Feedback Estimation Model for User Intent Recognition
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Solution Overview
Problem
Machine learning requires a large amount of training data, and existing methods lack efficient methods for classifying and validating this data, as well as continuously improving learning model performance post-deployment.
Innovation Solution
An electronic device and method for acquiring feedback information to continuously train a learning model based on user input and response, allowing for improved accuracy in executing specific functions and enhancing user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large amount of training data is used for machine learning, then the recognition rate and accuracy of the artificial intelligence system is improved, but the complexity of data classification and validation increases
Solution Approach 1:
The system enables automated data classification and validation through machine learning models that autonomously process and organize training data without requiring manual expert intervention. The model learns to identify patterns and categorize data independently, reducing the complexity burden on the system architecture.
Solution Approach 2:
The system dynamically adjusts data sampling parameters and classification thresholds based on the learned patterns from training data. By changing these parameters adaptively, the system optimizes the balance between data volume and classification complexity, maintaining high recognition rates while managing processing complexity.
2Reliability
If expert hand design schemes are used for data classification, then the initial model performance is improved, but the ability to continuously adapt and improve post-deployment is reduced
Solution Approach 1:
The system implements a feedback mechanism where user interactions and real-world performance data are continuously collected and fed back into the machine learning model. This enables the model to learn from actual usage patterns and continuously improve its performance beyond the initial expert-designed configuration, bridging the gap between reliable initial performance and adaptive long-term improvement.
Solution Approach 2:
The system transitions from static expert-designed classification rules to dynamic, adaptive machine learning-based classification. The classification behavior evolves continuously based on incoming data and performance feedback, allowing the system to adapt to changing conditions and improve over time while maintaining initial performance standards.
3Extent of automation
If unsupervised learning is used for data classification, then the automation level is improved, but the precision of validation and classification decreases
Solution Approach 1:
The system performs preliminary supervised learning training with expert-labeled data to establish accurate baseline classifications and validation criteria before deploying unsupervised learning for automated processing. This preliminary action ensures that the automated system inherits the precision of expert validation while maintaining high automation levels in subsequent operations.
Data Source
AI summary
Various embodiments of the present disclosure relate to an electronic device and a feedback information acquisition method therefor. The feedback information acquisition method of the electronic device includes: acquiring input feedback information of a user and first response information of the user, which are related to a specific function; training a feedback estimation model by using the input feedback information and the first response information; acquiring second response information of the user related to the specific function; and acquiring feedback information related to the specific function by applying the second response information to the trained feedback estimation model.