Methods and apparatus for coaching based on nutrition

Abstract

A method for enabling dynamic coaching feedback is disclosed. The method comprises generating a plurality of expected profiles for a plurality of data records within a user history database by grouping users into the plurality of expected profiles, generating heuristics and performance metrics for each of the plurality of expected profiles, and associating a first user with a first expected profile of the plurality of expected profiles. The method further includes receiving user input relating to nutrition consumption for the first user and, in response to receiving the user input relating to nutrition consumption, recommending an activity for the first user based on the first expected profile. The first expected profile includes at least one heuristic for generating dynamic feedback with the activity, and the at least one heuristic for generating the dynamic feedback comprises a rule for modifying the activity based on the nutrition consumption for the first user.

1 . A method for enabling dynamic coaching feedback at a client device, comprising:
 generating a plurality of expected profiles for a plurality of data records within a user history database by:
 using a machine learning model to group users into the plurality of expected profiles; and 
 using machine learning to generate heuristics and performance metrics for each of the plurality of expected profiles; 
   associating a first user with a first expected profile of the plurality of expected profiles;   receiving user input relating to nutrition consumption for the first user;   in response to receiving the user input relating to nutrition consumption, recommending a workout for the first user based on the first expected profile, wherein the first expected profile includes at least one heuristic for generating dynamic feedback associated with the workout, wherein the at least one heuristic for generating the dynamic feedback comprises a rule for modifying the workout based on the nutrition consumption for the first user; and   updating a user data record of the first user based on a logged performance corresponding to the workout.   
     
     
         2 . The method of  claim 1 , where the workout data records are obtained from a population of users. 
     
     
         3 . The method of  claim 2 , wherein the population of users are categorized based on at least one physiological or psychological trait. 
     
     
         4 . The method of  claim 1 , wherein the at least one heuristic for generating the dynamic feedback comprises a rule for motivating the first user based on an actual performance of the first user during the workout. 
     
     
         5 . The method of  claim 1 , wherein the first expected profile comprises an expected performance level for the workout. 
     
     
         6 . The method of  claim 1 , further comprising disassociating the first user with the first expected profile and associating the user with a second expected profile of the plurality of expected profiles based at least in part on the logged performance. 
     
     
         7 . The method of  claim 1 , wherein said nutrition consumption for the first user includes calorie consumption information based on meals logged by the first user. 
     
     
         8 . The method of  claim 1 , wherein said nutrition consumption for the first user includes macronutrient consumption information or micronutrient consumption information. 
     
     
         9 . The method of  claim 1 , further comprising identifying a caloric deficit or hydration deficit for the first user based on said nutrition consumption for the first user. 
     
     
         10 . The method of  claim 1 , wherein the user input relating to nutrition consumption for the first user is received from a nutrition tracking device associated with the first user. 
     
     
         11 . A method for enabling dynamic coaching feedback at a client device, comprising:
 generating a plurality of expected profiles for a plurality of data records within a user history database by:
 grouping users into the plurality of expected profiles; and 
 generating heuristics and performance metrics for each of the plurality of expected profiles; 
   associating a first user with a first expected profile of the plurality of expected profiles;   receiving user input relating to nutrition consumption for the first user;   in response to receiving the user input relating to nutrition consumption, recommending an activity for the first user based on the first expected profile, wherein the first expected profile includes at least one heuristic for generating dynamic feedback with the activity, wherein the at least one heuristic for generating the dynamic feedback comprises a rule for modifying the activity based on the nutrition consumption for the first user; and   updating a user data record of the first user based on a logged performance corresponding to the fitness activity.   
     
     
         12 . The method of  claim 11 , where the activity is a fitness activity. 
     
     
         13 . The method of  claim 12 , wherein the fitness activity is a workout. 
     
     
         14 . The method of  claim 11 , wherein the activity is an academic activity. 
     
     
         15 . The method of  claim 11 , wherein the activity is a subsequent nutrition consumption activity. 
     
     
         16 . The method of  claim 15 , wherein the activity is a meal or a post-workout snack. 
     
     
         17 . The method of  claim 11 , wherein grouping users into the plurality of expected profiles is accomplished using a supervised machine learning model, and wherein generating heuristics and performance metrics for each of the plurality of expected profiles is accomplished using unsupervised machine learning. 
     
     
         18 . A method for enabling dynamic coaching feedback at a client device, comprising:
 generating a plurality of expected profiles for a plurality of data records within a user history database by:   using a machine learning model to group users into the plurality of expected profiles; and   using machine learning to generate heuristics and performance metrics for each of the plurality of expected profiles;   associating a first user with a first expected profile of the plurality of expected profiles;   receiving user input relating to previous nutrition consumption for the first user;   in response to receiving the user input relating to previous nutrition consumption, providing a recommended nutrition consumption for the first user based on the first expected profile, wherein the first expected profile includes at least one heuristic for generating dynamic feedback associated with the recommended nutrition consumption, wherein the at least one heuristic for generating the dynamic feedback comprises a rule for modifying the recommended nutrition consumption for the first user; and   updating a user data record of the first user based on a logged nutrition consumption corresponding to the recommended nutrition consumption.   
     
     
         19 . The claim of  claim 18 , wherein said previous nutrition consumption for the first user includes calorie consumption information based on meals logged by the first user. 
     
     
         20 . The method of  claim 1 , wherein said previous nutrition consumption for the first user includes macronutrient consumption information or micronutrient consumption information.