Adaptive Psychological Counseling Scheme Using Feedback Classification
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Solution Overview
Problem
Existing psychological robots perform counseling based on preset procedures, which fail to satisfy individual user demands, leading to suboptimal counseling results and potential user dissatisfaction.
Innovation Solution
An electronic device and method for determining a psychological counseling training scheme, which involves obtaining user feedback after each counseling session, using a classification model to analyze the feedback, and adjusting the training scheme based on the analysis to better suit individual user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed counseling procedure is used, then the counseling process is simple to implement, but individual user demands cannot be satisfied leading to suboptimal counseling results
Solution Approach 1:
The counseling procedure transitions from a fixed static process to a dynamic adaptive process. The system continuously monitors user feedback (training feeling data) and adjusts the counseling scheme in real-time, making the procedure flexible and responsive to individual user needs while maintaining ease of implementation through automated adjustments.
Solution Approach 2:
The system introduces feedback mechanisms by collecting user feedback after each counseling session and using this information to adjust subsequent counseling schemes. The feedback loop enables the system to learn from user responses and personalize the counseling approach, resolving the contradiction between procedural simplicity and individualization.
2Adaptability or versatility
If practice effects vary among users, then personalized counseling is needed, but fixed procedures cannot adapt leading to negative feedback and user termination
Solution Approach 1:
The system enables self-adjustment of the counseling scheme based on user feedback. Rather than requiring external intervention to modify the procedure, the system automatically adapts to each user's practice effects and responses, maintaining reliability while providing personalization.
Solution Approach 2:
By continuously collecting and processing user feedback, the system maintains counseling effectiveness through adaptive adjustments. The feedback mechanism ensures that the counseling scheme remains reliable and effective for each individual user despite variations in practice effects.
3Quantity of substance
If a preset procedure requires multiple practices, then comprehensive training is provided, but users who practice less cannot achieve expected effects
Solution Approach 1:
The training scheme transitions from a fixed volume requirement to a dynamic adjustment based on user practice frequency and effectiveness. The system adapts the recommended number and intensity of practices to match each user's actual engagement level, ensuring that both frequent and infrequent practitioners can achieve expected effects.
Solution Approach 2:
The system changes key parameters of the training scheme (such as number of recommended practices, intensity, and timing) based on user feedback and observed practice patterns. This allows the training volume to be optimized for each user rather than applying a one-size-fits-all approach.
Data Source
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AI summary
Embodiments of the present disclosure provide a method and apparatus for determining a psychological counseling training scheme. The method includes: obtaining training feeling data of a user after each session of psychological counseling training through an interactive inquiry with the user; inputting the training feeling data into a first classification model, identifying a training result of the user after each session of psychological counseling training by using the first classification model, and collecting statistics about a training result of the user in a current training period; and determining a training scheme of the user for a next training period based on the training result in the current training period of the user. The invention resolves the problem that a counseling result is not ideal because individual demands of different users cannot be satisfied if a psychological robot performs psychological counseling on the user according to a preset counseling procedure.