Personalized Food Analysis Using Glucose Response Data
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Solution Overview
Problem
Current food and nutrition databases are inconsistent, unreliable, and fragmented, limiting their applicability and failing to provide personalized nutrition recommendations that evolve with an individual's lifestyle.
Innovation Solution
A system that continuously collects and analyzes data from various sources to create a food ontology, integrates biomarker information, and generates personalized nutrition recommendations using a device/data hub and insights and recommendation engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data are collected from multiple sources to improve comprehensiveness, then the quantity of data increases, but the consistency and reliability deteriorate
Solution Approach 1:
The patent merges multiple food databases into a single comprehensive database, integrating data from various sources while establishing standardized formats and validation rules to ensure consistency and reliability across all collected nutritional information
Solution Approach 2:
The system implements feedback mechanisms where user responses to food items are collected and used to continuously improve and validate the database, ensuring data quality and reliability through iterative refinement based on actual user metabolic responses
2Measurement precision
If databases target specific populations or time periods to improve specialization, then the measurement precision for specific groups improves, but the adaptability to general populations deteriorates
Solution Approach 1:
The patent creates a universal food database and system that serves multiple population groups simultaneously, using standardized nutritional information and metabolic response tracking that can be applied across different demographics while maintaining precision through personalized calibration
3Ease of manufacture
If data sources use different formats to improve source independence, then the ease of data collection improves, but the device complexity increases
Solution Approach 1:
The patent introduces a centralized database as an intermediary layer that standardizes and harmonizes data from multiple sources with different formats, allowing independent data collection while simplifying integration through a unified data structure and validation framework
4Adaptability or versatility
If personalized recommendations are generated to improve individualization, then the adaptability to individual needs improves, but the loss of information increases due to data fragmentation
Solution Approach 1:
The patent combines fragmented data about food composition, user consumption patterns, and metabolic responses into a unified comprehensive database, preventing information loss and enabling accurate personalized recommendations through complete data integration
Data Source
AI summary
Techniques disclosed herein relate to food analysis and glucose level management. In some examples, the techniques involve determining a consumption of a first food item by a user, receiving data indicative of a glucose level of the user, determining an effect of the first food item on the glucose level of the user based on a change of the glucose level of the user, and storing the effect of the first food item on the glucose level of the user to a database that includes data indicating effects of individual food items on the glucose level of the user. In some examples, the techniques also involve providing one or more recommendations for managing the glucose level of the user based on the data in the database and a food item consumed by the user.