Multi label classification of Artificial Intelligence related patents using Modified D2SBERT and Sentence Attention mechanism
This work addresses the need for automated patent classification in AI, which currently relies on manual efforts, saving time and money for patent information management.
The paper tackles the problem of classifying AI-related patents, which is difficult due to complex technologies and legal terms, by presenting a method using Modified D2SBERT and Sentence Attention mechanism, achieving the highest performance compared to other deep learning methods.
Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days. However, artificial intelligence-related patents are very difficult to classify because it is a mixture of complex technologies and legal terms. Moreover, due to the unsatisfactory performance of current algorithms, it is still mostly done manually, wasting a lot of time and money. Therefore, we present a method for classifying artificial intelligence-related patents published by the USPTO using natural language processing technique and deep learning methodology. We use deformed BERT and sentence attention overcome the limitations of BERT. Our experiment result is highest performance compared to other deep learning methods.