Service Ticket Escalation via Interaction Pattern Analysis
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Solution Overview
Problem
Large-scale computing systems face delays in resolving service incidents due to push-based escalation methods, which incur high costs and reduce user experience, as service tickets often take a long time to reach suitable support tiers.
Innovation Solution
A support management system with automated escalation based on interaction patterns, using predictive, keyword, spike, and time-based modules to identify and prioritize service tickets for immediate escalation to higher support tiers, reducing the need for intermediate tiers and minimizing delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If push-based escalation is used to route service tickets through intermediate support tiers, then support costs are reduced by filtering tickets through lower tiers first, but resolution time increases significantly as tickets wait at each tier
Solution Approach 1:
The system performs preliminary analysis of service tickets using multiple candidate modules (predictive, keyword, spike, time-based) to identify tickets requiring immediate escalation before they are routed through intermediate support tiers. This pre-screening action allows critical tickets to bypass lower tiers and go directly to appropriate support levels, eliminating waiting time while maintaining cost efficiency for non-critical tickets.
Solution Approach 2:
The patent implements a mechanism that allows service tickets to skip intermediate support tiers entirely when identified as high-priority through the candidate module evaluation. The framework enables direct routing from lower to higher support tiers for tickets matching escalation criteria, effectively rushing critical tickets through the support pipeline without the delays inherent in sequential tiered processing.
2Measurement precision
If multiple candidate modules are used to evaluate service tickets for escalation, then escalation accuracy improves by reducing duplicate escalation, but system complexity increases
Solution Approach 1:
The support management system is divided into distinct candidate modules (predictive module, keyword module, spike module, time-based module), each responsible for evaluating specific escalation criteria. This segmentation allows each module to specialize in particular aspects of ticket evaluation, improving overall accuracy while maintaining manageable complexity through modular design. The framework integrates these modules independently rather than requiring a monolithic complex system.
Solution Approach 2:
The framework serves multiple functions simultaneously: it evaluates predictive patterns, searches for keywords, detects spikes in ticket volume, and analyzes time-based patterns all within a single unified system. This multi-functionality reduces the need for separate specialized systems for each evaluation criterion, managing complexity while maintaining comprehensive escalation accuracy through a single versatile framework.
Data Source
AI summary
Techniques for managing support computing services in computing systems are disclosed. One example technique includes analyzing data representing a service ticket having data representing multiple messages exchanged between the user and a support entity of a support tier in the computing system. Based on the analysis, an interaction pattern in the multiple messages can be identified. The interaction pattern includes a sequence of words or phrases arranged in a chronological order. The method can further include determining whether the identified interaction pattern matches one or more interaction patterns corresponding to previously escalated service tickets and triggering a notification of the immediate escalation of the service ticket to another support entity in another support tier in response to determining that the interaction pattern matches one of the one or more interaction patterns corresponding to previously escalated service tickets.