Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas

Tom Hope, Ronen Tamari, Daniel Hershcovich, Hyeonsu B. Kang, Joel Chan, Aniket Kittur, Dafna Shahaf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


Large repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations. However, idea descriptions are typically in the form of unstructured text, lacking key structure that is required for supporting creative innovation interactions. Prior work has explored idea representations that were either limited in expressivity, required significant manual effort from users, or dependent on curated knowledge bases with poor coverage. We explore a novel representation that automatically breaks up products into fine-grained functional aspects capturing the purposes and mechanisms of ideas, and use it to support important creative innovation interactions: functional search for ideas, and exploration of the design space around a focal problem by viewing related problem perspectives pooled from across many products. In user studies, our approach boosts the quality of creative search and inspirations, substantially outperforming strong baselines by 50-60%.

Original languageAmerican English
Title of host publicationCHI 2022
Subtitle of host publicationProceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Number of pages15
ISBN (Electronic)9781450391573
StatePublished - 29 Apr 2022
Event2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 - Virtual, Online, United States
Duration: 30 Apr 20225 May 2022

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


Conference2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
Country/TerritoryUnited States
CityVirtual, Online

Bibliographical note

Funding Information:
This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant no. 852686, SIAM), US National Science Foundation, US-Israel Binational Science Foundation (NSF-BSF) grant no. 2017741.

Publisher Copyright:
© 2022 ACM.


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