Service Message Classification Using NLP for Ticket Analysis
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Solution Overview
Problem
Managing and analyzing large volumes of service tickets in complex application frameworks is computationally expensive, inefficiently strains computing resources, and requires manual or statistical analysis prone to human error and scalability issues.
Innovation Solution
Utilizing supervised and unsupervised machine learning models, particularly natural language processing, to classify and generate insights from service tickets, reducing resource usage and enabling automated dashboard visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual or statistical analysis methods are used to manage service tickets, then analysis can be performed, but computing resources are excessively strained and human error increases
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated machine learning models. Supervised learning models classify service tickets into predefined categories, while unsupervised learning models discover patterns and group similar tickets automatically. This substitution eliminates human error while reducing computing resource strain through efficient algorithmic processing.
Solution Approach 2:
The system enables self-service analysis through automated machine learning pipelines that continuously process service tickets without human intervention. The models automatically extract features, classify tickets, generate insights, and update dashboard visualizations, allowing the system to serve itself and eliminate the need for manual analysis while optimizing resource utilization.
2Productivity
If traditional analysis methods are used for service tickets, then processing can be performed, but scalability is limited and efficiency is reduced
Solution Approach 1:
The patent implements a universal machine learning framework that handles multiple service ticket analysis functions simultaneously. The same infrastructure supports both supervised classification and unsupervised pattern discovery, enabling the system to scale efficiently across diverse ticket types and volumes while maintaining high productivity through automated multi-functional processing.
3Loss of information
If more computing resources are allocated to analyze service tickets, then analysis depth can be increased, but resource strain and costs increase
Solution Approach 1:
The patent extracts only the most relevant features from service tickets using supervised learning models. By identifying and extracting key discriminative features rather than processing all raw data, the system maintains high insight quality while significantly reducing the quantity of computing resources required for analysis.
Solution Approach 2:
The system applies partial action by focusing computational effort on the most informative aspects of service tickets. The machine learning models process only critical features and generate targeted insights rather than attempting comprehensive analysis of all ticket attributes, optimizing the balance between insight quality and resource consumption.
Data Source
AI summary
Methods, apparatuses, or computer program products that process service message data objects via supervised machine learning to provide service message classifications. In some examples, a feature set is extracted from a plurality of service message data objects associated with an application framework, a supervised natural language processing model is applied to the feature set to generate a plurality of classification data objects associated with the plurality of service message data objects that classify a respective service message data object as belonging to a predefined class of a plurality of predefined classes, and a rendering of a dashboard visualization is initiated via an electronic interface based at least in part on the plurality of classification data objects.