Finding your MUSE: Mining Unexpected Solutions Engine
Artificial Intelligence
2025-09-08 v1 Computation and Language
Abstract
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.
Keywords
Cite
@article{arxiv.2509.05072,
title = {Finding your MUSE: Mining Unexpected Solutions Engine},
author = {Nir Sweed and Hanit Hakim and Ben Wolfson and Hila Lifshitz and Dafna Shahaf},
journal= {arXiv preprint arXiv:2509.05072},
year = {2025}
}