SYSTEM AND METHOD FOR GENERATING AN OCULAR DYSFUNCTION NOURISHMENT PROGRAM

1 . A system for generating an ocular dysfunction nourishment program, the system comprising a computing device configured to:

determine an ocular assessment as a function of receiving an ocular attribute datum;

generate an ocular profile as a function of the ocular assessment, wherein the generation includes:

determine at least an ocular vector as a function of the ocular attribute datum; and

generate a degree of variance as a function of the ocular vector and an ocular utopia;

identify at least an edible as a function of the ocular profile, a nourishment composition, and an edible classifier, wherein the identification includes:

determining an ocular dysfunction as a function of the ocular profile using a dysfunction training set correlating at least an ocular enumeration and a visual system effect to the ocular dysfunction; and

develop a profile outcome including a treatment outcome and a prevention outcome as a function of the edible.

2 . The system of claim 1 , wherein the ocular assessment includes an applanation tonometry.

3 . The system of claim 1 , wherein the ocular profile includes a visual health status.

4 . The system of claim 3 , wherein the ocular utopia represents an ideal visual health status.

5 . The system of claim 1 , wherein the computing device is configured to generate a degree of variance as a function of the ocular vector and the ocular utopia.

6 . The system of claim 1 , wherein the identifying at least an edible includes:

training an edible machine-learning model by training data that contains nourishment compositions and ocular profiles as inputs correlated to a plurality of edibles as outputs; and

outputting the edible as a function of training the edible machine-learning model.

7 . The system of claim 1 , wherein the computing device generates the edible classifier using a K-nearest neighbors (KNN) algorithm.

8 . The system of claim 1 , wherein the computing device identifies at least an edible as a function of a likelihood parameter and a user taste profile.

9 . The system of claim 1 , wherein the at least an edible contains one or more flavor variables.

10 . The system of claim 1 , wherein the computing device is further configured to develop a nourishment program as a function of the profile outcome and the at least an edible.

11 . A method for generating an ocular dysfunction nourishment program, the method comprising:

determining, by a computing device, an ocular assessment as a function of receiving an ocular attribute datum;

generating, by the computing device, an ocular profile as a function of the ocular assessment, wherein the generation includes:

determining at least an ocular vector as a function of the ocular attribute datum; and

generating a degree of variance as a function of the ocular vector and an ocular utopia;

identifying, by the computing device, at least an edible as a function of the ocular profile, a nourishment composition, and an edible classifier, wherein the identification includes:

determining an ocular dysfunction as a function of the ocular profile using a dysfunction training set correlating at least an ocular enumeration and a visual system effect to the ocular dysfunction; and

developing, by the computing device, a profile outcome including a treatment outcome and a prevention outcome as a function of the edible.

12 . The method of claim 11 , wherein the ocular assessment includes an applanation tonometry.

13 . The method of claim 11 , wherein the ocular profile includes a visual health status.

14 . The method of claim 13 , wherein the ocular utopia represents an ideal visual health status.

15 . The method of claim 11 , wherein the computing device is configured to generate a degree of variance as a function of the ocular vector and the ocular utopia.

16 . The method of claim 11 , wherein the identifying at least an edible includes:

training an edible machine-learning model by training data that contains nourishment compositions and ocular profiles as inputs correlated to a plurality of edibles as outputs; and

outputting the edible as a function of training the edible machine-learning model.

17 . The method of claim 11 , wherein the computing device generates the edible classifier using a K-nearest neighbors (KNN) algorithm.

18 . The method of claim 11 , wherein the computing device identifies at least an edible as a function of a likelihood parameter and a user taste profile.

19 . The method of claim 11 , wherein the at least an edible contains one or more flavor variables.

20 . The method of claim 11 , wherein the computing device is further configured to develop a nourishment program as a function of the profile outcome and the at least an edible.