Cluster-Based Guidebook Generation for Ticket Processing
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Solution Overview
Problem
It is challenging to identify the best ticket processing guidebook for a given customer service ticket and to keep ticket processing guidebooks updated in IT environments where numerous customer service tickets need to be processed efficiently.
Innovation Solution
A method that involves extracting features from customer service tickets, clustering them based on similarities, and assigning a data-driven processing guidebook generated using machine learning techniques from historical tickets, which outlines independent actions to resolve the issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to identify and update ticket processing guidebooks, then guidebooks can be maintained, but the process becomes inefficient and cannot keep up with the large volume of tickets
Solution Approach 1:
The system automatically generates and updates ticket processing guidebooks by analyzing historical ticket data and clustering similar tickets, eliminating the need for manual creation and maintenance of guidebooks. The machine learning model self-updates as new tickets are processed, continuously improving without human intervention.
Solution Approach 2:
The patent replaces manual mechanical processes of guidebook creation and updating with automated machine learning algorithms that analyze ticket data, identify patterns, and generate guidebooks automatically, significantly improving efficiency and reducing time loss.
2Adaptability or versatility
If multiple ticket processing guidebooks are maintained to cover different scenarios, then coverage is improved, but the complexity of selecting the appropriate guidebook increases
Solution Approach 1:
The patent segments the large set of guidebooks into clusters based on similarity of the tickets they address. Each cluster contains guidebooks for similar ticket types, and the system automatically identifies which cluster a new ticket belongs to, simplifying the selection process while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary that automatically matches incoming tickets to the most appropriate guidebook cluster based on learned patterns from historical data, eliminating the need for manual selection and reducing complexity while maintaining adaptability.
3Reliability
If guidebooks are updated frequently to reflect current issues, then relevance is improved, but the resources required for manual updates increase
Solution Approach 1:
The patent implements a dynamic system where guidebooks are automatically updated as new tickets are processed. The machine learning model continuously learns from new data and adapts the guidebook content accordingly, ensuring relevance without manual intervention and reducing maintenance complexity.
Solution Approach 2:
The system incorporates feedback loops where resolved tickets are analyzed to improve future guidebook recommendations. The machine learning model learns from the outcomes of previous ticket resolutions and automatically adjusts guidebook content to maintain high relevance without requiring manual updates.
Data Source
AI summary
Techniques are provided for customer service ticket processing using cluster-based data driven guidebooks. One method comprises obtaining a customer service ticket; extracting features related to the customer service ticket, wherein the features comprise a representation of a problem associated with the customer service ticket; assigning the customer service ticket to a given cluster of multiple customer service ticket clusters based on the features; obtaining a customer service ticket processing guidebook associated with the given cluster that identifies independent actions to perform to address the problem; and processing the customer service ticket based on the customer service ticket processing guidebook. A customer service ticket processing guidebook may be generated for each customer service ticket cluster using historical customer service tickets from the respective cluster. The customer service ticket processing guidebooks can be generated by clustering (i) possible independent actions and (ii) possible solutions identified in the historical customer service tickets of the given cluster.