Machine Learning Troubleshooting Engine for Computer System Root Cause Analysis
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Solution Overview
Problem
Diagnosing the root causes of complex computer system issues is time-consuming and resource-intensive, often requiring multiple teams and extensive analysis, as these issues can stem from various hardware and software compatibility problems, software corruption, or configuration issues, making it difficult for manufacturers and end-users to identify and resolve the primary cause effectively.
Innovation Solution
A centralized troubleshooting analysis engine utilizing artificial intelligence and machine learning-based classifiers to diagnose root causes by gathering system information, applying correlation rules, and identifying candidate root causes, thereby reducing the time and resources needed to resolve issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual troubleshooting methods are used to diagnose computer system issues, then thorough analysis can be performed, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The troubleshooting system segments the complex diagnosis process into distinct phases: data collection from multiple system components, correlation rule application to identify relationships, machine learning classification for root cause identification, and remediation recommendation. This segmentation enables automated parallel processing of different diagnostic aspects, maintaining thoroughness while reducing time consumption.
Solution Approach 2:
The patent introduces an intermediary automated troubleshooting system that acts as a mediator between the computer system and the user. This intermediary collects system data, applies correlation rules, and uses machine learning models to identify root causes, thereby eliminating the need for time-consuming manual analysis while preserving diagnostic accuracy.
2Reliability
If multiple teams perform extensive analysis to diagnose complex computer system issues, then comprehensive coverage of potential causes is achieved, but resource consumption increases
Solution Approach 1:
The troubleshooting system implements a universal automated diagnostic platform that can handle multiple types of computer system issues (hardware failures, software bugs, configuration problems, compatibility issues) through a single integrated system. This multi-functional approach maintains comprehensive diagnostic coverage while eliminating the need for multiple specialized teams, thereby reducing resource consumption.
Solution Approach 2:
The system enables self-service troubleshooting by automatically collecting system data, applying correlation rules, and using machine learning models to identify root causes without human intervention. The automated generation of remediation recommendations further reduces resource consumption by eliminating the need for manual analysis while maintaining high diagnostic reliability.
3Productivity
If automated systems are used to diagnose computer system issues, then speed and efficiency improve, but the complexity of the diagnostic system increases
Solution Approach 1:
The patent replaces manual mechanical troubleshooting processes with automated electronic systems. Machine learning models and correlation rule engines automatically analyze system data, identify patterns, and determine root causes, thereby improving troubleshooting efficiency. The complexity is managed through modular architecture where the automated system handles complex analysis while presenting simple results to users.
Data Source
AI summary
A process includes responsive to an issue occurring with the computer system, receiving, by a troubleshooting analysis engine, data from the computer system representing information about the computer system. The process includes processing, by the troubleshooting analysis engine, the data to identify a parameter of the computer system having an unexpected value; and searching, by the troubleshooting analysis engine, a design database to identify a design infrastructure of the computer system that is causally linked to the issue. The process includes analyzing, by the troubleshooting analysis engine, the design infrastructure using machine learning to identify a candidate cause of the issue.