Can large language models understand engineering design patents? An exploratory study

ABSTRACT: Patents contain valuable design insights, yet manual analysis remains time-consuming and complex. This study explores Large Language Models’ capacity to automate patent analysis for engineering design. GPT-5 and Gemini 2.5 Pro were evaluated across Motivation, Novelty, and Key Invention Features using three patents and expert evaluators assessed outputs through Accuracy & Fidelity, Comprehensiveness, and Analytical Depth. Results indicate LLMs demonstrate proficiency in feature synthesis but exhibit inferential limitations in motivation analysis, underscoring the necessity for human oversight.