IP research platforms use AI and NLP to build patent search engines that allow researchers to query 100M+ patent documents in natural language — replacing Boolean keyword-dependent searches with semantic understanding of invention concepts, dramatically improving patent landscape analysis quality.
IPRally built a custom ML platform using NLP on 120M+ global patent documents, creating an accurate, semantically searchable database that adds 200,000+ new patent sources per week. Enables patent professionals to find relevant prior art and conduct freedom-to-operate analysis through natural language queries.
Google Cloud ML/NLP (custom platform); vector search for semantic patent retrieval; BigQuery for scale; real-time ingestion pipeline for 200K+ weekly updates.