METHODS AND SYSTEMS FOR IDENTIFYING COMPATIBLE MEAL OPTIONS
A system for identifying compatible meal options the system comprising a processor the processor configured to receive a user selection identifying a dietary preference; select a meal option as a function of the dietary preference; calculate a user effective age measurement 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 a numerical food tolerance score as a function of the user effective age measurement; and identify a plurality of compatible meal options as a function of the numerical food tolerance score.
1 . A system for identifying compatible meal options the system comprising a processor wherein the processor is further configured to:
receive a user selection identifying a dietary preference;
select a meal option as a function of the dietary preference;
calculate a user effective age measurement 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 a numerical food tolerance score as a function of the user effective age measurement; and
identify a plurality of compatible meal options as a function of the numerical food tolerance score.
2 . The system of claim 1 , wherein the user selection identifies a diagnosis.
3 . The system of claim 1 , wherein the user selection identifies a food item and a symptomatic complaint.
4 . The system of claim 1 , wherein the meal option contains an ingredient and wherein the ingredient configured to conform to the dietary requirement.
5 . The system of claim 1 , wherein the biological marker further comprises a marker of mitochondrial function.
6 . The system of claim 1 , wherein the biological marker further comprises an indicator of a stress response.
7 . The system of claim 1 , wherein the biological marker further comprises a marker of cellular energy.
8 . The system of claim 1 , wherein the biological marker further comprises a toxicity measurement.
9 . The system of claim 1 , wherein determining the numerical food tolerance score further comprises:
generating a second machine-learning process, wherein the second machine-learning process is trained within training data correlating a plurality of effective age measurements to a plurality of effective age measurements; and
determining the numerical food tolerance score as a function of the second machine-learning process.
10 . The system of claim 1 , wherein the computing device is further configured to:
select a meal portion for each of the plurality of compatible meal options as a function of the user effective age measurement.
11 . A method of identifying compatible meal options the method comprising:
receiving by a processor, a user selection identifying a dietary preference;
selecting by the processor, a meal option as a function of the dietary preference;
calculating by the processor, a user effective age measurement 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 by the processor, a numerical food tolerance score as a function of the user effective age measurement; and
identifying by the processor, a plurality of compatible meal options as a function of the numerical food tolerance score.
12 . The method of claim 11 , wherein the user selection identifies a diagnosis.
13 . The method of claim 11 , wherein the user selection identifies a food item and a symptomatic complaint.
14 . The method of claim 11 , wherein the meal option contains an ingredient and wherein the ingredient configured to conform to the dietary requirement.
15 . The method of claim 11 , wherein the biological marker further comprises a marker of mitochondrial function.
16 . The method of claim 11 , wherein the biological marker further comprises an indicator of a stress response.
17 . The method of claim 11 , wherein the biological marker further comprises a marker of cellular energy.
18 . The method of claim 11 , wherein the biological marker further comprises a toxicity measurement.
19 . The method of claim 11 , wherein determining the numerical food tolerance score further comprises:
generating a second machine-learning process, wherein the second machine-learning process is trained within training data correlating a plurality of effective age measurements to a plurality of effective age measurements; and
determining the numerical food tolerance score as a function of the second machine-learning process.
20 . The method of claim 11 further comprising:
selecting a meal portion for each of the plurality of compatible meal options as a function of the user effective age measurement.