Genotype-Based Analysis System Using Phenotypic Interrogatories

The patent creates a computational model that copies the functionality of laboratory genotype testing through machine learning. The system trains a model on existing genotype data and phenotype information, then uses this copied knowledge to predict genotypes from phenotypic data without requiring actual laboratory testing. This resolves the contradiction by providing a time-efficient alternative that maintains predictive accuracy through model-based inference rather than direct measurement.

The patent performs preliminary action by pre-training the machine learning model on comprehensive genotype and phenotype data before deployment. The model learns the relationships between phenotypic characteristics and genotypes in advance, so that during actual use, genotype prediction can be performed rapidly without time-consuming laboratory tests. This preliminary training phase stores the knowledge needed for fast subsequent predictions.

The patent replaces expensive laboratory testing infrastructure with a computationally-based solution that uses inexpensive phenotypic data collection. Instead of requiring costly lab equipment, reagents, and personnel for genotype determination, the system uses affordable digital phenotyping assessments and processes them through the pre-trained machine learning model. This provides a low-cost alternative that maintains predictive capability.

The computational model copies the genotype determination function from expensive laboratory tests to an inexpensive digital platform. By training the model on existing lab test data and then using it to predict genotypes from phenotypic observations, the system replicates the value of laboratory testing without the associated costs of physical testing infrastructure.

The patent extracts the complex genotype analysis functionality from the user-facing system and embeds it within the pre-trained machine learning model. The model encapsulates the complex relationships between phenotypic data and genotypes, allowing the user interface to remain simple while maintaining access to sophisticated analytical capabilities. This separates the complexity of the analysis engine from the simplicity of the interaction interface.

The machine learning model serves as an intermediary between simple phenotypic data collection and complex genotype interpretation. The model translates easily-collected phenotypic observations into meaningful genotype predictions and symptom severity assessments, bridging the gap between simple input data and complex analytical output without requiring the user system to handle the complexity directly.

The patent replaces the mechanical and physical laboratory testing system with a digital computational system. Instead of requiring physical sample collection, transportation, and laboratory processing, the system uses digital phenotypic assessments processed by machine learning algorithms. This substitution maintains measurement precision through model-based prediction while dramatically improving ease of operation through remote, automated assessment.

The system enables self-service by allowing individuals to complete phenotypic assessments independently without requiring laboratory visits or professional testing personnel. The machine learning model automatically processes the collected phenotypic data and generates genotype predictions and symptom severity assessments, eliminating the need for manual laboratory operations and making the process accessible to anyone with internet connectivity.