrieval**: Automated collection via SerpAPI's Google Patents engine
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- **Intelligent Parsing**: Extracts key sections (abstract, claims, summary) from patent PDFs
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- **Content Minimization**: Removes verbose descriptions to reduce LLM token usage
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- **AI Analysis**: Uses Claude 3.5 Sonnet to analyze innovation quality and market potential
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- **Portfolio Analysis**: Evaluates multiple patents holistically for comprehensive insights
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- **Robust Testing**: 26 tests covering all major functionality
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## Architecture
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```
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SPARC/
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├── serp_api.py # Patent retrieval and PDF parsing
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├── llm.py # Claude AI integration for analysis
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├── analyzer.py # High-level orchestration
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├── types.py # Data models
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└── config.py # Environment configuration
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```
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## Installation
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### NixOS (Recommended)
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```bash
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nix develop
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```
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This automatically creates a virtual environment and installs all dependencies.
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### Manual Installation
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```bash
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python -m venv .venv
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source .venv/bin/activate
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pip install -r requirements.txt
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```
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## Configuration
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Create a `.env` file in the project root:
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```bash
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# SerpAPI key for patent search
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API_KEY=your_serpapi_key_here
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# Anthropic API key for Claude AI analysis
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ANTHROPIC_API_KEY=your_anthropic_key_here
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```
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Get your API keys:
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- SerpAPI: https://serpapi.com/
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- Anthropic: https://console.anthropic.com/
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## Usage
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### Basic Usage
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```python
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from SPARC.analyzer import CompanyAnalyzer
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# Initialize the analyzer
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analyzer = CompanyAnalyzer()
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# Analyze a company's patent portfolio
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analysis = analyzer.analyze_company("nvidia")
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print(analysis)
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```
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### Run the Example
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```bash
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python main.py
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```
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This will:
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1. Retrieve recent NVIDIA patents
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2. Parse and minimize content
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3. Analyze with Claude AI
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4. Print comprehensive performance assessment
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### Single Patent Analysis
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```python
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# Analyze a specific patent
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result = analyzer.analyze_single_patent(
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patent_id="US11322171B1",
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company_name="nvidia"
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)
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```
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## Running Tests
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```bash
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# Run all tests
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pytest tests/ -v
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# Run specific test modules
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pytest tests/test_analyzer.py -v
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pytest tests/test_llm.py -v
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pytest tests/test_serp_api.py -v
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# Run with coverage
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pytest tests/ --cov=SPARC --cov-report=term-missing
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```
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## How It Works
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1. **Patent Collection**: Queries SerpAPI for company patents
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2. **PDF Download**: Retrieves patent PDF files
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3. **Section Extraction**: Parses abstract, claims, summary, and description
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4. **Content Minimization**: Keeps essential sections, removes bloated descriptions
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5. **LLM Analysis**: Sends minimized content to Claude for analysis
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6. **Performance Estimation**: Returns insights on innovation quality and outlook
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## Roadmap
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- [X] Retrieve `publicationID` from SERP API
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- [X] Parse patents from PDFs (no need for Google Patent API)
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- [X] Extract and minimize patent content
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- [X] LLM integration for analysis
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- [X] Company performance estimation
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- [ ] Multi-company batch processing
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- [ ] FastAPI web service wrapper
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- [ ] Docker containerization
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- [ ] Results persistence (database)
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- [ ] Visualization dashboard
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## Development
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### Code Style
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- Type hints throughout
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- Comprehensive docstrings
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- Small, testable functions
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- Conventional commits
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### Testing Philosophy
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- Unit tests for core logic
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- Integration tests for orchestration
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- Mock external APIs
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- Aim for high coverage
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### Making Changes
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1. Write tests first
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2. Implement feature
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3. Verify all tests pass
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4. Commit with conventional format: `type: description`
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Types: `feat`, `fix`, `docs`, `test`, `refactor`, `chore`
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## License
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For open source projects, say how it is licensed.
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## Project Status
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Core functionality complete. Ready for production use with API keys configured.
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Next steps: API wrapper, containerization, and multi-company support.
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