Generative Design Ideation: A Natural Language Generation Approach

2022/03/28 by Qihao Zhu, Zhu, Qihao, Jianxi Luo +1
Engineering · Materials Science · #Computation and Language (cs.CL) #Design Education and Practice #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Manufacturing Process and Optimization

paper · pdf · doi:10.48550/arxiv.2204.09658

openalex publication_date 2022/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper aims to explore a generative approach for knowledge-based design ideation by applying the latest pre-trained language models in artificial intelligence (AI). Specifically, a method of fine-tuning the generative pre-trained transformer using the USPTO patent database is proposed. The AI-generated ideas are not only in concise and understandable language but also able to synthesize the target design with external knowledge sources with controllable knowledge distance. The method is tested in a case study of rolling toy design and the results show good performance in generating ideas of varied novelty with near-field and far-field source knowledge.

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