Finding your MUSE: Mining Unexpected Solutions Engine
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 39 references that are in the map. First posted on arXiv 5 Sep 2025.
Builds on
10 of 39 references.
References · 10
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
- 2022Emergent Analogical Reasoning in Large Language Modelscited by 240
- 2021A Recipe For Arbitrary Text Style Transfer with Large Language Modelscited by 107
- 2021Abstraction and Analogy-Making in Artificial Intelligencecited by 103
- 2017Accelerating Innovation Through Analogy Miningcited by 32
- 2022Guiding Data-Driven Design Ideation by Knowledge Distancecited by 10
- 2021Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideascited by 7
Built on by
Citing papers per year: 2026: 1.
1 paper.