Methods and systems for generating a supplement instruction set using artificial intelligence

1. A system for generating a supplement instruction set using artificial intelligence, the system comprising:

a computing device;

a diagnostic engine operating on the computing device, the diagnostic engine designed and configured to:

record a biological extraction pertaining to a user;

receive a user physiological history input;

receive a first training set including at least an element of physiological state data and at least a correlated first prognostic label; and

generate a diagnostic output utilizing a first machine-learning process trained by the first training set, as a function of the biological extraction pertaining to the user and the user physiological history input;

a plan generator module operating on the computing device, the plan generator module designed and configured to generate a nutrition instruction set, wherein generating the instruction set comprises:

training a second machine-learning process utilizing a second training set, the second training set including at least a biological extraction and user physiological history correlated to at least a nutrition instruction set; and

generating the nutrition instruction utilizing the diagnostic output from the first machine-learning process as an input to the second machine-learning process; and

a supplement generator module operating on the computing device, the supplement generator module designed and configured to calculate a supplement instruction set utilizing the diagnostic output, the nutrition instruction set, and nutritional availability, wherein nutritional availability measures the nutrition instruction set's availability as a function of a geographical location.

2. The system of claim 1 , wherein the plan generator module is further configured to:

identify available nutrients; and

generate the nutrition instruction set utilizing the identified available nutrients.

3. The system of claim 1 , wherein the plan generator module is further configured to generate the nutrition instruction set utilizing a second machine-learning process.

4. The system of claim 1 , wherein the plan generator module is further configured to:

determine a nourishment possibility;

identify a nutrient deficiency as a function of the nourishment possibility and the user biological extraction; and

transmit the nutrient deficiency to the supplement generator module.

5. The system of claim 1 , wherein the plan generator module is further configured to:

learn a user nourishment behavior pattern utilizing a user-specific learner;

identify a proposed nourishment that falls outside the user nourishment behavior pattern; and

determine if supplementation is necessary.

6. The system of claim 1 , wherein the supplement generator module is further configured to:

generate a supplement instruction set that identifies a supplement and a customized dose, and wherein the customized dose is generated utilizing a machine-learning process.

7. The system of claim 1 , wherein the supplement generator module is further configured to:

transmit the supplement instruction set to an advisor client device operated by a physical performance entity wherein the physical performance entity coordinates distribution of the supplement instruction set to the user.

8. The system of claim 1 , wherein the supplement generator module is further configured to:

identify a nutrient deficiency contained within the nutrition instruction set; and

locate a supplement intended to remedy the nutrient deficiency contained within the supplement instruction set.

9. The system of claim 1 , wherein the supplement generator module is further configured to:

receive an input from the plan generator module containing nutritional availability; and

generate the supplement instruction set utilizing nutritional availability.

10. A method of generating a supplement instruction set using artificial intelligence, the method comprising:

recording by a computing device, a biological extraction pertaining to a user;

receiving by the computing device, a user physiological history input;

receiving by the computing device, a first training set including at least an element of physiological state data and at least a correlated first prognostic label;

generating by the computing device, a diagnostic output utilizing a first machine-learning process trained by the first training set, as a function of the biological extraction pertaining to the user and the user physiological history input;

generating by the computing device, a nutrition instruction set, wherein generating the instruction set comprises:

training a second machine-learning process utilizing a second training set, the second training set including at least a biological extraction and user physiological history correlated to at least a nutrition instruction set; and

generating the nutrition instruction utilizing the diagnostic output from the first machine-learning process as an input to the second machine-learning process; and

calculating by the computing device, a supplement instruction set utilizing the diagnostic output the nutrition instruction set, and nutritional availability, wherein nutritional availability measures the nutrition instruction set's availability as a function of a geographical location.

11. The method of claim 10 , wherein generating the nutrition instruction set further comprises:

identifying available nutrients; and

generating the nutrition instruction set utilizing the identified available nutrients.

12. The method of claim 10 , wherein generating the nutrition instruction set further comprises utilizing a second machine-learning process.

13. The method of claim 10 , wherein generating the nutrition instruction set further comprises:

determining a nourishment possibility;

identifying a nutrient deficiency as a function of the nourishment possibility and the user biological extraction; and

utilizing the nutrient deficiency to generate the supplement instruction set.

14. The method of claim 10 , wherein generating the nutrition instruction set further comprises:

learning a user nourishment behavior pattern utilizing a user-specific learner;

identifying a proposed nourishment that falls outside the user nourishment behavior pattern; and

determining if supplementation is necessary.

15. The method of claim 10 , wherein calculating the supplement instruction set further comprises generating a supplement instruction set that identifies a supplement and a customized dose, and wherein the customized dose is generated utilizing a machine-learning process.

16. The method of claim 10 , wherein calculating the supplement instruction set further comprises transmitting the supplement instruction set to an advisor client device operated by a physical performance entity wherein the physical performance entity coordinates distribution of the supplement instruction set to the user.

17. The method of claim 10 , wherein calculating the supplement instruction set further comprises:

identifying a nutrient deficiency contained within the nutrition instruction set; and

locating a supplement intended to remedy the nutrient deficiency contained within the supplement instruction set.

18. The method of claim 10 , wherein calculating the supplement instruction set further comprises:

receiving an input containing nutritional availability; and

generating the supplement instruction set utilizing nutritional availability.