Prototype: An end-to-end learning project demonstrating a multi-agent MCP RAG pipeline that analyzes existing patents and suggests how to refine ideas to make them novel and potentially patentable.
Search 200M+ patents and scientific literature in natural language directly inside Claude, Cursor, or any MCP-compatible AI tool. Powered by PatSnap's global R&D intelligence database.
Detect potential patentable technical inventions in Git diffs, rank prior art, and produce attorney-reviewable claim drafts in CI.
PatentLLM generates R&D-focused patent intelligence reports from patent PDFs using section-aware parsing, embedding retrieval, local LLMs, claim-bank grounding, and citation-alignment audits.
Patent intelligence for AI agents — patent search, USPTO data, patent landscape & pgvector prior-art search. MCP + x402.
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