###### Abstract
By better understanding the semantics of patent texts, they not only improve the accuracy of search results but also enhance the user experience by simplifying retrieval operations. Importantly, these advanced techniques can also be applied to curated data traditionally used in patent databases, further enhancing the effectiveness of search results. Indeed, many databases have started integrating both traditional curation methods and advanced retrieval technologies to leverage the strengths of each approach. This hybrid approach allows researchers and practitioners to concentrate more on analysis and innovation rather than on the tedious and laborious tasks of data preprocessing. The integration of modern, computer-based search technologies represents a paradigm shift in patent retrieval. These methods address the limitations of traditional techniques and offer enhanced capabilities that are crucial for supporting the growing complexity and volume of patent data. This transition is pivotal for the future of patent searches, where efficiency, accuracy, and accessibility are paramount. A detailed analysis of the state of the art in patent retrieval, particularly focusing on patent prior art retrieval, can direct future studies by pinpointing research gaps and the shortcomings of existing approaches. Before this work, no systematic literature review had comprehensively addressed these dimensions within the domain of patent retrieval. This systematic literature review (SLR) analyzes new approaches, incorporates recent developments, and synthesizes novel insights that contribute to a comprehensive understanding of the domain. As such, it fills a critical gap in the literature and provides a crucial foundation for subsequent empirical studies that may explore the applicability and effectiveness of the discussed strategies in more depth. This systematic approach not only brings clarity to the field but also sets a benchmark for future research, offering a scaffold upon which ongoing and future studies can build. This SLR focuses on the last decade of research and development in the field of patent retrieval and patent prior art search, highlighting the transformative impact of technologieslike natural language processing, machine learning, and deep learning. These innovations have significantly enhanced the analysis and interpretation of patent data, improving the automation of classification, information extraction tasks, and the semantic understanding of patent texts. This has led to more accurate and relevant retrieval results, showcasing a leap in the effectiveness of modern retrieval systems. Despite our focus on recent technological advances, the foundational work from earlier studies remains crucial. For instance, a 2004 study introduced a query-based associative document retrieval method that laid the groundwork for later algorithmic approaches [14]. Another study published in 2010 enhanced prior art retrieval by expanding queries with pseudo-relevance feedback [15]. These early methods demonstrated the potential of algorithmic approaches in enhancing patent search capabilities and inspired subsequent developments in the field. Moreover, the integration of machine learning into retrieval systems during the early 2000s allowed for the initial exploration of automated learning from data, setting a precedent for the more complex deep learning models that now dominate the field. These foundational techniques, though now evolved, continue to influence current research and development, providing a historical context that enriches our understanding of the trajectory of patent retrieval technologies. The subsequent sections of the paper are organized as follows. Section 2: Patent Retrieval Tasks delves into various search tasks related to patent prior art retrieval, including a comprehensive review of related surveys, offering insights into their contributions and deficiencies.