Nutrient predictions based on food purchase data

A machine-learning model using datasets like FIES and Euromonitor predicts nutrient availability from food purchase data, addressing inaccuracies in traditional nutrition assessment methods by providing reliable and standardized nutrient information.

WO2026114846A1 Publication Date: 2026-06-04SOCIETE DES PRODUITS NESTLE SA
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Patent Information

Application Number
PCT/EP2025/084124
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-11-27
Filing Date
2025-11-25
Publication Date
2026-06-04

AI Technical Summary

Technical Problem

Traditional methods for assessing nutrition, such as dietary surveys and Global Dietary Databases, are cumbersome, unreliable, and lack accuracy due to self-misreporting biases, variability in food compositions, and inconsistent data quality, making it difficult to provide reliable and standardized nutrient information.

Method used

A machine-learning model trained on datasets like the Philippines Family Income Expenditure Survey and Euromonitor data is used to predict nutrient availability from food purchase data, incorporating feature mapping and optimization techniques to ensure accuracy and consistency across regions.

Benefits of technology

The model provides cost-effective, real-time nutrient predictions with high accuracy, enabling comprehensive insights into nutrient intake patterns and dietary diversity, benefiting individuals and stakeholders with actionable nutritional insights.

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Abstract

Nutrient prediction systems and methods can include systems and methods of training and testing machine-learning models and food purchase data collection and analysis. For example, a food purchase data collection and analysis system, under the control of at least one processor, can include a purchase data collection device to collect food purchase data at a point of sale of a food basket including information regarding food items purchased and location of the food purchase, an analysis server including a machine-learning model predicting nutrient availability based on the food purchase data, and a network for transmitting the food purchase data from the purchase data collection device to the analysis server for analysis using the machine-learning model.
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