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In addition to research papers, the Design Society is developing several valuable resources for those interested in the study of design. These include a repository of PhD theses, a library of case studies and transcripts of design activities, and an archive of our newsletters. Please note that these resources are accessible exclusively to Design Society members.

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

Jiang, Pingfei; Wang, Yuxuan; Han, Ji

Type:
Papers
DS 145: Proceedings of the DESIGN2026 19th International Design Conference
Year:
2026
Editor:
Štorga, M.; Škec, S.; Martinec, T.; Marjanović, D.; Pavković, N.
Author:
Jiang, Pingfei; Wang, Yuxuan; Han, Ji
Series:
DESIGN
Institution:
INDEX, University of Exeter, United Kingdom
Page(s):
2353-2362
DOI number:
10.1017/pds.2026.10593
ISSN:
2732-527X (Online)
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.
Keywords:
artificial intelligence (AI), engineering design, design knowledge, patent analysis, large language model (LLM)
DESIGN2026 - The 19th International Design Conference
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