Abstract: A lifestyle preferences management system is provided. The system may collect and organize data regarding various foods and/or beverages that one or more local restaurants may offer and recommend a particular restaurant and/or menu item to a particular user based on the known dietary preferences and geographical location of the user. The system also may generate custom menu items for each user based on a cross-section of the user's known dietary preferences and the availability of substitutions, additions and/or deletions for a given menu item resulting in a dish that conforms to the user's preferences. The system also may facilitate the ordering of food from one or more vendors for a group of users while considering the dietary and/or lifestyle preferences of each member of the group in aggregate. The system also may utilize machine learning to learn a user's eating habits (e.g., by type of food, time of day, day of the week, geographical location, weather conditions, etc.

Abstract: A lifestyle preferences management system is provided. The system may collect and organize data regarding various foods and/or beverages that one or more local restaurants may offer and recommend a particular restaurant and/or menu item to a particular user based on the known dietary preferences and geographical location of the user. The system also may generate custom menu items for each user based on a cross-section of the user's known dietary preferences and the availability of substitutions, additions and/or deletions for a given menu item resulting in a dish that conforms to the user's preferences. The system also may facilitate the ordering of food from one or more vendors for a group of users while considering the dietary and/or lifestyle preferences of each member of the group in aggregate. The system also may utilize machine learning to learn a user's eating habits (e.g., by type of food, time of day, day of the week, geographical location, weather conditions, etc.

Abstract: A lifestyle preferences management system is provided. The system may collect and organize data regarding various foods and/or beverages that one or more local restaurants may offer and recommend a particular restaurant and/or menu item to a particular user based on the known dietary preferences and geographical location of the user. The system also may generate custom menu items for each user based on a cross-section of the user's known dietary preferences and the availability of substitutions, additions and/or deletions for a given menu item resulting in a dish that conforms to the user's preferences. The system also may facilitate the ordering of food from one or more vendors for a group of users while considering the dietary and/or lifestyle preferences of each member of the group in aggregate. The system also may utilize machine learning to learn a user's eating habits (e.g., by type of food, time of day, day of the week, geographical location, weather conditions, etc.

Abstract: A lifestyle preferences management system is provided. The system may collect and organize data regarding various foods and/or beverages that one or more local restaurants may offer and recommend a particular restaurant and/or menu item to a particular user based on the known dietary preferences and geographical location of the user. The system also may generate custom menu items for each user based on a cross-section of the user's known dietary preferences and the availability of substitutions, additions and/or deletions for a given menu item resulting in a dish that conforms to the user's preferences. The system also may facilitate the ordering of food from one or more vendors for a group of users while considering the dietary and/or lifestyle preferences of each member of the group in aggregate. The system also may utilize machine learning to learn a user's eating habits (e.g., by type of food, time of day, day of the week, geographical location, weather conditions, etc.