## 4 Results and Discussion
### Research Questions' Findings  
Rq1: What innovative patent retrieval techniques have been developed and utilized for effective patent analysis? A wide range of techniques have been proposed in the literature, focusing on either general patent retrieval or retrieval specifically aiding prior art searches. These patent retrieval techniques can be divided into five (5) main categories:  
1. Bibliometric-based methods  Bibliometric-based methods, in the context of patent retrieval, leverage patent bibliographic data and citation data to enhance the effectiveness of the patent retrieval and analysis process. Bibliographic data include the invention's title, inventor(s), assignee(s), filing date, publication date, priority date, and patent classification codes, whereas citation data comprise information on the references cited within a patent document (backward citations) and references that cite the given patent document (forward citations) [39]. Extracting citations from patent manuscripts is challenging due to the lack of a standard format for patent references [8]. For citation analysis, citation graphs and temporal patterns are commonly employed. 1. Citation Graph/Network: It is a graphical representation of relationships between patent documents by analyzing their citations. This method aids in comprehending patterns, influences, and trends within a specific area of study [39, 40]. 2. Temporal Patterns: These are a type of citation analysis that utilizes backward citations to show how patents cite prior works over time, providing insight into technological advancements in a particular field [41]. 3. General Information Retrieval-Based Techniques General information retrieval-based techniques employ principal Information Retrieval (IR) methods to search and retrieve patent documents from patent databases using methods such as keyword-based searching, Boolean queries, and relevance ranking algorithms. These include: 1. Collection Selection Method: This method helps to decide which subsection of patent documents or patent data sources should be employed during the search or analysis. The selection of pertinent data sources is based on already set parameters such as relevance to the topic, and data quality [42, 43]; 2. Federated Search: It enables simultaneous search through several patent databases, thereby, consolidating outcomes into one interface for swift retrieval. This method accelerates the overall information retrieval process and saves time on searches [44, 45]; 3. Query Expansion/Construction: It is a process of refining (adding or removing terms to fine-tune), expanding (adding terms to broaden the scope), or augmenting (adding terms to enrich coverage of a topic) the original search query to increase the inclusiveness and relevance of search outcomes [39, 46, 47, 48]; \begin{table}
\begin{tabular}{c c c} \hline \hline
**Academic Databases** & **Total Articles Found** &
\begin{tabular}{c} **After Applying Inclusion/Exclusion** \\ **Criteria and Duplicate Removal** \\ \end{tabular} \\ \hline Google & 738 & 38 \\ \hline IEEE & 180 & 14 \\ \hline Springer & 308 & 16 \\ \hline ACM & 135 & 9 \\ \hline Science Direct & 80 & 1 \\ \hline
**Total** & 5 & 1441 & 78 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Article retrieval per academic database. Graph-Based Techniques: These techniques leverage graph theory to represent and examine patent documents as a network of connected nodes in a graphical layout, enhancing context-aware search, retrieval, and analysis [39; 48; 49];
* Dynamic Ranking: This method iteratively re-ranks search results to better reflect shifting user preferences and situations. Used by recommendation engines and search engines, dynamic ranking adaptively reorders search results to provide users with the most current and relevant content [50; 51]. * Machine Learning-Based Techniques Machine learning-based techniques, in the context of patent retrieval, recognize patterns and predict document relevance to enhance patent document search, categorization, and analysis [52].