Ticket Troubleshooting Support System Using ML Clustering
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Solution Overview
Problem
Conventional troubleshooting guides (TSGs) become outdated quickly, leading to inefficient and costly support processes as they rely on outdated information, causing many tickets to be escalated to engineering, wasting human and technological resources.
Innovation Solution
A machine learning model is trained using historical support tickets to identify common commands for resolving issues, clustering similar tickets, and applying these commands to new tickets based on problem similarity, eliminating the need for frequent TSG updates and enabling automatic or guided resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional troubleshooting guides (TSGs) are used, then support processes can be standardized, but the guides become outdated quickly leading to wasted resources and ticket escalations
Solution Approach 1:
The system enables self-updating of troubleshooting guides by automatically learning from resolved support tickets. The AI model continuously absorbs new problem-solution pairs from the support system, allowing the TSG to self-improve and stay current without manual intervention, thus maintaining reliability while preserving standardization benefits
Solution Approach 2:
The system implements feedback loops where resolved support tickets are fed back into the AI training dataset. The model learns from actual resolution outcomes and continuously updates the troubleshooting guides, ensuring the information remains accurate and current while maintaining standardized support processes
2Device complexity
If outdated TSGs are used, then existing support infrastructure can be maintained, but tickets are escalated to engineering wasting human and technological resources
Solution Approach 1:
The AI model serves as an intermediary layer between the simple existing support infrastructure and the need for accurate troubleshooting. It sits in the middle, taking inputs from resolved tickets and generating updated troubleshooting guidance, thus improving productivity without requiring complete infrastructure overhaul
Solution Approach 2:
The system performs preliminary actions by proactively learning from resolved tickets and updating troubleshooting guides before new issues arise. This anticipatory approach ensures accurate support information is available in advance, preventing ticket escalations and improving support efficiency without adding complex infrastructure
3Reliability
If manual TSG updates are performed, then troubleshooting information can be kept current, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The AI model performs self-updating of troubleshooting guides by automatically learning from resolved support tickets. This eliminates the need for manual TSG maintenance, keeping information current while freeing up time resources that would otherwise be spent on manual updates
Solution Approach 2:
The system enables continuous learning and updating of troubleshooting guides as new tickets are resolved. Instead of periodic manual updates, the AI model continuously absorbs new information, ensuring the TSG remains current without interrupting support operations or consuming additional time resources
4Extent of automation
If AI model is implemented, then automatic solution identification is achieved, but the system complexity increases
Solution Approach 1:
The AI model creates simplified representations (copies) of complex problem-solution patterns from historical tickets. By learning these patterns and storing them as training data, the system achieves automatic solution identification while managing complexity through pattern abstraction rather than raw data processing
Solution Approach 2:
The system manages complexity by changing parameters in the learning process, such as adjusting the training dataset composition, model architecture parameters, and confidence thresholds. These parameter adjustments allow the system to achieve automation while controlling the effective complexity at different operational stages
Data Source
AI summary
Systems and methods for providing ticket support using a machine learning model trained using clusters of support tickets that are clustered based on similarity of resolution commands are provided. The system extracts commands used to resolve prior tickets and creates clusters of resolved tickets based on similarity of the commands. For each cluster, problem statements are extracted from the resolved tickets. The system trains a machine learning model with the extracted problem statements to identify a cluster number for each cluster. With a new support ticket, the system extracts a problem statement from the new ticket and identifies a predicted cluster number by applying the trained machine learning mode! to the problem statement from the new ticket. Based on the predicted cluster number, one or more commands used to resolve the prior tickets in the cluster corresponding to the predicted cluster number are accessed and provided to a requesting user.