Patent intelligence in the age of AI: Unlocking strategic insights through granular classification

Patent intelligence is increasingly constrained by the volume, technical density, and linguistic variability of patent documents, which limit the feasibility of producing high-granularity, analyst-facing outputs, such as matrices, trajectories, and comparative maps, using conventional keyword- or bibliometric-based methods. This study addresses this feasibility gap by proposing an Artificial Intelligence (AI)-based framework designed to stabilize patent content for scalable strategic analysis. Methodologically, the framework transforms patent documents into structured semantic summaries optimized for large language model processing and uses these representations to support AI-driven clustering and multidimensional classification. A retrieval-augmented generation (RAG) architecture ensures that summaries and classifications remain grounded in the underlying patent corpus, enhancing reliability and transparency. The framework is demonstrated through a large-scale robotics case study focusing on Siemens, Toshiba, and Mitsubishi. Results show improved recall and precision compared with keyword-based searches for semantically ambiguous categories, illustrated through a legged-robot benchmark. The approach enables the systematic construction of technology–application matrices that reveal competitive positioning, white spaces, and cross-domain innovation patterns. It further supports cell-level deep dives via grounded clustering and evolutionary timelines, as well as technology-acceleration analysis and normalized portfolio benchmarking. An additional application to European funded-project data highlights misalignments between corporate patenting trajectories and public R&D investment priorities. Overall, the study demonstrates that AI-based semantic stabilization provides a robust and scalable foundation for advanced patent intelligence in complex technological domains.