AI Feedback Classification for Real-Time Trend Detection
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Solution Overview
Problem
Conventional techniques for processing feedback data are time- and labor-intensive, require manual review, and fail to capture actual subjects or analyze changes over time, limiting the ability to recognize trends and develop proactive solutions.
Innovation Solution
A system utilizing artificial intelligence and natural language processing to automate feedback data processing, classifying, filtering, and reducing data into descriptors displayed on a graphical user interface, enabling end users to filter by time and subject.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and search of feedback data is performed, then feedback data can be analyzed, but it is time- and labor-intensive for large volumes of data
Solution Approach 1:
The patent replaces manual mechanical review processes with automated natural language processing (NLP) systems. The NLP technology automatically classifies, filters, and summarizes feedback data packets, eliminating the need for human reviewers to manually examine each piece of feedback while maintaining or improving analysis accuracy through consistent application of classification criteria.
Solution Approach 2:
The feedback data processing system performs self-service by automatically analyzing, classifying, and summarizing feedback data without human intervention. The system autonomously processes large volumes of feedback data packets, identifying trends and generating insights that would otherwise require significant manual effort, thereby resolving the contradiction between analysis accuracy and time consumption.
2Ease of operation
If predefined subjects are used for feedback classification, then feedback can be organized, but the predefined subjects may not accurately capture the actual subjects in the feedback data
Solution Approach 1:
The patent implements dynamic subject classification where the system adapts to actual feedback content rather than forcing feedback into static predefined categories. The NLP technology analyzes the semantic content of feedback data packets and automatically determines relevant subjects, allowing the classification structure to evolve and adjust based on the actual topics present in the feedback data, thereby improving classification accuracy while maintaining organizational ease.
3Quantity of substance
If conventional feedback processing techniques are used, then feedback data can be captured, but changes in feedback data over time cannot be analyzed
Solution Approach 1:
The patent applies preliminary temporal segmentation to feedback data packets by associating each packet with time period identifiers before analysis. This preliminary action of organizing data chronologically enables subsequent temporal trend analysis, allowing the system to track changes in feedback characteristics over time while processing large volumes of data, thereby preventing loss of temporal information.
Data Source
AI summary
Disclosed are systems and methods that automatically classify, filter, and reduce large volumes of feedback data as a function of time using artificial intelligence technology. The aggregated feedback data is reduced by representing the feedback data as sets of descriptors corresponding to one or more time periods that are displayed on a graphical user interface. Feedback data packets are parsed by labeling the feedback data packets with a time period identifier. The feedback data packets are processed utilizing neural network technology to classify the feedback data according to one or more subject identifiers that are each associated with a subject vector. A descriptor analysis is used to process the subject vectors and the feedback data packets to generate descriptor sets comprising one or more descriptors as well as weighting data for each descriptor.