Apparatus and method for using a feedback loop to optimize meals
View Patent ↗The present disclosure is generally directed to an apparatus for using a feedback loop to optimize meals, may include at least a processor; and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to retrieve nutrition data from a database. The processor may be configured to generate an optimization score, wherein generating the optimization score may include training an optimization machine-learning model, wherein the optimization machine-learning model is trained with optimization training data, inputting a nutrient quantity to the optimization machine-learning model to output a target nutrient score, and generating an optimization score as a function of the nutrition data and the target nutrient score.
1. An apparatus for using a feedback loop to optimize meals, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to:
retrieve nutrition data for a plurality of users sharing a phenotype from a database;
generate a plurality of nutrient scores using a nutrient classifier, wherein the plurality of nutrient scores relate to an impact of one or more nutrients on one or more phenotypes, wherein generating the plurality of nutrient scores comprises:
training the nutrient classifier using nutrient training data, wherein the nutrient training data comprises historical nutrient data correlated to categories of nutrients;
generating the plurality of nutrient scores as a function of the nutrient classifier;
generate an optimization score, wherein generating the optimization score comprises:
training an optimization machine-learning model, wherein the optimization machine-learning model is trained using optimization training data;
inputting the nutrition data to the optimization machine-learning model to output a target nutrient score, wherein the target nutrient score comprises a nutrient score for a particular phenotype associated with a user from the plurality of nutrient scores; and
generating the optimization score as a function of the nutrition data and the target nutrient score; and
modify the nutrition data as a function of the optimization score, wherein modifying the nutrition data comprises reassigning at least one user to a different phenotype.
2. The apparatus of claim 1 , wherein the nutrition data comprises nutrient data.
3. The apparatus of claim 1 , wherein the nutrition data comprises at least a nutrient score.
4. The apparatus of claim 3 , wherein generating the optimization score comprises comparing the at least a nutrient score to the target nutrient score.
5. The apparatus of claim 1 , wherein the nutrition data is a function of a geofenced area, wherein the geofenced area comprises a predetermined geographic area.
6. The apparatus of claim 1 , wherein the optimization training data comprises historical nutrition data correlated to historical target nutrient scores.
7. The apparatus of claim 1 , wherein generating the optimization score comprises iteratively generating subsequent optimization scores.
8. The apparatus of claim 7 , wherein generating the subsequent optimization scores further comprises:
modifying the nutrition data as a function of the optimization score;
inputting modified nutrition data into the optimization machine-learning model to output a modified target nutrient score; and
generating the subsequent optimization scores as a function of the modified nutrition data and the modified target nutrient score.
9. The apparatus of claim 7 , further comprising generating the subsequent optimization scores over a predetermined time interval.
10. The apparatus of claim 1 , wherein the memory contains instructions further configuring the at least a processor to update a phenotype of a user as a function of the optimization score.
11. A method for using a feedback loop to optimize meals, the method comprising:
retrieving, by a processor, nutrition data for a plurality of users sharing a phenotype from a database;
generating, by the processor, a plurality of nutrient scores using a nutrient classifier, wherein the plurality of nutrient scores relate to an impact of one or more nutrients on one of more phenotypes, wherein generating the nutrient score comprises:
training the nutrient classifier using nutrient training data, wherein the nutrient training data comprises historical nutrient data correlated to categories of nutrients;
generating the plurality of nutrient scores as a function of the nutrient classifier;
generating, by the processor, an optimization score, wherein generating the optimization score comprises:
training an optimization machine-learning model, wherein the optimization machine-learning model is trained with optimization training data;
inputting a nutrient quantity to the optimization machine-learning model to output a target nutrient score, wherein the target nutrient score comprises a nutrient score for a particular phenotype associated with a user from the plurality of nutrient scores; and
generating the optimization score as a function of the nutrition data and the target nutrient score; and
modifying, by the processor, the nutrition data as a function of the optimization score, wherein modifying the nutrition data comprises reassigning at least one user to a different phenotype.
12. The method of claim 11 , wherein retrieving the nutrition data further comprises retrieving nutrient data.
13. The method of claim 11 , wherein retrieving the nutrition data further comprises retrieving at least a nutrient score.
14. The method of claim 13 , wherein generating the optimization score comprises comparing the at least a nutrient score to the target nutrient score.
15. The method of claim 11 , wherein retrieving the nutrition data further comprises retrieving the nutrition data as a function of a geofenced area, wherein the geofenced area comprises a predetermined geographic area.
16. The method of claim 11 , wherein training an optimization machine-learning model with training data, wherein the training data comprises historical nutrition data correlated to historical target nutrient scores.
17. The method of claim 11 , wherein generating the optimization score comprises iteratively generating subsequent optimization scores.
18. The method of claim 17 , wherein generating the subsequent optimization scores further comprises:
modifying the nutrition data as a function of the optimization score;
inputting modified nutrition data into the optimization machine-learning model to output a modified target nutrient score; and
generating the subsequent optimization scores as a function of the modified nutrition data and the modified target nutrient score.
19. The method of claim 17 , further comprising generating, by the processor, the subsequent optimization scores over a predetermined time interval.
20. The method of claim 11 , further comprising updating, by the processor, a phenotype of a user as a function of the optimization score.