Neurodiversity-Based User Interface Customization via Machine Learning

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Solution Overview

Problem

Existing user interfaces are not efficiently usable by neurodivergent individuals, as they do not account for the unique interaction patterns and preferences of users with neurological or developmental conditions such as autism spectrum disorder or ADHD.

Innovation Solution

A system and method utilizing machine learning models to analyze user interaction data from client devices, identifying neurodiversity categories, and generating customized user interfaces by merging user interface parameters specific to each category, allowing users to opt-in to the customized interface.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If user interfaces are designed for general usability, then they can be used by most people, but they do not efficiently serve neurodivergent users with unique interaction patterns

Engineering Contradiction:

Improveadaptability to neurodivergent usersVSAvoidease of operation for neurodivergent users

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system performs preliminary classification of users into neurodiversity categories by analyzing interaction patterns before providing customized interfaces. The machine learning model proactively identifies user characteristics and applies appropriate interface configurations in advance, eliminating the need for users to manually configure settings.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically changes user interface parameters such as color contrast, font size, navigation structure, and interaction modes based on the classified neurodiversity category. By adjusting these parameters automatically, the interface adapts to the specific needs of different neurodivergent users while maintaining ease of operation.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If machine learning models are used to classify neurodiversity categories, then customized interfaces can be generated, but user interaction data must be collected and processed

Engineering Contradiction:

Improvecustomization capabilityVSAvoiddata collection and processing complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system automatically collects interaction data and performs classification without requiring explicit user input or manual configuration. Users simply interact with the interface naturally, and the machine learning model self-adjusts based on observed patterns, eliminating the need for users to provide detailed information about their neurodiversity status.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The machine learning model acts as an intermediary between raw user interaction data and customized interface configurations. It processes and interprets interaction patterns, translating them into appropriate interface parameters, thereby simplifying the overall system architecture while enabling sophisticated customization.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of operation

If user interface parameters are customized based on neurodiversity categories, then user experience is enhanced, but the system must identify and classify users accurately

Engineering Contradiction:

Improveuser experience qualityVSAvoidneurodiversity classification accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system continuously monitors user interaction patterns and uses this feedback to refine the classification accuracy over time. The machine learning model learns from actual usage behavior and adjusts its classifications accordingly, improving precision while delivering enhanced user experience.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The classification system is dynamic and adapts to changing user behaviors and preferences. Rather than relying on static criteria, the model continuously updates its understanding of user patterns, allowing for more accurate classification that reflects the evolving needs of neurodivergent users.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250190101A1Customizing user interfaces based on neurodiverse classification

Publication Date: 2025.06.12 CITIBANK N A

AI summary

Systems and methods are described herein for novel uses and/or improvements for customizing user interfaces for neurodiversity categories using machine learning models. In particular, one or more neurodiversity categories corresponding to a user are identified based on inputting user interaction data into a machine learning model. Based on the output of the machine learning model of one or more neurodiversity categories, user interface parameters are determined for those neurodiversity categories and a customized user interface is generated based on the user interface parameters. One or more applications with which the user interacts are then updated using the customized user interface.