Granted Patent B2

US 12,406,763 · App. 17/541,399 · Granted Sep 2, 2025

Inventor: Kenneth Neumann (Lakewood, CO)

Assignee: KPN INNOVATIONS, LLC.

G16H20/60

Abstract

Claims (67)

1. A system for generating a cancer alleviation nourishment plan, the system comprising:

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

receive at least a cancer biomarker relating to a user, wherein the cancer biomarker indicates a presence of cancer;

retrieve a cancer profile related to the user;

assign the cancer profile to a cancer category, wherein the cancer category includes a determination of a type of tumor;

identify, using the cancer profile, a plurality of nutrition elements for alleviating the type of cancer, wherein identifying comprises:

calculating, according to the type of tumor in the cancer category, a plurality of nutrient amounts, wherein calculating the plurality of nutrient amounts includes:

inputting a result, wherein the result includes a type of tumor;

determining a respective effect of each nutrient amount of the plurality of nutrient amounts on the type of tumor in the cancer profile; and

calculating each of the nutrient amounts of the plurality of nutrient amounts as a function of the respective effect of each the plurality of nutrient amounts, wherein the plurality of nutrient amounts comprises a plurality of amounts intended to result in cancer alleviation corresponding to the type of tumor and further utilizing a nutrient machine-learning model comprising a linear regression model which further comprises:

 receiving a training data set, wherein the training data set comprises a plurality of data entries that correlates a magnitude of nutrient effect to a plurality of nutrient amounts for each type of tumor in the cancer category;

 training, iteratively, the nutrient machine-learning model using the training data set, wherein training the nutrient machine-learning model includes retraining the nutrient machine-learning model with feedback from previous iterations of the nutrient machine-learning model; and

 calculating the nutrient amounts using the trained nutrient machine-learning model;

identifying, as a function of the plurality of nutrient amounts, the plurality of nutrition elements for cancer alleviation; and

generate, using the plurality of nutrition elements, a cancer alleviation nourishment plan as a function of the type of tumor.

2. The system of claim 1 , wherein receiving the at least the cancer biomarker further comprises receiving a result of one or more tests relating the user.

3. The system of claim 1 , wherein retrieving the cancer profile further comprises:

receiving cancer profile training data;

training a cancer profile machine-learning model with training data that includes a plurality of data entries wherein each entry correlates cancer biomarkers to a plurality of types of tumors; and

generating the cancer profile as a function of the cancer profile machine-learning model and at least the cancer biomarker.

4. The system of claim 1 , wherein assigning the cancer category to a plurality of types of tumors further comprises:

classifying the cancer category to a plurality of types of tumors using a cancer classification machine-learning process; and

assigning the cancer category as a function of the classifying.

5. The system of claim 1 , wherein determining the effect of the plurality of nutrient amounts on the cancer profile further comprises retrieving a plurality of predicted effects of the plurality of nutrient amounts on the type of tumor.

6. The system of claim 1 , wherein generating the cancer alleviation nourishment plan further comprises:

generating a nourishment plan classifier using a nourishment classification machine-learning process to classify the plurality of nutrient amounts to the plurality of nutrition elements; and

outputting the plurality of nutrition elements as a function of the nourishment plan classifier.

7. The system of claim 1 , wherein the cancer alleviation nourishment plan comprises whole foods.

8. The system of claim 1 , wherein generating the cancer alleviation nourishment plan further comprises:

determining a change in incidence of cancer as a function of adherence to nourishment plan; and

updating the cancer alleviation nourishment plan as a function of the change in incidence of cancer.

9. The system of claim 1 , wherein generating the cancer alleviation nourishment plan further comprises:

receiving a user preference related to the plurality of nutrition elements; and

modifying the plurality of nutrition elements as a function of the user preference.

10. A method for generating a cancer alleviation nourishment plan, the method comprising:

receiving, by a computing device, at least a cancer biomarker relating to a user, wherein the cancer biomarker indicates a presence of cancer;

retrieving, by the computing device, a cancer profile related to the user;

assigning, by the computer device, the cancer profile to a cancer category, wherein the cancer category includes a determination of a type of tumor;

identifying, by the computing device and using the cancer profile, a plurality of nutrition elements for alleviating the type of cancer, wherein identifying comprises:

calculating, according to the type of tumor in the cancer category, a plurality of nutrient amounts, wherein calculating the plurality of nutrient amounts includes:

inputting a result, wherein the result includes a type of tumor;

determining a respective effect of each nutrient amount of the plurality of nutrient amounts on the type of tumor in the cancer profile; and

calculating each of the nutrient amounts of the plurality of nutrient amounts as a function of the respective effect of each the plurality of nutrient amounts, wherein the plurality of nutrient amounts comprises a plurality of amounts intended to result in cancer alleviation corresponding to the type of tumor and further utilizing a nutrient machine-learning model comprising a linear regression model which further comprises:

receiving a training data set, wherein the training data set comprises a plurality of data entries that correlates a magnitude of nutrient effect to a plurality of nutrient amounts for each type of tumor in the cancer category:

training, iteratively, the nutrient machine-learning model using the training data set, wherein training the nutrient machine-learning model includes retraining the nutrient machine-learning model with feedback from previous iterations of the nutrient machine-learning model; and

calculating the nutrient amounts using the trained nutrient machine-learning model;

identifying, as a function of the plurality of nutrient amounts, the plurality of nutrition elements for cancer alleviation; and

generating, by the computing device an using the plurality of nutrition elements, a cancer alleviation nourishment plan as a function of the type of tumor.

11. The method of claim 10 , wherein receiving the at least the cancer biomarker further comprises receiving a result of one or more tests relating the user.

12. The method of claim 10 , wherein retrieving the cancer profile further comprises:

receiving cancer profile training data;

training a cancer profile machine-learning model with training data that includes a plurality of data entries wherein each entry correlates cancer biomarkers to a plurality of types of tumors; and

generating the cancer profile as a function of the cancer profile machine-learning model and at least the cancer biomarker.

13. The method of claim 10 , wherein assigning the cancer category to a plurality of types of tumors further comprises:

classifying the cancer category to a plurality of types of tumors using a cancer classification machine-learning process; and

assigning the cancer category as a function of the classifying.

14. The method of claim 10 , wherein determining the effect of the plurality of nutrient amounts on the cancer profile further comprises retrieving a plurality of predicted effects of the plurality of nutrient amounts on the type of tumor.

15. The method of claim 10 , wherein generating the cancer alleviation nourishment plan further comprises:

generating a nourishment plan classifier using a nourishment classification machine-learning process to classify the plurality of nutrient amounts to the plurality of nutrition elements; and

outputting the plurality of nutrition elements as a function of the nourishment plan classifier.

16. The method of claim 10 , wherein the cancer alleviation nourishment plan comprises whole foods.

17. The method of claim 10 , wherein generating the cancer alleviation nourishment plan further comprises:

determining a change in incidence of cancer as a function of adherence to nourishment plan; and

updating the cancer alleviation nourishment plan as a function of the change in incidence of cancer.

18. The method of claim 10 , wherein generating the cancer alleviation nourishment plan further comprises:

receiving a user preference related to the plurality of nutrition elements; and

modifying the plurality of nutrition elements as a function of the user preference.

Assignments (1)

Continuity (2)

References Cited (18)