Helpdesk Ticket Explainer for Real-Time Incident Summaries
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Solution Overview
Problem
Customer-support ticketing systems in the gaming industry lack automated tools for real-time data analysis and visualization, requiring manual intervention for advanced analytics.
Innovation Solution
A cloud-based automated helpdesk explainer system that integrates Large Language Models (LLMs) and natural language processing (NLP) to analyze customer service tickets, generating real-time infographics and summaries for quick decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used for advanced analytics in customer service ticketing systems, then analysis accuracy can be maintained, but productivity and response time deteriorate due to the overwhelming number of tickets
Solution Approach 1:
The patent introduces an automated analytics system as an intermediary between the overwhelming volume of tickets and the need for accurate analysis. This system uses natural language processing and machine learning models to automatically analyze ticket data, generating insights and visualizations without requiring manual intervention for each ticket, thus maintaining productivity while preserving analysis accuracy through sophisticated automated algorithms
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. Instead of human analysts manually reviewing each ticket, the system uses natural language processing, topic modeling, and large language models to automatically process, analyze, and generate insights from ticket data, dramatically improving processing speed while maintaining analytical depth
2Productivity
If automated tools are implemented for real-time data analysis, then productivity and response time improve, but device complexity increases due to integration of multiple AI models and processing systems
Solution Approach 1:
The patent merges multiple AI components (natural language processing, topic modeling, large language models, and visualization systems) into a single integrated automated analytics platform. This consolidation allows the system to perform real-time analysis of customer service tickets while managing complexity through unified architecture and centralized processing pipelines
Solution Approach 2:
The automated analytics system is designed as a multi-functional platform that can handle various types of ticket data, generate multiple types of insights, create different visualizations, and serve multiple stakeholders. This universal design improves productivity across different use cases while managing complexity through a single versatile system rather than multiple specialized tools
3Measurement precision
If comprehensive data analysis is performed on all helpdesk tickets, then insight quality improves, but loss of time increases due to processing large volumes of data
Solution Approach 1:
The patent implements preliminary processing steps that prepare and structure ticket data before comprehensive analysis. This includes automatic ticket categorization, topic extraction, and data normalization that occur as tickets are created or received. By performing these preliminary actions, the system reduces the complexity of subsequent comprehensive analysis, maintaining insight quality while reducing overall processing time
Solution Approach 2:
The system employs periodic analysis at different stages - real-time preliminary analysis as tickets arrive, intermediate analysis at scheduled intervals, and comprehensive periodic reviews. This multi-stage periodic approach ensures high-quality insights from comprehensive analysis while minimizing total processing time by not requiring all analysis to be performed simultaneously on all data
Data Source
AI summary
An automated helpdesk explainer for use by a customer-support ticket platform is integrated with an online gaming platform. The automated helpdesk explainer receives a plurality of helpdesk tickets and determines a ticket volume parameter based on the plurality of helpdesk tickets. In response to the ticket volume parameter satisfying a predefined threshold, the automated helpdesk explainer applies one or more of the plurality of helpdesk tickets to a natural language processor configured to assign the one or more of the plurality of helpdesk tickets to one or more topics, creates an infographic image representing the one or more of the plurality of the helpdesk tickets based on the topics, applies at least a portion of the topics to a large language model configured to generate a natural language incident analysis summary, and provides the infographic image and incident analysis summary for presentation at a client device.