AutoTRIZ: Artificial Ideation with TRIZ and Large Language Models

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, TRIZ stands out as the most well-known approach, 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 practicability. This paper proposes AutoTRIZ, an artificial ideation tool that leverages large language models (LLMs) to automate and enhance the TRIZ methodology. By leveraging the broad knowledge and advanced reasoning capabilities of LLMs, AutoTRIZ offers a novel approach to design automation and interpretable ideation with artificial intelligence. We demonstrate and evaluate the effectiveness of AutoTRIZ through consistency experiments in contradiction detection and comparative studies with cases collected from TRIZ textbooks. 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 and innovation.