This work describes a scalable system for automated patent search and analysis that integrates large language models with function calling to support data retrieval and classification. The approach combines conventional data extraction from the US Patent and Trademark Office with semantic similarity search and structured function execution to enable accurate and reproducible patent management applicable to real-world institutional data analysis challenges.
