PAI-NET: Retrieval-Augmented Generation Patent Network Using Prior Art Information

Similar patent document retrieval is an essential task that reduces the scope of patent claimants’ searches, and numerous studies have attempted to provide automated patent search services. Recently, Retrieval-Augmented Generation (RAG) based on generative language models has emerged as an excellent method for accessing and utilizing patent knowledge environments. RAG-based patent search services offer enhanced retrieval ranking performance as AI search services by providing document knowledge similar to queries. However, achieving optimal similarity-based document ranking in search services remains a challenging task, as search methods based on document similarity do not adequately address the characteristics of patent documents. Unlike general document retrieval, the similarity of patent documents must take into account prior art relationships. To address this issue, we propose PAI-NET, a deep neural network for computing patent document similarities by incorporating expert knowledge of prior art relationships. We demonstrate that our proposed method outperforms current state-of-the-art models in patent document classification tasks through semantic distance evaluation on the USPD and KPRIS datasets. PAI-NET presents similar document candidates, demonstrating a superior patent search performance improvement of 15% over state-of-the-art methods.