Design innovation projects often generate large numbers of design ideas from designers, users and, increasingly, the crowd over the Internet. Such idea data are often used for selection and implementation, but in fact can also be used as sources of inspiration for further idea generation. In particular, the elementary concepts that underlie the original ideas can be recombined to generate new ideas. But it is not a trivial task to retrieve concepts from raw lists of ideas and data sources in a manner that can stimulate or generate new ideas. A significant difficulty lies in the fact that idea data are often expressed in unstructured natural languages. This paper develops a methodology that uses natural language processing to extract key words as elementary concepts embedded in massive idea descriptions and represents the elementary concept space in a core-periphery structure to direct the recombination of elementary concepts into new ideas. We apply the methodology to mine and represent the concept space underlying massive crowdsourced ideas and use it to generate new ideas for future transportation system designs in a real public-sector-sponsored project via humans and automated computer programs. Our analysis of the human and computer recombination processes and outcomes sheds light on future research directions for artificial intelligence in design ideation.
Mining and Representing the Concept Space of Existing Ideas for Directed Ideation
Contributed by the Design Theory and Methodology Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received March 1, 2019; final manuscript received July 25, 2019; published online xx xx, xxxx. Assoc. Editor: Ying Liu.
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He, Y., Camburn, B., Liu, H., Luo, J., Yang, M., and Wood, K. (August 1, 2019). "Mining and Representing the Concept Space of Existing Ideas for Directed Ideation." ASME. J. Mech. Des. doi: https://doi.org/10.1115/1.4044399
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