Abstract
In online conference scenarios, shared content detection is widely used to determine whether content being shared is text content or video content. Determining the type of shared content can impact whether the video mode for the online conference favors higher resolution (for text content) or smoother motion (for video content). A system is proposed herein that leverages predictive hints from user actions (e.g., opening applications or files) and contextual audio cues to anticipate upcoming shared content types (e.g., video or text). By combining these hints with real-time content analysis, the system preemptively switches to the appropriate video mode for an online conference, significantly reducing decision-making time and improving accuracy. This ensures a smoother user experience with faster and more seamless transitions.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Tan, Jieyu; Wurm, Karlheinz; Li, Soya; and Wang, Wallice, "SHARER INTENTION ANALYSIS FOR CONTENT TYPE DETECTION", Technical Disclosure Commons, (October 21, 2025)
https://www.tdcommons.org/dpubs_series/8741