Artificial intelligence methods and systems for generating zoological instruction sets from biological extractions

1. An artificial intelligence system for generating zoological instruction sets from biological extractions, the system comprising a computing device, the computing device designed and configured to:

retrieve a biological extraction pertaining to an animal, wherein the biological extraction comprises a metabolic rate test result that indicates the animal's resting metabolic rate;

generate a zoological classifier wherein the zoological classifier utilizes the biological extraction as an input and outputs a zoological profile, wherein generating the zoological classifier comprises training the zoological classifier with training data, wherein the training data comprises biological extraction data correlated to zoological profile data;

receive a zoological input from a remote device wherein the zoological input identifies a zoological habit;

select a zoological machine-learning model of a plurality of zoological machine-learning models utilizing the zoological input and the zoological profile, wherein:

the zoological machine-learning model comprises a supervised machine learning model;

the zoological machine-learning model utilizes the zoological input and the zoological profile as inputs and outputs a correlated zoological instruction set; and

selecting the zoological machine-learning model comprises:

generating an input classifier;

training the input classifier using input classifier training data comprising a plurality of zoological input data and a plurality of zoological profile data correlated to a plurality of corresponding zoological machine-learning model data;

classifying the zoological input and the zoological profile to the zoological machine-learning model of the plurality of zoological machine-learning model using the trained input classifier; and

selecting the zoological machine-learning model from the plurality of zoological machine-learning models using the trained input classifier and the classification;

train the selected zoological machine-learning model using zoological machine-learning model training data comprising a plurality of zoological input data set and a plurality of zoological profile data set correlated to a plurality of zoological instruction sets;

output the zoological instruction set utilizing the trained selected zoological machine-learning model;

receive an instruction set input from a remote device;

compare the instruction set input and the zoological instruction set; and

update the zoological instruction set as a function of the comparison utilizing the trained selected zoological machine learning model.

2. The system of claim 1 , wherein the zoological profile identifies one or more animal characteristics.

3. The system of claim 1 , wherein the zoological input identifies a nourishment element.

4. The system of claim 1 , wherein the zoological input identifies a supplementation element.

5. The system of claim 1 , wherein the computing device selects a zoological machine-learning model by matching an entry contained within a first zoological profile to a second zoological profile contained within the zoological machine-learning model.

6. The system of claim 1 , wherein the computing device is further configured to:

identify, in the zoological instruction set, an alimentary instruction set associated with the animal; and

generate an alimentary instruction set contained within the zoological instruction set.

7. The system of claim 1 , wherein the computing device is further configured to:

generate a physical performance instruction set utilizing the zoological instruction set; and

transmit the physical performance instruction set to a remote device operated by a physical performance entity.

8. The system of claim 7 , wherein the computing device is further configured to generate the physical performance instruction to contain a frequency datum.

9. An artificial intelligence method of generating zoological instruction sets from biological extractions, the method comprising:

retrieving, by a computing device, a biological extraction pertaining to an animal;

generating, by the computing device, a zoological classifier wherein the zoological classifier utilizes the biological extraction as an input and outputs a zoological profile;

receiving, by the computing device, a zoological input from a remote device wherein the zoological input identifies a zoological habit;

selecting, by the computing device, a zoological machine-learning model of a plurality of zoological machine-learning models utilizing the zoological input and the zoological profile, wherein:

the zoological machine-learning model comprises a supervised machine learning model;

the zoological machine-learning model utilizes the zoological input and the zoological profile as inputs and outputs a correlated zoological instruction set; and

selecting the zoological machine-learning model comprises:

generating an input classifier;

training the input classifier using input classifier training data comprising a plurality of zoological input data and a plurality of zoological profile data correlated to a plurality of corresponding zoological machine-learning model data;

classifying the zoological input to the zoological machine-learning model of the plurality of zoological machine-learning model using the trained input classifier; and

selecting the zoological machine-learning model from the plurality of zoological machine-learning models using the trained input classifier and the classification;

training, by the computing device, the selected zoological machine-learning model using zoological machine-learning model training data comprising a plurality of zoological input data set and a plurality of zoological profile data set correlated to a plurality of zoological instruction sets;

outputting, by the computing device, a zoological instruction set utilizing the trained selected zoological machine-learning model;

receiving, by the computing device, an instruction set input from a remote device;

comparing, by the computing device, the instruction set input and the zoological instruction set; and

updating, by the computing device, the zoological instruction set as a function of the comparison utilizing the trained selected zoological machine learning model.

10. The method of claim 9 , wherein generating the zoological classifier further comprises outputting a zoological profile identifying one or more animal characteristics.

11. The method of claim 9 , wherein receiving the zoological input further comprises identifying a nourishment element.

12. The method of claim 9 , wherein receiving the zoological input further comprises identifying a supplementation element.

13. The method of claim 9 , wherein selecting the zoological machine-learning model further comprises matching an entry contained within a first zoological profile to a second zoological profile contained within the zoological machine-learning model.

14. The method of claim 9 , wherein calculating the zoological instruction set further comprises:

identifying, in the zoological instruction set, an alimentary instruction set associated with the animal; and

generating an alimentary instruction set contained within the zoological instruction set.

15. The method of claim 9 , wherein calculating the zoological instruction set further comprises:

generating a physical performance instruction set utilizing the zoological instruction set; and

transmitting the physical performance instruction set to a remote device operated by a physical performance entity.

16. The method of claim 15 , wherein generating the physical performance instruction set further comprises generating the physical performance instruction to contain a frequency datum.