Our Proprietary Algorithms
Proprietary Multilingual Learning Intelligence
Defensibility: These algorithms represent novel, proprietary approaches to Multilingual learning that competitors cannot easily replicate. Each combines multiple ML factors (cognate analysis, cultural context, temporal decay, transfer learning) in unique ways that create a sustainable competitive advantage.
Test the Algorithms
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Multi-Dimensional Cognate Analysis
Proprietary innovation in Multilingual vocabulary learning
Core Innovation: Combines 5 analysis dimensions:
- Orthographic Similarity - Edit distance, pattern matching (e.g., -tion → -ción)
- Phonetic Analysis - IPA-based pronunciation comparison
- Semantic Verification - LLM-powered meaning overlap detection
- Frequency-Based Transfer - High-frequency cognates transfer better
- False Friend Detection - Penalizes words with meaning divergence
Core Formula:
Score = (Orthographic × 0.35) + (Phonetic × 0.25) + (Semantic × 0.25) + (Frequency × 0.15) + FalseFriendPenaltyAdaptive Scaffolding:
- Score ≥ 0.85: Minimal support (leverage cognate recognition)
- Score 0.65-0.84: Moderate support (guided cognate exploration)
- Score 0.4-0.64: Substantial support (explicit instruction)
- Score < 0.4: Full Multilingual scaffolding
Unique Value:
Unlike simple translation lookups, this algorithm predicts learning transfer probability and automatically adjusts instruction intensity. No competitor has this level of sophistication.