基于功效特征的专利聚类方法

当前专利是按照领域划分的，而基于功效特征可以实现跨领域的专利聚类，这在企业创新设计中具有重要意义，而精确提取专利功效特征和快速获得最优聚类结果是其中的关键任务。为此提出一种信息实体语义增强表示（ERNIE）和卷积神经网络（CNN）相结合的功效特征联合提取（FEI-Joint）模型来提取专利文献的功效特征，并且改进自组织神经网络（SOM）算法，从而提出具有早期拒绝策略与类合并思想的自组织神经网络（ERCM-SOM）来实现基于功效特征的专利聚类。对FEI-Joint模型与TF-IDF、狄利克雷分布（LDA）、CNN在特征提取后的聚类效果上进行比较和分析，结果表明其F-measure值比其他模型有明显提高。ERCM-SOM算法与K-Means算法、SOM算法相比，在F-measure值提高的同时，其时间较SOM算法有明显缩短。对比使用专利分类号（IPC）的专利分类，采用基于功效特征的聚类方法可实现跨领域的专利聚类效果，为设计者借鉴其他领域的设计方法奠定了基础。

Patent clustering method based on functional effect

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.