Unstructured IT Ticket Clustering via Vocabulary Indices Matrix
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Solution Overview
Problem
Existing IT ticket clustering methods are inefficient and slow, particularly when dealing with large datasets, and often require access to templates that may not be available, hindering effective routing and automatic resolution of IT issues.
Innovation Solution
A computer-implemented method and system for clustering unstructured IT tickets using tokenization, frequency analysis, and vocabulary indices matrix generation, allowing for rapid and accurate clustering without requiring access to specific templates, employing techniques like k-means or hierarchical clustering to improve efficiency and quality of clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional IT ticket clustering methods are used, then clustering can be performed with template access, but the process is inefficient and slow particularly with large datasets
Solution Approach 1:
The patent extracts and removes the dependency on template access by implementing template-independent clustering methods. The system processes unstructured ticket text directly through tokenization and frequency analysis, eliminating the bottleneck of template matching while maintaining clustering effectiveness.
Solution Approach 2:
The patent replaces traditional mechanical template-matching clustering mechanisms with computational linguistics approaches including tokenization, frequency analysis, and matrix-based similarity computation. This substitution enables parallel processing of large datasets, dramatically improving clustering speed.
2Adaptability or versatility
If template-based clustering is used, then clustering can be performed with available templates, but templates may not be available hindering effective routing and automatic resolution
Solution Approach 1:
The patent creates a universal clustering system that functions without templates by implementing template-independent text analysis. The vocabulary indices matrix and frequency-based similarity measures enable the system to adapt to any ticket format or structure, making it universally applicable across different IT environments.
Solution Approach 2:
The system performs self-service clustering by automatically generating vocabulary and computing similarity metrics from raw ticket text without requiring external templates. The algorithm autonomously identifies patterns and groups tickets based on intrinsic textual characteristics.
3Productivity
If individual ticket resolution is performed without considering structural similarity, then each ticket can be resolved independently, but the process is inefficient
Solution Approach 1:
The patent segments the ticket processing workflow into distinct phases: tokenization of ticket text, frequency analysis to build vocabulary, matrix construction to represent ticket characteristics, and similarity-based clustering. This segmentation transforms individual ticket processing into a systematic batch operation, improving efficiency while managing complexity through modular design.
Data Source
AI summary
Systems, methods, and computer program products relating to clustering unstructured data. A set of unstructured documents is tokenized to produce a plurality of tokens. A frequency at which terms appear in the plurality of tokens is analyzed, to generate a vocabulary of terms. A vocabulary indices matrix is generated based on the generated vocabulary of terms. The matrix relates to the set of unstructured documents. A plurality of rows in the vocabulary indices matrix are matched to generate a plurality of clusters for the set of unstructured documents.