At-Home Nutritional Response Measurement Accuracy via ML Validation
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Solution Overview
Problem
At-home measurements of nutritional responses are often less accurate than those taken in clinical settings due to factors like contamination, user compliance, and lack of controlled conditions, limiting the ability to provide personalized dietary recommendations to individuals.
Innovation Solution
The use of computing devices and data accuracy services that combine multiple measurements from various sources, such as continuous glucose monitors, at-home blood tests, and wearable devices, along with machine learning mechanisms to adjust and validate test data, improves the accuracy of nutritional response measurements by correcting for errors and inconsistencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If measurements are taken in a clinical setting, then measurement precision is improved, but cost increases and accessibility decreases
Solution Approach 1:
The patent creates a virtual replica of clinical-grade measurement conditions by using machine learning models trained on clinical data to process and validate at-home measurement data. This copying approach allows at-home devices to achieve clinical-level accuracy without requiring actual clinical infrastructure, thereby reducing cost and improving accessibility while maintaining measurement precision.
Solution Approach 2:
The patent introduces an intermediary layer of machine learning validation services that act as a bridge between at-home measurement devices and clinical-grade accuracy requirements. This intermediary validates at-home measurements by comparing them against clinical data patterns, enabling accurate nutritional response assessment without direct clinical involvement, thus reducing cost while maintaining precision.
2Ease of manufacture
If at-home measurements are used, then cost decreases and accessibility improves, but measurement precision deteriorates due to contamination and user compliance issues
Solution Approach 1:
The patent implements feedback mechanisms where machine learning models continuously learn from both at-home and clinical measurement data. The system provides feedback to users about proper measurement techniques and validates at-home measurements by comparing them against expected patterns derived from clinical data, thereby improving measurement precision while maintaining the cost and accessibility advantages of at-home testing.
Solution Approach 2:
The patent replaces the mechanical/physical control systems of clinical settings (trained staff, controlled environments, standardized procedures) with an information-based machine learning validation system. This substitution allows at-home measurements to achieve clinical-level accuracy by using computational methods to detect and correct errors related to contamination and user compliance, while maintaining the cost and accessibility benefits of at-home testing.
3Measurement precision
If multiple measurements from various sources are combined, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple data sources (continuous glucose monitors, at-home blood tests, wearable devices, clinical measurements) into a unified machine learning validation framework. By combining these diverse measurement sources and processing them through a single integrated learning system, the patent achieves improved measurement precision while managing complexity through unified data processing rather than separate analysis systems.
Data Source
AI summary
Techniques are disclosed herein for improving the accuracy of nutritional responses measured in a non-clinical setting. Using the technologies described herein, different techniques can be utilized to improve the accuracy of test data associated with one or more “at home” tests. In some examples, more than one test is utilized to improve the accuracy of test data associated with a particular biomarker. In other examples, a data accuracy service can programmatically analyze data received from an individual and determine whether the data is accurate. In some examples, a computing device is utilized to assist in determining what food item(s) are consumed, as well as determine whether a test protocol was followed.