A Deep Patent Landscaping Model using Transformer and Graph Embedding
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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. Because the process of patent landscaping requires several advanced resources and can be tedious, 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, we propose an automated patent landscaping model based on deep learning. The proposed model comprises a modified transformer structure for analyzing textual data present in patent documents and a graph embedding method using diffusion graph called Diff2Vec for analyzing patent metadata. Four patent landscaping benchmarking datasets, which were produced by querying to Google BigQuery based on search formula made by the Korean patent attorney, are proposed for comparing related research studies. Obtained results indicate that the proposed model with the datasets can attain state-of-the-art performance comparing current patent landscaping models.
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