US 12,406,763 B2
Systems and methods for generating a cancer alleviation nourishment plan
Kenneth Neumann, Lakewood, CO (US)
Assigned to KPN INNOVATIONS, LLC., Lakewood, CO (US)
Filed by KPN INNOVATIONS, LLC., Lakewood, CO (US)
Filed on Dec. 3, 2021, as Appl. No. 17/541,399.
Application 17/541,399 is a continuation in part of application No. 17/136,084, filed on Dec. 29, 2020.
Prior Publication US 2022/0208351 A1, Jun. 30, 2022
Int. Cl. G16H 20/60 (2018.01)
CPC G16H 20/60 (2018.01) 18 Claims
OG exemplary drawing
 
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.