Methods and systems for timing impact of nourishment consumption

1. A system for timing impact of nourishment consumption, the system comprising:

a computing device, the computing device configured to:

receive training data comprising physiological data, correlated to current nutrient levels of a subject, wherein physiological data comprises food intolerances of a subject;

train a nutrient machine-learning model using the training data;

generate a nutrient profile of the subject utilizing the nutrient machine-learning model, wherein the nutrient machine-learning model is configured to:

receive a nutritional input;

determine relationships between consumption of particular alimentary elements and the concentration of nutrients in physiological data; and

output the nutrient profile comprising a nutritional deficiency;

determine, using the nutrient profile, a nourishment consumption program,

wherein the nourishment consumption program includes:

at least an alimentary element; and

a consumption pattern for consuming the at least an alimentary element, wherein:

the consumption pattern includes a time of day; and

the time of day is determined as a function of the nutrient profile and the current nutrient levels of the subject;

provide, to the subject, the nourishment consumption program, wherein providing the nourishment consumption program further comprises:

providing a representation of the nourishment consumption program to a user device of the subject in the form of an audiovisual notification;

linking the nourishment consumption program to a calendar application of the user device of the subject; and

setting timed reminders on the user device of the subject to consume at least one alimentary element as a function of a current location of the subject;

determine defined time intervals using reactive computing, wherein the reactive computing comprises a model-view-controller architecture;

receive a set of nutrition consumption data of the subject as a function of the nourishment consumption program;

generate, using the reactive computing, an updated nutrient profile as a function of the set of nutrition consumption data and the defined time intervals; and

provide, to the subject, an updated consumption pattern of the nourishment consumption program as a function of the set of nutrition consumption data and the updated nutrient profile at each defined time interval.

2. The system of claim 1 , wherein providing an updated consumption pattern further comprises:

receiving updated training data correlating nutrient consumption data to consumption patterns;

training the nutrient machine-learning model with the updated training data, wherein the nutrient machine-learning model is configured to input nutrient consumption data and output consumption patterns; and

determine, using the nutrient machine-learning model, an updated consumption pattern of the nourishment consumption program.

3. The system of claim 1 , wherein the nourishment program further comprises a second consumption pattern for consuming a second alimentary element.

4. The system of claim 3 , wherein the consumption pattern is updated as a function of the second consumption pattern for consuming the second alimentary element.

5. The system of claim 1 , wherein the consumption pattern is determined as a function of a circadian rhythm of the subject.

6. The system of claim 1 , wherein the consumption pattern is determined as a function of a nutrient threshold.

7. The system of claim 1 , wherein the consumption pattern is determined as a function of cultural considerations of the subject.

8. The system of claim 1 , wherein the consumption pattern includes a nutrient consumption frequency.

9. The system of claim 1 , wherein the consumption pattern is updated as a function of a nutrient concentration of the subject.

10. The system of claim 1 , wherein the computing device is further configured to calculate a change in the nutrient profile as a function of timing a compatible alimentary element.

11. A method for timing impact of nourishment consumption using a computing device, the method comprising:

receiving, by a computing device, training data comprising physiological data, correlated to current nutrient levels of a subject, wherein physiological data comprises food intolerances of a subject;

training, by the computing device, a nutrient machine-learning model using the training data;

generating, by the computing device, a nutrient profile of the subject utilizing the nutrient machine-learning model, wherein the nutrient machine-learning model is configured to:

receive a nutritional input;

determine relationships between consumption of particular alimentary elements and the concentration of nutrients in physiological data; and

output the nutrient profile comprising a nutritional deficiency;

determining, using the nutrient profile, a nourishment consumption program, wherein the nourishment consumption program includes:

at least an alimentary element; and

a consumption pattern for consuming the at least an alimentary element, wherein:

the consumption pattern includes a time of day; and

the time of day is determined as a function of the nutrient profile and the current nutrient levels of the subject;

providing, to the subject, the nourishment consumption program, wherein providing the nourishment consumption program further comprises:

providing a representation of the nourishment consumption program to a user device of the subject in the form of an audiovisual notification;

linking the nourishment consumption program to a calendar application of the user device of the subject; and

setting timed reminders on the user device of the subject to consume at least one alimentary element as a function of a current location of the subject;

determining, by the computing device, defined time intervals using reactive computing, wherein the reactive computing comprises a model-view-controller architecture;

receiving a set of nutrition consumption data of the subject as a function of the nourishment consumption program;

generating, by the computing device using the reactive computing, an updated nutrient profile as a function of the set of nutrition consumption data and the defined time intervals; and

providing, to the subject, an updated consumption pattern of the nourishment consumption program as a function of the set of nutrition consumption data and the updated nutrient profile at each defined time interval.

12. The method of claim 11 , wherein providing an updated consumption pattern further comprises:

receiving updated training data correlating nutrient consumption data to consumption patterns;

training the nutrient machine-learning model with the updated training data, wherein the nutrient machine-learning model is configured to input nutrient consumption data and output consumption patterns; and

determine, using the nutrient machine-learning model, an updated consumption pattern of the nourishment consumption program.

13. The method of claim 11 , wherein the nourishment consumption program further comprises a second consumption pattern for consuming a second alimentary element.

14. The method of claim 13 , wherein the consumption pattern is updated as a function of the second consumption pattern for consuming the second alimentary element.

15. The method of claim 11 , wherein the consumption pattern is determined as a function of a circadian rhythm of the subject.

16. The method of claim 11 , wherein the consumption pattern is determined as a function of a nutrient threshold.

17. The method of claim 11 , wherein the consumption pattern is determined as a function of cultural considerations of the subject.

18. The method of claim 11 , wherein the consumption pattern includes a nutrient consumption frequency.

19. The method of claim 11 , wherein the consumption pattern is updated as a function of a nutrient concentration of the subject.

20. The method of claim 11 , wherein the method further comprises calculating, by the computing device, a change in the nutrient profile as a function of timing a compatible alimentary element.