Abstract
Existing network troubleshooting tools typically remediate network issues in a reactive and localized manner, thus generating solutions that lack awareness of other active or historical sessions across the same network. As a result, multiple users often investigate identical or derivative problems, which leads to inefficient use of operational and computational resources. To address these challenges, the techniques presented herein implement an artificial intelligence (AI)-driven network troubleshooting system configured to perform context correlation, influence-radius detection, and automatic context-aware predictive remediation with proactive user notifications.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
S Desai, Vishal and Barton, Robert, "PREDICTIVE REMEDIATION BY ANALYZING INFLUENCE-RADIUS ACROSS GENERATIVE WORKSPACE BOARDS", Technical Disclosure Commons, (March 05, 2026)
https://www.tdcommons.org/dpubs_series/9454