Methods and systems for optimizing supplement decisions

1. A system for optimizing supplement decisions, the system comprising a computing device, the computing device further configured to:

receive a longevity inquiry from a remote device; retrieve a first biological extraction from a user database;

identify a longevity element associated with a user as a function of the longevity inquiry and the first biological extraction;

select an ADME (Absorption, Distribution, Metabolism, and Excretion) model as a function of the first biological extraction;

generate a machine-learning algorithm utilizing the selected ADME model that is configured to use the longevity element associated with the user as an input and output an ADME factor; and

identify a tolerant longevity element as a function of the ADME factor, wherein identifying the tolerant longevity element further comprises:

identifying a second longevity element administered in conjunction with the identified tolerant longevity element;

generating a second ADME factor for the second longevity element; and

identifying a second tolerant longevity element as a function of the second ADME factor.

2. The system of claim 1 , wherein receiving the longevity inquiry from a remote device further comprises receiving at an image device located on the computing device a wireless transmission from the remote device containing a photograph of a longevity element.

3. The system of claim 1 , wherein identifying the longevity element associated with the user further comprises:

receiving dietary training data wherein dietary training data includes a plurality of biological extractions and a plurality of correlated longevity elements;

generating using a first machine learning algorithm a dietary model relating biological extractions to longevity elements;

receiving a second biological extraction; and

outputting a longevity element using the first machine learning algorithm.

4. The system of claim 3 , wherein the first machine learning algorithm further comprises a supervised machine-learning algorithm.

5. The system of claim 3 , wherein selecting an ADME model further comprises:

identifying a genetic marker contained within a third biological extraction;

generating using genetic training data including a plurality of genetic markers and a plurality of correlated ADME models, and using a classification algorithm, a genetic classifier, wherein the genetic classifier inputs a genetic marker and outputs an ADME model; and

selecting an ADME model as a function of generating the genetic classifier.

6. The system of claim 5 , wherein selecting the ADME model further comprises:

retrieving a fourth biological extraction from the user database wherein the fourth biological extraction further comprises a genetic marker containing an ADME marker; and

locating an ADME model containing the ADME marker.

7. The system of claim 1 , wherein identifying the tolerant longevity element further comprises selecting a tolerant longevity element compatible with the ADME factor.

8. The system of claim 1 , wherein identifying the tolerant longevity element further comprises:

identifying a third longevity element contraindicated with the identified tolerant longevity element; and

eliminating the third longevity element as a potential tolerant longevity element.

9. The system of claim 1 , wherein identifying the tolerant longevity element further comprises:

identifying that the identified tolerant longevity element comprises a first active ingredient;

identifying that the second tolerant longevity element comprises a second active ingredient;

determining that the first active ingredient relates to the second active ingredient; and

eliminating the second tolerant longevity element as a function of the determination.

10. A method of optimizing supplement decisions, the method comprising:

receiving by a computing device a longevity inquiry from a remote device;

retrieving by the computing device a first biological extraction from a user database;

identifying by the computing device a longevity element associated with the user as a function of the longevity inquiry and the first biological extraction;

selecting by the computing device an ADME (Absorption, Distribution, Metabolism, and Excretion) model as a function of the first biological extraction;

generating by the computing device a machine-learning algorithm utilizing the selected ADME model that is configured to use the longevity element associated with the user as an input and output an ADME factor; and

identifying by the computing device a tolerant longevity element as a function of the ADME factor;

wherein identifying the tolerant longevity element further comprises:

identifying a second longevity element administered in conjunction with the identified tolerant longevity element;

generating a second ADME factor for the second longevity element; and

identifying a second tolerant longevity element as a function of the second ADME factor.

11. The method of claim 10 , wherein receiving the longevity inquiry from a remote device further comprises receiving at an image device located on the computing device a wireless transmission from the remote device containing a photograph of a longevity element.

12. The method of claim 10 , wherein identifying the longevity element associated with the user further comprises:

receiving dietary training data wherein dietary training data includes a plurality of biological extractions and a plurality of correlated longevity elements;

generating using a first machine learning algorithm a dietary model correlating biological extractions with longevity elements;

receiving a second biological extraction; and

outputting a longevity element using the first machine learning algorithm.

13. The method of claim 12 , wherein generating using the first machine learning algorithm further comprises generating a supervised machine-learning algorithm.

14. The method of claim 12 , wherein selecting the ADME model further comprises:

identifying a genetic marker contained within a third biological extraction;

generating using genetic training data including a plurality of genetic markers and a plurality of correlated ADME models, and using a classification algorithm, a genetic classifier, wherein the genetic classifier inputs a genetic marker and outputs an ADME model; and

selecting an ADME model as a function of generating the genetic classifier.

15. The method of claim 14 , wherein selecting the ADME model further comprises:

retrieving a fourth biological extraction from the user database wherein the biological extraction further comprises a genetic marker containing an ADME marker; and

locating an ADME model containing the ADME marker.

16. The method of claim 10 , wherein identifying the tolerant longevity element further comprises selecting a tolerant longevity element compatible with the ADME factor.

17. The method of claim 10 , wherein identifying the tolerant longevity element further comprises:

identifying a third longevity element contraindicated with the identified tolerant longevity element; and

eliminating the third longevity element as a potential tolerant longevity element.

18. The method of claim 10 , wherein identifying the tolerant longevity element further comprises:

identifying that the identified tolerant longevity element comprises a first active ingredient;

identifying that the second tolerant longevity element comprises a second active ingredient;

determining that the first active ingredient relates to the second active ingredient; and

eliminating the second tolerant longevity element.