Optimizing Patent Prior Art Search: An Approach Using Patent Abstract and Key Terms

The rapid advancement of technology has led to a sustained accumulation of patent documents globally, as newly filed applications add to an ever-expanding repository of prior art. The need for innovation and progress within the patent system underscores the significance of robust patent investigation, which includes prior art searches. The swift expansion of the patent arena poses challenges for experts employing conventional qualitative practices to handle the increasing quantitative needs. In this study, we propose a novel method to enhance patent prior art search through the integration of advanced natural language processing (NLP) techniques. Our approach leverages the abstract and top terms of patent documents to generate a unique set of labelled databases. This database is then utilized to train Bidirectional Encoder Representations from Transformers (BERT) for patents, enabling domain-specific prior art searches. Testing our method on the Google Public Patent Database yielded an improved F1 score of 0.94 on the testing data. Not only does our method demonstrate superior accuracy compared to baseline approaches, but it also exhibits enhanced computational efficiency. The refined prior art search promises to provide valuable assistance to specialists in their decision-making processes, offering insightful analyses and relevant information that can significantly increase the efficiency and accuracy of their judgments.