What problem does it solve? Researchers, IP professionals, and R&D teams need to search massive patent and academic literature databases, but traditional Boolean search syntax and fragmented databases make prior art searches, competitive intelligence, and freedom-to-operate analysis slow and error-prone. ## Core Features & Use Cases - Natural Language Patent Search: Query 200M+ patents across 170+ jurisdictions (USPTO, EPO, WIPO) without learning Boolean syntax, with filters for assignee, inventor, IPC class, legal status, jurisdiction, and date range. - Scientific Literature Search: Search 216M+ academic papers with citation-count filtering, and combine with patent results for fusion research workflows. - Detailed Record Retrieval: Fetch full patent records (claims, descriptions, legal status, patent family) or literature metadata as Markdown by URL or publication number. - Use Case: Before filing a patent on a solid-state battery design, search granted US patents by assignee and date range, then fetch the full claims of the top results to assess prior art and freedom to operate. ## Quick Start Ask the AI to search PatSnap for granted US patents on solid state battery lithium metal anodes filed by Toyota since 2020, then fetch the detailed records of the top three results.