## 4 Results and Discussion
### Research Questions' Findings
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Techniques**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Number of** \\ **Studies** \\ \end{tabular} } & \multicolumn{1}{c}{**Study**} & \multicolumn{1}{c}{**General Category**} \\ \hline
**Citation Graphs/Network** & 6 & [39; 40] & Bibliometric-Based \\ \hline
**Paten Ranking** & 1 & [41] & Techniques \\ \hline
**Collection Selection method** & 2 & [42; 43] & \\ \hline
**Federated search** & 2 & [44; 45] & \\ \hline
**Query Expansion/Construction** & 23 & [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61] & General Information Retrieval \\ \hline
**Graph-Based Techniques** & 8 & [39; 48; 49] & \\ \hline
**Dynamic Ranking** & 2 & [50; 51] & \\ \hline
**Recommendation-based retrieval** & 2 & [41; 53] & \\ \hline
**Nearest-neighbor (NN) technique** & 1 & [54] & \\ \hline
**Dimension reduction model** & 1 & [55] & \\ \hline
**Ensemble method** & 1 & [56] & \\ \hline
**Fuzzy Logic** & 1 & [47] & Machine Learning-Based \\ \hline
**Genetic algorithm** & 2 & [57; 58] & \\ \hline
**Heuristic meaning comparison** & 1 & [59] & \\ \hline
**Deep Learning** & 3 & [60; 61; 62] & \\ \hline
**Clustering** & 3 & [63; 64; 65] & \\ \hline
**Topic Modelling** & 2 & [61; 66] & \\ \hline  & Bigram language Model & 1 & [46] \\ \cline{2-4}  & LM (Dirichlet smoothing, and & & \\  & Jelinek-Mercer smoothing) & 2 & [78; 79] \\ \cline{2-4}
**Statistical Analysis** &
\begin{tabular}{l} TF-IDF \\ \end{tabular} & 13 & [55; 58; 73; 75; 77; 80; 81; 83; 94; 95; 96] \\ \cline{2-4}  & Latent Dirichlet allocation (LDA) & 1 & [63] \\ \cline{2-4}  & Positional Language Model & 1 & [74] \\ \cline{2-4}  & Unigram language model & 2 & [39; 82] \\ \hline  & Doc2 Vec & 2 & [48; 70] \\ \hline
**Semantic Analysis** &
\begin{tabular}{l} Semantic Trees \\ \end{tabular} & 1 & [71] \\ \cline{2-4}  & Skip-gram model & 1 & [57] \\ \cline{2-4}  & Word2 vec & 5 & [56; 72; 73; 74; 75; 76; 77; 78; 79; 80; 82; 83; 84; 85; 86; 87; 88; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98] \\ \hline  & BERT & 10 & [40; 44; 53; 66; 67; 68; 69; 98] & Natural Language \\ \cline{2-4}  & BiLSTM (Bidirectional Long & 1 & [61] \\ \cline{2-4}  & Short-Term Memory) & \(1\) & [69] \\ \cline{2-4}  & Gated Recurrent Units (GRUs) & \(1\) & [69] \\ \cline{2-4}  & Conditional Random Fields (CRFs) & 1 & [68] \\ \hline
**Text Processing** & 6 & [51; 62; 84; 85; 86; 87] \\ \hline  & Approximate & & \\  & nearest-neighbor & 1 & [54] \\ \cline{2-4}
**Similarity Measures** &
\begin{tabular}{l} Kullback-Leibler \\ Divergence \\ \end{tabular} & 1 & [88] \\ \hline \hline  & Stanford CoreNLP & 1 & [89] \\ \cline{2-4}
**Tools** &
\begin{tabular}{l} OpenNLP \\ \end{tabular} & 1 & [90] \\ \cline{2-4}  & TreeTagger & 2 & [91; 99] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Patent retrieval techniques per category. Further analysis with the help of Figure 4 shows that query expansion/construction is the most used approach in the information retrieval category. Similarly, the semantic tree-based approach and cosine similarity-based approach have both been utilized once in the semantic analysis category. Furthermore, NLP-based approaches have also been widely exploited. _RQ2: How effective are the patent prior art retrieval techniques?_  
When undertaking a patent retrieval task with a specific patent, whether for a new application of an innovative idea, the objective is to identify patents within a database that are pertinent to the referenced patent. This process entails a detailed examination of the patent's claims, descriptions, and technical sphere to formulate search strategies capable of pinpointing similar inventions. The goal is to discover patents with shared technological, functional, or inventive traits, thereby providing a holistic view of the related prior art. Such an extensive search identifies not only exact matches but also patents with sufficient similarities to be deemed relevant, enriching the understanding of the patent environment surrounding the new invention.