AI Food Optimization Using Blood Saliva Biomarkers
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Solution Overview
Problem
Current methods for optimizing food nutrition and health do not effectively utilize personal blood and saliva chemistry data to provide tailored dietary recommendations, leading to inefficiencies in food consumption and waste.
Innovation Solution
A system that uses biomarkers from blood and saliva samples to optimize food intake by forming optimization algorithms based on linear and non-linear systems of vectors, considering individual preferences, health, ingredient weights, variety, flavoring, style, ethnicity, nutrition, and delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personalized dietary recommendations are provided based on blood and saliva chemistry analysis, then nutrition optimization and health improvement are achieved, but system complexity and cost increase
Solution Approach 1:
The system segments the complex health optimization problem into distinct components: blood chemistry analysis, saliva chemistry analysis, algorithmic processing, and personalized recommendation generation. Each component is handled separately by specialized modules, making the overall system more manageable and reliable
Solution Approach 2:
The patent introduces an intermediary computational system that processes raw blood and saliva chemistry data through optimization algorithms to generate personalized dietary recommendations. This intermediary layer translates complex biochemical data into actionable nutritional guidance, bridging the gap between laboratory analysis and practical dietary application
2Measurement precision
If blood and saliva sampling analysis is implemented to measure nutrition and body chemistry unbiasedly, then measurement accuracy is improved, but loss of time and convenience deteriorate
Solution Approach 1:
The system performs preliminary blood and saliva sampling to establish baseline chemistry profiles before implementing personalized dietary recommendations. This preliminary action captures the user's initial nutritional status, allowing the optimization algorithms to work from accurate baseline data rather than requiring continuous frequent sampling
Solution Approach 2:
The patent implements a feedback mechanism where blood and saliva chemistry results are continuously monitored and fed back into the optimization algorithm. This feedback loop allows the system to adjust personalized recommendations based on actual physiological responses, improving measurement accuracy over time while reducing the frequency of required sampling
3Productivity
If food consumption is reduced by optimizing nutrition intake, then health improvement and waste reduction are achieved, but nutrient sufficiency may be compromised
Solution Approach 1:
The system changes the parameters of food consumption from quantity-based to composition-based optimization. Instead of reducing overall food intake, the optimization algorithm adjusts the chemical composition and nutritional density of consumed foods based on blood and saliva chemistry profiles, ensuring adequate nutrient intake at optimized consumption levels
Solution Approach 2:
The patent applies local quality optimization by tailoring the nutritional composition of specific food items to individual physiological needs. The system identifies which specific nutrients require adjustment based on personal chemistry profiles, rather than uniformly reducing all food consumption, thereby maintaining nutrient sufficiency while achieving health optimization
Data Source
AI summary
A method, comprises: determining, by one or more computer processing units, a plurality of combinations based on a plurality of ingredients; training a neural network to determine a plurality of optimized weight values for a respective combination of the plurality of combinations for a user based on a plurality of expected blood chemistry values corresponding to the user and a plurality of standard deviation values corresponding to the user, wherein the optimized weight values correspond to neural network probability weightings with iterative feedback from one or more biological samples data from the user; determining, by the one or more computer processing units, a plurality of optimized combinations based on the plurality of optimized weight values, wherein the plurality of optimized combinations is a subset of the plurality of combinations; and providing data corresponding to at least one or more combinations of the plurality of optimized combinations.