Interactive engine to provide personal recommendations for nutrition, to help the general public to live a balanced healthier lifestyle

Abstract

An algorithm and method to provide personal recommendations for nutrition based on preferences, habits, medical and activity profiles for users, and constraints. The algorithm can also be fed and takes into account real-time feedback from the user. The method allows creating a personal nutritional schedule based on a set of constraints, which are solved using an optimization algorithm to find the diet best fitting each user. The method also includes analyzing a single user by applying various statistical techniques, enabling the algorithm to infer the user's preferences and updating of the constraints, analyzing and clustering of the general user population based on statistical principles, giving the algorithm insightful information and allowing improved performance by means of “machine-learning,” and creating a list of recommended food items/recipes to help users live a balanced, healthier lifestyle.

We claim: 
     
         1 . A method of determining a diet, the method comprising:
 installing an application on computing devices belonging to a plurality of users;   creating a personal nutritional schedule based on a set of constraints which are solved using an optimization algorithm;   analyzing a single user's eating habits by applying statistical techniques, enabling the algorithm to infer the user's preferences and update of the constraints;   statistically analyzing and clustering a plurality of users, giving the algorithm insightful information and allowing improved performance by means of “machine-learning;” and   creating a list of recommended food items/recipes based on the statistical analysis and clustering.   
     
     
         2 . The method of  claim 1 , further comprising asking individuals of the plurality of users several questions to determine their culinary preferences and medical and activity profiles. 
     
     
         3 . The method of  claim 1 , further comprising querying the user about dietary preferences using a game approach. 
     
     
         4 . The method of  claim 3 , wherein the game approach further comprises inferring the user's tastes and habits according to a game where the user iteratively chooses one of two options on the screen. 
     
     
         5 . The method of  claim 3 , wherein the game approach further comprises requiring the user to choose between two choices of at least one of two food items and two typical meals. 
     
     
         6 . The method of  claim 3 , wherein a list of questions is assembled in a manner that will dissect a set of preferences in an optimal way, and wherein each answer helps the algorithm benefit most from the previous question. 
     
     
         7 . The method of  claim 1 , further comprising breaking the recommended list into meals by a meal separator. The meal separator segments each daily diet into well-portioned meals and their recommended eating time. 
     
     
         8 . The method of  claim 1 , further comprising creating a diet profile by applying the updated constraints, wherein the diet profiler generates a list of diet constraints from user's information and provides user clustering based on dietary habits/eating habits. 
     
     
         9 . The method of  claim 8 , further comprising learning the users' habits in order to correct a diet instantly, and provide crowdsourcing for dietary/eating habits. 
     
     
         10 . The method of  claim 8 , further comprising finding nutritional correlation by a research system between the user's habits, food intake, workouts and reported well-being.