Automated Issue Identification in IT Support Tickets
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Solution Overview
Problem
Current systems for identifying issues in IT support tickets are manual, resource-intensive, and lack automation, failing to provide detailed insights due to large volumes of unstructured data and limited ability to integrate with various platforms, resulting in inefficient service improvements and cost optimization.
Innovation Solution
A method and system that automatically identify issues in tickets by generating sub-sequence patterns of n-grams from ticket data, determining their frequency and Part-of-Speech weightage, and scoring them to identify key patterns associated with the highest scores, providing detailed insights for service improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is performed to identify issues in tickets, then detailed insights can be obtained, but the process becomes tedious and time-consuming with huge resource involvement
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computational system that uses NLP techniques (n-gram extraction, POS tagging, frequency analysis) to identify issues in tickets. This substitution eliminates the need for human analysts to manually examine large volumes of unstructured ticket data while maintaining high precision in issue identification through algorithmic pattern recognition.
2Reliability
If existing supervised techniques are used to analyze tickets, then some patterns can be identified, but the systems lack robustness and generic applicability across different platforms
Solution Approach 1:
The patent creates a universal issue identification system that can process tickets from multiple different platforms and data sources without requiring platform-specific customization. The system uses generic NLP techniques (n-gram extraction, POS tagging, frequency analysis) that are applicable across diverse ticket formats and structures, making it adaptable to various IT support environments while maintaining consistent reliability in issue identification.
3Loss of information
If detailed analysis of unstructured data is performed to identify specific improvement areas, then service improvements can be ensured, but the process requires huge resources and is not scalable
Solution Approach 1:
The patent replaces resource-intensive manual analysis with an automated computational system that efficiently processes unstructured ticket data. The system extracts n-grams, applies POS tagging, calculates frequencies, and identifies issues automatically, maintaining detailed information extraction while dramatically improving productivity and scalability to handle large volumes of tickets across multiple platforms without proportional increases in human resources.
Data Source
AI summary
The present disclosure relates to method and system for automatically identifying one or more issues in one or more tickets of an organization. An issue identification system retrieves a sequence pattern from ticket data received from one or more data sources. The issue identification system generates one or more first sub-sequence patterns of the n-grams from the sequence pattern. Further, frequency of occurrence and Part-of-Speech (POS) weightage of each of the one or more first sub-sequence patterns of the n-grams are determined by the issue identification system. A first score is determined for each of the one or more first sub-sequence patterns of the n-grams based on both the frequency and the POS weightage. Upon determining the first score, the issue identification system identifies one or more issues in the one or more tickets automatically based on the first sub-sequence pattern of the n-grams associated with a highest first score.