The "Cascading Patent Office": A multi-agent RAG system

The challenge: Automating complex patent analysis

For a client in the Intellectual Property industry, the core challenge was to automate the highly manual and time-consuming process of analyzing a patent examiner's office action. The goal was to create a structured and reliable AI-driven system that could dissect the examiner's rejections, associate them with the correct prior art, and propose legally sound response strategies.

Our solution: A collaborative multi-agent system

We architected a sophisticated multi-agent Retrieval-Augmented Generation (RAG) system that operates like a "cascading patent office." Rather than relying on a single monolithic model, this approach uses a team of distinct, specialized agents that collaborate in a structured workflow to achieve the goal.

Built using LangChain, the system delegates specific tasks to the most suitable agent. For example, one agent is responsible for the initial intake and classification of all case documents using AI-powered OCR. Other agents then work in parallel to analyze each rejection, cross-reference the relevant prior art, and generate potential response strategies. In a final step, compliance-focused agents perform a quality check, filtering the proposed strategies against the relevant articles of the European Patent Convention (EPC) to ensure all recommendations are legally sound.

This "cascading" process ensures that each step builds upon a verified foundation, progressively refining the output from raw documents to actionable, compliant strategies.

The impact: Structured, reliable, high-quality results

The multi-agent system transformed a complex business process into a reliable and automated workflow. It can deliver high-quality, strategic outcomes, saving the client significant expert time and providing a structured, repeatable approach to a critical and previously subjective task.