Deep-Learning Mental Health Advisory System Using Acceptance and Commitment Therapy
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Solution Overview
Problem
Parents of children with special needs face significant challenges in accessing effective mental health services due to long waiting periods, geographical barriers, and self-stigma, leading to increased strain and manifestation of depression and anxiety symptoms.
Innovation Solution
A deep-learning-based mental health advisory system using Acceptance and Commitment Therapy (ACT) is developed, incorporating a knowledge database, a customized question bank, an external Large Language Model (LLM), a method for handling irrelevant responses, and an AI logic model based on BERT and RoBERTa architectures to provide personalized mental health advice.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional face-to-face mental health services are provided, then the quality of counseling is improved, but the accessibility and ease of obtaining services deteriorates due to geographical barriers and long waiting periods
Solution Approach 1:
The patent creates a digital copy of the counseling experience through an AI chatbot that simulates Acceptance and Commitment Therapy (ACT) counseling sessions. The system replicates the therapeutic interaction by using pre-trained NLP models to generate responses that mimic human counselors, making the service accessible without geographical or temporal constraints while maintaining therapeutic quality through structured ACT-based dialogue
2Reliability
If comprehensive mental health services are provided, then the effectiveness of treatment is improved, but the cost and device complexity increase
Solution Approach 1:
The patent segments the comprehensive mental health service into distinct functional modules: a knowledge database storing ACT therapeutic content, a pre-trained NLP model for natural language understanding, a counseling session management system, and an evaluation module. This modular architecture delivers effective treatment while reducing overall system complexity by making each component independent and manageable
Solution Approach 2:
The system performs preliminary actions by pre-training the NLP model with extensive ACT counseling knowledge and pre-structuring the question bank with life-contextual and problem-focused interview questions. This preliminary preparation enables the system to provide effective treatment during actual counseling sessions without requiring complex real-time processing
3Reliability
If personalized mental health advice is provided, then the relevance and effectiveness of counseling is improved, but the time and productivity are reduced due to extensive assessment requirements
Solution Approach 1:
The patent implements feedback mechanisms where the NLP model continuously analyzes user responses during the counseling session, automatically adjusting the dialogue flow and question selection based on detected mental health status. This real-time feedback enables personalized counseling advice while maintaining high productivity by eliminating manual assessment steps
Data Source
AI summary
A method for providing mental health advice, a method for training a deep-learning network and a deep-learning based mental health advisory system using Acceptance and Commitment Therapy (ACT). The method for providing mental health advice comprises the steps of: receiving textual input from a user in a consultation session; processing the textual input by applying a mental health condition relationship to the textual input to identify the mental health status of the user; and providing an output associated with the mental health status of the user.