Myfitnesspal, Inc.

Under Armour, Inc.

Chul Lee

Hesamoddin Salehian

Bryan Levine

Patrick Howell

Mark Allen

Joohyun Kim

Paul Radcliffe

Karlo Berket

Karthik Subramaniam

Jiang Xu

Aadhar Jain

Layla Martin

Surender Reddy Yerva

Iman Barjasteh

Yi Qiang

Kyler Eastman

Angela Hsiao

Murtaza Ali

Poojit Sharma

Sid Jatia

Madeline Taylor Rose

Jiaqi Sun

Mirela Spasova

Vaibhav Aggarwal

Hannah O'Neal

A method of operating a food categorization engine includes extracting features from each text description of a training food data set to generate a feature set. The feature set is analyzed to determine sets of food categorization features that correlate to one or more food categories, wherein the sets of food categorization features are defined by a plurality of category vectors. Individual words and/or characters of a text description of a food item received from a user are then analyzed in order to generate a numerical vector representative thereof. The numerical vector is multiplied by a matrix defined by the plurality of category vectors. One or more food categories with which to associate the received food item are automatically selected based at least in part on the multiplication, and the data record associated with the food item is amended to include the selected food categories.

System and method for food categorization

A method and system for providing more relevant search results and recommendation from a food database is disclosed. The method includes receiving a query, a first candidate food, and a second candidate food. The method includes generating vectors based on the query and food names of the first and second candidate foods using at least one embedding function of a machine learning model. The method includes determining nutrition content vectors from the nutritional data of the first and second candidate foods. The method includes generating a nutrition content vector based on the query using another embedding function of the machine learning model. The method includes determining which of the first and second candidate food is more relevant to the query based at least in part on the nutrition content vectors. The method includes providing search results or recommendation based on the determined relevance.

Deep multi-modal pairwise ranking model for crowdsourced food data

A method for operating a health tracking system, a health tracking system, and non-transitory computer-readable medium for operating a health tracking system are disclosed. The method comprises receiving a data record comprising at least a descriptive string and nutritional data regarding a consumable item to which the data record corresponds; determining a taste associated to the consumable item based on an evaluation of at least one of: (i) the descriptive string, and (ii) the nutritional data; and associating the determined taste with the data record in a database.

Method, system, and non-transitory computer-readable medium for associating taste with consumable records

Disclosed embodiments include apparatuses, methods and storage media associated with modifying a food record database. The method comprises receiving a plurality of food records from a plurality of sources, each of the plurality of food records comprising at least a food record description, the plurality of sources including (i) at least one non-user entity that is an owner of a third party food database and (ii) users of the food record database. The method further comprises receiving search requests from users, and returning one or more top search results from the food record database in response. The method also comprises determining a score for a particular food record identified by the top search results, wherein the score is calculated based at least in part on one of: a number of times the particular food record has been included in the top search results of the search requests or a number of times the particular food record has been logged.

Food description processing methods and apparatuses

A method for decreasing a number of individual entries in a database of user-created records which describe a single item by: receiving a plurality of user-created records, each of said records comprising at least a descriptive string; placing individual ones of the plurality of user-created records having a sufficiently similar descriptive string into one of a plurality of first groups; hashing the descriptive string of each of the plurality of first groups in order to place two or more groups into a single bin; performing a pair-wise comparison of the descriptive strings of the two or more groups in each bin; and when the comparison of the descriptive strings of the two or more groups in a bin results in a distance below a first threshold, merging the two or more groups into a combined group.

Health tracking system with verification of nutrition information

Apparatus and methods for deriving a dietary plan. In one embodiment, the method comprises enabling a user to enter a long-term dietary goal at a health tracking device; accessing user profile information relating to the user; generating at least one value representative of a short-term dietary goal for the user based at least in part on the long-term dietary goal and the user profile information; dividing the at least one value of the short-term dietary goal among a plurality of dietary portions; and enabling the user to adjust the relative amount of the at least one value apportioned to each of the plurality of dietary portions.

Health tracking system with meal goals

System and method for associating user-entered consumable item description to an entry in a consumable item database. In one embodiment, formally structured restaurant menu item is matched to a large database of food items that has been constructed via crowd-sourcing. A novel, practical, and scalable machine learning solution architecture, consisting of two major steps is utilized. First a query generation approach is applied, based on a Markov Decision Process algorithm, to reduce the time complexity of searching for matching candidates. That is then followed by a re-ranking step, using deep learning techniques, to ensure matching quality goals are met.

System and method for associating user-entered text to database entries

A system and method of providing nutritional data for a user is disclosed herein. The method includes receiving a selected restaurant from a health tracking device, and providing menu data for the user based on the selected restaurant. The method further includes receiving a selected menu item from the health tracking device, associating the selected menu item with a plurality of food items in a database, and providing the plurality of food items for the user. Furthermore, the method includes receiving a selected one of the plurality of food items from the health tracking device, and providing nutritional data based on the selected one of the plurality of food items.

Restaurant matching and filtering for use in a health tracking system

Methods for calculating nutrient content information. In one embodiment, the methods comprise: receiving a recipe having a list of ingredients and quantities, for each of the ingredients a corresponding record is found within a database of known records, the records are associated to quantities and nutritional values. The units of measurement of the recipe ingredients and the identified record are compared. When the units are the same, no conversion is performed. When the units are different, the units of the known record are converted using a conversion factor derived from a relationship between the differing units of measurement. In one variant, the conversion factor may be identified from a table of conversion factors relating various units of measurement to one another. Finally, the converted or the known nutritional values are multiplied by a ratio of the quantity of the ingredient in the recipe to the quantity of the known record.

Nutrient content identification method and apparatus

Apparatus and methods are given for providing enhanced product recommendations. In one embodiment, products are recommended to a consumer based on information manually entered by the consumer and/or derived from data relating to the consumer obtained from a health-monitoring platform. The recommended products are provided from a recommendation engine to a human operator or curator at an item selection entity, the curator selects a predetermined number of the recommended products for delivery to the consumer. Such delivery is established by the consumer to occur periodically (such as monthly) as part of a subscription service. Product recommendations are adjusted over time to relate to customer activity and interest data accumulated from the health-monitoring platform. In one embodiment, a recommendation engine is configured to specifically tailor its recommendation algorithms based on feedback information received from the curator and/or the consumer.

Growing community of inventors

San Francisco, CA, United States of America

Chul Lee