Granted Patent B2

US 12,340,893 · App. 18/522,039 · Granted Jun 24, 2025

Inventor: Kenneth Neumann (Lakewood, CO)

Assignee: KPN INNOVATIONS LLC

G16H20/60C12Q1/6883C12Q2600/156G16H50/20

Abstract

Claims (61)

1. A system for generating a gestational disorder nourishment program, the system comprising:

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

obtain a maternal marker, wherein the maternal marker comprises an uncertainty indicator;

receive a conception datum;

classify the conception datum to a gestational progression level;

determine a gestational disorder as a function of the maternal marker using one or more disorder machine-learning models, wherein determining the gestational disorder comprises:

obtaining a disorder training set that correlates at least a gestational enumeration and a gestational effect to the gestational disorder;

training a disorder machine-learning model of the one or more disorder machine-learning models as a function of the disorder training set, wherein the disorder machine-learning model comprises one or more disorder machine-learning processes;

determining the gestational disorder as a function of the maternal marker using the trained disorder machine-learning model; and

updating the disorder machine-learning model to incorporate a new gestational enumeration that is related to a modified gestational effect;

calculate a gestational phase as a function of the maternal marker, the gestational progression level, and the gestational disorder, wherein calculating the gestational phase further comprises determining a gestational divergence wherein the gestational divergence indicates a magnitude of divergence of the maternal marker from a gestational recommendation;

generate a gestational outcome wherein the gestation outcome comprises a treatment outcome, wherein the treatment outcome comprises at least a preventative measure associated with the gestational disorder determined using the trained disorder machine-learning model;

train a nourishment machine-learning model, wherein training the nourishment machine-learning model comprises:

receiving a nourishment training set comprising the generated gestational outcome as input correlated to an edible output; and

training the nourishment machine-learning model using the nourishment training set; and

generate a nourishment program as a function of the gestational phase and the trained nourishment machine-learning model.

2. The system of claim 1 , wherein the computing device is further configured to receive the maternal marker from an informed advisor, wherein the maternal marker further comprises a medical assessment.

3. The system of claim 1 , wherein the conception datum indicates a conception method.

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

determine that the maternal marker is not suitable for a first gestational phase; and

determine that the maternal marker is suitable for a second gestational phase wherein the second gestational phase occurs after the first gestational phase.

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

generate a gestational classification model comprising gestational classification algorithm, wherein the gestational classification model utilizes the conception datum as an input and outputs the gestational progression level; and

classify the conception datum to the gestational progression level as a function of the gestational classification model.

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

determine an edible as a function of the gestational phase; and

generate the nourishment program as a function of the edible.

7. The system of claim 1 , wherein the computing device is configured to update the nourishment machine-learning model to incorporate a new gestational outcome related to a modified edible using a remote device.

8. The system of claim 1 , wherein the gestational outcome comprises a treatment outcome.

9. The system of claim 1 , wherein the gestational outcome comprises a prevention outcome.

10. A method for generating a gestational disorder nourishment program, the method comprising:

obtaining, using a computing device, a maternal marker, wherein the maternal marker comprises an uncertainty indicator;

receiving, using the computing device, a conception datum;

classifying, using the computing device, the conception datum to a gestational progression level;

determining, using the computing device, a gestational disorder as a function of the maternal marker using one or more disorder machine-learning models, wherein determining the gestational disorder comprises:

obtaining a disorder training set that correlates at least a gestational enumeration and a gestational effect to the gestational disorder;

training a disorder machine-learning model of the one or more disorder machine-learning models as a function of the disorder training set, wherein the disorder machine-learning model comprises one or more disorder machine-learning processes;

determining the gestational disorder as a function of the maternal marker using the trained disorder machine-learning model; and

updating the disorder machine-learning model to incorporate a new gestational enumeration that is related to a modified gestational effect;

calculating, using the computing device, a gestational phase as a function of the maternal marker, the gestational progression level and the gestational disorder, wherein calculating the gestational phase further comprises determining a gestational divergence wherein the gestational divergence indicates a magnitude of divergence of the maternal marker from a gestational recommendation;

generating, using the computing device, a gestational outcome wherein the gestation outcome comprises a treatment outcome, wherein the treatment outcome comprises at least a preventative measure associated with the gestational disorder determined using the trained disorder machine-learning model;

training, using the computing device, a nourishment machine-learning model, wherein training the nourishment machine-learning model comprises:

receiving a nourishment training set comprising the generated gestational outcome as input correlated to an edible output; and

training the nourishment machine-learning model using the nourishment training set; and

generating, using the computing device, a nourishment program as a function of the gestational phase and the trained nourishment machine-learning model.

11. The method of claim 10 , further comprising:

receiving, using the computing device, the maternal marker from an informed advisor, wherein the maternal marker further comprises a medical assessment.

12. The method of claim 10 , wherein the conception datum indicates a conception method.

13. The method of claim 10 , further comprising:

determining, using the computing device, that the maternal marker is not suitable for a first gestational phase; and

determining, using the computing device, that the maternal marker is suitable for a second gestational phase wherein the second gestational phase occurs after the first gestational phase.

14. The method of claim 10 , further comprising:

generating, using the computing device, a gestational classification model comprising gestational classification algorithm, wherein the gestational classification model utilizes the conception datum as an input and outputs the gestational progression level; and

classifying, using the computing device, the conception datum to the gestational progression level as a function of the gestational classification model.

15. The method of claim 10 , further comprising:

determining, using the computing device, an edible as a function of the gestational phase; and

generating, using the computing device, the nourishment program as a function of the edible.

16. The method of claim 10 , further comprising:

updating, using the computing device, the nourishment machine-learning model to incorporate a new gestational outcome related to a modified edible using a remote device.

17. The method of claim 10 , wherein the gestational outcome comprises a treatment outcome.

18. The method of claim 10 , wherein the gestational outcome comprises a prevention outcome.

Assignments (1)

Continuity (2)

References Cited (34)