Patent Data for Engineering Design: A Review

DS 116: Proceedings of the DESIGN2022 17th International Design Conference

Year: 2022
Editor: Mario Štorga, Stanko Škec, Tomislav Martinec, Dorian Marjanović
Author: Shuo Jiang (1), Serhad Sarica (2), Binyang Song (3), Jie Hu (1), Jianxi Luo (4)
Series: DESIGN
Institution: 1: Shanghai Jiao Tong University, China; 2: Institute of High Performance Computing, A*STAR, Singapore; 3: Massachusetts Institute of Technology, United States of America; 4: Singapore University of Technology and Design, Singapore
Section: Design Information and Knowledge
Page(s): 723-732
DOI number: https://doi.org/10.1017/pds.2022.74
ISSN: 2732-527X (Online)

Abstract

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 design, data-driven design, artificial intelligence (AI), big data analysis, data mining