## 4 Results and Discussion
### Research Trends and Key Findings
#### 4.4.2 Research Questions: Key Findings  
* RQ1: Emerging Patent Retrieval Techniques  
The survey revealed that \"Query Construction/Expansion\" has traditionally been the most popular technique amongst researchers [60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74], assisting in patent retrieval tasks. The main focus of these techniques was to enhance recall by reducing query mismatch and ambiguity. Recently, with advancements in NLP, more sophisticated context and semantic-aware algorithms have been developed, yielding better results for patent retrieval tasks with improved precision, as seen in references [62; 99; 111]. Additionally, \"Graph-based techniques\" [40; 49; 108] have also been widely explored, due to their ability to represent both structured and unstructured patent data and their complex relationships, facilitating patent ranking and analysis by identifying the influential patents and refining the relevance and  
Figure 11: The number of conference and journal publications per publisher. Figure 10: Number of publications per publisher. quality of search results. Based on the surveyed studies, Figure 12 presents the classification of the patent retrieval and patent prior art search-based techniques. These techniques have been classified into five general categories: Bibliometric-Based Techniques, General Information Retrieval-Based Techniques, Machine Learning-Based Techniques, Natural Language Processing-Based Techniques, Semantic Analysis-Based Techniques, and Others. The sub-categories are also shown in the figure. * RQ2: Top Performing Patent Prior Art Retrieval Techniques  
Two studies, [50; 51], based on dynamic ranking techniques, reported a perfect recall rate for retrieval, with reference [50] utilizing the Yeast dataset while reference [51] utilized the USPTO dataset to perform experimentation. This highlights the importance of dynamic ranking techniques in retrieving a high number of relevant patent documents on versatile datasets. However, precision rates of 89% and 95% were reported by these studies, respectively. The top precision of 99% was achieved using a Nearest Neighbor-based technique [54]. The use of similarity measurement to retrieve relevant patent documents  
Figure 12: Classification of patent retrieval/prior art search techniques. can be one of the contributing factors that yielded such high precision for Nearest Neighbor-based techniques. Balancing between precision and recall, the highest performing model employed an NLP-based technique [62], achieving an F1 score of 95%. This underscores the effectiveness of NLP techniques not only in accurately identifying relevant patent documents but also in minimizing the detection of irrelevant ones. * RQ3: Popular Datasets  
Figure 13 illustrates the use of patent datasets over the surveyed years, showcasing a strong preference for the USPTO and CLEF_IP databases among researchers. More than 70% of our surveyed studies make use of the two most popular datasets, with 26 studies making use of the United States Patent and Trademark Office (USPTO) dataset [81; 92; 98] and 25 studies employing the Cross-Language Evaluation Forum for Intellectual Property (CLEF-IP) [39; 43; 82]. These choices highlight the versatility and relevance of the corpora, as both remain favored for their comprehensive and high-quality data. The USPTO database, renowned for its extensive coverage of U.S. patents and patent applications, includes a record number of up to 10 million records utilized in the most extensive study [83]. This database's breadth makes it an invaluable resource for patent prior art searches, widely recognized across numerous industries for its utility in research and analysis. Meanwhile, CLEF-IP, particularly the 2011 variant which is the most widely used, sees up to 3.5 million records employed from its datasets for a single study [69]. Designed specifically for patent prior art search tasks, CLEF-IP offers a well-curated and organized collection that provides standardized and well-annotated patent documents.