## 4 Results and Discussion
### Limitations and Future Directions  
This systematic literature review (SLR) is subject to several limitations that should be considered when interpreting its findings. Firstly, the temporal scope of this review is confined to the past decade. While this allows for a focus on recent advancements and trends, it may omit relevant insights and foundational work published before this period, possibly skewing the understanding of the evolution and development of patent retrieval techniques. Secondly, the review is specifically concentrated on patent prior art retrieval. This focus provides depth in one area but limits the breadth of the investigation, potentially overlooking broader aspects of patent retrieval such as infringement checks or patent validity analysis, which could provide a more holistic view of the field. Thirdly, the limitation of sourcing only from journal articles and conference papers might miss significant insights found in other forms of literature, such as book chapters, industry reports, and technical reports. These sources often contain valuable practical applications and case studies that could offer a different perspective or complement the academic viewpoints presented. Figure 14: Classification of NLP-based techniques. Moreover, the review exclusively considers English-language articles, thereby restricting the diversity of viewpoints and potentially missing important contributions from non-English-speaking regions. This language barrier may introduce a bias towards English-speaking researchers' perspectives and methodologies, possibly overlooking innovative approaches developed in other linguistic contexts. Additionally, the exclusion of non-text-based data might lead to overlooking complex innovations that are not easily described in text form. This exclusion could limit the comprehensiveness of the review, especially in fields where visual data are paramount. Lastly, the omission of documents for which full-text access is not available could result in overlooking critical studies. This reliance on accessible full-text documents might bias the review towards more readily available or popular sources, potentially missing out on pivotal but less accessible research. Each of these limitations not only frames the current findings but also sets the stage for future research directions. Addressing these limitations in subsequent studies could expand the understanding of patent retrieval practices, offering a more comprehensive and inclusive perspective. Future research could aim to include a wider range of sources, extend the temporal coverage, incorporate multilingual studies, and consider non-textual data to enhance the richness and applicability of the findings. The introduction of Generative AI in patent retrieval offers a promising avenue to address some of these limitations by enhancing search accuracy, automating patent classification, and providing advanced summarization and translation features. With its sophisticated ability to comprehend the nuances of search queries, Generative AI could revolutionize prior art searches, forecast innovation trends, and enable more dynamic and interactive querying processes. However, the integration of this technology must be approached with caution, carefully considering potential challenges such as bias, data privacy, and the costs associated with implementation. Future research should also broaden the scope of sources used in patent retrieval. This includes incorporating multilingual and non-textual data to better capture the global and diverse range of innovations. Additionally, while non-patent literature may not traditionally fall within the strict bounds of patent retrieval, its integration is crucial for comprehensive prior art searches to establish novelty. Exploring non-patent literature alongside traditional patent databases enriches the context and depth of searches, potentially uncovering prior art that patent documents alone might miss.