Enhancing TRIZ Contradiction Resolution with AI-Driven Contradiction Navigator (AICON)

Abstract

This paper presents the AI-driven Contradiction Navigator (AICON), a tool engineered to address the inherent limitations of the traditional TRIZ Contradiction Matrix. By integrating advanced AI technologies, AICON enhances the identification, mapping, and resolution of contradictions with advanced precision and context sensitivity. Central to its architecture is the incorporation of Retrieval-Augmented Generation (RAG) AI, enabling the system to access and utilize diverse knowledge domains dynamically. This capability allows AICON to identify new inventive principles and effectively cover previously unaddressed areas within the TRIZ matrix. The system’s architecture is designed to facilitate AI-driven data enrichment, adaptive contradiction mapping, and tailored solution generation, all within an iterative learning framework that continuously refines its problem-solving efficacy. Preliminary research outcomes highlight AICON’s success in discovering novel inventive principles and expanding the TRIZ matrix’s applicability with contextually relevant solutions. These advancements underline AICON’s potential to improve inventive problem-solving methodologies.