Abstract
This document proposes a novel approach using Artificial Intelligence (AI), including Large Language Models (LLMs) and AI agents, to address the challenge of correlating low Quality of Experience (QoE) with underlying network Quality of Service (QoS) parameters in multi-user communication platforms like online teleconferencing systems. The proposed techniques facilitate real-time monitoring of audio/video feeds from teleconference participants to identify and label communication disruptions and correlate these disruptions with network parameters. This enables proactive network and application optimization to enhance user experience.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Rezaee, Arman and Gundavelli, Sri, "REAL-TIME MONITORING AND ENHANCEMENT OF MULTI-USER COMMUNICATIONS WITH AI AGENTS", Technical Disclosure Commons, (April 08, 2025)
https://www.tdcommons.org/dpubs_series/7983