  Researchers and innovators have made enormous efforts in developing ideation
methods, such as morphological analysis and design-by-analogy, to aid
engineering design ideation for problem solving and innovation. Among these,
the Theory of Inventive Problem Solving (TRIZ) stands out as one of the most
well-known approaches, widely applied for systematic innovation. However, the
complexity of TRIZ resources and concepts, coupled with its reliance on users'
knowledge, experience, and reasoning capabilities, limits its practicality.
Therefore, we explore the recent advances of large language models (LLMs) for a
generative approach to bridge this gap. This paper proposes AutoTRIZ, an
artificial ideation tool that uses LLMs to automate and enhance the TRIZ
methodology. By leveraging the broad knowledge and advanced reasoning
capabilities of LLMs, AutoTRIZ offers a novel approach for design automation
and interpretable ideation with artificial intelligence. AutoTRIZ takes a
problem statement from the user as its initial input, and automatically
generates a solution report after the reasoning process. We demonstrate and
evaluate the effectiveness of AutoTRIZ through consistency experiments in
contradiction detection, and a case study comparing solutions generated by
AutoTRIZ with the experts' analyses from the textbook. Moreover, the proposed
LLM-based framework holds the potential for extension to automate other
knowledge-based ideation methods, including SCAMPER, Design Heuristics, and
Design-by-Analogy, paving the way for a new era of artificial ideation for
design innovation.
