## 2 Background
### Existing Works on Patent Retrieval
The primary objective of patent retrieval (PR) is to retrieve relevant patent documents based on a search query, which may take the form of an array of keywords, a brief, or a complete patent document. Reference [23] performs a comparative analysis of various pre-application prior art search strategies, including partial application search and query reformulation approaches from the perspective of inventors determining the patentability of their ideas before filing a full application. The complexity of lengthy queries was considered, taking into account the necessity for query reduction to remove irrelevant terms as wellas query extension to include more relevant terms. The results of this study indicated that, in terms of both writing effort and retrieval efficacy, querying with an abstract is the most balanced choice [23]. However, sole emphasis has been placed on understanding and evaluating the efficacy of various strategies linked to query reformulation, with no thorough investigation of alternative techniques used to retrieve patent papers. In the domain of patent information retrieval, patent mining and patent retrieval are two distinct but related concepts. Patent mining uses text mining and machine learning to obtain insights from patent documents. In contrast, patent retrieval focuses on the efficient search and retrieval of relevant patent documents based on particular criteria or search queries to aid patent experts in tasks such as patent landscape analysis. Reference [24] employs bibliometric and keyword-based network analysis to map out the evolution of patent analysis and patent mining, uncovering the key players and delineating three pivotal stages (metadata and citation analysis, cluster and network analysis, and patent mining enabled by text mining and auxiliary approaches) of development in this field, while also examining recent advancements in information retrieval and pattern analysis. A comprehensive survey of the latest trends in data mining relevant to patents, to enrich the understanding of patent analysts on the landscape of data mining, is given in reference [25]. The survey delves into multiple facets of patent mining, including patent retrieval, patent classification, and patent visualization, to offer an in-depth understanding of the area [25]. However, the survey only discusses patent retrieval as a single component of a larger investigation within the subject of patent mining. Table S1 in Section S2 of the Supplementary Materials provides detailed summaries of the shortcomings of current studies in the areas of patent retrieval [8; 12; 20; 21; 22; 23; 24; 25]. This table highlights the gaps and challenges that persist in the field, underscoring the necessity for ongoing research and development in patent retrieval techniques. This systematic literature review (SLR) on patent prior art retrieval fills a gap in existing research by systematically synthesizing and analyzing the available patent retrieval techniques from the last decade, focusing on patent \"prior art\" and \"novelty\" searches. It presents a thorough analysis of the state-of-the-art approaches in patent prior art retrieval, analyzing a substantial corpus of 78 articles. Additionally, this study exhibits clarity by thoroughly describing every database used, and its well-organized and sectioned layout makes it simple to navigate and comprehend the information presented. Also, the detailed tabular and graphical representations provide insightful illustrations of important features of patent prior art retrieval, enhancing the understanding of the results reported in the selected articles under review.