Microsoft Technology Licensing, LLC

Cellavision Ab

Oscar Beijbom

Neel Suresh Joshi

Daniel Morris

T Scott Saponas

Martin Almers

Sven Hedlund

Siddharth Khullar

A 'Food Logger' provides various approaches for learning or training one or more image-based models (referred to herein as 'meal models') of nutritional content of meals. This training is based on one or more datasets of images of meals in combination with &#x201c;meal features&#x201d; that describe various parameters of the meal. Examples of meal features include, but are not limited to, food type, meal contents, portion size, nutritional content (e.g., calories, vitamins, minerals, carbohydrates, protein, salt, etc.), food source (e.g., specific restaurants or restaurant chains, grocery stores, particular pre-packaged foods, school meals, meals prepared at home, etc.). Given the trained models, the Food Logger automatically provides estimates of nutritional information based on automated recognition of new images of meals provided by (or for) the user. This nutritional information is then used to enable a wide range of user-centric interactions relating to food consumed by individual users.

Food logging from images

Restaurant-specific food logging from images

Growing community of inventors

La Jolla, CA, United States of America

Oscar Beijbom