Automated Pattern Detection in IT Service Tickets
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Solution Overview
Problem
Organizations face challenges in efficiently addressing an increasing number of IT service tickets without hiring additional IT Support Technicians, as existing methods lack effective automated systems for early detection of IT-related issues.
Innovation Solution
The use of statistical process control, natural language processing, artificial intelligence, and machine learning to analyze service tickets, identify keyword patterns, and generate reports that highlight implicated IT resources and affected users, enabling proactive issue resolution through a master service ticket system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If additional IT Support Technicians are hired to address increasing service tickets, then service ticket resolution capacity is improved, but labor cost and operational complexity increase
Solution Approach 1:
The system enables self-service through automated pattern detection and master service ticket generation. The analytics computer application automatically analyzes service tickets, identifies patterns, and creates master service tickets that consolidate multiple related tickets, reducing the need for manual intervention by individual support technicians while maintaining high resolution capacity
Solution Approach 2:
The system performs preliminary action by proactively detecting patterns in service tickets before they escalate into widespread issues. The analytics computer application continuously monitors incoming tickets, identifies emerging patterns, and generates master service tickets in advance, allowing IT operations to address systemic issues before they require extensive manual intervention
2Measurement precision
If manual analysis of service tickets is performed to detect IT issues, then issue detection accuracy is improved, but time consumption and labor resources increase
Solution Approach 1:
The system replaces the mechanical system of manual ticket analysis with an automated computer-based analytics system. The analytics computer application uses natural language processing and pattern recognition algorithms to analyze service tickets, substituting human manual review with automated computational analysis that achieves comparable or superior detection accuracy while dramatically reducing time consumption
Solution Approach 2:
The system introduces an intermediary analytics computer application between the service tickets and human support technicians. This intermediary automatically processes and analyzes tickets, identifying patterns and generating master service tickets, thereby filtering and preparing information before it reaches human analysts and reducing their time burden while maintaining detection accuracy
3Speed
If service tickets are analyzed in real-time to detect patterns, then response speed is improved, but computational resource consumption increases
Solution Approach 1:
The system implements periodic action by analyzing service tickets in batches or at scheduled intervals rather than continuously processing each ticket immediately upon arrival. The analytics computer application processes tickets in organized cycles, identifying patterns over time windows, which reduces peak computational resource consumption while maintaining effective pattern detection and appropriate response speeds
Solution Approach 2:
The system applies segmentation by dividing the service ticket analysis process into distinct phases and components. The analytics computer application segments tickets into groups based on characteristics, processes them in manageable batches, and analyzes patterns at different levels (individual tickets, grouped tickets, overall trends), reducing computational resource demands while maintaining detection effectiveness
4Measurement precision
If comprehensive analysis of all service tickets is performed, then pattern detection accuracy is improved, but system complexity and processing overhead increase
Solution Approach 1:
The system extracts only the essential and relevant features from service tickets for pattern analysis. The analytics computer application identifies and extracts key parameters such as error messages, affected systems, user actions, and ticket categories, focusing computational resources on the most discriminative features rather than analyzing every aspect of each ticket, thereby maintaining detection accuracy while reducing system complexity
Data Source
AI summary
Systems and methods pattern detection in service tickets are disclosed. According to one embodiment, an analytics computer application may: (1) determine a service ticket threshold for an Information Technology (IT) resource; (2) receive a plurality of service tickets for the IT resource; (3) pre-process text of the plurality of service tickets; (4) identify keyword patterns in the pre-processed text of the plurality of service tickets; (5) group the plurality of service tickets into subsets of service tickets based on the keyword patterns; (6) determine that a number of service tickets in one of the subsets of service tickets exceeds the service ticket threshold; (7) identify an IT component associated with the subset of service tickets; (8) identify users affected by the IT component; and (9) notify the users affected by the IT component of an issue with the IT component.