Patent Data for Engineering Design: A Review

S. Jiang; S. Sarica; B. Song; J. Hu; J. Luo

doi:10.1017/pds.2022.74

Patent Data for Engineering Design: A Review

S. Jiang^*
, S. Sarica
, B. Song
, J. Hu
, J. Luo

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

27 Downloads (CityUHK Scholars)

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. © The Author(s), 2022.

Original language	English
Title of host publication	DESIGN2022
Publisher	Cambridge University Press
Pages	723-732
DOIs	https://doi.org/10.1017/pds.2022.74
Publication status	Published - 2022
Externally published	Yes
Event	17th International Design Conference (DESIGN 2022) - Virtual, Croatia Duration: 23 May 2022 → 26 May 2022 https://www.designconference.org/past-events

Publication series

Name	Proceedings of the Design Society
Volume	2
ISSN (Electronic)	2732-527X

Conference

Conference	17th International Design Conference (DESIGN 2022)
Place	Croatia
Period	23/05/22 → 26/05/22
Internet address	https://www.designconference.org/past-events

Research Keywords

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

Publisher's Copyright Statement

This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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@inproceedings{74c1c93bcc5d4292a2549a9de6a5c489,

title = "Patent Data for Engineering Design: A Review",

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. {\textcopyright} The Author(s), 2022.",

keywords = "artificial intelligence (AI), big data analysis, data mining, data-driven design, engineering design",

author = "S. Jiang and S. Sarica and B. Song and J. Hu and J. Luo",

year = "2022",

doi = "10.1017/pds.2022.74",

language = "English",

series = "Proceedings of the Design Society",

publisher = "Cambridge University Press",

pages = "723--732",

booktitle = "DESIGN2022",

address = "United Kingdom",

note = "17th International Design Conference (DESIGN 2022) ; Conference date: 23-05-2022 Through 26-05-2022",

url = "https://www.designconference.org/past-events",

}

TY - GEN

T1 - Patent Data for Engineering Design

T2 - 17th International Design Conference (DESIGN 2022)

AU - Jiang, S.

AU - Sarica, S.

AU - Song, B.

AU - Hu, J.

AU - Luo, J.

PY - 2022

Y1 - 2022

N2 - 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. © The Author(s), 2022.

AB - 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. © The Author(s), 2022.

KW - artificial intelligence (AI)

KW - big data analysis

KW - data mining

KW - data-driven design

KW - engineering design

UR - https://www.scopus.com/pages/publications/85131377343

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85131377343&origin=recordpage

U2 - 10.1017/pds.2022.74

DO - 10.1017/pds.2022.74

M3 - RGC 32 - Refereed conference paper (with host publication)

T3 - Proceedings of the Design Society

SP - 723

EP - 732

BT - DESIGN2022

PB - Cambridge University Press

Y2 - 23 May 2022 through 26 May 2022

ER -

Patent Data for Engineering Design: A Review

Abstract

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Research Keywords

Publisher's Copyright Statement

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