Machine Learning Console for Enterprise Network Troubleshooting
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Solution Overview
Problem
Complexity in enterprise networks leads to inefficiencies in troubleshooting, as multiple technicians may work on similar issues without sharing knowledge, resulting in wasted time and effort.
Innovation Solution
A support assistance console connects to client systems, gathering data through agents to provide a graphical representation of network components and their status, enabling technicians to select components and attributes for analysis. A machine learning module compares data to identify similar issues in other systems, predicting solutions based on historical data and providing recommendations from experienced technicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple technicians work on similar issues independently without knowledge sharing, then each technician can focus on their own task, but time and effort are wasted due to duplicate work
Solution Approach 1:
The patent combines multiple technicians' troubleshooting activities into a unified system where data from all technicians is aggregated and analyzed. The machine learning model merges information from multiple case records, network configurations, and troubleshooting actions to generate comprehensive solutions, preventing duplicate work and improving overall productivity.
Solution Approach 2:
The system implements feedback mechanisms where troubleshooting results from one technician are immediately available to others through the centralized platform. The machine learning model continuously learns from new troubleshooting outcomes and updates its recommendations in real-time, creating a feedback loop that prevents redundant troubleshooting efforts.
2Measurement precision
If technicians manually analyze complex enterprise network data, then detailed analysis can be performed, but the process becomes time consuming
Solution Approach 1:
The patent replaces the mechanical manual analysis process with an automated machine learning system. The ML model automatically analyzes network configurations, logs, and diagnostic data, performing complex pattern recognition and root cause analysis without human intervention, thereby maintaining high accuracy while dramatically reducing troubleshooting time.
Solution Approach 2:
The system performs preliminary analysis of network data automatically before a technician begins troubleshooting. The machine learning model pre-processes large volumes of network data, identifies potential issues, and prepares diagnostic reports in advance, so when a technician needs to intervene, the groundwork is already done, reducing overall troubleshooting duration.
3Loss of information
If a centralized system collects and analyzes data from multiple client systems, then knowledge sharing improves, but system complexity increases
Solution Approach 1:
The patent implements a universal centralized platform that handles multiple functions: data collection from various client systems, machine learning model training and inference, knowledge base management, and technician interface. This multi-functional system consolidates what would otherwise require multiple separate systems, managing complexity through integration rather than proliferation of components.
Solution Approach 2:
The machine learning model acts as an intermediary between the raw data from multiple client systems and the technicians needing information. The ML layer processes, filters, and synthesizes data from diverse sources into actionable insights, simplifying the interface between data collection and knowledge application without requiring direct complex connections between all system components.
4Reliability
If machine learning models are trained on historical data to predict solutions, then solution accuracy improves, but data processing requirements increase
Solution Approach 1:
The patent extracts only the most relevant features and patterns from historical troubleshooting data for machine learning training. Rather than processing entire raw datasets, the system identifies and extracts key diagnostic indicators, network configuration parameters, and troubleshooting patterns that are most predictive of successful resolutions, reducing data processing requirements while maintaining prediction accuracy.
Data Source
AI summary
In some examples, after a client system encounters a problem, a technical support specialist may connect to the client system via a console. The console may display a graphical representation of a client system that includes a plurality of components. The console may execute a machine learning module to determine one or more potential solutions to the particular problem. Each solution of the one or more solutions may correspond to a previously resolved problem that is similar to the particular problem and may have an associated confidence level determined based on: a similarity of the particular problem to the previously resolved problem, a similarity of the plurality of components to a second plurality of components included in a second client system associated with the previously resolved problem, a similarity of a network topology of the client system to a second network topology of the second client system.