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Assessing Concept Novelty Potential with Lexical and Distributional Word Similarity for Innovative Design

Part of: Design Methods and Tools

Published online by Cambridge University Press:  26 July 2019

Yutaka Nomaguchi ,
Takahiro Kawahara ,
Koki Shoda  and
Kikuo Fujita

Abstract

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Generating novel design concepts is a cornerstone for producing innovative products. Although many methods have been proposed for supporting the task, their performance depends on human ability. The goal of this research is to build a method supporting designers to generate novel design concepts with the knowledge of what factors have positive effects on the novelty. Toward the goal, this research assumes that the more distant two function concepts chosen, the more novel idea would come up with by the combination of the two concepts. Based on the assumption, this paper introduces a notion of novelty potential of the combination of two function concepts, and proposes a method to assess it by the function similarity. It is calculated with the integration of a lexical database for natural language called WordNet and a distributional semantics method called word2vec. The proposed method is adapted to case studies in which students perform design concept generation for given design tasks. The correlation analysis is performed to verify the assessment performance of the proposed method. This paper discusses its possibility based on the results of the case studies.

Keywords

Conceptual designCreativityEarly design phasesDesign methodsWord similarity
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1413 - 1422
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Altshuller, G. (2005), 40 Principles: TRIZ Keys to Technical Innovation, Extended edition, Technical Innovation Center Worchester, MA.Google Scholar
Chan, J., Fu, K., Schunn, C., Cagan, J., Wood, K. and Kotovsky, K. (2011), “On the Benefits and Pitfalls of Analogies for Innovative Design: Ideation Performance based on Analogical Distance, Commonness, and Modality of Examples”, Journal of Mechanical Design, Transactions of the ASME, Vol. 133 No. 8, p. 081004. https://doi.org/10.1115/1.4004396Google Scholar
Cheong, H., Li, W., Cheung, A., Nogueira, A. and Iorio, F. (2017), “Automated Extraction of Function Knowledge from Text”, Journal of Mechanical Design, Transactions of the ASME, Vol. 139 No. 11, p. 111407. https://doi.org/10.1115/1.4037817Google Scholar
Daly, S. R., Seifert, C. M., Yilmaz, S. and Gonzalez, R. (2016), “Comparing Ideation Techniques for Beginning Designers”, Journal of Mechanical Design, Transactions of the ASME, Vol. 138 No. 10, p. 101108.Google Scholar
De Brentani, U. (2001), “Innovative versus Incremental New Business Services: Different Keys for Achieving Success”, Journal of Product Innovation Management, Vol. 18 No. 3, pp. 169–187. https://doi.org/10.1016/S0737-6782(01)00071-6Google Scholar
Douali, M. G. and Silver, J. D. (2004), “Self-optimised Vision Correction with Adaptive Spectacle Lenses in Developing Countries”, Ophthalmic and Physiological Optics, Vol. 24 No. 3, pp. 234–241. https://doi.org/10.1111/j.1475-1313.2004.00198.xGoogle Scholar
Fu, K., Fuge, M. and Brown, D. C. (2018), “Design Creativity”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 32 No. 4, pp. 363–364. https://doi.org/10.1017/S089006041800015XGoogle Scholar
Georgiev, G.V. and Georgiev, D.D. (2018) “Enhancing User Creativity: Semantic measures for Idea Generation”, Knowledge-Based Systems, Vol. 151, pp. 1–15. https://doi.org/10.1016/j.knosys.2018.03.016Google Scholar
Han, J., Shi, F., Chen, L. and Childs, P. R. N. (2018), “A Computational Tool for Creative Idea Generation based on Analogical Reasoning and Ontology”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 32 No. 4, pp. 462–477. https://doi.org/10.1017/S0890060418000082Google Scholar
Hatchuel, A. and Weil, B. (2009), “C-K design theory: An advanced formulation”, Research in Engineering Design, Vol. 19 No. 4, pp. 181–192. https://doi.org/10.1007/s00163-008-0043-4Google Scholar
Mikolov, T., Chen, K., Corrado, G. and Dean, J. (2013), “Efficient Estimation of Word Representations in Vector Space”. https://arxiv.org/abs/1301.3781Google Scholar
Miller, G. A. (1995), “WordNet: A Lexical Database for English”, Communications of the ACM, Vol. 38 No. 11, pp. 39–41. https://doi.org/10.1145/219717.219748Google Scholar
Pandey, R. and Pesala, B. (2016), “Heat and Mass Transfer Analysis of a Pot-in-pot Refrigerator Using Reynolds Flow Model”, Journal of Thermal Science and Engineering Applications, Vol. 8 No. 3, p. 031006. https://doi.org/10.1115/1.4033010Google Scholar
Rada, R., Mili, H., Bicknell, E. and Blettner, M. (1989), “Development and Application of a Metric on Semantic Nets”, IEEE Transactions on Systems, Man and Cybernetics, vol. 19 No. 1, pp. 17–30. https://doi.org/10.1109/21.24528Google Scholar
Ranjan, B. S. C., Siddharth, L. and Chakrabarti, A. (2018), “A Systematic Approach to Assessing Novelty, Requirement Satisfaction, and Creativity”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 32 No. 4, pp. 390–414. https://doi.org/10.1017/S0890060418000148Google Scholar
Shah, J. J., Vargas-Hernandez, N. and Smith, S. M. (2003), “Metrics for Measuring Ideation Effectiveness”, Design Studies, Vol. 24 No. 2, pp. 111–134. https://doi.org/10.1016/S0142-694X(02)00034-0Google Scholar
Shimomura, Y., Yoshioka, M., Takeda, H., Umeda, Y. and Tomiyama, T. (1998), “Representation of Design Object based on the Functional Evolution Process Model”, Journal of Mechanical Design, Transactions of the ASME, Vol. 120 No. 2, pp. 221–229. https://doi.org/10.1115/1.2826962Google Scholar
Suryadi, D. and Kim, H. (2017), “A Clustering and Word Similarity based Approach for Identifying Product Feature Words”, Proceedings of the International Conference on Engineering Design, ICED, Vol. 6 No. DS87-6, pp. 71–80.Google Scholar
Taura, T. and Nagai, Y. (2013), “A Systematized Theory of Creative Concept Generation in Design: First-Order and High-Order Concept Generation”, Research in Engineering Design, Vol. 24 No. 2, pp. 185–199. https://doi.org/10.1007/s00163-013-0152-6Google Scholar
Tomiyama, T., Breedveld, P. and Birkhofer, H. (2010), “Teaching Creative Design by Integrating General Design Theory and the Pahl & Beitz Methodology”, Proceeding of the ASME Design Engineering Technical Conference & Computers and Information in Engineering Conference, Montreal, DETC2010-28444.Google Scholar
Yoshikawa, H. (1981), “General Design Theory and a CAD System”, in Sata, T. and Warman, E. (Ed.), Man-Machine Communication in CAD/CAM, North-Holland, Amsterdam, pp. 35–38.Google Scholar