SYSTEM AND METHOD FOR GENERATING A RATION PROTOCOL AND INSTITUTING A DESIRED ENDOCRINAL CHANGE

1 . A method of generating a ration protocol for instituting a desired endocrinal change comprising:

receiving, using a computing device, at least an endocrinal representation and a ration record;

calculating, using the computing device, a desired endocrinal change as a function of the at least an endocrinal representation;

generating, using the computing device and the ration record, at least a change of nutrition, wherein generating the at least a change of nutrition further comprises:

receiving training data correlating nutritional elements to endocrinal representations;

training a machine learning model as a function of a machine learning algorithm and the training data; and

generating at least a change of nutrition as a function of the machine learning model, and the ration record; and

outputting, using the computing device, the ration protocol as a function of the at least a change of nutrition.

2 . The method of claim 1 wherein outputting the ration protocol further comprises:

receiving ration classification training data correlating a plurality of rations to a plurality of bins;

training, using the computing device, a ration classification model as a function of a ration classification algorithm and the ration classification training data;

classifying, using the computing device, at least a ration from the ration record to at least a bin of the plurality of bins, as a function of the ration classification model and the ration record;

selecting, using the computing device, a new ration classified to the at least a bin, as a function of the at least a change of nutrition; and

outputting, using the computing device, the ration protocol, wherein the ration protocol comprises the new ration.

3 . The method of claim 2 , classifying the at least a ration to the at least a bin further comprises generating a probability of classification.

4 . The method of claim 1 , wherein calculating the desired endocrinal change further comprises calculating a distance between the endocrinal representation and an endocrinal standard.

5 . The method of claim 4 , wherein the endocrinal standard comprises a normal range of hormone levels.

6 . The method of claim 4 , wherein calculating the distance between the endocrinal representation and the endocrinal standard further comprises:

representing the endocrinal representation as a first vector;

representing the endocrinal standard as a second vector;

calculating a similarity between the first vector and the second vector; and

calculating the distance as a function of the similarity between the first vector and the second vector.

7 . The method of claim 1 , wherein generating the at least a change of nutrition further comprises:

generating the at least a change of nutrition as a function of the machine learning model, the desired endocrinal change, and the at least a ration record.

8 . The method of claim 1 , wherein generating the at least a change of nutrition further comprises:

generating a nutrition standard as a function of the machine learning model and an endocrinal standard;

calculating a distance between the ration record and the nutrition standard; and

generating the at least a change of nutrition as a function of the distance.

9 . The method of claim 8 wherein outputting the ration protocol further comprises:

receiving ration classification training data correlating a plurality of rations to a plurality of bins;

classifying, using the computing device, at least a ration from the ration record to at least a bin of to the plurality of bins, as a function of a ration classification model and the ration record;

selecting, using the computing device, a new ration classified to the at least a bin, as a function of the at least a change of nutrition; and

outputting, using the computing device, the ration protocol, wherein the ration protocol comprises the new ration.

10 . The method of claim 1 , wherein the machine learning model comprises a convolutional neural network.

11 . A system for generating a ration protocol for instituting a desired endocrinal change comprising a computing device configured to:

receive at least an endocrinal representation and a ration record;

calculate the desired endocrinal change as a function of the at least an endocrinal representation;

generate, using the ration record, at least a change of nutrition, wherein generating the at least a change of nutrition, further comprises:

receiving training data correlating nutritional elements to endocrinal representations;

training a machine learning model as a function of a machine learning algorithm and the training data; and

generating at least a change of nutrition as a function of the machine learning model, and the ration record; and

output the ration protocol as a function of the at least a change of nutrition.

12 . The system of claim 11 wherein outputting the ration protocol further comprises:

receiving ration classification training data correlating a plurality of rations to a plurality of bins;

training a ration classification model as a function of a ration classification algorithm and the ration classification training data;

classifying at least a ration from the ration record to at least a bin of the plurality of bins, as a function of the ration classification model and the ration record;

selecting a new ration classified to the at least a bin, as a function of the at least a change of nutrition; and

outputting the ration protocol, wherein the ration protocol comprises the new ration.

13 . The system of claim 12 , wherein classifying the at least a ration to the at least a bin further comprises generating a probability of classification.

14 . The system of claim 11 , wherein calculating the desired endocrinal change further comprises calculating a distance between the endocrinal representation and an endocrinal standard.

15 . The system of claim 14 , wherein the endocrinal standard comprises a normal range of hormone levels.

16 . The system of claim 14 , wherein calculating the distance between the endocrinal representation and the endocrinal standard further comprises:

representing the endocrinal representation as a first vector;

representing the endocrinal standard as a second vector;

calculating a similarity between the first vector and the second vector; and

calculating the distance as a function of the similarity between the first vector and the second vector.

17 . The system of claim 11 , wherein generating the at least a change of nutrition further comprises:

generating the at least a change of nutrition as a function of the machine learning model, the desired endocrinal change, and the ration record.

18 . The system of claim 11 , wherein generating the at least a change of nutrition further comprises:

generating a nutrition standard as a function of the machine learning model and an endocrinal standard;

calculating a distance between the ration record and the nutrition standard; and

generating the at least a change of nutrition as a function of the distance.

19 . The system of claim 18 wherein outputting the ration protocol further comprises:

receiving ration classification training data correlating a plurality of rations to a plurality of bins;

classifying, using the computing device, at least ration from the ration record to at least a bin of the plurality of bins, as a function of a ration classification model and the ration record;

selecting, using the computing device, a new ration classified to the at least a bin, as a function of the at least a change of nutrition; and

outputting, using the computing device, the ration protocol, wherein the ration protocol comprises the new ration.

20 . The system of claim 11 wherein the machine learning model comprises a convolutional neural network.