Methods and systems for ordered food preferences accompanying symptomatic inputs

1. A system for ordered food preferences accompanying symptomatic inputs, the system comprising a computing device, the computing device designed and configured to:

retrieve a food profile pertaining to a user;

select a first food element as a function of the food profile;

select a second food element as a function of the first food element;

create a food preference menu wherein the food preference menu contains the first food element and the second food element, wherein creating the food preference menu comprises:

identifying a previous meal selection;

classifying, using a Naïve Bayes classifier derived from training data comprising meal selections data correlated to meal category data, the previous meal selection to a meal category; and

creating the food preference menu as a function of the meal category; and

modify the food preference menu as a function of an entry contained within a symptomatic database.

2. The system of claim 1 , wherein the food profile is retrieved as a function of a genetically related food preference.

3. The system of claim 1 , wherein the first food element is selected as a function of a user taste preference.

4. The system of claim 1 , wherein the first food element is selected as a function of a symptomatic input.

5. The system of claim 1 , wherein the second food element is selected to enhance the nutrition of the first food element.

6. The system of claim 1 , wherein creating the food preference menu further comprises generating a machine-learning process, wherein the machine-learning process utilizes the food profile as an input, and outputs the food preference menu.

7. The system of claim 6 , wherein the machine-learning process further comprises a feature learning process.

8. The system of claim 1 , wherein modifying the food preference menu further comprises adjusting a portion size contained within a nutritional plan.

9. The system of claim 8 , wherein adjusting the portion size further comprises:

locating a symptomatic neutralizer containing a numerical score;

selecting a default portion size;

comparing the numerical score to the default portion size; and

adjusting the portion size as a function of the symptomatic neutralizer.

10. A method for ordered food preferences accompanying symptomatic inputs, the method comprising:

retrieving by a computing device, a food profile pertaining to a user;

selecting by the computing device, a first food element as a function of the food profile;

selecting by the computing device, a second food element as a function of the first food element;

creating by the computing device, a food preference menu wherein the food preference menu contains the first food element and the second food element, wherein creating the food preference menu comprises:

identifying a previous meal selection;

classifying, using a Naïve Bayes classifier derived from training data comprising meal selections data correlated to meal category data, the previous meal selection to a meal category; and

creating the food preference menu as a function of the meal category; and

modifying by the computing device, the food preference menu as a function of an entry contained within a symptomatic database.

11. The method of claim 10 , wherein retrieving the food profile further comprises retrieving the food profile as a function of a genetically related food preference.

12. The method of claim 10 , wherein selecting the first food element further comprises selecting the first food element as a function of a user taste preference.

13. The method of claim 10 , wherein selecting the first food element further comprises selecting the first food element as a function of a symptomatic input.

14. The method of claim 10 , wherein selecting the second food element further comprises selecting the second food element to enhance the nutrition of the first food element.

15. The method of claim 10 , wherein creating the food preference menu further comprises generating a machine-learning process, wherein the machine-learning process utilizes the food profile as an input, and outputs the food preference menu.

16. The method of claim 15 , wherein generating the machine-learning process further comprises generating a feature learning process.

17. The method of claim 10 , wherein modifying the food preference menu further comprises adjusting a portion size contained within a nutritional plan.

18. The method of claim 17 , wherein adjusting the portion size further comprises:

locating a symptomatic neutralizer containing a numerical score;

selecting a default portion size;

comparing the numerical score to the default portion size; and

adjusting the portion size as a function of the symptomatic neutralizer.