Reliable Multi-View Deep Patent Classification

Patent classification has long been regarded as a crucial task in patent information management and patent knowledge mining. In recent years, studies combining deep learning automatic patent classification methods with deep neural networks have significantly increased. Although great efforts have been made in the patent deep classification task, they mainly focus on information extraction from a single view (e.g., title or abstract view), but few studies concern multi-view deep patent classification, which aims to improve patent classification performance by integrating information from different views. To that end, we propose a reliable multi-view deep patent classification method. Within this method, we fuse multi-view patent information at the evidence level from the perspective of evidence theory, which not only effectively improves classification performance but also provides a reliable uncertainty estimation to solve the unreliability of classification results caused by property differences and inconsistencies in the different patent information sources. In addition, we theoretically prove that our approach can reduce the uncertainty of classification results through the fusion of multiple patent views, thus facilitating the performance and reliability of the classification results. The experimental results on 759,809 real-world multi-view patent data in Shanghai, China, demonstrate the effectiveness, reliability, and robustness of our approach.