A Deep Patent Landscaping Model using Transformer and Graph Convolutional Network

Patent landscaping is a method that is employed for searching related patents during the process of a research and development (R&D) project. To avoid the risk of patent infringement and to follow the current trends of technology development, patent landscaping is a crucial task that needs to be conducted during the early stages of an R&D project. Generally, the process of patent landscaping requires several advanced resources and can be tedious. Furthermore, the patent landscaping process has to be repeated throughout the duration of an R&D project. Owing to such reasons, the demand for automated patent landscaping is gradually increasing. However, the shortage of well-defined benchmarking datasets and comparable models makes it difficult to find related research studies. In this paper, an automated patent landscaping model based on deep learning is proposed. The proposed model comprises a modified transformer structure for analyzing textual data present in patent documents and a graph convolutional network for analyzing patent metadata. Twelve patent landscaping benchmarking datasets, which were processed by the Korean patent attorney, are proposed for determining the resources required for comparing related research studies. Obtained results indicate that the proposed model with the proposed datasets can attain state-of-the-art performance , and mean classification accuracy of 98% can be achieved.