Hierarchical Service Message Classification for Real-Time Insight
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Solution Overview
Problem
Managing and optimizing large volumes of dynamic data objects generated by complex application frameworks is computationally expensive and strains resources, with manual and statistical analysis being inefficient and prone to errors, hindering real-time insights.
Innovation Solution
Utilizing unsupervised and supervised natural language processing models, combined with large language models, to extract topics, themes, and classifications from service message data objects, generating dashboard visualizations for efficient insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual and statistical analysis methods are used to process service message data objects, then analysis can be performed with simple tools, but the process is inefficient and prone to errors, hindering real-time insights
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated machine learning models. Specifically, unsupervised learning models automatically extract topics from service messages, and supervised learning models classify these topics, eliminating the need for manual statistical analysis while improving both efficiency and accuracy through automated pattern recognition and classification algorithms.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw service message data and human analysts. These models act as mediators that automatically process, extract, and classify data, providing structured insights that reduce human effort while maintaining high reliability through consistent algorithmic application across all data points.
2Productivity
If traditional data processing methods are used, then system complexity remains low, but computing resource usage is high and real-time insights are hindered
Solution Approach 1:
The patent segments the data processing task into distinct stages performed by different specialized models: unsupervised learning models handle topic extraction from raw messages, while supervised learning models handle classification of extracted topics. This segmentation allows each model to be optimized for its specific function, improving overall efficiency and enabling real-time processing through parallel execution of specialized tasks.
Solution Approach 2:
The patent transforms unstructured service message data into structured representations through feature extraction and topic modeling. By changing the parameter representation from raw text to extracted topics and classifications, the system enables more efficient processing and querying, reducing the computational resources needed for analysis while providing real-time insights.
3Ease of operation
If extensive technical expertise is required for data analysis, then analysis can be thorough, but ease of operation decreases and collaboration is hindered
Solution Approach 1:
The patent implements self-service capabilities where the machine learning system automatically performs data extraction, topic identification, and classification without requiring user intervention or technical expertise. The system serves itself by autonomously processing service messages and generating structured insights, making the analysis accessible to users regardless of their technical background while maintaining high quality through algorithmic consistency.
Data Source
AI summary
Methods, apparatuses, or computer program products that process service message data objects via large language modeling to provide service message classifications. In some examples, a first large language model is applied to a plurality of service message data objects associated with an application framework to generate a first feature set for the plurality of service message data objects, a plurality of topic data objects representative of respective hierarchical topic classifications for the plurality of service message data objects is generated based on the first feature set, a second feature set is extracted from the plurality of topic data objects, and a second large language model is applied to the second feature set to generate a plurality of theme data objects representative of respective hierarchical theme classifications for the plurality of topic data objects.