METHODS AND SYSTEMS FOR IDENTIFYING COMPATIBLE MEAL OPTIONS
A system for identifying compatible meal options. The system including a processor configured to receive a user biological marker. The processor may also be configured to determine a food tolerance score as a function of the user biological marker. The processor may be further configured to generate a food tolerance instruction set as a function of the food tolerance score. The processor may be configured to receive a geofence, where the geofence includes a predetermined geographic area selected by the user and identify statistical makeup data of a population as a function of the geofence. The processor may then be configured to generate a micronutrient band as a function of the statistical makeup data and generate alimentary data as function of the micronutrient band. One or more meal options may be identified as a function of the food tolerance instruction set and the alimentary data.
1 . A system for identifying compatible meal options, the system comprising a processor wherein the processor is configured to:
receive a user biological marker, wherein the user biological marker comprises physiological data of a user;
determine a food tolerance score as a function of the user biological marker, wherein the food tolerance score relates to a user ability to tolerate a food item;
generate a food tolerance instruction set as a function of the food tolerance score;
receive a geofence, wherein the geofence comprises a predetermined geographic area selected by the user;
identify statistical makeup data as a function of the geofence;
generate a micronutrient band as a function of the statistical makeup data;
generate alimentary data as function of the micronutrient band, wherein the alimentary data comprises a recommended nutrient intake; and
identify one or more meal options as a function of the food tolerance instruction set and the alimentary data.
2 . The system of claim 1 , wherein the processor is further configured to:
calculate a conicity index as a function of the statistical makeup data; and
generate the alimentary data as a function of the micronutrient band and the conicity index.
3 . The system of claim 1 , wherein the alimentary data comprises a nutrition deficiency of a demographic category of the user.
4 . The system of claim 1 , wherein the processor is further configured to determine a plurality of phenotype clusters within the geofence as a function of the alimentary data.
5 . The system of claim 1 , wherein the user biological marker comprises a plurality of user body measurements.
6 . The system of claim 5 , wherein the body measurements include at least a genetic body measurement.
7 . The system of claim 1 , wherein the statistical makeup data comprises a demographic category.
8 . The system of claim 1 , wherein the processor is configured to determine a plurality of phenotype clusters as a function of the micronutrient band.
9 . The system if claim 1 , wherein:
the processor is further configured to generate a menu as a function of the alimentary data; and
the menu options as a function of the food tolerance instruction set and the menu.
10 . The system of claim 1 , wherein the processor is further configured to:
generate, using the food analysis module of the processor, a food tolerance instruction set as a function of the food tolerance score; and
generate, using a menu generator module of the processor, a plurality of menu options as a function of the food tolerance instruction set and the alimentary data.
11 . The system of claim 1 , wherein the processor is further configured to display the plurality of menu options using a graphical user interface.
12 . 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.
13 . A method of identifying compatible meal options, the method comprising:
receiving a user biological marker, wherein the user biological marker comprises physiological data of a user;
determining a food tolerance score as a function of the user biological marker, wherein the food tolerance score relates to a user ability to tolerate a food item;
generating a food tolerance instruction set as a function of the food tolerance score;
receiving a geofence, wherein the geofence comprises a predetermined geographic area selected by the user;
identifying statistical makeup data as a function of the geofence;
generating a micronutrient band as a function of the statistical makeup data;
generating alimentary data as function of the micronutrient band, wherein the alimentary data comprises a recommended nutrient intake; and
identifying one or more meal options as a function of the food tolerance instruction set and the alimentary data.
14 . The method of claim 13 , further comprising:
calculating a conicity index as a function of the statistical makeup data; and
generating the alimentary data as a function of the micronutrient band and the conicity index.
15 . The method of claim 13 , wherein the alimentary data comprises a nutrition deficiency of a demographic category of the user.
16 . The method of claim 13 , further comprising determining a plurality of phenotype clusters within the geofence as a function of the alimentary data.
17 . The method of claim 13 , wherein the user biological marker comprises a plurality of user body measurements.
18 . The method of claim 13 , wherein the body measurements include at least a genetic body measurement.
19 . The method of claim 13 , wherein the statistical makeup data comprises a demographic category.
20 . The method of claim 13 , wherein:
the processor is further configured to generate a menu as a function of the alimentary data; and
the menu options as a function of the food tolerance instruction set and the menu.