Adaptive Troubleshooting Pattern Learning System
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Solution Overview
Problem
Troubleshooting in complex computing environments is challenging due to the need for deep domain expertise, manual processes, and the lack of context-sensitive guidance, leading to increased turnaround times and reliance on experienced troubleshooters, with existing methods being difficult to document and maintain.
Innovation Solution
A system that records and transforms troubleshooting sessions into a graph-based pattern learning unit, recommending next queries based on similarity, frequency, and relevance, allowing for adaptive learning and guidance in live sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If troubleshooting patterns are documented in documents or articles, then knowledge is preserved, but they are difficult to use and maintain and lack context-sensitive guidance
Solution Approach 1:
The patent replaces manual document-based troubleshooting systems with an automated machine-learning system that dynamically generates context-sensitive guidance. The system uses natural language processing and graph-based pattern recognition to automatically analyze troubleshooting sessions and provide relevant guidance, eliminating the need for manual documentation creation and maintenance while delivering context-aware recommendations.
Solution Approach 2:
The troubleshooting system performs self-learning by automatically analyzing past troubleshooting sessions and generating updated guidance without requiring manual intervention. The machine learning model continuously improves by processing new troubleshooting data, automatically adapting to system changes and emerging patterns, thereby maintaining itself without human effort.
2Productivity
If experienced troubleshooters rely on tribal knowledge, then issue resolution is faster, but it takes time for novice troubleshooters to come up to speed
Solution Approach 1:
The system captures and replicates the expertise of experienced troubleshooters by analyzing their troubleshooting sessions and encoding their decision-making patterns into a machine learning model. This creates a digital copy of tribal knowledge that can be instantly accessed by any troubleshooter, regardless of experience level, thereby transferring expertise without requiring lengthy training periods.
Solution Approach 2:
The system provides real-time feedback to troubleshooters by analyzing their current troubleshooting actions and suggesting optimal next steps based on patterns from experienced practitioners. This feedback mechanism guides novice troubleshooters through the troubleshooting process, effectively transferring expert knowledge interactively and reducing the time needed to develop expertise.
3Adaptability or versatility
If systems change, then new issues are observed, but existing troubleshooting patterns lose relevance and require time to learn
Solution Approach 1:
The troubleshooting guidance system is designed to be dynamic and adaptive, continuously learning from new troubleshooting sessions and system changes. The machine learning model automatically updates its knowledge base as new issues emerge, adapting to system evolution without requiring manual reconfiguration or extensive retraining, thereby maintaining relevance in changing environments.
Solution Approach 2:
The system proactively learns and adapts to new troubleshooting patterns by continuously analyzing incoming troubleshooting sessions. Rather than waiting for patterns to become obsolete, the system performs preliminary learning actions by processing new data in real-time, ensuring that troubleshooting guidance remains current and relevant as systems evolve.
4Measurement precision
If troubleshooting is performed manually with deep domain expertise, then accurate problem diagnosis is achieved, but turnaround time increases
Solution Approach 1:
The patent introduces an intelligent intermediary system that acts as a bridge between raw troubleshooting data and expert diagnosis. The machine learning model processes and analyzes troubleshooting information, applying learned patterns to suggest accurate diagnoses and next steps, thereby capturing the diagnostic accuracy of experts while significantly reducing the time required for problem resolution.
Data Source
AI summary
The system may include a troubleshooting activity recorder configured to record troubleshooting sessions. Each troubleshooting session may include a sequence of queries and query results. The troubleshooting activity recorder may include a query transformer configured to transform the queries and the query results into transformed queries and transformed query results before recording the troubleshooting sessions. The troubleshooting activity recorder may be configured to record the transformed queries and the transformed query results as troubleshooting session information in a troubleshooting activity database. The system may include a troubleshooting pattern learning unit including a graph builder configured to generate a troubleshooting pattern graph having query nodes and links between the query nodes based on the troubleshooting session information.