## 2 Background
### Existing Works on Patent Retrieval  
The challenge of finding and understanding relevant patent documents amid the escalating volume of patent documents has led to the automation of patent retrieval systems, which traditionally relied on domain experts. Nevertheless, existing information retrieval (IR) approaches still struggle to effectively handle the distinctive attributes of patent documents. Reference [12] highlights numerous research efforts at refining existing information retrieval strategies or applying standard procedures at various stages of patent retrieval. Despite the numerous efforts, patent retrieval remains an unresolved research area, with conventional information retrieval methods proving inefficient in tackling the unique challenges posed by the specific features of patent documents [12]. However, the study lacks detailed sectioning of the information gathered from the articles under review. Additionally, there are not enough graphical and tabular representations of articles based on different aspects that are crucial to patent retrieval such as which databases were utilized and which specific sections of patent documents were employed for patent retrieval. Furthermore, the study discusses patent retrieval in general and does not specifically focus on patent prior art retrieval. Another study [8] presents a thorough review of patent retrieval (PR) techniques, highlighting the need for patent domain-specific modifications in retrieval systems as traditional information retrieval approaches, such as simple web searches, are insufficient for retrieving relevant patent documents. Additionally, the review suggests a need to create interactive search tools compatible with the practices and requirements of patent domain experts [8]. Although the review attempts to categorize articles under review according to all the existing patent retrieval approaches, it does not discuss the latest state-of-the-art natural language processing (NLP) approaches. Also, it broadly mentions patent retrieval, rather than providing an in-depth review of patent prior art retrieval. Reference [20] examines the way information retrieval research has influenced and altered patent search strategies over time; however, it lacks coverage of the most recent advancements in patent retrieval techniques as it was conducted more than ten years ago [20]. In the field of patent analysis, novel approaches have been developed using deep learning and natural language processing. The use of deep learning in patent analysis was reviewed by summarizing state-of-the-art approaches and categorizing 40 research publications based on datasets and deep learning methodologies, as well as identifying possible research directions where patent analysis meets deep learning [16]. However, as the sole focus was on deep learning methodologies for patent analysis, there is a lack of comprehensive comparison with conventional or hybrid approaches. An in-depth examination of the merits and shortcomings of both deep learning and non-deep learning approaches is essential for a deeper understanding of their specific functions and effectiveness in various patent analysis tasks. Reference [21] examines the existing natural language processing (NLP) methodologies for summarizing, simplifying, and generating patent texts while acknowledging the unique challenges posed by patents in the research and development process [21]. However, the study does not directly address the patent retrieval process. Reference [22] probes the current landscape of patent analysis, its various tasks (such as forecasting technological trends, strategic technology planning, and identifying patent infringements), the prevalent tools and methodologies for efficient patent analysis, as well as the limitations of the existing tools. However, the review only briefly touches on patent retrieval tasks and predominantly centers around other aspects of patent analysis [22].