关键词: 专利聚类, 信息实体语义增强表示, 卷积神经网络, 跨领域, 自组织神经网络

Abstract: At present, patents are divided according to their domains, and cross-domain patent clustering can be realized based on the functional effect, which is of great significance in enterprise innovation design. Accurate extraction of patent functional effect and fast acquisition of optimal clustering results are the key tasks in it. Therefore, a Functional Effect Information-Joint (FEI-Joint) model combining Enhanced Language Representation with Informative Entities (ERNIE) and Convolutional Neural Network (CNN) was proposed to extract the functional effects of patent documents, and the Self-Organizing Map (SOM) algorithm was improved, so as to propose an Early Reject based Class Merge Self-Organizing Map (ERCM-SOM) to realize the patent clustering based on functional effect. FEI-Joint model was compared with Term-Frequency-Inverse-Document-Frequency (TF-IDF), Latent Dirichlet Allocation (LDA) and CNN in the clustering effect after feature extraction, and the results show that the F-measure value of the proposed model was obviously improved than those of other models. Compared with K-Means algorithm and SOM algorithm, ERCM-SOM algorithm has higher F-measure value while has significantly shorter time than that of SOM algorithm. Compared with the patent classification using International Patent Classification (IPC), the clustering method based on functional effect can achieve cross-domain patent clustering effect, which lays a foundation for designers to learn from design methods in other domains.

Key words: patent clustering, Enhanced Language Representation with Informative Entities (ERNIE), Convolutional Neural Network (CNN), cross-domain, Self-Organizing Map (SOM)