Incident Ticket Impact Prediction With Real-Time Infrastructure Data
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Solution Overview
Problem
Incident tickets often contain incomplete and inaccurate information, leading to inefficient and time-consuming manual review processes during root cause analysis.
Innovation Solution
A computerized method using a machine learning prediction model to analyze real-time system data and user-provided incident tickets, identifying potentially impacted infrastructure components and users, and updating tickets with additional details to enhance root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of user-generated incident tickets is performed, then incomplete and inaccurate information can be identified, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated machine learning model that analyzes incident tickets. The model uses natural language processing and classification algorithms to extract, validate, and enrich incident information automatically, eliminating the need for manual human review while maintaining or improving information accuracy.
Solution Approach 2:
The incident ticket system performs self-service by automatically enriching and validating ticket information through the machine learning model. The system autonomously identifies missing information, classifies incidents, and prioritizes tickets without requiring external manual intervention, thereby reducing review time while maintaining quality.
2Measurement precision
If detailed information is collected in incident tickets, then root cause analysis accuracy improves, but the complexity of ticket processing increases
Solution Approach 1:
The patent segments the complex ticket processing task into distinct components handled by the machine learning model: information extraction, validation, enrichment, and classification. Each component processes specific aspects of the incident data independently, reducing overall processing complexity while comprehensively gathering detailed information for accurate root cause analysis.
3Reliability
If standardized incident tickets are generated, then data quality improves, but the automation requirements increase
Solution Approach 1:
The patent implements comprehensive automation by replacing manual ticket standardization processes with a machine learning-based system. The model automatically enforces data quality standards, validates information completeness, and standardizes ticket formats through algorithmic processing, achieving high data quality with full automation rather than partial manual intervention.
Data Source
AI summary
The disclosure herein describes predicting potential impact of issues reported in incident ticket data on infrastructure element. A ticket manager component includes an impact model utilizing machine learning to analyze real-time event and metric data with incident-related data to generate predicted impact data. The predicted impact data identifies potentially impacted infrastructure elements, such as, potentially impacted users, predicted infrastructure components impacted by the issue and/or an updated time-period associated with the issue. The ticket manager component creates labeled incident tickets by updating user-generated incident tickets with additional data generated by the impact model, including predicted impact data and/or additional details associated with the issue. The labeled incident tickets are provided back to the model as training data to further refine predictions generated by the model.