Mental Health Assistant Device for Ambient Distress Detection

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Solution Overview

Problem

Current technologies face challenges in promoting positive mental health, as they struggle to detect and respond to positive emotional responses effectively, and are ill-equipped to quantify and alleviate environmental distress without user input.

Innovation Solution

A device and method that utilize sensors and machine learning to measure ambient environmental conditions, assign thresholds, and trigger ameliorative actions when distress levels exceed predetermined limits, promoting positive feedback loops for mental health improvement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If a device uses algorithms optimized to display provocative content to drive engagement, then user engagement increases, but user mental health deteriorates

Engineering Contradiction:

Improveuser engagementVSAvoidmental health harm

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The patent converts the harmful effect of negative emotional triggers into a beneficial detection mechanism. The system intentionally exposes users to negative content triggers and uses machine learning to detect the resulting emotional distress responses, then counters with positive content. This transforms the harmful engagement mechanism into a useful diagnostic tool for mental health monitoring.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

Solution Approach 2:

The system implements a feedback loop where user responses to content are continuously monitored, analyzed, and used to adjust future content delivery. The machine learning algorithm learns from user emotional responses and adjusts content selection to promote positive mental health outcomes while maintaining engagement, creating a self-correcting system that balances productivity with well-being.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If a device monitors ambient environmental conditions continuously to detect distress signals, then detection accuracy improves, but energy consumption increases

Engineering Contradiction:

Improvedistress signal detection accuracyVSAvoiddevice energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system employs periodic monitoring of ambient conditions rather than continuous monitoring. The machine learning algorithm processes environmental data at intervals and only triggers full analysis when distress patterns are detected, reducing energy consumption while maintaining detection accuracy through strategic sampling of the environment.

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The system performs preliminary filtering and preprocessing of ambient data using lightweight algorithms before committing to energy-intensive analysis. The machine learning model is trained beforehand to recognize patterns of interest, allowing the device to skip detailed analysis during normal conditions and only activate full monitoring when potential distress signals are present.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If a device requires user input to quantify environmental distress, then measurement accuracy improves, but ease of operation deteriorates

Engineering Contradiction:

Improveenvironmental distress measurement accuracyVSAvoiddevice usability

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system performs self-calibration and self-measurement by using its own sensors and machine learning algorithms to automatically quantify environmental distress without requiring user input. The device independently monitors ambient conditions, compares them against learned patterns of distress, and generates assessments autonomously, eliminating the need for users to manually report their environment.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The machine learning algorithm acts as an intermediary that translates complex ambient sensor data into meaningful distress assessments. Rather than requiring users to interpret or input environmental data, the system processes raw sensor readings through the algorithmic mediator and presents simplified, actionable insights to users, improving both accuracy and ease of use.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20230200698A1Mental health assistant device and methods of training and using the same for ameliorating audible distress in an ambient environment

Publication Date: 2023.06.29 BECK CHAYA

AI summary

A method for improving the mental health of children, families and homes using “positive feedback loop” technology is described. One or more aspects of the method include obtaining a device; training the device to assign a predetermined threshold to at least one input, where the at least one input comprises a measurement of at least one condition of an ambient environment; measuring the at least one input; assigning a score to the measurement of the at least one condition of an ambient environment; evaluating whether the score exceeds the predetermined threshold; alerting at least one user when the score exceeds the predetermined threshold; and repeating the measuring, assigning, and evaluating steps until the score exceeds the predetermined threshold when the score does not exceed the predetermined threshold.