https://github.com/bparanj/patents
AI-driven TRIZ analysis of cybersecurity patents reveals novel threat detection insights, strengthening your patent portfolio and IP strategy.
https://github.com/bparanj/patents
ai cybersecurity triz
Last synced: 5 months ago
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AI-driven TRIZ analysis of cybersecurity patents reveals novel threat detection insights, strengthening your patent portfolio and IP strategy.
- Host: GitHub
- URL: https://github.com/bparanj/patents
- Owner: bparanj
- License: mit
- Created: 2024-12-19T15:17:24.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-19T15:56:07.000Z (over 1 year ago)
- Last Synced: 2025-06-01T17:23:31.520Z (about 1 year ago)
- Topics: ai, cybersecurity, triz
- Homepage:
- Size: 17.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CyberShu
**CyberShu** is an open-source project that combines the systematic problem-solving methodology of TRIZ with AI to analyze cybersecurity patents. By automating the analysis of contradictions, resolutions, and trends, CyberShu aims to uncover hidden insights in the realm of cybersecurity threat detection.
---
## **Features**
- **TRIZ-Driven Patent Analysis**: Leverages the principles of TRIZ to extract contradictions and map inventive solutions.
- **AI-Powered Insights**: Uses advanced natural language processing (NLP) and machine learning models to analyze large patent datasets.
- **Cybersecurity Focus**: Tailored specifically to uncover trends, gaps, and opportunities in threat detection innovation.
- **Visualization**: Generates intuitive visualizations for trends, contradictions, and interdisciplinary insights.
---
## **Goals**
1. Automate Altshuller’s TRIZ methodology to analyze patents at scale.
2. Uncover new patterns and contradictions that humans may overlook.
3. Inspire innovation in cybersecurity by identifying gaps and novel opportunities.
---
## **Repository Structure**
- **`data/`**: Contains raw and processed patent datasets.
- **`models/`**: AI models for NLP, clustering, and trend analysis.
- **`notebooks/`**: Jupyter Notebooks for experiments and demonstrations.
- **`scripts/`**: Python scripts for preprocessing, analysis, and visualization.
- **`results/`**: Outputs such as visualizations and reports.
- **`docs/`**: Documentation for users, contributors, and license details.
---
## **Getting Started**
### **Prerequisites**
- Python 3.10+
- Required Libraries:
```bash
pip install pandas numpy nltk spacy gensim transformers faiss-cpu bertopic matplotlib plotly scikit-learn
```
### **Setup**
1. Clone the repository:
```bash
git clone https://github.com/yourusername/CyberShu.git
cd CyberShu
```
2. Set up the environment:
```bash
python -m venv env
source env/bin/activate # On Windows: env\Scripts\activate
pip install -r requirements.txt
```
3. Prepare datasets:
- Place raw patent files in the `data/raw/` directory.
4. Run example notebooks or scripts:
```bash
python scripts/preprocess.py
python scripts/analyze.py
```
---
## **Contributing**
We welcome contributions! Please see [CONTRIBUTING.md](docs/CONTRIBUTING.md) for guidelines.
---
## **License**
This project is licensed under the MIT License. See [LICENSE.md](docs/LICENSE.md) for details.
---
## **Contact**
For questions or collaboration:
- Email: bparanj@zepho.com
- GitHub Issues: [CyberShu Issues](https://github.com/yourusername/CyberShu/issues)
---
## **Acknowledgments**
- Inspired by Genrich Altshuller’s TRIZ methodology.
- Special thanks to the open-source community for tools and datasets that power this project.