AI for patent searching: From prior art to future frontiers

AI tools for intellectual property management, like large language models, retrieval systems, embeddings, and sematic searches, are multiplying, and so are the vendor claims. AI is touted as the answer to patent search problems, but it isn't that simple, especially for complex scientific fields. Patent literature is written to protect claims, not to explain science. Inconsistent language, deliberate obfuscation, and hundreds of thousands of documents make this some of the hardest text an AI model will ever encounter.

This webinar goes past the marketing to show you which AI approaches are built for patent-scale problems, how they're engineered to handle claim language and technical inconsistency, and why the same model that performs well on general text can fail on prior art. You will discover:

• What types of AI are being applied to patent search and why general-purpose language models struggle with patent-specific language and structure.

• Where AI delivers measurable value across the patent workflow and where it still falls short through a clear-eyed look at current AI capability.

• How AI-powered analysis at scale can reveal where a scientific field is heading through AI applied to real patent workflows, including AI-driven findings from a recent CAS pharma patent landscape report analyzing more than 368,000 filings across 90+ patent offices.