Graph Convolutional Networks Based Standard Essential Patents Identification

The standard essential patents are some patents for which the patent technology solutions are adopted by the technological standard. The identification of standard essential patents is an important task in the standardization. With the sharply increasing number of patents and much more diversity of standard domains, the manual standard essential patents identification methods to check the patent by word for word, are not applicable for the big patent data processing. The automatic standard essential patents identification methods face some challenges from the extraction and representation of features to the construction and learning of models. To solve the above issues, we develop a graph convolutional network based standard essential patents identification. We propose a hypothesis that the standard essential patents under the same standard are structure-association and the associated relations can be used in standard essential patents identification. Herein, we extract the co-occurrence relations including keyword-keyword relation, document-keyword relation. We construct a heterogeneous graph consisting of patent nodes, keyword nodes and their co-occurrence relations, which include rich structural information for the identification of standard essential patents. Thereafter, a graph convolutional network is developed to identify the potential standard essential patents. Since the number of standard essential patents and nonstandard essential patents is imbalance in practice, we utilize the sampling method to alleviate the imbalanced issue on the graph. To validate the effectiveness of the proposed model, we conduct experiments on five datasets of patent in the real world and the experimental results show that the proposed model outperforms existing research in the identification of standard essential patents.