Machine Learning Feedback Auditing for Consistent Reclassification
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Solution Overview
Problem
Conventional customer relationship management (CRM) systems are ineffective in managing large volumes of user feedback data, leading to misclassification and data inconsistencies, which hinders the service provider's ability to understand user issues and improve products or services effectively.
Innovation Solution
Implementing a machine learning-based user feedback auditing tool that applies various machine learning models to reclassify user communications, using techniques such as data preprocessing, feature extraction, semantic analysis, clustering, and supervised learning to generate a more accurate second set of feedback categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification by customer service agents is used, then human judgment and context understanding are applied, but classification errors and data inconsistencies occur
Solution Approach 1:
The patent replaces the manual mechanical classification process with an automated machine learning-based classification system. The system uses trained ML models to automatically classify user feedback data, eliminating human error and inconsistency while maintaining high accuracy through algorithmic decision-making and continuous model training.
Solution Approach 2:
The classification system performs self-service by automatically processing and classifying user feedback without requiring manual intervention. The ML models continuously learn from new data and automatically adjust classification patterns, enabling the system to serve itself and improve over time without human retraining.
2Productivity
If conventional CRM systems are used to manage large volumes of user feedback, then existing infrastructure is leveraged, but the systems are ineffective and cannot process the data volume
Solution Approach 1:
The patent transforms the processing capability by changing the fundamental parameter of how data is analyzed - moving from manual text processing to machine learning-based semantic analysis. This parameter change enables the system to handle large volumes of unstructured feedback data efficiently while maintaining high classification accuracy and reliability.
Solution Approach 2:
The system replaces conventional CRM manual processing mechanisms with automated ML-based processing. The trained machine learning models automatically ingest, analyze, and classify large volumes of user feedback data, dramatically increasing productivity while ensuring consistent and reliable classification results.
3Adaptability or versatility
If manual classification is used, then flexibility in handling diverse feedback types is maintained, but misclassification errors increase
Solution Approach 1:
The patent creates a universal classification system using machine learning models that can handle multiple types of user feedback (text, audio, images, video) through a single unified platform. The ML models are trained to recognize patterns across diverse feedback formats, providing both adaptability to different feedback types and high classification accuracy simultaneously.
Solution Approach 2:
The system changes the processing parameter from manual interpretation to algorithmic pattern recognition, enabling consistent and accurate classification across diverse feedback types. The ML models automatically adapt to different feedback formats while maintaining high precision through learned patterns and continuous training.
Data Source
AI summary
Methods and systems are presented for auditing user feedback data corresponding to user communications received via at least one interface of a service provider. The user feedback data includes a first set of feedback categories associated with a first classification of the user communications. A first feature representation of the communications is generated from the user feedback data. The first feature representation includes a first set of textual data features extracted from the communications. A second feature representation is generated from the first feature representation using a first machine learning model. The second feature representation includes a second set of textual data features including semantic equivalents of the first set of features. A second machine learning model is trained with the second feature representation. A second classification of the user communications according to a second set of feedback categories is generated using the trained second machine learning model.