Patent Claims Explorer

A computer-implemented system comprising six algorithm services operating in parallel through a Synchronization Engine.

Real-time intervention delivery within 500ms when engagement crisis is detected.

Sync Engine coordinates outputs from all six algorithms with conflict detection and resolution.

Dynamic weight assignment to algorithm outputs based on learner state and context.

Automated resolution when algorithms produce contradictory recommendations.

Cross-algorithm synergy ratio computation for combined recommendation quality.

All algorithm computations complete within a configurable latency budget.

PALP dual-path system with Hot Path for real-time interventions and Cold Path for deep analytics.

Background processing pipeline for deep learner modeling and long-term pattern analysis.

Hot Path can override Cold Path recommendations when immediate intervention is needed.

Maintains learner in optimal difficulty zone (0.60-0.85 success probability) using Bayesian estimation.

Binary search over question bank to find questions matching target difficulty level.

Real-time Bayesian estimation of learner's success probability using item response theory.

Exponential smoothing prevents jarring difficulty jumps between consecutive questions.

Predicts memory decay using modified Ebbinghaus curves with interference factors.

Optimally schedules concept reviews based on predicted retention probability.

Detects when similar concepts interfere with each other in memory and adjusts scheduling.

Models concept relationships as a Bayesian network for optimal learning path selection.

Ensures prerequisite concepts are mastered before advancing to dependent topics.

Strengthens concept connections through repeated co-activation (Hebbian learning).

PID-controlled scaffolding that dynamically adjusts support level based on learner performance.

Gradually reduces scaffolding as learner demonstrates independent competence.

Provides feedback across multiple dimensions (hints, explanations, worked examples).

Real-time estimation of learner's cognitive capacity using dual-process theory.

Selects optimal content presentation format based on cognitive load and learning style.

Prevents cognitive overload by reducing content complexity when capacity is exceeded.

Derives affective state from behavioral signals (timing, accuracy, patterns) without requiring sensors.

Continuous monitoring of learner engagement through interaction pattern analysis.

Detects engagement crisis when stress exceeds threshold and triggers immediate intervention.

Multi-tier intervention system (nudge, pause, encourage, reduce difficulty) based on stress severity.

System supports concurrent multi-subject learning with per-subject algorithm calibration.

Tracks individual algorithm contributions to learning outcomes for accountability.

Full session state persistence enabling session resume and cross-device continuity.

Real-time system metrics for monitoring algorithm performance and system health.

Cache service maintains hit rate of 85% or higher for learner state retrieval.

End-to-end API processing completes within 200ms latency budget.

Structured question bank with difficulty scoring, concept tagging, and prerequisite mapping.

Comprehensive learner model tracking cognitive, affective, and behavioral dimensions.

WebSocket-based real-time communication for live algorithm updates and interventions.

Microservice-ready architecture with independent algorithm services and centralized orchestration.