Abstract: A method of operating a health tracking system is disclosed. The method comprises: storing a food knowledge graph having a plurality of labels describing consumable items and a plurality of relationships between pairs of labels, some of the labels being generic names for consumable items; receiving a data record having a descriptive string regarding a consumable item from a first health tracking device; matching the descriptive string to at least one label in the plurality of labels; and updating one or more information fields of the data record to associate the data record with the at least one label to which the descriptive string was matched. In some embodiments, the method further includes receiving a request for data records from a health tracking device and matching the request to the plurality of labels to provide an improved response to the request for data records.

Abstract: 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.

Abstract: 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.

Abstract: 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 text feature vectors based on the query and food names of the first and second candidate foods using at least one first 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 a second 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 on the text feature vectors and the nutrition content vectors. The method includes providing search results or recommendation based on the determined relevance.

Abstract: A method of operating a health tracking system is disclosed. The method comprises: storing a food knowledge graph having a plurality of labels describing consumable items and a plurality of relationships between pairs of labels, some of the labels being generic names for consumable items; receiving a data record having a descriptive string regarding a consumable item from a first health tracking device; matching the descriptive string to at least one label in the plurality of labels; and updating one or more information fields of the data record to associate the data record with the at least one label to which the descriptive string was matched. In some embodiments, the method further includes receiving a request for data records from a health tracking device and matching the request to the plurality of labels to provide an improved response to the request for data records.

Abstract: 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 text feature vectors based on the query and food names of the first and second candidate foods using at least one first 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 a second 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 on the text feature vectors and the nutrition content vectors. The method includes providing search results or recommendation based on the determined relevance.