Machine Learning Model for Product Feedback Classification
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Solution Overview
Problem
Existing feedback compilation systems are limited in efficiently and accurately analyzing and classifying product feedback, requiring manual review by companies and consumers, which is time-consuming and ineffective in assessing product issues across the supply chain.
Innovation Solution
A computer-implemented method and system that trains a machine learning model using a dataset of consumer feedback entries to classify them into categories, allowing for automated classification and visualization of product feedback, flagging discrepancies for manual review, and enabling data-driven decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of product feedback is used, then classification accuracy can be maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between raw product feedback and manual review. The model pre-classifies feedback entries into categories (e.g., positive, negative, neutral) and flags only uncertain or conflicting classifications for manual review, thereby reducing time consumption while maintaining accuracy.
Solution Approach 2:
The system performs preliminary classification of product feedback using trained machine learning models before manual review. This preliminary action filters out clearly classified feedback, allowing human reviewers to focus only on edge cases and improving overall efficiency without sacrificing accuracy.
2Reliability
If manual review of all feedback is performed, then comprehensive assessment is achieved, but productivity and processing speed decrease
Solution Approach 1:
Instead of manually reviewing all feedback entries, the system applies partial action by automatically classifying the majority of clear-cut cases and reserving manual review only for borderline or conflicting cases. This approach maintains comprehensive assessment reliability while significantly improving processing speed.
Solution Approach 2:
The system implements a feedback loop where machine learning classifications are reviewed manually when confidence is low, and these reviewed cases are used to retrain and improve the model. This creates a self-improving system that maintains reliability while scaling productivity.
3Loss of information
If more feedback data is collected for analysis, then insight quality improves, but system complexity and processing requirements increase
Solution Approach 1:
The patent segments the feedback analysis system into modular components: data collection module, preprocessing module, machine learning classification module, and manual review module. This segmentation allows the system to handle large volumes of feedback data efficiently while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The machine learning model serves multiple functions: initial classification, confidence scoring, and identifying cases for manual review. This multi-functionality reduces the need for separate systems for each task, thereby improving insight quality without proportionally increasing system complexity.
Data Source
AI summary
Systems and methods for classifying product feedback by an electronic device are described. According to certain aspects, an electronic device may receive consumer feedback entries associated with various products, where each entry may include an initial classification. The electronic device may analyze each entry using a machine learning model to determine a subsequent classification for the entry. When there is a mismatch between classifications, the electronic device may present information associated with the entry for review by a user, where the user may specify a final classification for the entry, and the electronic device may update the machine learning model for use in subsequent analyses.