The emergence of artificial intelligence-generated contents (AIGC) technology has posed great challenges to the economy and society. Most of the existing research discusses aspects of technical regulation and development history; few empirical studies are conducted on the patent layout of AIGC leading enterprises. This paper aims to direct its attention towards the comprehensive examination and scrutiny of the patent layout of enterprises at the forefront of AIGC in the United States. To accomplish this objective, this paper selects 14 leading startups and 4 technology giants in the field of AIGC in the United States to analyze their patent IPC network. Intricate network analysis methods and K-means clustering algorithms are employed.It is found that the patent layout of AIGC emerging enterprises in the United States focuses on technical fields such as electrical digital data processing, graphic data reading and presentation. Patent layout is analyzed by knowledge width, knowledge depth, knowledge cohesion, knowledge fragment level and knowledge consistency clustering. Through this meticulous study, a profound understanding of the patent layout is achieved, leading to the identification of three distinct categories: professional players, technology giants and leaders, and innovators. Professional players refer to AIGC companies that have a focused and specialized patent layout, indicating a deep understanding and expertise in specific technical fields. Technology giants and leaders are AIGC enterprises that have established themselves as industry leaders, with a patent layout that reflects their dominance and influence in the field. Innovators are AIGC startups that demonstrate a unique and novel approach in their patent layout, showcasing their ability to introduce new ideas and technologies to the AIGC industry. These categories provide invaluable insights into the diverse strategies implemented by the aforementioned enterprises.Furthermore, an in-depth exploration of the patent core knowledge areas and bridging knowledge areas is conducted, ultimately revealing the focal points of knowledge and the areas of expertise within AIGC companies. Core knowledge refers to the essential and fundamental knowledge areas that are central to the patent layout of AIGC enterprises. These knowledge areas represent the key technical fields in which the companies focus their patent filings. Bridging knowledge areas, on the other hand, are the knowledge areas that connect or bridge different technical fields within the patent layout of AIGC enterprises. These areas indicate the interdisciplinary nature of the companies' patent filings and their ability to integrate knowledge from multiple domains. The identification of core knowledge areas and bridging knowledge areas helps in understanding the strategic focus and innovation capabilities of AIGC companies, as well as the potential for cross-pollination of ideas and technologies across different technical fields.The paper's pivotal contribution lies within its empirical analysis of the patent layout, which presents a wealth of valuable insights into the intricate clustering patterns and specialized knowledge areas of the leading enterprises in the field of AIGC. Firstly, it provides empirical insights into the patent layout of leading AIGC enterprises. Secondly, it employs complex network analysis methods and K-means clustering algorithms in machine learning to examine the patent IPC network of AIGC companies, offering a novel approach to investigating their patent layouts. Thirdly, it categorizes the patent layout for AIGC enterprises into three distinct groups: professional players, technology giants and leaders, and innovators. These categories are determined based on factors such as knowledge width, depth, cohesion, fragment level, and consistency clustering. Fourthly, it identifies the core knowledge areas and bridging knowledge areas within the patent layouts of AIGC companies, thereby shedding light on their technical focus, interdisciplinary integration, and innovation capabilities.In summary, this analysis of the layout of patent networks for companies specializing in AIGC encompass enables a comprehensive understanding of the technical focus and specialization of AIGC companies based on the arrangement of their patents. Then it allows for the identification of prominent industry players and technology leaders within the AIGC sector based on the structure of their patent layouts, and facilitates an assessment of the innovation capabilities and novel approaches employed by AIGC companies, as reflected in their patent layouts. Furthermore, this analysis suggests policy measures for the development of the AIGC industry in China, with a specific focus on algorithm, computing power, and data.