## 2 Literature Review
### Dynamic Topic Modeling for Analyzing Technological Evolution  
Topic modeling has emerged as a powerful technique in the realm of patent analysis, offering valuable insights into the complex landscape of technological innovation. Many existing methods harness the information in patent documents to identify and classify technical topics. The typical output of a topic modeling algorithm is a set of topics, either fixed or flexible in number, with each topic represented by a list of top words [14]. DTMs introduce a novel approach by creating topics defined by a set of words and associated with a timestamp or time range [15]. They employ a Bayesian framework to model the temporal changes in the relative proportions of topics within documents. This Bayesian modeling enables the identification of evolving trends and patterns over time in the corpus [34]. Furthermore, building upon DTMs, topic evolution analysis [18] has been used in a varietyof applications, such as discovering the evolution of research topics and innovations in scientific archives [35] and understanding the evolution trend in patent analysis [36]. When it comes to statistical modeling for deriving insights from data, the research of DTMs mainly falls into two categories: Probabilistic Dynamic Topic Models (PDTMs) and Algorithmic Dynamic Topic Models (ADTMs) [34]. Probabilistic topic models assume that each document in a corpus is a mixture of topics, with each topic represented as a probability distribution over the words in the corpus. PDTMs, a subset of these models, assign probabilities to different words and topics across time. This temporal probability assignment allows them to infer the underlying themes and patterns as they change [37; 38]. For example, the classic DTM [15] was among the first PDTMs, which is a variant of the LDA and incorporates temporal components. Despite their extensive application across a range of studies [39; 40], PDTMs exhibit elevated computational costs when confronted with substantial archives containing vast vocabularies [41]. Consequently, the work extends the class of tractable priors from Wiener processes to the more general class of Gaussian processes. This approach allows the model to be applied to large collections of text and explore topics that evolve smoothly over a long period. Despite all the efforts to address the problems of PDTMs, they still show less coherence and diversity in topic representation compared to ADTMs. In contrast, ADTMs leverage advancements in neural networks to transform the underlying temporal probability distributions of words and documents into a fixed-length vector representation space. These models not only retain crucial words in topic descriptions but also form dense clusters of documents that are highly interpretable. For instance, BERTopic [14] is a widely-used ADTM. It leverages LLMs to generate document representations, thereby embedding context within the generated topics. This model integrates BERT and c-TF-IDF, creating dense clusters of interpretable topics while safeguarding important words in the topic descriptions. By calculating the c-TF-IDF topic representation for each time window, BERTopic enables dynamic topic modeling. This approach allows for a more nuanced understanding of how topics evolve over time, as the c-TF-IDF representation captures the relative importance of terms within each time-specific cluster, and the use of LLMs and BERT ensures that the topics are rich in context and semantic information. ANTM [34] can capture the dynamic topic over the periods using aligned UMAP to enable a flexible number of topics. Additionally, BERTrend [42] extends from BERTopic by operating within an online learning framework to dynamically classify topics. Despite their advantages, current dynamic topic modeling approaches cannot be directly applied to identify emerging technological trends in patent analysis. Firstly, the landscape of 6G is complex and full of domain-specific technological terms.