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Real time Hunger Level Based on Predictive Analytics with Matching Food Recommendations Disclosure Number: IPCOM000243928D
Publication Date: 2015-Oct-28
Document File: 4 page(s) / 88K

Publishing Venue

The Prior Art Database


Disclosed is a system and method to analytically derive the hunger level of an individual at any time by leveraging data captured from wearable sensors/devices. The system performs vector matching between each food item’s (e.g., in a vending machine, on a digital menu, etc.) information vector and the individual’s health status vector, and then digitally recommends appropriate food items in real time based on the determined hunger level.

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Real time Hunger Level Based on Predictive Analytics with Matching Food Recommendations

Existing food/snack vending machines offer food items for which the details displayed are static. Likewise, existing digital menu cards display the detailed description of food items available at a restaurant, along with caloric information, pricing, etc., which is also static and general information. Neither food providing system offers customized information for an individual's specific needs in terms of:

 Hunger Level

 Calorie requirement for a given individual at any given instance of time

 Health vectors such as sugar Level, blood pressure level, cholesterol level, etc.

A system and method are needed to help individuals make informed food choices based on personal hunger levels and health status, as well as nutritional information about the food.

The novel contribution is a system and methodology to analytically determine the real time hunger level for any given individual. The system leverages the technology of, and data captured by, wearable devices. Using the information from wearable health monitoring devices, the system enables automatic food vending machines, digitally connected hotel menu cards etc., to identify hunger level and then suggest appropriate healthy food options from the existing list of items or menu.

The recommendation system is an automatic food vending machine and the digital menu is displayed on mobile devices held by individuals. The recommendation system is a digital billboard with capability to display dynamic customized content.

The system determines hunger level based on an individual's current glucose level, which is determined by continuous glucose monitoring leveraging wearable sensors. In addition, the system uses biosensors and biomarkers to determine hunger based on an individual's saliva as well as the levels of the hormones leptin and ghrelin.

Each food item 'n' in the menu is represented as a vector Fn, comprising dimensions such as calorie content, hunger level rating with consideration to the quantity, spice level (e.g., hot, medium, mild), health sub-dimensions (e.g., age, cholesterol, sugar, etc.), price, etc. The hunger level rating of the food items can be derived from experiments
on how filling a particular food item is to a set of individuals, calorie content, quantity, ingredients, composition etc.

For example, when the system makes food recommendations for individual 'm', it considers the individual's:

 Calorie intake for the day,

 Balance of calorie intake left for the day (recommended calorie intake minus the calorie intake for the day),

 Hunger level at the moment (in a scale of 1 to 10),


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 Spice preference (hot, medium or mild), and

 Health parameters (age, cholesterol, sugar content etc.)

It maps these to heal...