ML Engine for Proactive Network Maintenance
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Solution Overview
Problem
The performance of network devices can decrease over time or unexpectedly, leading to inefficient and costly customer service operations for vendors and multiple service operators (MSOs).
Innovation Solution
A system that utilizes proactive network maintenance (PNM) data collected by network devices and analyzed by a machine learning (ML) engine to generate an ordered list of predetermined operations predicted to increase network device performance. This list is provided to operators who can execute the operations to improve network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional customer service operations are used to troubleshoot network device performance issues, then operators can resolve problems through manual intervention, but the cost of supporting network devices increases and service efficiency decreases
Solution Approach 1:
The system performs preliminary analysis of PNM data using machine learning algorithms before customer service intervention is needed. The ML engine pre-processes and analyzes network device data to identify potential issues and generate predicted solutions in advance, so that when a performance issue occurs, operators receive ready-to-apply solutions rather than needing to manually diagnose from scratch.
Solution Approach 2:
The system enables self-service through automated ML analysis that independently processes PNM data and generates troubleshooting solutions without requiring extensive human intervention. The machine learning engine autonomously identifies patterns, predicts failures, and recommends specific operations, reducing the need for operator expertise and manual labor in routine troubleshooting.
2Ease of repair
If manual troubleshooting methods are employed by operators, then network device issues can be resolved through human expertise, but the cost associated with supporting network devices increases
Solution Approach 1:
The system replaces the mechanical human troubleshooting process with an automated machine learning system. Instead of operators manually analyzing PNM data and determining troubleshooting steps, the ML engine automatically processes the data, identifies issues, and generates predicted solutions, substituting human cognitive labor with automated computational processes that reduce support costs.
Solution Approach 2:
The machine learning engine acts as an intermediary between raw PNM data and operator intervention. Rather than operators directly analyzing complex network data or implementing ad-hoc troubleshooting, the ML engine serves as a mediator that transforms raw data into structured, actionable predicted solutions, making the repair process easier and more systematic while reducing the expertise burden on operators.
3Reliability
If reactive customer service is used where operators respond to reported issues, then problems can be addressed after they occur, but time is lost in troubleshooting and repairs
Solution Approach 1:
The system performs preliminary analysis of PNM data continuously in the background, building predictive models and identifying trends before actual performance degradation occurs. By pre-processing and analyzing network data proactively, the system prepares predicted solutions in advance, so that when a performance issue is reported, operators receive immediate actionable guidance rather than needing time for diagnostic analysis.
Solution Approach 2:
The system implements a feedback loop where PNM data from network devices is continuously collected, analyzed by the ML engine, and used to generate predicted solutions that are fed back to operators. This closed-loop feedback mechanism ensures that the system learns from past performance data and improves its predictive accuracy over time, reducing troubleshooting time with each iteration while maintaining reliable network performance.
Data Source
AI summary
Methods, systems, and devices for network maintenance are described. A controller may receive an indication that the performance of a network device has decreased. The indication may include case identification data associated with the network device. The controller may request proactive network maintenance (PNM) data associated with the network device based on receiving the case identification data. The controller may transmit the PNM data to a machine learning (ML) engine. The ML engine may transmit, to the controller, an indication of a predetermined operation predicted to improve the performance of the network device. The controller may transmit the predetermined operation to a client device based on receiving the indication of the predetermined operation. The ML engine may receive feedback indicating whether an execution of the predetermined operation improved the performance of the network device. The ML engine may a training data set based on the feedback.