AI Chatbot Sentiment Analysis for Psychological Support
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Solution Overview
Problem
Conventional psychological cure methods struggle to detect and address strong negative emotions, such as suicidal tendencies, in an automatic and efficient manner, as individuals often hide their unhappiness and emotions are difficult to detect even for trained psychologists due to limited resources and the reluctance to share stressors.
Innovation Solution
A chatbot system that performs sentiment analysis on text, voice, and image inputs to detect negative emotions and provide personalized cure strategies through a Dynamic Memory Network (DMN)-based reasoning framework, assisting in psychological testing and counseling, and acting as a virtual psychologist or assistant to real-world psychologists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional psychological cure methods are used, then psychologists can provide care, but the ability to detect strong negative emotions automatically is insufficient due to limited resources and human limitations
Solution Approach 1:
The patent introduces an AI chatbot as an intermediary between users and psychologists. The chatbot performs sentiment analysis on user inputs (text, voice, images) to automatically detect negative emotions and generates cure strategies, thereby extending the reach of psychological services while maintaining detection capabilities that would be difficult for human psychologists to achieve at scale.
Solution Approach 2:
The patent replaces manual psychological assessment with automated AI-based sentiment analysis. The system processes user inputs through machine learning models to detect emotions, replacing the mechanical process of human psychologists manually evaluating patient statements with an automated computational system that can analyze multiple modalities simultaneously.
2Adaptability or versatility
If AI chatbot performs sentiment analysis on multiple input types, then detection capability improves, but system complexity increases
Solution Approach 1:
The patent segments the complex sentiment analysis task into distinct processing streams for different input modalities (text, voice, images). Each modality is processed by specialized components that extract relevant features, which are then integrated by the Dynamic Memory Network. This segmentation allows the system to handle multiple input types without overwhelming complexity in any single processing path.
Solution Approach 2:
The patent employs a Dynamic Memory Network (DMN) that adapts its reasoning process based on the specific situation. The DMN dynamically adjusts which memory elements to activate and how to weight different input sources based on the detected emotion type and severity, allowing the system to handle versatile inputs with adaptive complexity rather than fixed rigid processing for all cases.
3Reliability
If chatbot provides personalized cure strategies, then psychological support effectiveness improves, but resource requirements increase
Solution Approach 1:
The patent implements pre-trained sentiment analysis models and cure strategy databases that are prepared in advance. When a user interacts with the chatbot, the system leverages these pre-computed resources rather than generating everything from scratch, significantly reducing real-time computational requirements while maintaining personalized effectiveness through dynamic adaptation to individual user inputs.
Solution Approach 2:
The patent adjusts the level of personalization and computational intensity based on detected parameters such as emotion severity, user history, and crisis level. For routine concerns, the system uses lighter processing with standardized strategies, while escalating to more intensive personalized analysis only when necessary, thereby optimizing resource usage while maintaining reliability when it matters most.
Data Source
AI summary
Method and apparatus for assisting psychological cure in automated chatting. At least one message may be received in a chat flow, a psychological test may be performed based on the at least one message, and a cure strategy may be provided based at least on the psychological test. A first request for obtaining user information of a user may be received in a chat flow, the user information may be provided based on the first request, a second request for obtaining a suggested cure strategy for the user may be received, and the suggested cure strategy may be provided based on the second request, wherein the suggested cure strategy may be determined based at least on the user information.