Methods and systems for identifying compatible meal options

1. A system for identifying compatible meal options, the system comprising a processor wherein the processor is configured to:

receive a user selection identifying a biological marker comprising an epigenetic body measurement;

calculate a user effective age using a first machine learning process, wherein the first machine learning process is trained with training data correlating a plurality of biological markers to a plurality of effective age measurements;

determine, using a body analysis module, a clustering label, wherein determining the clustering label comprises:

classifying a plurality of unclassified data points to a classified dataset, wherein the plurality of unclassified data points comprises anonymous patient survey data from multiple geographic locations, wherein classifying the unclassified data points to a classified data set utilizing a hierarchal classifying algorithm, wherein the hierarchal classifying algorithm performs linkage measurements between each data point of the plurality of unclassified data points;

ranking the linkage measurements based on a degree of similarity; and

determining a clustering label as a function of the ranking;

determine, using a food analysis module, a food tolerance score as a function of the user effective age, wherein the food tolerance score relates to a user ability to tolerate a food item, wherein the food item is a supplement;

identify a plurality of compatible meal options as a function of the food tolerance score and the clustering label, wherein at least a compatible meal option of the plurality of compatible meal options includes at least a supplement; and

receive, via a graphical user interface, a selection of a meal option from the plurality of compatible meal options.

2. The system of claim 1 , wherein:

the user selection further identifies a user chronological age; and

the processor is further configured to calculate a difference between the user effective age and the user chronological age.

3. The system of claim 2 , wherein determining the food tolerance score comprises determining the food tolerance score as a function of the difference between the user effective age and the user chronological age and includes generating a second machine-learning process, wherein the second machine-learning process is trained with training data correlating a plurality of effective age measurements to a plurality of food tolerance scores and determining the numerical food tolerance score as a function of the second machine-learning process.

4. The system of claim 1 , wherein the processor is further configured to:

generate, using the food analysis module, a food tolerance instruction set as a function of the food tolerance score; and

generate, using a menu generator module, a plurality of menu options as a function of the food tolerance instruction set.

5. The system of claim 4 , wherein the processor is further configured to display the plurality of menu options using a graphical user interface.

6. The system of claim 4 , wherein identifying the plurality of compatible meal options further comprises:

receiving a plurality of meal option inputs;

receiving the plurality of menu options from the menu generator module; and

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

7. The system of claim 6 , wherein identifying the plurality of compatible meal options further comprises identifying the plurality of compatible meal options as a function of generating the k-nearest neighbors algorithm.

8. The system of claim 4 , wherein generating the plurality of menu options comprises using a supervised machine-learning process that receives the food tolerance instruction set as an input and produces an output containing the plurality of menu options.

9. The system of claim 1 , wherein determining the food tolerance score further comprises:

generating a second machine-learning process, wherein the second machine-learning process is trained with training data correlating a plurality of effective age measurements to a plurality of food tolerance scores; and

determining the food tolerance score as a function of the second machine-learning process.

10. The system of claim 1 , wherein determining the food tolerance score comprises using a supervised machine-learning process that receives the user effective age and produces an output containing the food tolerance score.

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

receiving by a processor, a user selection identifying a biological marker comprising an epigenetic body measurement;

calculating, by the processor, a user effective age using a first machine learning process, wherein the first machine learning process is trained with training data correlating a plurality of biological markers to a plurality of effective age measurements;

determining, using a body analysis module operated by the processor, a clustering label, wherein determining the clustering label comprises:

classifying a plurality of unclassified data points to a classified dataset, wherein the plurality of unclassified data points comprises anonymous patient survey data from multiple geographic locations, wherein classifying the unclassified data points to a classified data set utilizing a hierarchal classifying algorithm, wherein the hierarchal classifying algorithm performs linkage measurements between each data point of the plurality of unclassified data points;

ranking the linkage measurements based on a degree of similarity; and

determining a clustering label as a function of the ranking;

determining, using a food analysis module operated by the processor, a food tolerance score as a function of the user effective age, wherein the food tolerance score relates to a user ability to tolerate a food item, wherein the food item is a supplement;

identifying, by the processor, a plurality of compatible meal options as a function of the food tolerance score and the clustering label, wherein at least a compatible meal option of the plurality of compatible meal options includes at least a supplement; and

receiving, from a graphical user interface (GUI), a selection of a meal option from the plurality of compatible meal options.

12. The method of claim 11 , further comprising calculating a difference between the user effective age and a user chronological age, wherein the user selection further identifies the user chronological age.

13. The method of claim 12 , wherein determining the food tolerance score comprises determining the food tolerance score as a function of the difference between the user effective age and the user chronological age and includes generating a second machine-learning process, wherein the second machine-learning process is trained with training data correlating a plurality of effective age measurements to a plurality of food tolerance scores and determining the numerical food tolerance score as a function of the second machine-learning process.

14. The method of claim 11 , further comprising:

generating, using the food analysis module, a food tolerance instruction set as a function of the food tolerance score; and

generating, using a menu generator module, a plurality of menu options as a function of the food tolerance instruction set.

15. The method of claim 14 , further comprising displaying the plurality of menu options using a graphical user interface.

16. The method of claim 14 , wherein identifying the plurality of compatible meal options further comprises:

receiving a plurality of meal option inputs;

receiving the plurality of menu options from the menu generator module; and

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

17. The method of claim 16 , wherein identifying the plurality of compatible meal options further comprises identifying the plurality of compatible meal options as a function of generating the k-nearest neighbors algorithm.

18. The method of claim 14 , wherein generating the plurality of menu options comprises using a supervised machine-learning process that receives the food tolerance instruction set as an input and produces an output containing the plurality of menu options.

19. The method of claim 11 , wherein determining the food tolerance score further comprises:

generating a second machine-learning process, wherein the second machine-learning process is trained with training data correlating a plurality of effective age measurements to a plurality of food tolerance scores; and

determining the food tolerance score as a function of the second machine-learning process.

20. The method of claim 11 , wherein determining the food tolerance score comprises using a supervised machine-learning process that receives the user effective age and produces an output containing the food tolerance score.