## 5 Conclusions  
In this study, we aimed to address the challenges of analyzing the technological evolution of 6G through patent data. Given the rapid development and complexity of 6G technologies, traditional topic modeling approaches were insufficient. We proposed a novel dynamic topic modeling framework to overcome these limitations and provide more interpretable alternative tools to analyze emerging trends in the field. Specifically, we contributed to the usage of LLM-based tools for cost-effective and efficient summarization techniques to pre-process the large volume of patent data. The experimental results show that long summarization improved topic coherence (TC) in most fields, while short summarization enhanced topic diversity (TD). This indicates that different summarization strategies could be applied according to specific analysis requirements, providing a more flexible and efficient way to handle patent data. Then, we conducted a comparative analysis of various embedding techniques and found no single model was superior across all metrics and datasets. Furthermore, we optimized the hyperparameters of the BERTopic-based framework. Through random searches based on the DBCV value, we significantly improved the performance of the model. The optimized model showed a remarkable increase in TC (about 40% on average) and TD (about 30% on average), enabling more accurate topic representation and a deeper understanding of emerging trends in 6G technology. Finally, we applied the weak signal analysis method using TEMs in our dynamic topic modeling framework. By analyzing the five 6G patent datasets (NSTs, RISs, ISAC, ISAGSC, and EAIs), we identified numerous emerging technologies. For example, in the NST dataset, topics like '17_channel estimation_sparse_millimeter wave channel_deep learning' and '18_millimeter wave radar_wave radar_doppler_target detection' were recognized as weak signals, indicating emerging research directions in millimeter-wave communication. In the RIS dataset, '5_terahertz_terahertz wave_vanadium dioxide_resonator' and other topics showed potential for future development in terahertz-related and optical-based RIS technologies. In conclusion, our proposed dynamic topic modeling framework effectively uncovers 6G technological evolution patterns. It provides valuable insights for strategic decision-making in the 6G industry and enables stakeholders to identify emerging technologies, allocate resources rationally, and stay ahead in the highly competitive 6G technology race. Future research could further explore more advanced hyperparameter optimization techniques and expand the scope of patent data analysis to other 6G-related fields to enhance a comprehensive understanding of 6G technological development. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15073783/s1](https://www.mdpi.com/article/10.3390/app15073783/s1)]([https://www.mdpi.com/article/10.3390/app15073783/s1](https://www.mdpi.com/article/10.3390/app15073783/s1)). **Author Contributions:** Conceptualization, J.J. and F.Y. ; methodology, F.Y. ; software, F.Y. and R.D. ; validation, J.J. and R.D. ; formal analysis, F.Y. ; investigation, J.J. and R.D. ; resources, J.J.; data curation, J.J.; writing--original draft preparation, J.J.; writing--review and editing, J.J., F.Y., and R.D. ; visualization, F.Y. ; supervision, J.J.; funding acquisition, F.Y. ; project administration, F.Y. and R.D. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China, NO. 32327801. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the sensitivity of 6G technology competition-related information.