## 1 Introduction  
In the relentless pursuit of advancing wireless communication technologies, the transition from 5G to 6G emerges as a pivotal milestone, promising to transcend the limitations of current networks and unlock a new era of connectivity [1]. As the next frontier in wireless communication, the landscape of 6G technologies is characterized by its rapid evolution and increasing complexity, driven by the need to address global challengessuch as ubiquitous connectivity, ultra-high data rates, and intelligent applications [2; 3]. Unlike its predecessor, 5G, which has significantly enhanced data rates, reduced latency, and enabled massive machine-type communications, 6G is poised to deliver even more profound advancements by exploiting higher frequency bands, including the mid band (1-24 GHz), the mmWave (24-92 GHz), and sub-THz (92-300 GHz) spectrum [4]. Among these bands, the sub-terahertz band and the upper mid-band (7-24 GHz) have recently garnered significant attention as candidate frequency bands. Recent advancements in new spectrum technologies (NSTs) have highlighted the critical role of the mid-to-high frequency spectrum (7.125-24.25 GHz, FR3) as the most promising candidate for 6G deployment [5], owing to its advantages in capacity, coverage, and spectral efficiency, as well as its integration with integrated sensing and communication (ISAC) technologies [6], and it has been identified by the ITU as a critical technology for enabling key 6G applications and underscores its growing importance in the evolution of next-generation wireless systems. To enable seamless, ubiquitous connectivity in the 6G era, the integrated space-air-ground-sea communication (ISAGSC) framework has emerged as a highly promising heterogeneous network solution to address coverage challenges in harsh environments such as forests, deserts, and oceanic regions [7]. Within this framework, AI/ML-enhanced air interface (EAI) technologies have become a key research focus, with 3 GPP Release 18 actively exploring the potential of AI/ML-based algorithms to enhance the air interface [8]. Current research focuses on advancements in channel state information feedback for massive multi-input multi-output, reference signals for channel estimation, beam management, and wireless positioning. In the context of communication transmission environments, reconfigurable intelligent surfaces (RISs) [9] are regarded as a transformative technology for 6G. By altering the electrical and magnetic properties of material surfaces to control the reflection of electromagnetic waves, RISs can operate without high-energy-consuming sources such as radio frequency chains, offering cost advantages over massive multi-input multi-output and providing unique wireless communication capabilities for 6G [10]. These improvements are not just incremental; they represent a paradigm shift in the capabilities and applications of wireless networks. The significance of 6G lies in its potential to catalyze the integration of communication, computing, and sensing, thereby enabling a wide array of transformative applications. Understanding the evolution of 6G technologies is crucial for researchers, industry stakeholders, and policymakers as it will shape the future landscape of communication and drive the development of new applications and services that can significantly impact various sectors of society [11]. However, the rapid proliferation and diversification of 6G technologies, coupled with their intricate interdependencies, pose significant challenges for field-related experts in manually analyzing and tracking technological evolution trends, as the dynamic landscape often outpaces traditional analytical capabilities. This highlights the necessity for more advanced and automated approaches to effectively navigate and understand the complex and fast-evolving 6G technological domain. Analyzing patents related to 6G is an effective way to understand the complex technological landscape and fast-evolving trends in this field [11].