An AI-powered patent novelty assessment platform that automates prior art searches across patent databases and academic literature, powered by AWS Bedrock Agent Core and multi-agent orchestration.
Watch the complete demonstration of the Patent Novelty Assessment System:
Click the image above to watch the demo (opens in Google Drive)
| Description | Link |
|---|---|
| Overview | Overview |
| Architecture | Architecture |
| Detailed Architecture | Detailed Architecture |
| Prerequisites | Prerequisites |
| User Flow | User Flow |
| Deployment | Deployment |
| Usage | Usage |
| Infrastructure | Infrastructure |
| Modification Guide | Modification Guide |
| Credits | Credits |
| License | License |
This application combines AI-powered document processing with intelligent patent and literature search to deliver comprehensive prior art analysis. Built on a serverless architecture with multi-agent orchestration, automated workflow, and professional PDF report generation.
- Multi-Agent AI System powered by AWS Bedrock with Claude Sonnet 4.5
- Automated Document Processing using Amazon Bedrock Data Automation
- Intelligent Patent Search via PatentView API with LLM-powered relevance scoring
- Academic Literature Search via Semantic Scholar with semantic evaluation
- Early Commercial Assessment for market viability analysis
- Professional PDF Reports with prior art analysis and abstracts
- Real-time Web Interface with drag-and-drop upload and progress tracking
The application implements a serverless, event-driven architecture with a multi-agent AI system at its core, combining automated document processing with intelligent search and evaluation.
For a detailed deep dive into the architecture, including core principles, component interactions, data flow, security, and implementation details, see docs/architectureDeepDive.md.
For a detailed overview of the user journey and application workflow, including step-by-step user interactions, see docs/userGuide.md.
For detailed deployment instructions, including prerequisites and step-by-step guides, see docs/deploymentGuide.md.
For detailed backend testing and usage instructions, including configuration steps and how to test the application from AWS Console, see docs/usage.md.
For frontend user guide and application features, see docs/userGuide.md.
For a detailed overview of the application infrastructure, including component interactions, AWS services, and data flow, see docs/architectureDeepDive.md.
- API Documentation - Comprehensive API reference for PatentView and Semantic Scholar
- API Gateway Endpoints - Internal API Gateway documentation
Steps to implement optional modifications such as changing the Bedrock model, adding more agents, or customizing the frontend can be found here.
This application was architected and developed by Shaashvat Mittal, Sahajpreet Singh, and Ashik Tharakan with solutions architect Arun Arunachalam, program manager Thomas Orr and product manager Rachel Hayden. Thanks to the ASU Cloud Innovation Center Technical and Project Management teams for their guidance and support.
See LICENSE file for details.