TRIZ-GPT: An LLM-augmented method for problem-solving

TL;DR

This paper introduces TRIZ-GPT, a novel approach that integrates Large Language Models with the TRIZ problem-solving framework to enhance innovation and practicality in generating solutions across various domains.

Contribution

It develops a workflow combining LLMs with TRIZ, creates a case collection for empirical testing, and demonstrates practical application through a mechanical engineering case study.

Findings

LLMs can effectively generate solutions aligned with traditional TRIZ methods.

The workflow improves problem transformation and solution validation.

Case study shows GPT-4's solutions closely match original inventive solutions.

Abstract

TRIZ, the Theory of Inventive Problem Solving, is derived from a comprehensive analysis of patents across various domains, offering a framework and practical tools for problem-solving. Despite its potential to foster innovative solutions, the complexity and abstractness of TRIZ methodology often make its acquisition and application challenging. This often requires users to have a deep understanding of the theory, as well as substantial practical experience and knowledge across various disciplines. The advent of Large Language Models (LLMs) presents an opportunity to address these challenges by leveraging their extensive knowledge bases and reasoning capabilities for innovative solution generation within TRIZ-based problem-solving process. This study explores and evaluates the application of LLMs within the TRIZ-based problem-solving process. The construction of TRIZ case collections establishes a solid empirical foundation for our experiments and offers valuable resources to the TRIZ community. A specifically designed workflow, utilizing step-by-step reasoning and evaluation-validated prompt strategies, effectively transforms concrete problems into TRIZ problems and finally generates inventive solutions. Finally, we present a case study in mechanical engineering field that highlights the practical application of this LLM-augmented method. It showcases GPT-4's ability to generate solutions that closely resonate with original solutions and suggests more implementation mechanisms.