## 4 Results and Discussion
### Rq3: What are the most widely used patent data collections?
The integration of semantic search and natural language processing (NLP) in the domain of patent retrieval enhances the efficiency and precision in locating relevant patent documents for researchers, inventors, and patent examiners. This combination enables a more refined matching process, capable of ranking complex-natured patent documents according to their semantic similarity with the search query by considering the contextual understanding of the content, which includes technical terminologies, legal jargon, and specialized acronyms. Figure S2 in Section S3.3 of the Supplementary Material summarizes the overall NLP-based techniques reported to have been used in various surveyed research, while Table 9 shows the details of the NLP models used. For instance, SBERT is one of the most widely deployed BERT variants. Among embeddings, word embedding is the most popular embedding technique, with eight studies reported to have used it. Another significant finding from the survey is that almost all studies published from 2019 onward have incorporated NLP as their main patent retrieval technique. Moreover, the majority of those studies have included embeddings to aid the retrieval process. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**NLP Model** & **Type** & **Number of Studies** & **Embedding** & **Study** \\ \hline
**Kullback-Leibler Divergence** & 1 & & [88] \\ \hline
**LM (Dirichlet smoothing, and jelinek-Mercer smoothing)** & 2 & [78; 79] \\ \hline
**Positional Language Model** & 1 & [74] \\ \hline
**Approximate nearest-neighbor techniques** & 1 & [54] \\ \hline \hline  & 2 & Sentence and Word & [44; 66] \\ \hline  & PL1 & 1 & Passage & [106] \\ \hline  & ColBERT & 1 & Passage & [81] \\ \cline{2-4} BERT & DistilBERT & 1 & Word & [53] \\ \cline{2-4}  & ParaBERT & 1 & Word & [110] \\ \cline{2-4}  & SBERT & 3 & Sentence & [40; 67; 111] \\ \cline{2-4}  & TinyBERT & 1 & Word & [98] \\ \hline
**BiLSTM** & 1 & Word & [61] \\ \hline
**Bigram language Model** & 1 & [46] \\ \hline
**Conditional Random Fields (CRFs)** & 1 & [68] \\ \hline
**Doc2 Vec** & 2 & Document & [48; 70] \\ \hline
**Gated Recurrent Units (GRUs)** & 1 & Word & [69] \\ \hline
**LDA** & 1 & [63] \\ \hline
**POS Tagging** & 6 & [51; 62; 84; 85; 86; 87] \\ \hline
**Semantic Trees** & 1 & [71] \\ \hline
**Skip-gram model** & 1 & [119] \\ \hline
**TF-IDF** & 13 & [55; 58; 73; 75; 76; 77; 78; 80; 81; 83; 94; 95; 96] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of the utilized NLP models. Table S6 in Section S3.3 of the Supplementary Materials offers a detailed overview of the natural language processing (NLP) techniques employed in the selected studies, providing a concise description of each method and its application within the research context [142; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159; 160; 161; 162; 163; 164; 165; 166; 167; 168; 169]. A total of 57 studies reported to have used NLP-based techniques, and 55 studies utilized semantic or contextual searches for patent retrieval tasks. Furthermore, 21 studies employed embeddings such as word, sentence, passage, and document embeddings to retrieve the patents of interest. This information has been summarized in Figure 6.