Sensor-Driven Nutritional Composition for Individualized Biomarker Dosing
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Solution Overview
Problem
Existing health care systems fail to provide personalized and efficient nutritional compositions based on individual biomarker data, leading to disparities in health outcomes and access to care.
Innovation Solution
A system and method for automatically generating a customized nutritional composition using a datastore, processing component, and recommending engine to determine dose values and mix nutrients based on biomarker data from various sensors, including biochemical and wearable sensors, and employing machine learning techniques to optimize the composition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual nutritional assessment and recommendation methods are used, then personalized nutritional guidance can be provided, but the process is time-consuming and difficult to scale
Solution Approach 1:
The system enables automated self-assessment through digital questionnaires and sensor data collection, where users independently complete health assessments and the system automatically processes their data to generate personalized nutritional recommendations without requiring manual intervention from nutritionists
Solution Approach 2:
Manual nutritional assessment processes are replaced with automated digital systems including electronic questionnaires, sensor data collection, and algorithm-driven analysis that automatically generates personalized nutritional compositions, eliminating the time-consuming manual evaluation process
2Measurement precision
If comprehensive biomarker data collection is implemented, then personalized nutritional compositions can be generated, but system complexity increases
Solution Approach 1:
The complex data collection system is segmented into multiple independent components including wearable sensors for continuous monitoring, periodic laboratory tests for detailed biomarker analysis, and digital questionnaires for lifestyle assessment, allowing each component to be optimized independently while maintaining overall system precision
Solution Approach 2:
The system integrates multiple data collection functions into a unified platform that processes wearable sensor data, laboratory test results, and questionnaire responses through a common analytical framework, reducing complexity by providing a multi-functional solution rather than separate systems for each data type
3Productivity
If automated nutritional composition generation is implemented, then productivity and scalability improve, but the need for multiple components increases system complexity
Solution Approach 1:
The system merges data collection, analysis, and composition generation functions into an integrated automated platform where wearable sensors, laboratory tests, and questionnaires all feed into a unified algorithmic system that generates personalized nutritional compositions, improving productivity while managing complexity through integration rather than separate standalone systems
Data Source
AI summary
The present invention relates to a system and a method comprising an automated composition of an individualized nutritional composition. The present invention provides a datastore configured to store and receive sensor dataset(s) from at least one or a plurality of sensors. The invention further provides a processing component, wherein the processing component is configured to extract the sensor dataset(s) from the datastore. Further, the processing component is configured to automatically generate structured biomarker data based on the extracted sensor dataset(s). The invention also provides a recommending engine, wherein the recommending engine is configured to determine a dose value based on the structured biomarker data. In a further embodiment the invention provides a mixer. The mixer is configured to automatically generate an optimized nutritional composition based on the dose value.