AT&T Patent for Network Troubleshooting Using Unsupervised Learning
Summary
The USPTO has published a new patent application from AT&T Intellectual Property I, L.P. for a network troubleshooting system utilizing unsupervised learning. The application, filed on September 23, 2024, describes a method for root cause analysis and solution recommendation based on clustering historical call flow failure traces.
What changed
This document is a publication of a patent application (US20260089051A1) filed by AT&T Intellectual Property I, L.P. on September 23, 2024. The patent describes a system for network troubleshooting that employs unsupervised learning, specifically clustering algorithms, to analyze historical call flow failure traces. Upon receiving a new failure trace, the system assigns it to a cluster to perform root cause analysis and recommend solutions.
As this is a patent application, it does not impose new regulatory obligations or compliance deadlines on entities. However, it signifies potential future technological advancements in network management and troubleshooting that companies in the telecommunications and technology sectors may wish to monitor for competitive or operational insights.
Source document (simplified)
NETWORK TROUBLESHOOTING WITH UNSUPERVISED LEARNING
Application US20260089051A1 Kind: A1 Mar 26, 2026
Assignee
AT&T Intellectual Property I, L.P.
Inventors
Anandarup Sarkar, Syed Anwar Aftab, Abbas Munawar Fazal
Abstract
Aspects of the subject disclosure may include, for example, performing an unsupervised learning by clustering historical call flow failure traces using a clustering algorithm suitable for use with categorical data. When a new call flow failure trace is received, it is assigned to a cluster, and a root cause analysis and solution recommendation is performed based on the cluster to which the new call flow failure trace is assigned. Other embodiments are disclosed.
CPC Classifications
H04L 41/0631 H04L 41/0677 H04L 41/16
Filing Date
2024-09-23
Application No.
18893099
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