Composite Nutritional Index via Inference Engine
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Solution Overview
Problem
Current methods for generating personalized nutritional recommendations fail to effectively account for individual physiological and genetic specificities, leading to incompatible and non-standard recommendations, and lack the ability to automatically and quickly calculate daily nutrient intakes that are compatible with each other.
Innovation Solution
A method that selects an individual, acquires phenotypical and genotypical data, applies predefined rules to generate normalized indices, and uses an inference engine with a knowledge base to calculate personalized daily nutrient intake values, grouping them by metabolic function for optimized nutritional guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a wide range of phenotypical and genotypical parameters are integrated to generate personalized nutritional recommendations, then the individualization and precision of recommendations are improved, but the complexity of the system and difficulty of establishing causal relationships increase
Solution Approach 1:
The patent segments the complex nutritional recommendation system into distinct modules: phenotypical data processing module, genotypical data processing module, rule engine module, and inference engine module. Each module handles specific types of data and operations, making the overall system more manageable despite processing numerous parameters. The segmentation allows independent optimization of each module while maintaining system-wide integration through standardized data structures.
Solution Approach 2:
The patent introduces normalized indices as intermediary representations between raw phenotypical/genotypical data and final nutritional recommendations. These indices serve as standardized intermediaries that simplify the integration of diverse data types (age, weight, genetic markers, physiological signs) into a unified format that the rule engine can process efficiently, reducing the complexity of direct multi-parameter integration.
2Productivity
If automated calculation of daily nutrient intakes is implemented to quickly generate personalized recommendations, then the productivity and speed of recommendation generation are improved, but the need for complex data models and inference engines increases system complexity
Solution Approach 1:
The patent implements preliminary action by pre-configuring the inference engine with extensive rule sets and knowledge bases during system initialization. Common nutritional relationships, metabolic pathways, and interaction patterns are pre-encoded into the system. This allows the automated calculation to proceed rapidly during runtime by applying pre-established rules to individual patient data, rather than performing complex reasoning from scratch for each recommendation.
Solution Approach 2:
The patent utilizes parameter changes by transforming raw phenotypical and genotypical data into normalized indices with standardized scales and units. This parameter transformation enables the inference engine to process diverse data types uniformly and apply mathematical operations consistently across different parameter types, improving computational efficiency while maintaining the ability to handle complex relationships.
3Stability of the object's composition
If phenotypical and genotypical data are processed through predefined rules to generate normalized indices, then the compatibility and consistency of recommendations are improved, but the time required for data processing and index calculation increases
Solution Approach 1:
The patent applies parameter changes by implementing efficient normalization algorithms that transform raw data into standardized indices using pre-defined mathematical transformations. These parameter changes are optimized to reduce computational overhead while maintaining consistency. The normalized indices enable direct comparison and integration of diverse data types without requiring extensive processing time for each transformation.
Solution Approach 2:
The patent implements local quality by applying different normalization strategies and processing methods tailored to specific data types (phenotypical vs. genotypical) and specific parameters (age, weight, genetic markers). Each data type receives customized processing appropriate to its characteristics, optimizing the balance between processing speed and consistency for each local segment of the data pipeline while maintaining overall system compatibility.
Data Source
AI summary
A method for generating a composite nutritional index includes selecting an individual; acquiring a first set of phenotypical data for the individual characterizing phenotypic descriptors; acquiring a second set of data for a genotype characterizing genotypical descriptors for the individual; applying a set of predefined rules; generating a set of personalized phenotypical and genotypical indices for an individual; calculating a target value of a daily intake of the at least one nutrient from the application of an inference engine and determining a composite nutritional index including an operation to associate a plurality of target values of a daily intake of the at least one nutrient with at least one metabolic function.