Computational Behavioral Phenotyping for Autism Detection

The patent replaces traditional mechanical/clinical assessment methods with computational and automated systems. Machine learning models analyze user interactions with digital applications to detect neurodevelopmental disorders, substituting manual clinical evaluation with algorithm-based detection that achieves high accuracy (AUC=0.90) while being scalable to real-world settings through mobile devices and digital platforms.

The patent introduces digital applications and computational algorithms as intermediaries between the user and the detection process. These intermediaries capture behavioral data through app interactions, process it through machine learning models, and generate diagnostic predictions, thereby bridging the gap between simple screening and accurate detection without requiring direct clinical intervention for every assessment.

The patent implements self-service assessment where users complete digital applications and motor skills tasks independently without requiring trained clinicians for every interaction. The system automatically captures interaction data, processes it through machine learning models, and generates predictions autonomously, enabling high-throughput screening while reducing the need for specialized human resources in each assessment instance.

The patent creates a multi-functional system that combines behavioral phenotyping through app interactions, motor skills assessment through touchscreen tasks, and diagnostic prediction through machine learning models. This universal platform serves multiple assessment functions through a single integrated system, increasing productivity while managing complexity through consolidation rather than requiring separate specialized tools for each assessment type.

The patent performs preliminary action by pre-training machine learning models on extensive datasets before deployment. The system prepares assessment protocols and digital applications in advance, and pre-processes data through automated pipelines. This preliminary preparation enables rapid real-time assessment without requiring trained clinicians during the actual evaluation, significantly reducing assessment time while maintaining high automation levels.

The patent substitutes manual clinical assessment mechanics with automated computational processes. Machine learning models automatically analyze behavioral and motor data, replacing the need for trained clinicians to manually evaluate each patient. This substitution dramatically reduces assessment time and eliminates the dependency on specialized human resources while maintaining or improving detection accuracy.

The patent creates a universal assessment platform that functions across diverse populations through a single system. The machine learning models are trained on diverse datasets representing different demographics, and the digital applications are designed to be culturally and linguistically adaptable. This universal system maintains consistent measurement standards while accommodating population diversity, improving accessibility without requiring separate specialized systems for different groups.

The patent adapts assessment parameters and thresholds based on population characteristics through the machine learning framework. The system adjusts for demographic variables, cultural differences, and population-specific norms by modifying model parameters and interpretation thresholds dynamically. This allows the same core system to serve diverse populations accurately by changing parameters rather than requiring fundamentally different assessment approaches for each group.