Myfitnesspal, Inc.

Under Armour, Inc.

Bryan Levine

Chul Lee

Mark Allen

Paul Radcliffe

Karlo Berket

Karthik Subramaniam

Jiang Xu

Aadhar Jain

Joohyun Kim

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

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

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

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 government agency 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.

Disclosed embodiments include apparatuses, methods and storage media associated with food item categorization. In embodiments, an apparatus may include one or more processors and a food categorization engine. The food categorization engine may be operated by the one or more processors to cause the apparatus to receive a description of a food item; analyze the description of the food item; and select one or more food categories with which to associate the food item based at least in part on a result of the analysis. Other embodiments may be described and claimed.

Disclosed embodiments include apparatuses, methods and storage media associated with standardization or normalization of a food description. In embodiments, an apparatus may include one or more processors and a food data pre-processing engine. The food data processing engine may, when operated by the one or more processors, cause the apparatus to receive a description of a food item, process the description to generate a standardized or normalized description for the food item, and store the standardized or normalized description in a food record in a food data storage arrangement of a fitness management system. Other embodiments may be described and claimed.

Techniques are provided for calculating nutrient content information. A server that hosts a fitness management application receives text information that describes food recipe information. The server parses the text information to identify relevant food information. The relevant food information includes a first text portion that corresponds to food ingredient information and a second text portion that corresponds to food quantity information. The server matches the food ingredient information to the first text portion with a known food ingredient in a database of food ingredient information. The server converts the food quantity information in the second text portion to a known food quantity type. The server calculates nutrient content information of the food ingredient information using nutritional information of the known food ingredient and the known food quantity type.

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Oakland, CA, United States of America

Bryan Levine