Hybrid Ticket Classification Using Confidence Thresholds and Super-Clusters
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Solution Overview
Problem
As enterprise computing systems grow larger, manual ticket classification becomes inefficient, and existing automatic classification systems struggle to handle large volumes and respond to new types of tickets encountered during training.
Innovation Solution
A hybrid ticket classification system employing both supervised and unsupervised learning to label tickets, using a ticket classifier that assigns confidence scores and clusters tickets by semantic similarity, forming super-clusters to automatically respond to tickets with below-threshold confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual classification of tickets is used, then classification accuracy is maintained, but productivity decreases and the system cannot scale to handle large volumes of tickets
Solution Approach 1:
The classification system is segmented into multiple stages: an initial classifier provides quick classification for straightforward cases, while a secondary refinement stage handles ambiguous or complex tickets. This segmentation allows the system to maintain high throughput for simple tickets while ensuring accuracy for difficult cases, resolving the contradiction between productivity and automation capability.
Solution Approach 2:
The system dynamically adjusts its classification approach based on ticket characteristics. For high-confidence cases, the system uses rapid automated classification; for low-confidence or complex cases, it applies more sophisticated analysis methods or escalates to human reviewers. This dynamic behavior enables the system to scale efficiently while maintaining classification quality.
2Productivity
If existing automatic classification systems are used, then productivity increases, but the systems cannot respond to new types of tickets encountered during training
Solution Approach 1:
The system performs preliminary clustering and analysis on incoming tickets to identify potential new ticket types before full classification occurs. By detecting patterns and anomalies in advance, the system can adapt its classification rules dynamically, allowing it to handle new ticket types without sacrificing the productivity gains from automated processing.
Solution Approach 2:
The classification system incorporates feedback mechanisms where classification results and ticket characteristics are continuously analyzed to improve the model's ability to recognize new ticket types. This feedback loop enables the system to learn from encountered tickets and adapt to new patterns while maintaining high throughput classification performance.
Data Source
AI summary
Systems and methods for ticket classification and response include labeling tickets with a ticket classifier that assigns a ticket label and an associated confidence score to each ticket. Tickets are clustered according to semantic similarity to form ticket clusters. A template associated with each ticket cluster is determined. Templates and the respective ticket clusters are clustered according to semantic similarity to form one or more ticket super-clusters. Tickets that have below-threshold confidence scores are labeled according to the one or more ticket super-clusters. The tickets are automatically responded to.