## 5 Conclusions  
Patent prior art retrieval is essential for maintaining the robustness and integrity of the patent system. It ensures that new patents are truly innovative and non-obvious by comparing them against existing inventions. Effective retrieval of prior art helps protect intellectual property rights and prevents costly legal disputes over patent infringement. Moreover, understanding the landscape of prior art supports strategic R&D investments, guiding inventors and companies away from already patented technologies.  
This systematic literature review (SLR) offers a comprehensive examination of state-of-the-art methodologies and their effectiveness, significant data repositories, frequently utilized patent components, the impacts of semantic search and natural language processing, and the key barriers within the domain of patent prior art retrieval. To the best of our knowledge, this is the first survey of its kind in the existing literature. A total of 78 articles, published between 2013 and 2023, focusing on patent retrieval techniques for novelty and prior art searches were extracted from five main research databases through a rigorous search strategy. These articles were thoroughly and meticulously scrutinized, resulting in the identification of a number of research trends and challenges in the domain.  
Traditionally, query construction and graph-based techniques have been the preferred methods among researchers for performing patent retrieval. However, recent trends show a significant shift toward employing Natural Language Processing (NLP)-based approaches, which have notably improved the recall rates of patent retrieval tasks. The Cross-Language Evaluation Forum for Intellectual Property (CLEF-IP) and United States Patent and Trademark Office (USPTO) datasets were prominently used in the majority of the studies. To enhance the results of the patent retrieval tasks, most studies made use of the full patent document, despite the resource burden of considering the whole document. Moreover, inconsistent document structure and formats and sophisticated patent language and vocabulary have been the most widely reported challenges in the surveyed studies.  
The insights and trends highlighted in this SLR aim to serve as a foundational resource for researchers and practitioners involved in patent novelty and prior art searches, as well as general patent retrieval. Looking ahead, the application of specialized NLP transformer models, such as BERT for Patents, needs to be explored to further enhance the efficacy of novelty and prior art search processes. Moreover, the use of Generative AI also holds immense potential to transform the process of retrieving patents by improving search accuracy, automating patent classification, and providing advanced summarization and translation features.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/asi7050091/s1](https://www.mdpi.com/article/10.3390/asi7050091/s1)]([https://www.mdpi.com/article/10.3390/asi7050091/s1](https://www.mdpi.com/article/10.3390/asi7050091/s1)), Section S1: Introduction; Section S2: Supplementary Materials to Section 2; Section S3: Supplementary Materials to Section 3 Review Methodology Background.  
Conceptualization, A.A., A.T. and P.E.A.; methodology, A.A., A.T., P.E.A.; validation, A.A., A.T., L.C.D.S. and P.E.A.; formal analysis, A.A., A.T. and P.E.A.; investigation, A.A. and A.T.; resources, A.A. and P.E.A.; data curation, A.A., A.T., L.C.D.S. and P.E.A.; writing--original draft preparation, A.A., A.T.; writing--review and editing, A.A., A.T. and P.E.A.; visualization, A.A. and A.T.; supervision, L.C.D.S. and P.E.A.; funding acquisition, P.E.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universiti Brunei Darussalam grant number UBD/RSCH/1.3/FICBF(b)/2024/023.  
The authors declare no conflict of interest.