This study aims to identify generative AI (GenAI) applications and develop a roadmap for the near, mid, and far future. Structural topic modeling (STM) is used to discover latent semantic patterns and identify the key application areas from a text corpus comprising 2,398 patents published between 2017 and 2023. The study identifies six latent topics of GenAI application, including object detection and identification; medical applications; intelligent conversational agents; image generation and processing; financial and information security applications; and cyber-physical systems. Emergent topic terms are listed for each topic, and inter-topic correlations are explored to understand the thematic structures and summarize the semantic relationships among GenAI application areas. Finally, a technology roadmap is developed for each identified application area for the near, mid, and far future. This study provides valuable insights into the evolving GenAI landscape and helps practitioners make strategic business decisions based on the GenAI roadmap.

中文翻译：

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表征生成人工智能应用：支持文本挖掘的技术路线图


本研究旨在确定生成式人工智能 (GenAI) 应用并制定近期、中期和远期的路线图。结构主题建模（STM）用于发现潜在语义模式，并从包含 2017 年至 2023 年期间发布的 2,398 项专利的文本语料库中识别关键应用领域。该研究确定了 GenAI 应用的六个潜在主题，包括对象检测和识别；医疗应用；智能对话代理；图像生成和处理；金融和信息安全应用；和网络物理系统。为每个主题列出了新出现的主题术语，并探索主题间的相关性以理解主题结构并总结 GenAI 应用领域之间的语义关系。最后，为每个确定的应用领域制定近期、中期和远期的技术路线图。这项研究为不断发展的 GenAI 格局提供了宝贵的见解，并帮助从业者根据 GenAI 路线图做出战略业务决策。