Animal Wellness Profiling Using Genotype-Phenotype Risk Prediction
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Solution Overview
Problem
Existing methods lack effective and efficient ways to assess and manage health conditions in non-human subjects, such as mammals, using genetic and phenotypic data to identify conditions and risks, particularly in companion animals like felines and canines.
Innovation Solution
A machine learning-based approach that utilizes genetic data from genomic loci and phenotypic data to create a genotype-phenotype profile, applying a prediction model to identify conditions or risks, and providing personalized wellness recommendations, including behavioral modifications and therapeutic interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional health assessment methods are used for non-human subjects, then the process is simple and easy to operate, but the accuracy and precision of condition identification is insufficient
Solution Approach 1:
The system segments the health assessment process into distinct functional modules: data collection module (genetic data, phenotypic data, environmental data), data processing module (quality control, normalization), machine learning analysis module (risk prediction, condition identification), and recommendation module. This segmentation enables high precision through specialized processing in each module while managing overall system complexity through modular architecture.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw health data and condition identification results. The ML model processes complex multi-omics data and phenotypic information, transforming them into actionable health assessments. This intermediary handles the computational complexity internally while providing precise output to users.
2Reliability
If comprehensive genetic and phenotypic data analysis is performed, then the reliability of health condition identification improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and quality controlling genetic and phenotypic data before main analysis. Data normalization, missing value imputation, and quality filtering are conducted in advance, reducing the computational burden during actual health assessment and enabling faster, more reliable results when needed.
Solution Approach 2:
The patent transforms raw genetic data into standardized parameters and features suitable for machine learning analysis. By changing the parameter representation (e.g., genotype encoding, phenotypic scaling, feature selection), the system maintains high reliability through comprehensive analysis while reducing computational complexity and processing time through optimized parameter formats.
3Productivity
If personalized wellness recommendations are provided based on detailed analysis, then the effectiveness of health management improves, but the complexity of data processing and interpretation increases
Solution Approach 1:
The system implements feedback mechanisms where machine learning models continuously learn from health assessment results and recommendation outcomes. The system processes comprehensive data to generate personalized recommendations, then uses follow-up data to refine future recommendations, improving wellness management effectiveness while the ML model handles the computational complexity internally.
Solution Approach 2:
The patent enables the system to automatically process complex data and generate personalized wellness recommendations without requiring users to manually analyze or interpret raw data. The machine learning model performs self-service by autonomously integrating multi-omics data, phenotypic information, and environmental factors to produce actionable health insights and personalized intervention recommendations.
Data Source
AI summary
A health and wellness system for a non-human subject comprising analyzing genetic data and phenotypic data of the non-human subject with a machine learning algorithm and making a recommendation or recommendation for products or activities for the non-human subject.