Generative Design Ideation: A Natural Language Generation Approach
This work addresses design ideation for engineers or designers by providing a knowledge-based generative approach, though it is incremental as it applies existing language models to a new domain.
The paper tackles the problem of generating design ideas by fine-tuning a pre-trained language model on the USPTO patent database, resulting in AI-generated ideas that synthesize target designs with external knowledge at controllable distances and demonstrate varied novelty in a rolling toy case study.
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.