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 from a database;
generate a nutrient target score as a function of the nutrition data;
generate an optimization score as a function of the nutrition data and the nutrient target score;
generate an edible chain as a function of the nutrition data and the target nutrient score, wherein the edible chain comprises a plurality of ranked edible elements and generating the edible chain comprises:
generating chain training data, wherein the chain training data comprises correlations between exemplary nutrition data, exemplary target nutrient scores and exemplary edible chains;
training a chain machine-learning model using the chain training data; and
generating the edible chain using the trained chain machine-learning model; and
update the edible chain as a function of a user input received through a user interface, wherein updating the edible chain comprises:
iteratively updating the chain training data on a feedback loop as a function of the updated edible chain.
2. The apparatus of claim 1 , wherein the memory contains instructions further configuring processor to generate the plurality of edible elements of the edible chain as a function of an edible quantity and a consumption time interval.
3. The apparatus of claim 1 , wherein the memory contains instructions further configuring processor to rank the plurality of edible elements of the edible chain as a function of a preparation difficulty of the plurality of edible elements.
4. The apparatus of claim 1 , wherein the memory contains instructions further configuring processor to determine a nutrition supplement for the edible chain as a function of the nutrition data.
5. The apparatus of claim 1 , wherein the edible chain comprises a plurality of meal plans, wherein each of the plurality of meal plans comprises differently ranked edible elements.
6. The apparatus of claim 1 , wherein the memory contains instructions further configuring processor to generate an edible data structure for the edible chain using a large language model.
7. The apparatus of claim 1 , wherein the user input comprises a rank request.
8. The apparatus of claim 1 , wherein the memory contains instructions further configuring the at least a processor to iteratively generate subsequent optimization scores as a function of the optimization score.
9. The apparatus of claim 8 , wherein the memory contains instructions further configuring the at least a processor to:
modify the nutrition data as a function of the optimization score;
input the modified nutrition data into an optimization machine-learning model to output a modified target nutrient score; and
generate the subsequent optimization scores as a function of the modified nutrition data and the modified target nutrient score.
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, using at least a processor, nutrition data from a database;
generating, using the at least a processor, a nutrient target score as a function of the nutrition data;
generating, using the at least a processor, an optimization score as a function of the nutrition data and the nutrient target score;
generating, using the at least a processor, an edible chain as a function of the nutrition data and the target nutrient score, wherein the edible chain comprises a plurality of ranked edible elements and generating the edible chain comprises:
generating chain training data, wherein the chain training data comprises correlations between exemplary nutrition data, exemplary target nutrient scores and exemplary edible chains;
training a chain machine-learning model using the chain training data; and
generating the edible chain using the trained chain machine-learning model; and
updating, using the at least a processor, the edible chain as a function of a user input received through a user interface, wherein updating the edible chain comprises:
iteratively updating the chain training data on a feedback loop as a function of the updated edible chain.
12. The method of claim 11 , further comprising:
generating, using the at least a processor, the plurality of edible elements of the edible chain as a function of an edible quantity and a consumption time interval.
13. The method of claim 11 , further comprising:
ranking, using the at least a processor, the plurality of edible elements of the edible chain as a function of a preparation difficulty of the plurality of edible elements.
14. The method of claim 11 , further comprising:
determining, using the at least a processor, a nutrition supplement for the edible chain as a function of the nutrition data.
15. The method of claim 11 , wherein the edible chain comprises a plurality of meal plans, wherein each of the plurality of meal plans comprises differently ranked edible elements.
16. The method of claim 11 , further comprising:
generating, using the at least a processor, an edible data structure for the edible chain using a large language model.
17. The method of claim 11 , wherein the user input comprises a rank request.
18. The method of claim 11 , further comprising:
iteratively generating, using the at least a processor, subsequent optimization scores as a function of the optimization score.
19. The method of claim 18 , further comprising:
modifying, using the at least a processor, the nutrition data as a function of the optimization score;
inputting, using the at least a processor, the modified nutrition data into an optimization machine-learning model to output a modified target nutrient score; and
generating, using the at least a processor, the subsequent optimization scores as a function of the modified nutrition data and the modified target nutrient score.
20. The method of claim 11 , further comprising:
updating, using the at least a processor, a phenotype of a user as a function of the optimization score.