Machine Learning Model for Unstructured Support Ticket Tagging

The patent introduces an intermediary system (machine learning model with NLP capabilities) that sits between the unstructured problem descriptions and the problem identification process. This intermediary translates the vague, symptom-based unstructured text into structured problem representations, enabling both capture of user symptoms and accurate problem identification without requiring support professionals to write precise statements.

The system changes the parameter representation from unstructured text to structured data by applying NLP processing and machine learning models. This transformation converts the vague nature of unstructured descriptions into precise problem identifiers, maintaining the versatility of capturing user symptoms while achieving accurate problem classification through computational analysis.

The system enables self-service problem identification by automatically analyzing unstructured problem descriptions using machine learning models. Instead of requiring support professionals to manually read and interpret each description, the system performs this analysis autonomously, providing reliable problem understanding while significantly reducing the time investment required from human analysts.

The patent replaces the mechanical process of manual text analysis with an automated computational system using NLP and machine learning. This substitution eliminates the time-consuming manual reading and interpretation process while maintaining reliable problem identification, as the computational models process and understand unstructured text just as effectively as human professionals would.

The system segments the problem representation into multiple components: it captures the original unstructured user input to preserve nuance, processes it through NLP to extract key concepts, and creates structured problem identifiers and categories. This segmentation allows the system to maintain both the interpretability of structured data and the nuance of unstructured user inputs by keeping them as separate but connected representations.

The patent adds another dimension to the data representation by transforming unstructured text into multiple structured dimensions (problem category, severity level, affected components, etc.). This dimensional transformation enables the system to capture the full nuance of user inputs while presenting the data in structured, easily interpretable formats for tracking and analysis.