As the patent landscape continues to grow, so does the complexity of retrieving relevant “prior art”, “background art”, or “state of the art” from an expanding pool of publicly available patent data, a critical step in establishing novelty. However, retrieving this information presents significant challenges due to its volume and complexity. This systematic literature review surveys patent retrieval techniques over the past decade, focusing on ‘prior art’ and ‘novelty’ searches. Adhering to the PRISMA 2020 guidelines, our research includes 78 pertinent articles selected from a corpus of 1441, providing an in-depth overview of recent advancements, emerging trends, challenges, and future directions in the field of patent prior art retrieval. The review addresses six research questions: defining the current state of the art, evaluating the efficacy of various approaches, examining commonly used patent data collections, exploring the impact of semantic search and natural language processing (NLP) technologies, identifying frequently used components of patent documents, and discussing ongoing challenges in the domain of patent prior art search and retrieval. Our findings highlight the growing use of NLP to enhance the precision and comprehensiveness of patent searches, particularly on the Cross-Language Evaluation Forum for Intellectual Property (CLEF-IP) and the United States Patent and Trademark Office (USPTO) databases. Despite advancements, the specialized and technical nature of patent language continues to pose significant challenges in achieving high accuracy in patent retrieval.

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.