PatentConceptor: Generating Innovative Product Concepts with Patent Knowledge and Large Language Models

Product concept generation is essential to innovative engineering design. However, it is often constrained by designers’ domain fixation and their limited access to cross-domain technological knowledge. This paper presents PatentConceptor, an intelligent system for cross-domain innovative design. The system integrates patent knowledge retrieval with large language model (LLM) capabilities. It comprises three functional modules: design requirement analysis (DRA), patent knowledge retrieval (PKR), and product concept generation (PCG). Two principal innovations are proposed. First, a query-type-adaptive hybrid retrieval mechanism dynamically fuses TF-IDF and LSA by mapping query characteristics to optimal lexical-semantic weightings, rather than using a fixed fusion strategy. Second, a domain-constrained LLM generation paradigm enforces hard IPC-domain restrictions during concept generation, grounding concepts in retrieved patents and mitigating hallucination risk. Quantitative experiments on 50 design problems and 520,634 patent documents demonstrate the system’s strong retrieval performance (P@5 = 0.836, CP@5 = 0.212) and high concept novelty (mean novelty score = 5.29 on a 7-point scale). An end-to-end case study was conducted on a wearable system for emotional body gesture recognition. The results demonstrate that concepts generated by PatentConceptor can be successfully implemented as functional prototypes. The resulting prototype achieved a cross-subject recognition accuracy of 71.5%.