A Convolutional Neural Network-based Patent Image Retrieval Method for Design Ideation
This addresses the need for better visual information retrieval in patent databases for engineering design, though it is incremental as it builds on existing CNN methods.
The authors tackled the problem of retrieving patent images for design ideation by proposing a CNN-based method with a Dual-VGG architecture, which achieved improved retrieval of useful visual information compared to traditional keyword-based and Google image searches.
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.