Abstract
Traditional video segmentation methods that offer limited granularity, static chapter structures. Such solutions lack deep semantic understanding and do not incorporate user interaction data for video segmentation, leading to inefficient information discovery and a poor user experience. This disclosure describes the use of generative artificial intelligence (genAI) to automatically create and dynamically refine smart chapters for long-form videos. These chapters are rich in detail, including titles, summaries, key concepts, and preview clips. With user permission, user interaction data can be used to dynamically adapt the chapter structure for individual viewers or the overall population. The techniques transform static videos into dynamic, easily navigable content with a chapter structure that improves over time and eases navigation within a video.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Labzovsky, Ilia and Karmon, Danny, "Automated Self-Improving and User-Responsive Video Chapter Generation Using Generative Artificial Intelligence", Technical Disclosure Commons, (August 20, 2025)
https://www.tdcommons.org/dpubs_series/8494