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Patent Data for Engineering Design: A Review

Published online by Cambridge University Press:  26 May 2022

S. Jiang ,
S. Sarica ,
B. Song ,
J. Hu  and
J. Luo
S. Jiang*
Affiliation:
Shanghai Jiao Tong University, China
S. Sarica
Affiliation:
Institute of High Performance Computing, A*STAR, Singapore
B. Song
Affiliation:
Massachusetts Institute of Technology, United States of America
J. Hu
Affiliation:
Shanghai Jiao Tong University, China
J. Luo
Affiliation:
Singapore University of Technology and Design, Singapore
*
jsmech@sjtu.edu.cn

Abstract

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Patent data have been utilized for engineering design research for long because it contains massive amount of design information. Recent advances in artificial intelligence and data science present unprecedented opportunities to mine, analyse and make sense of patent data to develop design theory and methodology. Herein, we survey the patent-for-design literature by their contributions to design theories, methods, tools, and strategies, as well as different forms of patent data and various methods. Our review sheds light on promising future research directions for the field.

Keywords

engineering designdata-driven designartificial intelligence (AI)big data analysisdata mining
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 723 - 732
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), 2022.

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