Transformer-Based Feedback Categorization System
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Solution Overview
Problem
Manually categorizing feedback items from surveys and social media platforms is time-consuming and subjective, lacking an automated form of analysis to identify frequent topics and sentiments effectively.
Innovation Solution
A categorization system utilizing transformer-based machine learning models to analyze data items, generate visualizations of topic frequency, and sentiment analysis, which selects appropriate models based on data parameters, compares data items to known topics, and updates models periodically to ensure accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual categorization of feedback items is performed, then analysis accuracy can be maintained through human judgment, but time consumption and labor resources increase significantly
Solution Approach 1:
The patent replaces the mechanical human categorization process with an automated machine learning system. The system uses trained models to classify feedback items into topics and sentiments, eliminating manual human effort while maintaining consistent categorization accuracy across large volumes of data.
Solution Approach 2:
The system enables self-service categorization where the machine learning models autonomously process and categorize feedback items without human intervention. The automated pipeline includes data ingestion, model application, and result generation, allowing the system to serve itself in processing feedback at scale.
2Adaptability or versatility
If multiple individuals are involved in manual review, then diverse perspectives can be obtained, but human error and inconsistencies increase
Solution Approach 1:
The system changes the parameter of categorization from human subjective judgment to objective model-based classification. By transforming the categorization parameter into a standardized computational process, the system eliminates human inconsistency while maintaining reliable and repeatable results across different feedback items.
Solution Approach 2:
The patent segments the categorization task into distinct components handled by specialized machine learning models - one for topic classification and another for sentiment analysis. This segmentation allows each model to focus on specific aspects of feedback, improving overall reliability and consistency.
3Productivity
If automated analysis is implemented, then processing speed increases, but complexity of the system increases
Solution Approach 1:
The patent implements a universal machine learning framework that handles multiple functions - topic categorization and sentiment analysis - within a single integrated system. This multi-functionality approach increases productivity by processing both aspects simultaneously while managing system complexity through unified architecture rather than separate independent systems.
4Loss of information
If manual categorization is performed, then subjective human insight can be applied, but scalability to large datasets is limited
Solution Approach 1:
The system replaces manual human categorization with automated machine learning models that can process large datasets at scale. The models are trained to recognize nuanced patterns in feedback text, retaining the ability to capture subtle meanings and contexts while achieving scalability to handle volumes of data impossible for manual review.
Data Source
AI summary
A categorization system can include a computing device that is configured to obtain a plurality of data items over a threshold analysis period from an incoming data database in response to a threshold analysis interval elapsing. The computing device can also be configured to select a categorization model from a model database. The computing device can also be configured to, for each data item of the plurality of data items, apply the categorization model to the data item to identify at least one topic associated with the corresponding data item. The computing device can also be configured to generate a categorization visualization indicating a frequency of data items corresponding to each topic. The computing device can also be configured to transmit the categorization visualization to at least one of: (i) a user interface of an analyst device and (ii) a categorized database.