## 1 Introduction
Patents are technical documents that contain detailed technological information and play a vital role in revealing the evolutionary trajectory of core technologies [12]. In the fast-evolving field of 6G technologies, the rapidly growing number of patent documents poses an enormous challenge for retrieving and analyzing timely information from patent data to identify technical themes and unveil technological evolution processes. In recent years, based on deep learning (DL) methods for natural language processing, many novel approaches have been developed in the field of patent analysis to reduce costs by automating tasks that previously only domain experts could solve [13]. Among them, topic modeling techniques are widely used in exploratory data analysis for organizing, understanding, and summarizing large amounts of text data [14]. Dynamic Topic Models (DTMs) are a variant of topic models designed specifically for temporal archives [15]. Unlike static topic models, such as Latent Dirichlet Allocation (LDA) [16], they possess the unique ability to adapt and refine their assessments of underlying topics as new documents are added to the corpus, allowing the identification of evolving trends and patterns in the archive [17]. This adaptability feature facilitates the identification of evolving trends and patterns in patent-based technological development [18], particularly in rapidly evolving fields like 6G. DTM-based topic evolution analysis can help track how technical concepts and innovation trends in patents change over time, thereby contributing to a more comprehensive understanding of the technological evolution in this field. However, these approaches come with certain limitations and challenges in analyzing the evolving technology in 6G. Firstly, traditional statistics-based methods based on the Bag-of-words (BOW) assumption, such as the Term Frequency-Inverse Document Frequency model (TF-IDF) and LDA, face difficulties in effectively capturing topic semantics and dealing with high-dimensional data, resulting in potentially inaccurate and inefficient topic extraction and connection identification via similarity measurements [19]. For instance, in the early research on DTMs [15], the authors compared DTMs with static models in terms of capturing the topic evolution in Science articles. The quantitative results showed that the dynamic topic model outperformed LDA-based static topic models in predicting the content of the next year's Science articles, consistently assigning higher likelihood values. Additionally, in a study comparing LDA and BERTopic on a corpus of 22,101 climate protection articles from Austrian newspapers, LDA-like models often produced topics with very broad terminologies. For example, a housing-related topic in LDA might include general terms like 'city', 'new', and 'project', among others. At the same time, BERTopic identified more specific salient words such as 'heat pump' and 'oil heating' for the same kind of topic, making it more interpretable and better at highlighting specific aspects [20]. Nevertheless, DL-based DTM methods like BERTopic [21] handle complex semantic topic representation by utilizing high-dimensional transformer-based embedding. The study comparing LDA and BERTopic on Twitter data related to travel and COVID-19 found that LDA identified fewer topics, some of which were rather general and uninformative [17]. In dynamic topic modeling, its assumption of independent topics and reliance on word frequency might lead to overlooking topic correlations over time. For instance, it may not effectively capture how the 'government response' topic evolves in relation to other topics like 'travel restrictions' as the situation changes, making it less suitable for tracking dynamic topic changes. On the other hand, BERTopic, which uses a combination of Bidirectional Encoder Representations from Transformers (BERT) and c-TF-IDF, generated more specific and interpretable topics. For example, its c-TF-IDF algorithm can highlight the most representative terms within a cluster.