3D Tooth Position Scoring for Missing-Teeth Orthodontic Cases

The patent replaces the mechanical/mathematical constraint-based system with a machine learning classification system. Instead of using raw tooth point measurements and enforcing rigid body constraints and collision avoidance through mathematical models, the system uses trained classification models (e.g., neural networks, random forests) that have learned optimal tooth position patterns from historical data. This substitution eliminates the need for complex real-time constraint satisfaction algorithms while improving determination accuracy.

The patent transforms the input parameters from raw tooth point measurements to engineered features that capture essential tooth position characteristics. By changing the parameter representation to include features like tooth spacing, alignment metrics, and arch form descriptors, the system achieves better determination accuracy without requiring complex constraint handling mechanisms.

The patent applies preliminary action by pre-training classification models on datasets that include various missing tooth scenarios. During the training phase, the system learns optimal patterns for handling missing teeth and their impact on overall tooth arrangement. When processing new cases with missing teeth, the pre-trained model directly applies learned knowledge without requiring complex real-time reasoning about missing tooth implications, thereby maintaining high reliability.

The machine learning system performs self-service by automatically adapting to missing tooth cases through its learned patterns. The classification models have internally encoded knowledge about how missing teeth affect tooth positioning, allowing the system to handle these cases autonomously without requiring explicit rule-based logic or manual intervention, thus maintaining reliability while providing versatile handling of incomplete dentition.

The patent applies preliminary action by pre-computing and storing engineered features from historical tooth position data during the training phase. The system pre-processes raw measurements into meaningful features (e.g., tooth spacing, alignment, arch form) and stores these for rapid retrieval during automated scoring. This preliminary feature engineering enables fast automated assessment while maintaining high measurement precision through the use of pre-validated feature sets.

The patent replaces manual or simple automated scoring with sophisticated machine learning classification models that have been trained to recognize accurate tooth position patterns. These models automatically perform feature extraction, evaluation, and scoring in an integrated pipeline, achieving both high productivity through automation and high measurement precision through learned patterns from extensive training data.