Passive Cognitive Impairment Detection From User Interactions

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Solution Overview

Problem

Existing computing systems fail to adequately accommodate users with cognition impairments, often requiring manual adjustments and specialized equipment, and do not account for false positives or negatives in cognitive impairment detection, leaving impaired users excluded from everyday activities.

Innovation Solution

A cognition analysis and response system that uses unsupervised machine learning to identify cognitive impairments through user interactions, integrates with various data sources, and applies automated adaptive measures to assist users, including interface modifications and support services.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual adjustments and specialized equipment are used to accommodate users with cognition impairments, then accessibility for impaired users is improved, but device complexity and ease of operation are worsened

Engineering Contradiction:

ImproveAccessibility for impaired usersVSAvoidManual adjustments and specialized equipment

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system automatically detects cognitive impairment through analysis of user interactions with the computing system and autonomously applies adaptive measures without requiring manual intervention from the user. The processor monitors user behavior patterns, identifies impairment indicators, and triggers appropriate interface modifications automatically.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary detection and classification of cognitive impairment before the user needs assistance. By continuously monitoring user interactions and analyzing behavior patterns in advance, the system proactively identifies impairment and prepares adaptive measures, preventing operational difficulties before they occur.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If automated cognitive impairment detection is implemented, then accuracy and passivity of detection are improved, but false positives and negatives occur

Engineering Contradiction:

ImproveCognitive impairment detection accuracyVSAvoidFalse positives and negatives

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The detection process is divided into multiple independent stages: data collection from user interactions, feature extraction from behavior patterns, classification model application, and result validation. Each stage processes specific aspects of cognitive impairment detection separately, allowing for more precise and reliable overall assessment.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system incorporates feedback mechanisms where detection results are continuously refined based on ongoing user interaction data. The classification model is trained and adjusted using feedback from labeled training data and real-world user behavior patterns, improving accuracy while reducing false positives and negatives over time.

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If computing systems integrate into every human activity, then versatility and productivity are improved, but exclusion of impaired users increases

Engineering Contradiction:

ImproveIntegration into human activitiesVSAvoidExclusion of impaired users

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The cognitive impairment detection system is designed to work across multiple computing systems and various human activities simultaneously. The same detection methodology and adaptive measures apply whether the user is driving, sharing photos, or performing other tasks, making the solution universally applicable to all computing system interactions.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12431235B1Automatic identification of, and responding to, cognition impairment

Publication Date: 2025.09.30 UNITED SERVICES AUTOMOBILE ASSOCIATION (USAA)

AI summary

Aspects of the present disclosure are directed to a cognition analysis and response system that can A) identify and classify machine learning training data and use the training data to build a cognitive impairment machine learning model; B) identify cognitive impairments by applying the cognitive impairment machine learning model and performing a false positive/false negative analysis; and C) apply various automated adaptive measures for users with identified cognitive impairment. The cognition analysis and response system can generate training data by using an unsupervised training process and verifying results with validations of training items that have been pre-classified. The cognition analysis and response system can then iterate through each set of inputs paired with cognitive impairment classification, applying a machine learning model and updating the model based on a comparison of the model output to the cognitive impairment classification.