Methods and systems for identifying compatible meal options

1. A system for identifying compatible meal options the system comprising a processor wherein the processor further comprises:

a body analysis module wherein the body analysis module is further configured to:

receive a user biological marker, wherein the user biological marker contains a plurality of user body measurements including an element of microbiome data, an element of physical user gut-wall data, and an element of user genetic data;

select a clustering dataset from a clustering database wherein the clustering dataset further comprises a plurality of unclassified datapoints;

generate a hierarchical clustering algorithm using the clustering dataset as input, wherein the hierarchical clustering algorithm outputs a definite number of classified dataset clusters each containing a cluster label;

assign the plurality of user body measurements to a first classified dataset cluster containing a first cluster label; and

select the first classified dataset cluster containing the first cluster label;

a food analysis module wherein the food analysis module is further configured to:

receive from the body analysis module the first classified dataset cluster containing the first cluster label and the user biological marker;

select a food training set from a food database as a function of the user biological marker wherein the food training set correlates the element of microbiome data, the element of physical user gut-wall data, and the element of user genetic data to numerical food tolerance scores;

generate using a supervised machine-learning process a food model that receives the assigned plurality of user body measurements as an input and produces an output containing a numerical food tolerance score utilizing the food training set;

generate a food tolerance instruction set using the numerical food tolerance score; and

display on a graphical user interface located on the processor the output containing the food tolerance instruction set;

a menu generator module wherein the menu generator module is further configured to:

receive the food tolerance instruction set from the food analysis module;

select a menu training set from a menu database as a function of the food tolerance instruction set wherein the menu training set correlates numerical food tolerance scores to menu options;

generate using a supervised machine-learning process a menu model that receives the food tolerance instruction set as an input and produces an output containing a plurality of menu options utilizing the menu training set; and

display on the graphical user interface located on the processor the output containing the plurality of menu options; and

a local selector module wherein the local selector module is further configured to:

receive a plurality of meal option inputs from at least a meal preparer device wherein the meal option inputs contain available menu listings;

receive the output containing the plurality of menu options from the menu generator module;

generate a k-nearest neighbors algorithm utilizing the plurality of meal option inputs and the plurality of menu options;

identify a plurality of compatible meal options as a function of generating the k-nearest neighbor algorithm; and

display the plurality of compatible meal options on the graphical user interface located on the processor.

2. The system of claim 1 , wherein the plurality of user body measurements further comprises at least a microbiome body measurement and at least a genetic body measurement.

3. The system of claim 1 , wherein selecting a clustering dataset further comprises:

classifying a biological marker to a body dimension;

generating a classification label containing a body dimension label; and

selecting a clustering dataset as a function of matching the body dimension label to a clustering dataset containing unclassified datapoints related to the body dimension label.

4. The system of claim 1 , wherein selecting a first classified dataset cluster containing a first cluster label further comprises:

receiving the plurality of user body measurements;

generating a clustering algorithm using the user body measurements as input and wherein the clustering algorithm outputs a plurality of cluster labels containing a body dimension; and

selecting the first classified dataset cluster as a function of the body dimension.

5. The system of claim 1 , wherein generating the food tolerance instruction set further comprises:

receiving a user entry from a user client device containing a user food tolerance aversion input; and

filtering the food tolerance instruction set as a function of the user entry.

6. The system of claim 1 , wherein selecting a menu training set further comprises:

receiving a model training set correlating food tolerance instruction sets to menu training sets; and

generating using a supervised machine-learning process and the model training set a training set model that receives the food tolerance instruction set as an input and produces an output containing menu training set.

7. The system of claim 6 further comprising executing a lazy learning process as a function of the model training set and the food tolerance instruction set and producing an output containing menu training set.

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

receive a user input from a user client device wherein the user input further comprises a meal option element indicator;

generate a loss function utilizing the user input and the plurality of meal option inputs; minimize the loss function; and

select a compatible meal option from the plurality of compatible meal options as a function of minimizing the loss function.

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

receive an element of user geolocation data; and

receive a plurality of meal option inputs from at least a meal preparer device related to the element of user geolocation data.

10. The system of claim 1 , wherein the local selector module is further configured to:

generate a k-means clustering algorithm utilizing the plurality of menu options and the plurality of meal option inputs; and

identify a compatible meal option as a function of generating the k-means clustering algorithm.

11. A method of identifying compatible meal options the method comprising:

receiving by a processor a user biological marker wherein the user biological marker contains a plurality of user body measurements including an element of microbiome data, an element of physical user gut-wall data, and an element of user genetic data;

selecting by the processor a clustering dataset from a clustering database wherein the clustering dataset further comprises a plurality of unclassified datapoints;

generating by the processor a hierarchical clustering algorithm using the clustering dataset as input and wherein the hierarchical clustering algorithm outputs a definite number of classified dataset clusters each containing a cluster label;

assigning by the processor the plurality of user body measurements to a first classified dataset cluster containing a first cluster label;

selecting by the processor the first classified dataset cluster containing the first cluster label;

selecting by the processor a food training set from a food database as a function of the user biological marker wherein the food training set correlates the element of microbiome data, the element of physical user gut-wall data, and the element of user genetic data to numerical food tolerance scores;

generating by the processor using a supervised machine-learning process a food model that receives the assigned plurality of user body measurements as an input and produces an output containing a numerical food tolerance score utilizing the food training set;

generating by the processor a food tolerance instruction set using the numerical food tolerance score;

displaying by the processor on a graphical user interface the output containing the food tolerance instruction set containing the numerical food tolerance score;

selecting by the processor a menu training set from a menu database as a function of the food tolerance instruction set wherein the menu training set correlates numerical food tolerance scores to menu options;

generating by the processor using a supervised machine-learning process a menu model that receives the food tolerance instruction set as an input and produces an output containing a plurality of menu options utilizing the menu training set;

displaying by the processor on the graphical user interface the output containing the plurality of menu options;

receiving by the processor a plurality of meal option inputs from at least a meal preparer device wherein the meal option inputs contain available menu listings;

generating by the processor a k-nearest neighbors algorithm utilizing the plurality of meal option inputs and the plurality of menu options;

identifying by the processor a plurality of compatible meal options as a function of generating the k-nearest neighbor algorithm; and

displaying by the processor the plurality of compatible meal options on the graphical user interface.

12. The method of claim 11 , wherein the plurality of user body measurements further comprises at least a microbiome body measurement and at least a genetic body measurement.

13. The method of claim 11 , wherein selecting a clustering dataset further comprises:

classifying a biological marker to a body dimension;

generating a classification label containing a body dimension label; and

selecting a clustering dataset as a function of matching the body dimension label to a clustering dataset containing unclassified datapoints related to the body dimension label.

14. The method of claim 11 , wherein selecting a first classified dataset cluster containing the cluster label further comprises:

receiving the plurality of user body measurements;

generating a clustering algorithm using the plurality of user body measurements as input and wherein the clustering algorithm outputs a plurality of cluster labels containing a body dimension; and

selecting a first classified dataset cluster as a function of the body dimension.

15. The method of claim 11 , wherein generating the food tolerance instruction set further comprises:

receiving a user entry from a user client device containing a user food tolerance aversion input; and

filtering the food tolerance instruction set as a function of the user entry.

16. The method of claim 11 , wherein selecting a menu training set further comprises:

receiving a model training set correlating food tolerance instruction sets to menu training sets; and

generating using a supervised machine-learning process and the model training set a training set model that receives the food tolerance instruction set as an input and produces an output containing menu training set.

17. The method of claim 16 further comprising executing a lazy learning process as a function of the model training set and the food tolerance instruction set and producing an output containing menu training set.

18. The method of claim 11 , further comprising:

receiving, by a processor, a user input from a user client device wherein the user input further comprises a meal option element indicator;

generating, by a processor, a loss function utilizing the user input and the plurality of meal option inputs;

minimizing, by the processor, the loss function; and

selecting, by a processor, a compatible meal option from the plurality of compatible meal options as a function of minimizing the loss function.

19. The method of claim 11 , further comprising:

receiving, by the processor, an element of user geolocation data; and

receiving, by the processor, a plurality of meal option inputs from at least a meal preparer device related to the element of user geolocation data.

20. The method of claim 11 , further comprising:

generating, by the processor, a k-means clustering algorithm utilizing the plurality of menu options and the meal option inputs; and

identifying, by the processor, a compatible meal option as a function of generating the k-means clustering algorithm.