Finding your MUSE: Mining Unexpected Solutions Engine
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908521298132992 |
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| author | Sweed, Nir Hakim, Hanit Wolfson, Ben Lifshitz, Hila Shahaf, Dafna |
| author_facet | Sweed, Nir Hakim, Hanit Wolfson, Ben Lifshitz, Hila Shahaf, Dafna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05072 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Finding your MUSE: Mining Unexpected Solutions Engine Sweed, Nir Hakim, Hanit Wolfson, Ben Lifshitz, Hila Shahaf, Dafna Artificial Intelligence Computation and Language 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. |
| title | Finding your MUSE: Mining Unexpected Solutions Engine |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.05072 |