## 5 Discussions  
This study demonstrates the potential of ML methods to optimize document relevance filtering, particularly in multidisciplinary patent datasets. The results reveal critical insights into the strengths and weaknesses of different methodologies applied to the five technology domains. By systematically evaluating different methods of UML and SML, the study highlights their relative effectiveness in handling large and complex corpora with diverse terminologies.  
The key insight from the results is that SML methods are superior in this application. SML models consistently outperformed UML methods in classifying document relevance. Metrics such as AUC, classification accuracy, and F1-score highlight the robustness of models like GB, XGB, and D-ANNs. The ability of SML models to leverage labeled data allowed for precise discrimination between relevant and irrelevant documents, which is particularly evident in domains with distinct terminologies like eVTOL and LiDAR. Topic modeling techniques (LDA and NMF) demonstrated moderate success, with SSB achieving the highest F1-score among the technology domains analyzed. UML experienced significant challenges in aligning topic labels with document relevance. Clustering using t-SNE and k-means provided mixed results. Lower confusion indices in domains like eVTOL and LiDAR reflect effective clustering, while higher confusion in the domains of SSB and EVC  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **SSB** & **EVC** & **CV** & **eVTOL** & **LiDAR** & **Mean** \\ \hline Best Model & NB & GB & D-ANN & GB & XGB & \\ AUC & 0.806 & 0.998 & 0.994 & 0.987 & 0.948 & 0.95 \\ CA & 0.890 & 0.981 & 0.983 & 0.962 & 0.901 & 0.94 \\ F1 & 0.876 & 0.981 & 0.983 & 0.963 & 0.899 & 0.94 \\ No. AUC90 & 0 & 8 & 10 & 9 & 7 & 6.8 \\ C(ANN/Best) & 53.5 & 13.4 & 18.1 & 15.1 & 23.7 & 24.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification based on supervised ML.  
suggests that just clustering features such as keyword frequency and positional occurrence may not fully capture domain-specific nuances.  
There were a few domain-specific observations. The SSB domain achieved the highest precision in UML methods, indicating the effectiveness of LDA in separating topics. However, SML models provided a more balanced trade-off between precision and recall. The LiDAR domain showed exceptional recall, suggesting high coverage of relevant documents but with the trade-off of increased false positives in UML methods. CV and eVTOL domains benefited significantly from SML approaches, where advanced models like D-ANN captured the complexity of interrelated features better than UML methods. However, D-ANN models required significantly higher computational resources compared with the statistical SML methods.  
The limitations of this study stem primarily from its scope and methodology. The literature search focusing on methods of patent document relevance filtering returned very few recent studies. The UML methods relied on basic features such as keyword frequency and positional occurrence. While effective for certain domains, these features struggled to encapsulate domain-specific complexities, particularly for SSB and EVC. Although those features worked well for the SML models, they require labeled datasets, which may not be readily available for emerging domains or new datasets. This study focuses on classical machine learning models due to their accessibility, lower computational cost, and established performance in document relevance filtering. While large language models (LLMs) offer potential advantages, their adaptation requires significant resources, making them a topic for future research. Future work will explore richer feature sets, including semantic embeddings and contextual word representations, and include a wider range of technology domains to generalize the findings.