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Characterizing generative artificial fi cial intelligence applications: Text- mining-enabled technology roadmapping
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DOI：10.1016/j.jik.2024.100531.png)
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摘要
En 中文
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. (c) 2024 The Authors. Published by Elsevier Espa & ntilde;a, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Keyword:
Generative AI
Technology roadmapping
Patents
Text-mining
Structural topic modeling
Patent data mining
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期刊
IF:
15.5
论文数:
912
被引数:
6.4K
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