Human designers’ artistic ideation for idea era has been aided by intuitive or structured ideation strategies reminiscent of brainstorming, morphological evaluation, and thoughts mapping. Amongst such strategies, the Concept of Ingenious Downside Fixing (TRIZ) is extensively adopted for systematic innovation and has turn out to be a widely known method. TRIZ is a knowledge-based ideation methodology that gives a structured framework for engineering problem-solving by figuring out and overcoming technical contradictions utilizing ingenious ideas derived from a large-scale patent database.
Current developments combine machine studying and pure language processing with TRIZ to streamline its reasoning course of. Methods like PAT-ANALYZER and PaTRIZ robotically extract contradictory data from patent texts. Another strategies make use of text-mining methods for ingenious downside formulation or map TRIZ ideas to patents utilizing subject modeling. Nonetheless, most of those works make the most of algorithms to enhance particular steps of the TRIZ course of. These strategies nonetheless demand vital person reasoning.
Researchers from the Singapore College of Know-how and Design and the Metropolis College of Hong Kong current AutoTRIZ, a man-made ideation software that makes use of LLMs to automate and enhance the TRIZ methodology. By harnessing LLMs’ in depth information and superior reasoning capabilities, AutoTRIZ gives a brand new method to design automation and interpretable ideation with synthetic intelligence. It generates options for user-provided downside statements, adhering to the TRIZ considering circulate and reasoning course of.
AutoTRIZ begins with a user-provided downside assertion and conducts a four-step reasoning course of based mostly on TRIZ ideas. It generates an in depth answer report outlining the reasoning course of and proposed options. The system makes use of a set information base segmented into three TRIZ-related segments to information managed reasoning. AutoTRIZ emphasizes controlling the problem-solving course of whereas drawing problem-related information from the pre-trained large-scale corpora used to coach the LLM.
AutoTRIZ’s detection outcomes had been in contrast with human consultants’ analyses from textbooks categorized into full match, half match, and no match situations. Whereas human knowledgeable evaluation carries subjectivity and bias, it serves as a benchmark for comparability. Outcomes point out that in 7 out of 10 circumstances, AutoTRIZ’s high 3 detections utterly or partially matched the textbook analyses, demonstrating a level of overlap between AutoTRIZ and human knowledgeable outcomes.
In conclusion, The analysis introduces AutoTRIZ, a man-made ideation software that employs LLMs to automate and improve the TRIZ methodology. By means of three LLM-based reasoning modules and a pre-defined perform module interacting with a set information base, AutoTRIZ generates interpretable answer experiences from user-provided downside statements. The tactic’s effectiveness is demonstrated by quantitative experiments and case research, suggesting potential extensions to different knowledge-based ideation strategies past TRIZ.
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