TY - GEN
T1 - A convolutional neural network-based patent image retrieval method for design ideation
AU - Jiang, Shuo
AU - Luo, Jianxi
AU - Pava, Guillermo Ruiz
AU - Hu, Jie
AU - Magee, Christopher L.
PY - 2020
Y1 - 2020
N2 - The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we propose a convolutional neural network (CNN)-based patent image retrieval method. The core of this approach is a novel neural network architecture named Dual-VGG that is aimed to accomplish two tasks: visual material type prediction and international patent classification (IPC) class label prediction. In turn, the trained neural network provides the deep features in the image embedding vectors that can be utilized for patent image retrieval and visual mapping. The accuracy of both training tasks and patent image embedding space are evaluated to show the performance of our model. This approach is also illustrated in a case study of robot arm design retrieval. Compared to traditional keyword-based searching and Google image searching, the proposed method discovers more useful visual information for engineering design. © 2020 ASME.
AB - The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we propose a convolutional neural network (CNN)-based patent image retrieval method. The core of this approach is a novel neural network architecture named Dual-VGG that is aimed to accomplish two tasks: visual material type prediction and international patent classification (IPC) class label prediction. In turn, the trained neural network provides the deep features in the image embedding vectors that can be utilized for patent image retrieval and visual mapping. The accuracy of both training tasks and patent image embedding space are evaluated to show the performance of our model. This approach is also illustrated in a case study of robot arm design retrieval. Compared to traditional keyword-based searching and Google image searching, the proposed method discovers more useful visual information for engineering design. © 2020 ASME.
KW - Convolutional neural network
KW - Design ideation
KW - Image retrieval
KW - Patent analysis
UR - http://www.scopus.com/inward/record.url?scp=85096127631&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85096127631&origin=recordpage
U2 - 10.1115/DETC2020-22048
DO - 10.1115/DETC2020-22048
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9780791883983
VL - 9: 40th Computers and Information in Engineering Conference (CIE)
T3 - International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
BT - ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
PB - American Society of Mechanical Engineers
T2 - ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE 2020)
Y2 - 17 August 2020 through 19 August 2020
ER -