Support Ticket Summarizer and Resolution Forecaster
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Solution Overview
Problem
Current support ticket systems lack efficient summarization and resolution forecasting capabilities, relying on limited metadata that fails to capture the evolution of support tickets and often requires manual effort, leading to suboptimal customer satisfaction and resource allocation.
Innovation Solution
A machine learning-based system that trains models to identify topic sequences in support ticket communications, classify similar tickets, and predict subsequent topics, providing automated summarization and resolution forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual summarization and resolution forecasting are used by support agents, then contextual understanding and accuracy can be achieved, but time consumption and workload increase significantly
Solution Approach 1:
The support ticket system performs self-service by automatically generating summaries and forecasting resolutions using machine learning models, eliminating the need for manual agent intervention in these tasks while maintaining high accuracy through trained algorithms that analyze ticket communications independently
Solution Approach 2:
The patent replaces the mechanical manual process of summarization and forecasting with an automated machine learning-based system that processes ticket communications, identifies topic sequences, and predicts outcomes without human physical intervention, thereby reducing time consumption while preserving accuracy
2Device complexity
If limited metadata is used for support ticket representation, then data processing simplicity is maintained, but informational enrichment and resolution forecasting capability are insufficient
Solution Approach 1:
The patent segments the support ticket data into distinct topic sequences extracted from communications, allowing the system to process and analyze different aspects of ticket evolution separately while maintaining overall informational completeness and enabling sophisticated forecasting capabilities
Solution Approach 2:
The patent adds a new dimension to support ticket representation by introducing topic sequences that capture the temporal evolution and contextual flow of communications, transforming static metadata into dynamic, information-rich representations that enable accurate resolution forecasting without overwhelming processing complexity
3Productivity
If support agents handle multiple tickets simultaneously, then productivity is maintained, but problem resolution quality and customer satisfaction may deteriorate
Solution Approach 1:
The machine learning model provides feedback to support agents by forecasting likely resolutions and identifying relevant topic sequences, enabling agents to quickly assess ticket priorities and allocate attention appropriately while maintaining high resolution quality across multiple concurrent tickets
Solution Approach 2:
The patent introduces an automated summarization and forecasting system as an intermediary between the support ticket and the agent, providing enriched contextual information that enables agents to efficiently manage multiple tickets while maintaining high resolution quality through informed decision-making
Data Source
AI summary
A support ticket summarizer, similarity classifier, and resolution forecaster are described. A system trains a machine learning model to identify topic sequences for support ticket communications, identify topic sequences that are classified as similar, and predict subsequent topics for multiple support ticket communications, in response to receiving the support ticket communications. The machine learning model receives a communication for a support ticket, and then identifies a sequence of topics for the communication for the support ticket. The machine-learning model identifies historical sequences of topics, for historical support tickets, which are classified as similar to the sequence of topics. The machine-learning model uses the historical sequences of topics to predict at least one subsequent topic for the sequence of topics. The system outputs the at least one subsequent topic.