## 4 Results and Discussion
### What open challenges does prior art patent search and retrieval face?
Relevant documents might not be retrieved due to the use of synonyms and specialized terms in the query; therefore, researchers must develop techniques that bridge the gap between user queries and patent documents. Some studies have reported the challenge of 'information asymmetry and overload' [47; 63; 68; 75; 92; 93], which impacts the patent retrieval process due to imbalances in the availability of information among patent offices, applicants, and researchers, and the sheer volume of information related to patents. Applicants and researchers often lack access to cutting-edge tools and comprehensive databases, unlike patent offices, and this limited access can deter their ability to conduct exhaustive patent retrieval searches. This asymmetric access can impact the outcomes of patent searches and the overall patent application process. The'requirement of a high recall' has also been listed as one of the major challenges faced by the patent retrieval process, particularly for prior art searches [39; 50; 66; 89; 107]. High recall is necessary in prior art searches to ensure thorough scrutiny of all potential references that may impact the novelty of a new patent or invention application. Without achieving high recall, patent retrieval systems risk overlooking significant relevant patent documents and potentially facing legal issues. Several studies have reported challenges related to the 'limitations of keyword-based searches' [66; 114], where the focus is on overcoming the intrinsic shortcomings of keyword searches, including their inability to recognize synonyms and capture semantic meanings. Moreover, formulating complex queries involving multiple concepts or special operators, such as Boolean and proximity operators, is challenging in keyword-based searches. This can affect the outcome of patent retrieval systems. Likewise, some studies have dedicated efforts to enhancing'retrieval results accuracy and efficiency'. Precision and recall are both crucial in patent retrieval systems since high precision ensures that the number of retrieved patents is relevant, while high recall ensures that all relevant patents are retrieved. Nevertheless, it is a challenge to strike a balance between the two. Researchers must develop techniques that consider not just user requirements but also the context of the required search. For example, references [57; 84; 111] propose efficient techniques based on the Skip-gram and TF-IDF models. Some challenges have been reported by just one study, such as 'commercial patent analysis' [115], typically conducted to aid strategic decision-making; 'finding the most relevant classification codes' [117], required as part of accurately categorizing the patent applications; 'lack of data for training the BERT' [44], related to the insufficiency of diverse datasets for BERT training; 'examiner citations recommendation problem' [81], referring to the challenges that patent examiners might face in conducting prior art searches where proper referencing is not performed on the existing patent literature; and 'patent ranking' [75], related to the process of assigning ranking based on relevance.