Double Right Triangle Phase Prediction with Base-12 Geometry and Modular Constraints

This work presents a novel method for predicting relative quantum or classical phase shifts in a two-arm interferometric system with 90° separation using a discrete base-12 framework. By representing phase angles as modular ticks (30° per tick), the model simplifies prediction via modular arithmetic and a lookup table derived from geometric constraints. This approach enables exact phase class identification from a single intensity measurement and supports time-evolution forecasting with minimal computational overhead. The method offers a clean alternative to continuous-wave or Bayesian models, and aligns naturally with harmonic and geometric phase theory. Modeled with ChatGPT-5 Patent Pending