Finding your MUSE: Mining Unexpected Solutions Engine
This addresses the challenge for innovators in exploring novel alternatives, though it appears incremental as it builds on prior work with improvements in scale and quality.
The paper tackled the problem of cognitive fixation in innovation by introducing Functional Concept Graphs (FCGs) to support abstraction and analogical inspiration, resulting in a large-scale FCG computed on 500K patents and the MUSE algorithm for generating creative inspirations.
Innovators often exhibit cognitive fixation on existing solutions or nascent ideas, hindering the exploration of novel alternatives. This paper introduces a methodology for constructing Functional Concept Graphs (FCGs), interconnected representations of functional elements that support abstraction, problem reframing, and analogical inspiration. Our approach yields large-scale, high-quality FCGs with explicit abstraction relations, overcoming limitations of prior work. We further present MUSE, an algorithm leveraging FCGs to generate creative inspirations for a given problem. We demonstrate our method by computing an FCG on 500K patents, which we release for further research.