Dyslexia Detection via Machine Learning Interaction Analysis
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Solution Overview
Problem
Current methods for diagnosing dyslexia are expensive, require professional oversight, and are often disliked by children, leading to late detection and inadequate support.
Innovation Solution
A data processing system that uses human-computer interaction measures, such as eye-tracking and mouse-tracking data, in combination with machine learning to detect dyslexia. The system includes a web-based game, 'Dytective,' designed to identify individuals at risk of dyslexia through linguistic and attentional exercises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current professional diagnosis procedures are used, then diagnostic accuracy is improved, but cost and complexity increase significantly
Solution Approach 1:
The patent replaces complex mechanical/professional diagnostic systems with a computer-based automated system that uses machine learning algorithms to analyze human-computer interaction data. The system substitutes professional oversight with algorithmic processing of behavioral metrics from game interactions, achieving accurate dyslexia detection without requiring expert intervention.
Solution Approach 2:
The patent creates a simplified digital model of professional diagnosis by collecting and analyzing interaction data that mirrors professional assessment behaviors. The system copies the essential diagnostic functions into a software-based evaluation framework that processes linguistic and attentional exercise performance without requiring physical professional assessment tools.
2Measurement precision
If current professional diagnosis procedures are used, then diagnostic accuracy is improved, but cost increases
Solution Approach 1:
The patent implements a self-service diagnostic system where users independently complete linguistic and attentional exercises without professional intervention. The system automatically collects interaction data, processes it through machine learning algorithms, and generates diagnostic predictions, eliminating the need for paid professional services while maintaining diagnostic accuracy.
Solution Approach 2:
The patent replaces expensive professional diagnostic services with inexpensive automated computer-based assessments. The system uses low-cost digital interaction data collection and processing that can be deployed widely without requiring costly professional resources, making dyslexia detection economically accessible.
3Measurement precision
If traditional diagnosis methods are used, then diagnostic accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent replaces complex professional diagnostic procedures with an accessible computer-based game interface. The system substitutes intimidating professional testing with engaging digital exercises that users can complete independently, improving ease of operation while maintaining diagnostic accuracy through automated analysis of interaction patterns.
4Loss of time
If early detection is achieved, then intervention effectiveness is improved, but detection difficulty increases
Solution Approach 1:
The patent performs preliminary detection of dyslexia risk before formal education begins, using linguistic and attentional exercises that assess foundational cognitive skills. The system evaluates children's interactions with digital exercises to identify dyslexia indicators early, enabling timely intervention before academic struggles develop.
Solution Approach 2:
The patent uses computer-based game exercises as an intermediary tool to indirectly assess dyslexia risk. Rather than directly testing reading skills that may not yet be developed, the system uses linguistic and attentional exercises as mediators that reveal underlying cognitive patterns associated with dyslexia, making early detection feasible.
Data Source
AI summary
This document describes a data processing system for processing a feature vector that comprises features (one or more) that are indicative of dyslexic behavior that are indicative of dyslexic behavior. The data processing system includes a feature classification engine that generates classification metrics for a feature vector. Machine learning logic is used to determine a classification metric for each feature. Features that have a classification metric below a pre-determined threshold are removed. The data processing system includes a prediction engine that generates a prediction value indicative of a predicted likelihood of dyslexia. The prediction engine assigns, to each remaining feature, based on the classification metric of the respective remaining feature, a prediction weight and determines the prediction value based on the prediction weights.