MUMBAI, India, June 30 -- Intellectual Property India has published a patent application (202541035772 A) filed by Srinivas Tirukappa Venkataravana on April 12, 2025, for System And Method For Closed-Loop Generation Of Customized Learning Material Using Longitudinal Learner-State Estimation.
Inventor includes Srinivas Tirukappa Venkataravana.
The application for the patent was published on June 26, 2026, under issue no. 26/2026.
Abstract: SYSTEM AND METHOD FOR CLOSED-LOOP GENERATION OF CUSTOMIZED LEARNING MATERIAL USING LONGITUDINAL LEARNER-STATE ESTIMATION The present invention provides a computer-implemented system and method for generating customized learning material using a closed-loop adaptive architecture. The invention receives learner-specific and curriculum-specific inputs. A learner-state estimation engine constructs and persistently updates, across multiple learning sessions, a multidimensional learner-state representation comprising concept mastery, prerequisite readiness, misconception patterns, learning pace, response behaviour, retention tendency, modality effectiveness, and interruption-aware context signals. A curriculum-constraint engine, storing prerequisite relationships as a dependency structure among learning units, identifies a permissible set of next learning actions. An action-selection engine selects a next learning action prior to content generation. A content-synthesis engine generates customized multimodal learning material conditioned on the selected action. An interaction- monitoring engine captures behavioral, interface, device, and performance signals. The invention achieves a technical improvement in computational control and resource efficiency by restricting content generation to curriculum-valid and learner-state-compatible action paths, by classifying interruption signals before state update, and by reducing unnecessary computation and generation, storage, and delivery of irrelevant learning content while enabling longitudinal cross-session personalization. Figure of abstract: FIG. 1.
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