## 3 Review Methodology
### Research Questions  
A total of six research questions were formulated to define the scope and purpose of the systematic literature review (SLR). These research questions aim to guide researchers on the available and most utilized data collections, innovative and most effective state-of-the-art approaches, key performing parts of the patent documents, criteria for evaluating the quality and applicability of the approaches, the prominent challenges, and the role of natural language processing (NLP) and semantic search in improving patent prior art retrieval techniques.  
_Rq1: What innovative patent retrieval techniques have been developed and utilized for effective patent analysis?_  
This question explores the evolution of patent retrieval techniques over the last decade, considering technological advancements and their impact on patent analysis. Techniques have evolved from keyword-based and Boolean searches to context-based searches to improve patent retrieval efficiency. The integration of natural language processing (NLP) and machine learning (ML) techniques with semantic analysis has enhanced search capabilities by analyzing the meaning and context of searches. Collectively, these progresses have expedited patent retrieval, providing users with more precise and accurate findings from patent archives, hence creating more intelligent, context-aware, and efficient patent retrieval systems.  
_Rq2: How effective are the patent prior art retrieval techniques?_  
Prior art search involves investigating publicly available patent data to determine if an invention is novel [27]. The efficacy of patent prior art retrieval systems is measured by their ability to effectively and accurately retrieve relevant patent documents in response to specific information needs or search queries. This research question reviews widely used evaluation metrics such as recall, precision, Mean Average Precision (MAP), F1-score, and accuracy to assess the performance of patent information retrieval techniques.  
_Rq3: What are the most widely used patent data collections?_  
In the domain of patent prior art retrieval, numerous evaluation tracks and datasets have been developed to measure the performance of systems and algorithms. This research question explores the datasets utilized by the studies surveyed.  
_Rq4: What is the impact of semantic search and natural language processing on the patent retrieval process?_  
Patent retrieval enhanced by semantic search and natural language processing (NLP) has transformed traditional keyword-based approaches by utilizing advanced, contextualized methods to comprehend the meaning and context behind patent search requests, allowing for the retrieval of conceptually linked patents. Such context-aware systems can scan patent data and identify relevant patent documents, including prior art and technical information, in accordance with the semantics of the request. This research question examines the impact of semantic search and NLP on enhancing the patent retrieval process.  
_Rq5: Which parts of the patent document are widely used for prior art searches?_  
Patent prior art search is complex due to the intricate nature of patent documents. Each component of a patent document serves a distinct function and varies in terms of significance, with claims containing the legal aspect of the inventions. Researchers utilize various components and combinations thereof, including abstracts, claims, descriptions, and references, to comprehend the context of the invention. This question explores the different parts of the patent document that have been widely employed for patent prior art searches.  
_Rq6: What open challenges does prior art patent search and retrieval face?_  
This question delves into the challenges within the domain of prior art search and retrieval processes. Despite the improvements in search and retrieval accuracy, numerous challenges persist, with different researchers highlighting several key challenges pertinent to the domain.