Mental State Classifier with Buddy System for Risk Intervention

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Solution Overview

Problem

Current systems lack effective methods to detect and address changes in psychological states, particularly for individuals at risk of mental health issues like suicidality, addiction, weight loss, and financial distress, especially in remote areas without access to clinical services.

Innovation Solution

A mobile application using a mental state classifier and machine learning models to assess real-time risk values, generate dialogues for peer-to-peer intervention, and track edits to adapt future dialogues, providing personalized support and resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If machine learning models and real-time monitoring are implemented to detect psychological state changes, then the precision of risk detection is improved, but the complexity of the system increases

Engineering Contradiction:

Improverisk detection precisionVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces a buddy system as an intermediary between the user and clinical services. The buddy receives automated alerts and guidance from the monitoring system, simplifying the user's interaction while maintaining high detection precision through the underlying machine learning models.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs self-service by automatically monitoring user data, executing machine learning predictions, and generating alerts without requiring manual intervention. This automation maintains high detection precision while managing system complexity through standardized processes.

Inventive Principle:

Principle #25Self-service

2Productivity

If real-time monitoring and automated alert systems are deployed, then the productivity of intervention response is improved, but the loss of time for system setup and configuration increases

Engineering Contradiction:

Improveintervention response efficiencyVSAvoidsystem setup time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-configuring monitoring parameters, machine learning models, and alert protocols during initial setup. This allows the system to immediately begin real-time monitoring and intervention when deployed, reducing the time loss associated with ongoing configuration.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables parameter changes to be made during operation, allowing customization of monitoring thresholds, alert frequencies, and intervention protocols without requiring complete system reconfiguration. This maintains high intervention response efficiency while reducing initial setup time.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If personalized intervention dialogues and peer-to-peer support are implemented, then the adaptability of the system to individual needs is improved, but the complexity of dialogue generation and management increases

Engineering Contradiction:

Improvepersonalization capabilityVSAvoiddialogue system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system uses copying by generating dialogue templates and response patterns that can be replicated and adapted for different users. This maintains high personalization capability while managing dialogue system complexity through reusable templates rather than entirely unique dialogues for each user.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The dialogue system is segmented into modular components including alert generation, dialogue selection, and personalized messaging. This segmentation allows the system to maintain high adaptability through configurable modules while reducing overall complexity by breaking down the dialogue generation process into manageable segments.

Inventive Principle:

Principle #1Segmentation

4Reliability

If comprehensive tracking and monitoring of user data are implemented, then the reliability of risk assessment is improved, but the loss of information privacy increases

Engineering Contradiction:

Improverisk assessment reliabilityVSAvoidprivacy loss

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The system extracts and processes only the minimum necessary data required for risk assessment while maintaining reliability. By taking out only essential information for monitoring and analysis, the system reduces privacy loss while preserving assessment reliability through focused data collection on relevant psychological and behavioral indicators.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20210045696A1Assistance in response to predictions in changes of psychological state

Publication Date: 2021.02.18 POULIN HOLDINGS LLC

AI summary

Computer implemented techniques for classifying mental states of individuals and providing tailored support are described. The techniques determine sets of features that are associated with multiple groups having different mental status, and a classification model is used to classify one group against another group. The techniques also include receiving user set goal, querying a system database to determine whether there is a machine learning model to predict risk associated with the received goal assessing changes in a real-time risk value associated with the goal, generating an automated dialog associated with assessed changes in a real-time risk value associated with the goal, posting to a buddy system the real time risk value with the generated dialog, tracking edits made on the buddy system, and finally a comparison of users and their assisted goal accomplishment.