Machine Learning Network Event Remediation System
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Solution Overview
Problem
Telecommunications network disruptions due to equipment damage or malfunctions, such as severed fibers, are not efficiently addressed by existing technologies, leading to service disruptions and inefficiencies in remediation.
Innovation Solution
A system utilizing machine learning and automated techniques to identify network events, estimate remediation time, and autonomously initiate remediation, including communication with affected users and selection of appropriate remedial measures, such as reconfiguration of network components or dispatching technicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine learning techniques are implemented to identify and remediate network events, then remediation speed and efficiency are improved, but system complexity increases
Solution Approach 1:
The system enables automated self-service remediation where the network management system automatically identifies network events, determines appropriate remediation actions, and executes repairs without human intervention. Machine learning models analyze network data to autonomously diagnose issues and implement fixes, allowing the system to service itself and reduce dependency on manual operations.
Solution Approach 2:
The patent replaces manual mechanical processes with automated electronic systems. Instead of human operators physically examining and repairing network equipment, machine learning algorithms process network data electronically to detect events and trigger automated remediation workflows, substituting human mechanical actions with intelligent automated systems.
2Reliability
If manual monitoring and remediation processes are used for network events, then system complexity is kept low, but service disruption time increases
Solution Approach 1:
The system performs preliminary actions by proactively monitoring network parameters and detecting events before they cause significant service disruptions. Machine learning models analyze trends and patterns to identify potential issues early, enabling preemptive remediation actions that prevent or minimize service interruptions before they affect users.
Solution Approach 2:
The system implements continuous feedback loops where network performance data is constantly collected, analyzed by machine learning models, and used to adjust remediation strategies in real-time. The system monitors the effectiveness of remediation actions and automatically adjusts its behavior based on outcomes, creating a closed-loop control system that improves network reliability through adaptive learning.
Data Source
AI summary
A system described herein may use automated techniques, such as machine learning techniques, to identify network events (e.g., based on user-submitted reports and/or system-generated alerts). The system may identify a type and/or attributes of the network event, and may identify past network events that share the same or similar attributes. An estimated time to remediate the network event, and/or one or more remedial measures, may be determined and implemented based on remediation measures taken to remediate the past network events. The determined remedial measures may include one or more temporary remedial measures effected until a permanent solution is put into place. Users who are affected, or who are likely to be affected, may be contacted to indicate the estimated remediation time. Feedback may be used to refine the estimation and/or remediation for future similar network events.