###### Abstract  
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 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. patent retrieval (PR); prior art search; patent search; survey; systematic literature review (SLR); patent analysis; patent mining +
Footnote †: journal: _J. Therefore, an efficient patent retrieval system can aid the experts as well as enhance all the stages of the patent lifecycle, which involve some kind of patent retrieval. Traditional manual patent retrieval methods, while reliable in some respects, are fraught with inefficiencies and bottlenecks. Manually sifting through massive patent databases is not only labor-intensive but also time-consuming, often resulting in incomplete and inconsistent results. Even well-trained and experienced patent officers find identifying related prior work to be a tedious and laborious operation [9], with a significant risk of overlooking important documents [10,11]. These limitations have prompted a growing interest from researchers and practitioners in developing more efficient and effective approaches for patent retrieval to streamline the process, enhance search accuracy, and accelerate access to relevant patent information. The automation of the patent retrieval process began with basic Boolean and keyword-based searches but has evolved significantly over the years to complex context-based searches facilitated by deep learning and natural language processing (NLP). Early systems, reproducible but yet often ineffective, missed relevant documents or retrieved numerous irrelevant ones, necessitating labor-intensive and expertly crafted queries [12]. The development of more sophisticated techniques, such as the Vector Space Model (VSM), BM25, and Language Models (LM), brought improvements. These models evaluate the similarity between queries and documents [13] but often struggle with issues of scalability and fully comprehending the complex content of patents. To address these deficiencies, state-of-the-art technologies including deep learning, machine learning, and natural language processing (NLP) have been introduced. NLP techniques excel at recognizing and analyzing semantic relationships within patent texts, producing search results that are highly relevant and contextually appropriate. Similarly, deep learning helps automate the analysis of sequences and feature extraction, enhancing the speed and scalability of retrieval systems. These advanced methods significantly outperform traditional approaches in terms of efficiency, accuracy, and scalability.