Topic Model Clustering With Interface Feedback
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Solution Overview
Problem
Current information processing systems for service event analysis rely heavily on inefficient manual activities, such as manual screening and rule-based processing of unstructured text data, which is tedious and time-consuming, especially for large volumes of data.
Innovation Solution
A machine learning system for automated classification of documents using a clustering module that assigns documents to topics based on a topic model, with an interface for feedback to update the model, eliminating the need for manual screening and rule customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual screening and rule-based processing are used for unstructured text data, then service personnel can review and classify data, but the process becomes tedious and time-consuming especially for large volumes of data
Solution Approach 1:
The system enables automated self-service classification of unstructured text data through machine learning models that automatically analyze and categorize service requests without requiring manual review, thereby reducing processing time while maintaining classification accuracy
Solution Approach 2:
The patent replaces the mechanical manual screening process with an automated machine learning-based classification system that uses natural language processing to analyze text data, substituting human labor with computational processes that are both faster and scalable
2Productivity
If static sets of predetermined problem and resolution codes are used for service forms, then service personnel can efficiently complete forms, but the codes become overly general and vague
Solution Approach 1:
The system dynamically generates and updates problem and resolution codes based on patterns learned from historical service data, allowing the classification scheme to evolve and become more specific over time rather than remaining static and general
Solution Approach 2:
The machine learning model changes the parameters of classification by automatically generating specific problem and resolution codes based on the content of service requests, transforming the rigid predetermined code system into a flexible adaptive system that produces more accurate and specific classifications
3Loss of information
If unstructured text data is added to service forms, then more detailed information is captured, but the data requires special treatment including manual screening or customization of large rule sets
Solution Approach 1:
The patent replaces complex manual processing mechanisms with automated machine learning models that can handle unstructured text data efficiently, using natural language processing to extract meaningful information without requiring manual screening or extensive rule customization
Solution Approach 2:
The system enables the unstructured text data to be self-processed through automated classification algorithms that independently analyze and categorize the information, eliminating the need for manual intervention and reducing processing complexity while preserving information completeness
Data Source
AI summary
An apparatus comprises a processing platform configured to implement a machine learning system for automated classification of documents comprising text data of at least one database. The machine learning system comprises a clustering module configured to assign each of the documents to one or more of a plurality of clusters corresponding to respective topics identified from the text data in accordance with at least one topic model, and an interface configured to present portions of documents assigned to a particular one of the clusters by the clustering module and to receive feedback regarding applicability of the corresponding topic to each of one or more of the presented portions on a per-portion basis. The topic model is updated based at least in part on the received feedback. The feedback may comprise, for example, selection of a confidence level for applicability of the topic to a given one of the presented portions.