Systems and methods for providing meal plans

• US 10,496,798 B2
• Filed: 06/03/2016
• Issued: 12/03/2019
• Est. Priority Date: 06/04/2015
• Status: Active Grant

• Current Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Original Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Inventors: Wilkinson, Bruce Walter
• Primary Examiner(s): Pauls, John A
• Application Number:
  US15/173,298
• Publication Number:
  US 20160357942A1
• Time in Patent Office: 1,278 Days
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets

Abstract

A system ingests the purchases of a user at various retail outlets and are analyzed to characterize a user'"'"'s taste profile as well as the complexity and effort of meals prepared by the user. A meal plan including the preparation of recipes corresponding to the user'"'"'s taste, complexity, and effort profiles. Meal plan media including instructions for preparing the meals of the meal plan are provided. Interactions or lack thereof and the date of interactions are monitored and analyzed to determine whether a meal was executed and the date of execution. A weekly schedule profile of the user is developed based on the complexity and effort scores of meals and the determination of whether the meals are or are not executed. Subsequent meal plans are generated based on the schedule profile and the taste profile updated per determinations of which meals are executed.

Claims

1. A method for meal plan generation, the method comprising:

receiving, by a server system, from a first computer associated with a first user a recipe corpus defining a plurality of recipes each having a plurality of attributes;

receiving, by the server system, from the first user a subscription restriction for the recipe corpus;

receiving, by the server system, from a second computer associated with a second user a request to access the recipe corpus, the request satisfying the subscription restriction;

receiving, by a server system, records of purchases for a plurality of products by the second user;

analyzing, by the server system, attributes of the plurality of products;

generating, by the server system, a user profile for the second user according to the analyzing of the attributes of the plurality of products;

generating, by the server system, an initial meal plan including a plurality of meals conforming to the user profile of the second user and including recipes selected from the plurality of recipes of the recipe corpus;

transmitting, by the server system, the initial meal plan to the second user;

receiving, by the server system, feedback on the initial meal plan from the second user;

transmitting, by the server system, the feedback to the first user; and

training, by the server system, a machine learning model based on the feedback including;

determining at least one complexity score for each meal in the initial meal plan, the complexity score representing the level of skill and the degree of transformation required to complete a meal,

determining at least one effort score for each meal in the initial meal plan, the effort score representing the time required to complete a meal; and

updating, by the server system, and based on the machine learning model, the initial meal plan to create a dynamic meal plan for the user to favor scheduling meals having at least one of a complexity score or an effort score closer to those preferred by the user.

2. The method of claim 1, wherein transmitting the initial meal plan to the second user comprise transmitting menu media including instructions for executing the plurality of meals;

wherein receiving, by the server system, feedback on the initial meal plan from the second user comprises;

monitoring, by the server system, accessing of the menu media by the second user;

updating, by the server system, the user profile of the second user according to the monitoring of accessing of the menu media;

updating, by the server system, the dynamic meal plan, conforming to the updating of the user profile of the second user; and

transmitting, by the server system, the dynamic meal plan to the second user.

3. The method of claim 2, wherein monitoring, by the server system, accessing of the menu media further comprises:

receiving notification of first actions taken with respect to playback of video content in the menu media for a first meal of the plurality of meals of the initial meal plan, the actions including one or more of playing of the video content, pausing of the video content, and one or more additional playbacks of the video content; and

determining that the actions taken with respect to playback indicate that the menu media for the first meal was executed by the second user.

4. The method of claim 3, wherein monitoring, by the server system, accessing of the menu media further comprises:

determining that video content in the menu media for a second meal of the plurality of meals has not been the subject of the first actions;

determining that the second meal was not executed by the second user in response to determining that the video content in the menu media for the second meal has not been the subject of the first actions.

5. The method of claim 4, wherein updating, by the server system, the user profile of the second user according to the monitoring of accessing of the menu media further comprises:

training the machine learning model to favor meals having attributes of the first meal and disfavor meals having attributes of the second meal, the attributes including the at least one complexity score and the at least one effort score for each meal.

6. The method of claim 4, wherein updating, by the server system, the user profile of the second user according to the monitoring of accessing of the menu media further comprises:

determining a date of execution of the first meal according to timing of the first actions;

determining a scheduled execution date for the second meal according to the initial meal plan;

updating a schedule profile of the user to favor scheduling meals having at least one of complexity scores and effort scores closer to the at least one of the first complexity score and the first effort score than to the at least one of the second complexity score and the second effort score on a weekday corresponding to the date of execution of the first meal; and

updating the schedule profile of the user to disfavor scheduling meals having at least one of complexity scores and effort scores closer to the at least one of the second complexity score and the second effort score than to the at least one of the first complexity score and first effort score on a weekday corresponding to the scheduled execution date for the second meal.

7. The method of claim 4, wherein updating, by the server system, the user profile of the second user according to the monitoring of accessing of the menu media further comprises:

determining an estimated date of execution of the first meal according to timing of the first actions;

determining a scheduled date for the second meal according to the initial meal plan;

updating a complexity profile of the user to favor scheduling meals having at least one of complexity scores and effort scores closer to the at least one of the first complexity score and the first effort score than to the at least one of the second complexity score and the second effort score.

8. The method of claim 4, wherein updating, by the server system, the user profile of the second user according to the monitoring of accessing of the menu media further comprises:

updating a taste profile of the user to favor scheduling meals having ingredients more similar to those of the first meal than those of the second meal.

9. The method of claim 4, wherein transmitting, by the server system, the feedback to the first user further comprises:

transmitting to the first user notification of the execution of the first meal by the second user and non-execution of the second meal by the second user.

10. The method of claim 1, further comprising, for each meal of the plurality of meals of the initial meal plan and dynamic meal plan:

determining ingredients for the each meal; and

invoking shipping of at least a portion of the ingredients for the each meal on a date associated with the each meal.

11. A system for meal plan generation, the system comprising one or more processors and one or more memory devices operably coupled to the one or more processors and storing executable and operational code effective to cause the one or more processors to:

receive from a first computer associated with a first user a recipe corpus defining a plurality of recipes each having a plurality of attributes;

receive from the first computer a subscription restriction for the recipe corpus;

receive from a second computer associated with a second user a request to access the recipe corpus, the request satisfying the subscription restriction;

retrieve from a customer information database records of purchases for a plurality of products by the second user;

analyze attributes of the plurality of products;

generate a user profile for the second user according to the analyzing of the attributes of the plurality of products;

store the user profile for the second user in a user profile database;

generate an initial meal plan including a plurality of meals conforming to the user profile of the second user and including recipes selected from the plurality of recipes of the recipe corpus;

transmit the initial meal plan to the second user;

receive feedback on the initial meal plan from the second user;

transmit the feedback to the first user; and

training a machine learning model based on the feedback including;

determining at least one complexity score for each meal in the initial meal plan, the complexity score representing the level of skill and the degree of transformation required to complete a meal,

determining at least one effort score for each meal in the initial meal plan, the effort score representing the time required to complete a meal; and

updating, based on the machine learning model, the initial meal plan to create a dynamic meal plan for the user to favor scheduling meals having at least one of a complexity score or an effort score closer to those preferred by the user.

12. The system of claim 11, wherein the executable and operational code are further effective to cause the one or more processors to:

transmit the initial meal plan to the second user by transmitting menu media including instructions for executing the plurality of meals;

receive feedback on the meal plan from the second user by;

monitoring, by the server system, accessing of the menu media by the second user;

updating, by the server system, the user profile of the second user according to the monitoring of accessing of the menu media;

updating, by the server system, the dynamic meal plan conforming to the updating of the user profile of the second user and the at least one complexity score and the at least one effort score; and

transmitting, by the server system, the dynamic meal plan to the second user.

13. The system of claim 12, wherein the executable and operational code are further effective to cause the one or more processors to monitor accessing of the menu media by:

receiving notification of first actions taken with respect to playback of video content in the menu media for a first meal of the plurality of meals of the initial meal plan, the actions including one or more of playing of the video content, pausing of the video content, and one or more additional playbacks of the video content; and

determining that the actions taken with respect to playback indicate that the menu media for the first meal was executed by the second user.

14. The system of claim 13, wherein the executable and operational code are further effective to cause the one or more processors to monitor accessing of the menu media by:

determining that video content in the menu media for a second meal of the plurality of meals has not been the subject of the first actions;

determining that the second meal was not executed by the second user.

15. The system of claim 14, wherein the executable and operational code are further effective to cause the one or more processors update the user profile of the second user according to the monitoring of accessing of the menu media by:

training the machine learning model to favor meals having attributes of the first meal and disfavor meals having attributes of the second meal, the attributes including the at least one complexity score and the at least one effort score for each meal.

16. The system of claim 14, wherein the executable and operational code are further effective to cause the one or more processors to update the user profile of the second user according to the monitoring of accessing of the menu media by:

determining a date of execution of the first meal according to timing of the first actions;

determining a scheduled execution date for the second meal according to the initial meal plan;

updating a schedule profile of the user to favor scheduling meals having at least one of complexity scores and effort scores closer to the at least one of the first complexity score and the first effort score than to the at least one of the second complexity score and the second effort score on a weekday corresponding to the date of execution of the first meal; and

updating the schedule profile of the user to disfavor scheduling meals having at least one of complexity scores and effort scores closer to the at least one of the second complexity score and the second effort score than to the at least one of the first complexity score and first effort score on a weekday corresponding to the scheduled execution date for the second meal.

17. The system of claim 14, wherein the executable and operational code are further effective to cause the one or more processors to update the user profile of the second user according to the monitoring of accessing of the menu media by:

determining an estimated date of execution of the first meal according to timing of the first actions;

determining a scheduled date for the second meal according to the initial meal plan;

updating a complexity profile of the user to favor scheduling meals having at least one of complexity scores and effort scores closer to the at least one of the first complexity score and the first effort score than to the at least one of the second complexity score and the second effort score.

18. The system of claim 14, wherein the executable and operational code are further effective to cause the one or more processors to update the user profile of the second user according to the monitoring of accessing of the menu media by:

updating a taste profile of the user to favor scheduling meals having ingredients more similar to those of the first meal than those of the second meal.

19. The system of claim 14, wherein executable and operational code are further effective to cause the one or more processors to transmit the feedback to the first user by:

transmitting to the first user notification of the execution of the first meal by the second user and non-execution of the second meal by the second user.

20. The system of claim 11, wherein executable and operational code are further effective to cause the one or more processors to, for each meal of the plurality of meals of the initial meal plan and dynamic meal plan:

determine ingredients for the each meal; and

invoke shipping of at least a portion of the ingredients for the each meal on a date associated with the each meal.