## 3 Review Methodology
### Quality Assessment  
After applying inclusion and exclusion criteria to remove irrelevant articles, the remaining articles were assessed for quality using seven detailed quality criteria (QCs) addressing research robustness, publication prestige, and community value. These criteria evaluate factors such as the reputation of the publication venue, the presence of reasonable methodology, performance assessment based on real data, dataset size, acknowledgment of study limitations, citation impact, and proposed future research directions. A three-point scoring system was used for each criterion (1 for full compliance, 0.5 for partial compliance, and 0 for no compliance). The criteria and their descriptions are defined in Table 4.  
Resources used to assess for Quality Criteria QC1: Publication Venue include the Scimago journal ranking [30], impact factor and indexing from Journal Citation Reports [31; 32; 33], core conference ranking [34], and Oxford List of Conferences [35]. Additionally, thorough searches were also performed on the publisher's website for other relevant information including technical sponsors, event type, and the frequency of the event, as indications of quality. Each selected study was assessed for the quality of the proposed framework or methodology for Quality Criteria QC2. Thorough reading of the proposed framework or methodology was performed and a comparative analysis with other selected studies also aided in the quality evaluation process. Quality Criteria QC3 focuses on the presence of a proof of concept through simulation, mathematical modeling, or real-time implementations. For Quality Criteria QC4, datasets ranging from half a million to a million are deemed adequate, reflecting standards from reviewed patent retrieval research. Each study was carefully scrutinized to determine if study limitations were clearly defined for Quality Criteria QC5. For citation analysis (i.e., Quality Criteria QC6), data from Google Scholar [36], Scopus [37], and Web of Science Citation Report [31] were considered, taking into account yearly citations and comparative article citations to gauge impact. To identify future directions for Quality Criteria QC7, each study was meticulously examined with special attention paid to the conclusion section.  
Scores of each article for the seven criteria were added together to give the total score for each article. For a more accurate comparison, the final quality assessment scores were standardized using the min-max normalization technique [38]. This approach adjusts the scores to a common scale, enhancing the comparability of article quality across different metrics. The normalization formula applied converts raw scores into a standardized range, facilitating a uniform evaluation framework and ensuring that the quality assessment is both fair and reflective of each article's relative merits within the broader research context. The following min-max normalization formula is used for the conversion:  
\[D_{normalized}=\frac{D-\text{min}(D)}{\text{max}(D)-\text{min}(D)}\times 100 \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Number** & **Criteria Name** & **Criteria Definition** & **Decision** \\ \hline
5 & Article Type & Journal and Conference/Workshop & Included \\ \hline
6 & Article Type & Thesis, unpublished work, non-peer-reviewed, review, and experimental studies & Excluded \\ \hline
7 & Article Access & Full access & Included \\ \hline
8 & Article Access & Non-full access & Excluded \\ \hline
9 & Article & Non-text-based patent retrieval and & \\  & Relevance & techniques not focusing on prior art & Excluded \\  & search or patent retrieval & & \\ \hline
10 & Article & Text-based patent retrieval & Included \\  & Relevance & & \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._where \(D_{normalized}\) represents the normalized score on the scale of 0-100, \(D\) represents the score to be normalized, \(\min(D)\) represents the minimum score in the given set of values, and \(\max(D)\) represents the maximum score in the given set of values.