Granted Patent B2

US 12,334,208 · App. 18/225,546 · Granted Jun 17, 2025

Inventor: Kenneth Neumann (Lakewood, CO)

G16H20/60

Abstract

Claims (49)

1. A system for generating a nourishment program for nociception disorders, the system comprising:

a computing device, wherein the computing device is configured to:

retrieve a nociception parameter associated with a subject;

classify the nociception parameter to a nociception grouping, wherein the nociception grouping comprises a nutrition-linked nociception disorder grouping, wherein classifying the nociception parameter comprises:

generating a nociception classifier using nociception training data comprising a plurality of data entries correlating nociception parameter inputs to nociception grouping outputs based on trends observed in data for subsets of subjects;

updating the nociception training data as a function of classifying the nociception parameter to the nociception grouping; and

iteratively training the nociception classifier as a function of the updated training data;

identify a plurality of nutrition elements as a function of the nutrition-linked nociception disorder grouping;

generate a non-medicated treatment plan as a function of the nociception grouping; and

generate a nociception nourishment program as a function of the plurality of nutrition elements, wherein generating the nociception nourishment program comprises generating a nociception nourishment index using an indexing model.

2. The system of claim 1 , wherein the computing device is further configured to modify the plurality of nutrition elements as a function of a subject preference.

3. The system of claim 1 , wherein classifying the nociception parameter to the nutrition-linked nociception disorder grouping further comprises:

training the nociception classifier using the nociception training data, wherein the nociception training data comprises a plurality of data entries correlating the nociception parameter from a subset of categorized subjects to the nutrition-linked nociception disorder grouping; and

classifying the nociception parameter to the nutrition-linked nociception disorder grouping using the trained nociception classifier.

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

receive user feedback comprising user implementation data; and

calculate a user implementation score as a function of the user implementation data and the nociception nourishment program.

5. The system of claim 1 , wherein identifying the plurality of nutrition elements comprises calculating at least a nutritional level as a function of the nociception parameter.

6. The system of claim 1 , wherein the nociception nourishment program comprises a frequency and a magnitude associated with consumption of the plurality of nutritional elements.

7. The system of claim 1 , wherein the nutrition-linked nociception disorder grouping comprises a causal relationship between the nociception parameter and the nociception grouping.

8. The system of claim 1 , wherein generating the nociception nourishment index additionally comprises:

receiving a nutritional input based on a subject interaction with a client device;

generating the indexing model using training data comprising a plurality of data entries correlating the plurality of nutrition elements as inputs to the nociception nourishment program as an output; and

generating the nociception nourishment index as a function of the nutritional input using the trained indexing model.

9. The system of claim 1 , wherein the computing device is additionally configured to generate a plurality of nutritional metrics associated with reduction of nociception as a function of the nutrition-linked nociception disorder grouping.

10. A method for generating a nourishment program for nociception disorders, the method comprising:

retrieving, using a computing device, a nociception parameter associated with a subject;

classifying, using the computing device, the nociception parameter to a nociception grouping, wherein the nociception grouping comprises a nutrition-linked nociception disorder grouping, wherein classifying the nociception parameter comprises:

generating a nociception classifier using nociception training data comprising a plurality of data entries correlating nociception parameter inputs to nociception grouping outputs based on trends observed in data for subsets of subjects;

updating the nociception training data as a function of classifying the nociception parameter to the nociception grouping; and

iteratively training the nociception classifier as a function of the updated training data;

identifying, using the computing device, a plurality of nutrition elements as a function of the nutrition-linked nociception disorder grouping;

generating, using the computing device, a non-medicated treatment plan as a function of the nociception grouping; and

generating, using the computing device, a nociception nourishment program as a function of the plurality of nutrition elements, wherein generating the nociception nourishment program comprises generating a nociception nourishment index using an indexing model.

11. The method of claim 10 , wherein the method further comprises modifying, using the computing device, the plurality of nutrition elements as a function of a subject preference.

12. The method of claim 10 , wherein classifying the nociception parameter to the nutrition-linked nociception disorder grouping further comprises:

training the nociception classifier using the nociception training data, wherein the nociception training data comprises a plurality of data entries correlating the nociception parameter from a subset of categorized subjects to the nutrition-linked nociception disorder grouping; and

classifying the nociception parameter to the nutrition-linked nociception disorder grouping using the trained nociception classifier.

13. The method of claim 10 , further comprising:

receiving, by the computing device, user feedback comprising user implementation data; and

calculating, by the computing device, a user implementation score as a function of the user implementation data and the nociception nourishment program.

14. The method of claim 10 , wherein identifying the plurality of nutrition elements comprises calculating at least a nutritional level as a function of the nociception parameter.

15. The method of claim 10 , wherein the nociception nourishment program comprises a frequency and a magnitude associated with consumption of the plurality of nutritional elements.

16. The method of claim 10 , wherein the nutrition-linked nociception disorder grouping comprises a causal relationship between the nociception parameter and the nociception grouping.

17. The method of claim 10 , wherein generating the nociception nourishment index additionally comprises:

receiving a nutritional input based on a subject interaction with a client device;

generating the indexing model using training data comprising a plurality of data entries correlating the plurality of nutrition elements as inputs to the nociception nourishment program as an output; and

generating the nociception nourishment index as a function of the nutritional input using the trained indexing model.

18. The method of claim 10 , wherein the method further comprises generating, using the computing device, a plurality of nutritional metrics associated with reduction of nociception as a function of the nutrition-linked nociception disorder grouping.

Assignments (1)

Continuity (2)

References Cited (17)