## 4 Results and Discussion
### Research Trends and Key Findings
#### 4.4.2 Research Questions: Key Findings
This makes it easier and more efficient to evaluate prior art search and retrieval systems. Recently, Google Patents has also begun to gain recognition from the research community due to its easy access, comprehensive coverage, and availability of structured data, signaling a potential shift in preferred resources or a broadening of the tools available for patent research. Despite the introduction of newer platforms like Google Patents, USPTO and CLEF_IP remain the cornerstones for most patent retrieval research, consistently chosen for their robust datasets that support the development of innovative prior art search and patent retrieval techniques. Their continued popularity underscores their critical role in shaping the future directions of patent retrieval research. * RQ4: Impact of Natural Language Processing on Patent Retrieval  
Many of the recent studies have shifted their focus to using NLP-based techniques for prior art searches. Our survey revealed that almost all studies published from 2019 onward  
Figure 13: Patent datasets used over the years. have included NLP as their main patent retrieval technique. This shift from traditional approaches to NLP may be attributed to various advantages that NLP-based techniques offer, including semantic and contextual understanding, multi-lingual support, and the availability of advanced deep learning-based algorithms. The majority of the surveyed studies have made use of embeddings to aid the retrieval process. Embeddings are vector representations of words, sentences, and documents that help the retrieval process with better semantic and syntactic understanding of data. Among the different NLP techniques, BERT is an emerging choice by researchers, deployed in a total of 10 studies. BERT provides various advantages, including pre-trained contextualized word embeddings, assisting transfer learning, and supporting deep bidirectional representation learning. Another important trend observed in our surveyed articles is that the highest recall rates have been achieved by all those studies that deployed NLP as their main technique. For example, studies such as those in references [51, 62, 64, 77, 80, 91] showed above 90% recall rates, indicating a significant impact of NLP-based approaches on patent retrieval tasks, especially for novelty and prior art searches. Figure 14 presents a classification of NLP-based techniques. These techniques have been placed into seven categories: Topic Modelling Techniques, Statistical Analysis Techniques, Semantic Analysis Techniques, Contextual Modelling Techniques, Text Processing Techniques, Similarity Measures Techniques, and Tools. The sub-categories have also been shown in the figure. * RQ5: Popular Patent Document Sections Analysis of the selected studies revealed that 14 studies utilized the full patent document for prior art and other patent retrieval searches. While the full document provides comprehensive information on the patent, thereby potentially enhancing retrieval outcomes, it should be noted that utilizing the full document is resource-intensive. This is because a single full document contains a large amount of information, requiring significant computational power and storage capacity to process effectively. 13 out of those 14 studies utilized NLP as their search technique. Another 12 studies reported utilizing \"Title, Abstract, Claims, and Description\" to perform patent retrieval tasks, proving the effectiveness and usability of this combination. Similar to the above, \"Title, Abstract, Claims and Description\" is also a common combination being utilized by NLP-based approaches [113, 122]. A significant number of studies achieving a recall rate of 90% or higher utilized either the \"Title\" section alone or the combination of \"Title and Abstract\" to conduct patent retrieval [64, 77, 80, 117]. * RQ6: Widely Reported Challenges The surveyed studies have reported encountering numerous challenges in conducting patent prior art or novelty searches.