Neural Network Ticket Pattern Grouping for ITSM Automation
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Solution Overview
Problem
Current IT support ticketing systems face inefficiencies in identifying and resolving issues across multiple tickets with similar symptoms, leading to increased resolution time and resource allocation challenges.
Innovation Solution
An ITSM system utilizing artificial neural networks to identify patterns across tickets, grouping them into primary and secondary tickets, and implementing automated resolution steps from a database to address the primary ticket, which can also resolve secondary tickets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing of each ticket is performed, then individual ticket accuracy is maintained, but resolution time and resource consumption increase significantly
Solution Approach 1:
The system enables automated self-service ticket processing through neural network-based pattern recognition. The ANN automatically identifies patterns across tickets, groups them, and selects resolution steps without human intervention, allowing the system to serve itself in resolving common issues while maintaining the option for human oversight when needed.
Solution Approach 2:
The system performs preliminary actions by pre-grouping tickets into patterns and pre-selecting resolution steps before actual ticket processing. The neural network analyzes historical ticket data and pre-establishes resolution pathways, so when a new ticket arrives, the system can quickly match it to existing patterns and apply predetermined solutions, significantly reducing resolution time.
2Loss of time
If automated pattern recognition is implemented, then resolution efficiency improves, but system complexity and initial resource allocation increase
Solution Approach 1:
The system replaces manual mechanical ticket processing with an automated neural network-based intelligent system. Instead of human analysts manually reviewing and categorizing each ticket, the ANN automatically performs pattern recognition, ticket grouping, and resolution step selection, substituting human cognitive work with machine learning algorithms that process tickets much faster and more consistently.
3Productivity
If tickets are grouped into patterns, then resource allocation efficiency improves, but the accuracy of individual ticket resolution may decrease
Solution Approach 1:
The system dynamically adjusts the level of automation and grouping based on ticket characteristics. For common, well-defined patterns, the system applies automated grouped resolution to maximize efficiency. For unique or complex tickets that don't match existing patterns, the system can escalate to manual processing or create new pattern groups, ensuring individual ticket accuracy is maintained when needed while preserving resource efficiency for standard issues.
Data Source
AI summary
A plurality of tickets can be received. A pattern pertaining to an issue with an application can be determined by at least one artificial neural network. The pattern can be indicated among at least a portion of the plurality of tickets. The portion of the plurality of tickets can be grouped into a group comprising a primary ticket and at least one secondary ticket. Automation steps can be accessed from a database. The automation steps can be implemented to resolve the issue for the primary ticket. Responsive to the resolving the issue by implementing the automation steps from the database for the primary ticket, the secondary ticket can be closed.