Customer Feedback Classification System for Product Risk Assessment
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Solution Overview
Problem
The vast amount of customer feedback from various sources, such as websites and hotline calls, is difficult to process manually, making it challenging to determine if a product needs to be recalled or if there are other issues with the product, such as potential hazards or reliability concerns.
Innovation Solution
A customer feedback classification system comprising a capture module, scoring module, filtering module, transformation module, and risk module, which captures text, scans for sentiment scores, filters text into parts of speech and keywords, transforms it into a term-document matrix, and calculates a risk score, ultimately reporting the product and risk score to a subject matter expert for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing of customer feedback is used, then analysis accuracy can be maintained, but processing efficiency deteriorates due to the vast amount of feedback data
Solution Approach 1:
The feedback processing system is divided into multiple independent modules: capture module for data collection, scoring module for sentiment analysis, filtering module for noise removal, transformation module for data structuring, and risk module for priority assessment. Each module handles a specific aspect of feedback processing, enabling parallel operation and improved efficiency while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces intermediate processing layers between raw feedback and final analysis. The scoring module generates sentiment scores as intermediate representations, the filtering module creates cleaned text as an intermediate form, and the transformation module produces structured data formats. These intermediaries enable automated processing while preserving the essential information needed for accurate product safety assessment.
2Productivity
If automated processing systems are implemented, then processing efficiency improves, but measurement precision deteriorates due to difficulty in accurately assessing sentiment and risk
Solution Approach 1:
The patent replaces manual mechanical reading and analysis of feedback with automated computational processing. The scoring module uses automated sentiment analysis algorithms to evaluate customer feedback, the transformation module automatically structures unstructured text data, and the risk module computationally assesses product safety risks. This substitution enables processing of large volumes of feedback while maintaining consistent and objective measurement through algorithmic evaluation.
3Loss of information
If all customer feedback is processed in detail, then comprehensive analysis is achieved, but loss of time increases due to the volume of data to be reviewed
Solution Approach 1:
The filtering module extracts and removes irrelevant information from customer feedback, such as common phrases, stop words, and non-essential text elements. This extraction process eliminates noise while preserving the core meaningful content related to product safety and performance. By taking out only the essential information, the system achieves comprehensive analysis of relevant feedback without wasting time on redundant data.
Solution Approach 2:
The risk module implements prioritized processing by assessing and ranking feedback items based on their potential impact on product safety. Instead of processing all feedback uniformly, the system applies partial detailed analysis to high-risk items while using more efficient processing for lower-priority feedback. This selective approach ensures that critical safety concerns receive thorough examination while maintaining overall processing efficiency across the entire feedback volume.
Data Source
AI summary
Systems, methods, and machine readable media are provided for classifying customer feedback. In exemplary embodiments, text is captured from at least one source relating to at least one product. The text is scanned and a score is produced for sentiment for the at least one product. The text is filtered into parts of speech and key words to produce filtered text. The filtered text is transformed into a term-document matrix. A risk score is calculated and prioritized based on the term-document matrix and the sentiment score. The product and the associated risk score are reported to a subject matter expert (SME), where a determination is made whether the product is reportable or non-reportable.