Machine Learning Scoring for Figure Drawing Visuospatial Tests

The patent creates a digital copy of the human grading process through a machine learning model. The model is trained on extensively annotated datasets of figure drawing test responses, capturing the grading criteria and patterns. When new test responses are submitted, the trained model generates scores automatically, replicating the expert human grading process without requiring actual human graders for each new case.

The patent replaces the mechanical human grading system with an automated computational system. Instead of human neuropsychologists manually evaluating each drawing, the system uses machine learning algorithms that process digital images of drawings, extract relevant features, and generate scores based on trained patterns and criteria, thereby eliminating the time-consuming manual process.

The patent captures the expertise of trained professionals by training machine learning models on extensively annotated datasets that encode grading criteria and patterns. The trained model replicates the scoring behavior of qualified professionals, making the scoring capability available anywhere without requiring physical presence or specialized training of human graders.

The patent transforms the scoring system from a human-dependent parameter (availability of trained professionals) to a system-independent parameter (availability of computing resources and trained models). This allows the scoring service to be deployed remotely and scaled without being constrained by the limited pool of qualified human graders.