## 4 Results and Discussion
### Rq3: What are the most widely used patent data collections?
* Non-Patent Datasets  
Two non-patent datasets are used in the selected literature to assess the patent search and retrieval processes. The first, the Yeast ([[https://archive.ics.uci.edu/dataset/110/yeast](https://archive.ics.uci.edu/dataset/110/yeast)]([https://archive.ics.uci.edu/dataset/110/yeast](https://archive.ics.uci.edu/dataset/110/yeast)) URL (accessed on 29 March 24)) dataset, was created in 1996 and is used in biology to predict the cellular localization sites of proteins. This dataset serves classification tasks and has been employed in two studies [50,108]. The second dataset, the 20 Newsgroups ([[https://www.kaggle.com/datasets/crawford/20-newsgroups](https://www.kaggle.com/datasets/crawford/20-newsgroups)]([https://www.kaggle.com/datasets/crawford/20-newsgroups](https://www.kaggle.com/datasets/crawford/20-newsgroups)) URL (accessed on 29 March 24)) dataset, comprises approximately 20,000 documents across 20 diverse newsgroups. It covers a broad range of topics from computer graphics and hardware debates to sports, politics, and religion. This dataset is particularly valuable for evaluating machine learning techniques in text-based applications such as text classification and clustering, as demonstrated in the same two studies [50,108]. Two datasets have been overwhelmingly used by about 70% of our surveyed studies. A total of 25 studies made use of the Cross-Language Evaluation Forum for Intellectual Property (CLEF-IP) dataset and another 26 studies used the dataset provided by the United States Patent and Trademark Office (USPTO), as shown in Figure 5. The remaining datasets have been used much less frequently compared to the previously reported two datasets. The Google Patents dataset has been used by five studies, whereas three studies have reported their studies using the Indian Patents dataset, and another three have utilized the Russian Patents dataset. Table 8 shows more details of the CLEF-IP tracks. CLEF-IP 2011 has been widely used and has been reported by 12 studies, whereas CLEF-IP 2012 has just been used by two studies [82; 87]. The table caters for the cases where a single study uses more than one CLEF-IP track. For instance, if a study uses both CLEF-IP 2009 and CLEF-IP 2010, each is reported separately in the table. The preferred databases for prior art searches in the selected studies appear to be USPTO and CLEF-IP. There could be a few possible reasons for their selection. One potential reason for the USPTO could be its comprehensive coverage of patents, incorporating a wide array of inventions. Moreover, the USPTO enjoys a remarkable reputation amongst numerous industries, making it a significant resource for research and analysis. Lastly, the USPTO provides a broad spectrum of patents and patent applications filed in the United States, making it a rich source for prior art document searches. The likely explanation for favoring CLEF-IP could be that it provides a well-curated and organized set of patents intended solely for patent prior art search tasks. Additionally, this dataset offers a well-annotated and standardized collection of patent documents, making the evaluation of prior art search and retrieval systems easier and more efficient. Figure 5: Dataset utilized for validation of the proposed patent retrieval techniques. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Tracks** & **Number of Studies** \\ \hline \hline \multirow{6}{*}{**CLEF-IP**} & NS & 1 \\ \cline{2-3}  & 2009 & 1 \\ \cline{2-3}  & 2010 & 9 \\ \cline{2-3}  & 2011 & 12 \\ \cline{2-3}  & 2012 & 2 \\ \cline{2-3}  & 2013 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Breakdown of CLEF-IP tracks. RQ4: What is the impact of semantic search and natural language processing on the patent retrieval process? Semantic search leverages natural language processing (NLP) techniques to interpret and comprehend the intent of the search query and the content of the documents being searched, facilitating the identification of more relevant and accurate results.