Abstract
During live game streaming, the high volume and speed of audience chat can present challenges for a streamer attempting to identify and act upon crowd-sourced information. This disclosure describes a system that may use an artificial intelligence agent to ingest and analyze multimodal data streams, which can include live chat, game video, and game audio. The agent can classify chat messages, score their contextual relevance, and use techniques such as semantic clustering to synthesize consensus-based advice from multiple viewers. This distilled guidance may then be rendered as a non-disruptive visual hint, for example, an on-screen object highlight or path indicator, which can be configured for visibility to the streamer. Such a system may assist streamers in leveraging collective audience intelligence in real-time, potentially reducing information overload and enhancing the collaborative gameplay experience, and can be designed to operate without direct game integration.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Labzovsky, Ilia and Karmon, Danny, "Artificial Intelligence Powered Distillation and Visualization of Audience Chat for Live Game Streaming", Technical Disclosure Commons, (October 14, 2025)
https://www.tdcommons.org/dpubs_series/8717