  Generating novel and useful concepts is essential during the early design
stage to explore a large variety of design opportunities, which usually
requires advanced design thinking ability and a wide range of knowledge from
designers. Growing works on computer-aided tools have explored the retrieval of
knowledge and heuristics from design data. However, they only provide stimuli
to inspire designers from limited aspects. This study explores the recent
advance of the natural language generation (NLG) technique in the artificial
intelligence (AI) field to automate the early-stage design concept generation.
Specifically, a novel approach utilizing the generative pre-trained transformer
(GPT) is proposed to leverage the knowledge and reasoning from textual data and
transform them into new concepts in understandable language. Three concept
generation tasks are defined to leverage different knowledge and reasoning:
domain knowledge synthesis, problem-driven synthesis, and analogy-driven
synthesis. The experiments with both human and data-driven evaluation show good
performance in generating novel and useful concepts.
