AI Incident Ticket Management for Automated Classification and Resolution
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Solution Overview
Problem
Current IT incident ticket management systems struggle to handle large volumes of support tickets efficiently, often failing to classify, prioritize, and resolve complex queries promptly, leading to delayed resolutions and potential loss of tickets.
Innovation Solution
An artificial intelligence-based incident ticket management system that classifies, prioritizes, and resolves incidents using natural language processing, sentiment analysis, and machine learning to automate ticket classification, prioritization, and resolution, integrating business rules and incident logs for proactive outage prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional IT incident handling systems are used to manage large volumes of support tickets, then the system structure remains simple and easy to operate, but the system fails to classify, prioritize, and resolve complex queries promptly, leading to delayed resolutions
Solution Approach 1:
The patent introduces an artificial intelligence intermediary that mediates between the incident ticket input and the resolution process. The AI system processes and analyzes incident tickets, extracting key information and determining appropriate resolutions without requiring complex human intervention at each step, thereby improving productivity while managing complexity through automation
Solution Approach 2:
The system enables self-service automation where the AI automatically classifies, prioritizes, and generates resolutions for incident tickets based on learned patterns from historical data. This self-service capability allows the system to handle routine incidents autonomously, improving resolution efficiency without proportionally increasing operational complexity
2Loss of time
If manual classification and prioritization of incident tickets are performed, then the system requires minimal automation resources, but the turnaround time for ticket resolution increases and tickets may be lost
Solution Approach 1:
The system performs preliminary classification and prioritization actions automatically using AI algorithms before human agents need to intervene. By pre-processing incident tickets through automated classification and prioritization, the system reduces the time agents need to spend on each ticket while maintaining high automation levels for routine decisions
Solution Approach 2:
The system implements feedback loops where AI models continuously learn from resolved incident tickets and improvement suggestions from agents. This feedback mechanism allows the automation to improve over time, reducing turnaround time further while the system adapts to organizational-specific patterns and requirements
3Reliability
If comprehensive incident analysis and root-cause investigation are conducted, then the quality of resolution improves, but the time required for each ticket increases
Solution Approach 1:
The AI system applies local quality analysis by focusing investigative efforts on specific relevant aspects of each incident based on its classification and the organization's historical data. Rather than conducting comprehensive analysis on every ticket uniformly, the system tailors the depth and scope of analysis to the specific incident type and complexity, improving resolution quality while reducing unnecessary analysis time
Data Source
AI summary
Methods, systems, and computer-program products for ticket management. The methods, systems, and computer-program products include receiving input data having information relating to one or more incidents, classifying each incident of the one or more incidents, escalating an incident of the one or more incidents based, at least in part, on at least one of the classification of each incident or a prioritization level associated with each incident and generating a resolution for the incident, the resolution including at least one of a solution to the incident or an assignment of the incident to a related team.