AI Root Cause Detection System for Computing Environment Issues
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Solution Overview
Problem
In computing systems, identifying and correcting the root cause of numerous issues that occur frequently is challenging due to the complexity of hundreds of computers and applications, leading to performance degradation and customer dissatisfaction.
Innovation Solution
A root cause detection system utilizing machine learning, neural networks, and large language models to analyze trends, identify root causes, and provide recommendations for resolving issues in a computing system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and analysis of root causes is performed in a computing system with hundreds of computers and applications, then human expertise and judgment can be applied, but the time and resources required increase significantly making it difficult to identify and correct issues promptly
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated machine learning system that uses AI models to detect adverse trends, identify issues, determine root causes, and generate remediation recommendations. This automated system processes computing system data without human intervention, resolving the contradiction by eliminating the time-consuming manual analysis while maintaining or improving identification accuracy through algorithmic pattern recognition.
Solution Approach 2:
The patent introduces an intermediary machine learning system that acts as a bridge between raw computing system data and actionable insights. This intermediary automatically performs data collection, analysis, and recommendation generation, freeing human operators from time-consuming manual tasks while providing structured, consistent analysis that improves both speed and accuracy of root cause identification.
2Loss of information
If comprehensive monitoring and analysis of hundreds of computers and applications is implemented, then complete visibility into system issues is achieved, but the complexity of the system increases making it harder to manage and analyze
Solution Approach 1:
The patent segments the complex analysis task into distinct automated components: an AI model for detecting adverse trends, another for identifying specific issues, a third for determining root causes, and a fourth for generating remediation recommendations. This segmentation transforms the overwhelming complexity of analyzing hundreds of computers and applications into manageable, automated stages, maintaining complete system visibility while reducing management burden through systematic decomposition.
Solution Approach 2:
The patent implements a self-service automated system that independently collects data, analyzes trends, identifies issues, determines root causes, and generates remediation recommendations without requiring human intervention at each step. This self-service capability maintains comprehensive system monitoring while eliminating the complexity of manual management, as the system autonomously handles the entire analysis workflow.
3Productivity
If automated machine learning systems are deployed to detect and analyze issues, then analysis speed and consistency are improved, but the initial setup complexity and computational resources required increase
Solution Approach 1:
The patent employs preliminary action by pre-training machine learning models with historical computing system data before deployment. The models are prepared in advance with learned patterns and relationships, enabling them to quickly detect adverse trends and identify issues upon deployment. This preliminary training phase, while requiring initial computational resources, establishes the foundation for rapid, consistent analysis without requiring complex real-time computation during operational issue detection.
Data Source
AI summary
Systems, methods, and computer program products for using artificial intelligence (AI) models, machine learning, and large language models to identify root causes of issues in a computing environment where multiple applications and computing devices operate. One or more AI models may determine adverse trends from one or more issue metrics, where an issue metric corresponds to issues occurring in a computing system. The AI models may identify issues corresponding to the adverse trends. From the identified issues, the AI models may determine root causes from the issues and the impacted area information and process information from the issues. From the issues and the impacted area information and process information from the issues the AI models may determine recommendations for rectifying the issues. The relations between the root causes, the impacted area information, the process information, and the issues may be formatted and displayed as a traversable network graph.