Cognitive Impairment Detection Through Adaptive User Interfaces

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Solution Overview

Problem

Existing computing systems fail to adequately accommodate users with cognition impairments, often requiring overt actions and specialized understanding, leaving them unable to interact effectively with user interfaces.

Innovation Solution

A cognition analysis and response system that uses unsupervised machine learning to identify cognitive impairments through user interactions, applying automated adaptive measures such as interface modifications and support services to assist users.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual accommodations (larger controls, icons, text) are enabled, then interface accessibility is improved, but user burden and complexity increase

Engineering Contradiction:

Improveinterface accessibilityVSAvoidaccommodation configuration

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 enables appropriate interface accommodations without requiring the user to manually configure settings or undergo formal assessment

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors user interactions and uses this feedback to identify cognitive impairment patterns, then automatically adjusts interface settings based on the detected impairment level

Inventive Principle:

Principle #23Feedback

2Measurement precision

If cognitive impairment detection requires formal assessment, then identification accuracy is improved, but accessibility and ease of use deteriorate

Engineering Contradiction:

Improveimpairment identification accuracyVSAvoiddetection accessibility

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The computing system performs self-assessment by automatically analyzing user interaction patterns to detect cognitive impairment without requiring the user to seek out or complete formal assessment procedures

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces formal clinical assessment mechanisms with automated computational analysis of digital interaction data, substituting mechanical/paper-based evaluation with algorithmic detection

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If interface accommodations are made proactively, then user inclusion is improved, but system complexity and resource usage increase

Engineering Contradiction:

Improveuser inclusionVSAvoidadaptive system architecture

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system uses a unified machine learning model that performs multiple functions: analyzing various types of user interactions, detecting different types of cognitive impairment, and determining appropriate accommodations across diverse computing contexts

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12555033B1Automatic identification of, and responding to, cognition impairment

Publication Date: 2026.02.17 UNITED SERVICES AUTOMOBILE ASSOCIATION (USAA)

AI summary

Aspects of the present disclosure are directed to a cognition analysis and response system that can A) generate machine learning training data and use it 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 apply the cognitive impairment machine learning model and can analyze these results to identify false positive and false negative cognitive impairment determinations by analyzing result confidence values, determining whether the result matches an expected progression of cognitive impairment, and/or analyzing external factors which may have caused incorrect results. The cognition analysis and response system can then validate or invalidate the result or exclude or apply weighting factors to parts of the input before regenerating the result.