Granted Patent B2

US 12,640,252 · App. 17/464,011 · Granted May 26, 2026

Inventor: Kenneth Neumann (Lakewood, CO)

Assignee: KPN INNOVATIONS, LLC

G16H20/60G16B40/00G16H10/40G16H20/90G16H50/20G16H50/30G16H50/70

Abstract

Claims (51)

1 . A system for producing a homeopathic program for managing genetic disorders, the system comprising:

a computing device configured to:

receive at least one genetic marker and an input comprising genetics test data;

identify a plurality of biological indices of a disease state as a function of the genetics test data, wherein the plurality of biological indices comprises at least one biological index related to a genetic disease state;

generate a genetic disorder classifier, wherein the generating the genetic disorder classifier comprises:

receiving genetic disorder training data, wherein the genetic training data correlates biological indices of genetic disorders and genetic markers to genetic disorder labels; and

training, iteratively, the genetic disorder classifier using the genetic disorder training data, wherein training the genetic disorder classifier includes retraining the genetic disorder classifier with feedback from previous iterations of the genetic disorder classifier;

input the at least one biological index and the at least one genetic marker into the genetic disorder classifier;

classify, using the trained genetic disorder classifier, the at least one biological index and the at least one genetic marker to a genetic disorder which comprises:

receiving a predisposition index comprising a biological entity in at least a body fluid which indicates a likelihood of a disease; and

classifying the at least one genetic marker to the genetic disorder as a function of the predisposition index; and

produce a homeopathic program as a function of the genetic disorder, wherein producing the homeopathic program further comprises:

receiving a homeopathic guideline; and

producing the homeopathic program as a function of the homeopathic guideline and the genetic disorder utilizing a homeopathic machine-learning model which comprises:

receiving homeopathic training data, wherein the homeopathic training data correlates a plurality of homeopathic guidelines to a plurality of genetic disorder;

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

generating the homeopathic program as a function of the trained homeopathic machine-learning model, wherein the homeopathic program comprises at least a treatment goal designed to reverse the genetic disorder comprising at least a recommendation of edible supplementation.

2 . The system of claim 1 , wherein the genetics test data includes a user body measurement.

3 . The system of claim 1 , wherein the homeopathic program includes a sustenance plan.

4 . The system of claim 1 , wherein receiving the homeopathic guideline further comprises obtaining a homeopathic input.

5 . The system of claim 1 , wherein the homeopathic program includes an epigenetic goal.

6 . The system of claim 1 , wherein the homeopathic program includes a mitigation goal.

7 . The system of claim 1 , wherein producing the homeopathic program further comprises:

determining a genetic locus; and

producing the homeopathic program as a function of the genetic locus.

8 . The system of claim 1 , wherein producing the homeopathic program further comprises determining an inheritance element.

9 . A method for producing a homeopathic program for managing genetic disorders, the method comprising:

receiving, by a computing device, at least one genetic marker and an input comprising genetics test data;

identifying, by the computing device, a plurality of biological indices of a disease state as a function of the genetics test data, wherein the plurality of biological indices comprises at least one biological index related to a genetic disease state;

generating, by the computing device, a genetic disorder classifier, wherein the generating the genetic disorder classifier comprises:

receiving genetic disorder training data, wherein the genetic training data correlates biological indices of genetic disorders and genetic markers to genetic disorder labels; and

training, iteratively, the genetic disorder classifier using the genetic disorder training data, wherein training the genetic disorder classifier includes retraining the genetic disorder classifier with feedback from previous iterations of the genetic disorder classifier;

inputting, by the computing device, the at least one biological index and the at least one genetic marker into the genetic disorder classifier;

classifying, by the computing device, using the trained genetic disorder classifier, the at least one biological index and the at least one genetic marker to a genetic disorder which comprises:

receiving a predisposition index comprising a biological entity in at least a body fluid which indicates a likelihood of a disease; and

classifying the at least one genetic marker to the genetic disorder as a function of the predisposition index; and

producing, by the computing device, a homeopathic program as a function of the genetic disorder, wherein producing the homeopathic program further comprises:

receiving a homeopathic guideline; and

producing the homeopathic program as a function of the homeopathic guideline and the genetic disorder utilizing a homeopathic machine-learning model which comprises:

receiving homeopathic training data, wherein the homeopathic training data correlates a plurality of homeopathic guidelines to a plurality of genetic disorder;

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

generating the homeopathic program as a function of the trained homeopathic machine-learning model, wherein the homeopathic program comprises at least a treatment goal designed to reverse the genetic disorder comprising at least a recommendation of edible supplementation.

10 . The method of claim 9 , wherein the genetics test data includes a user body measurement.

11 . The method of claim 9 , wherein the homeopathic program includes a sustenance plan.

12 . The method of claim 9 , wherein receiving the homeopathic guideline further comprises obtaining a homeopathic input.

13 . The method of claim 9 , wherein the homeopathic program includes an epigenetic goal.

14 . The method of claim 9 , wherein the homeopathic program includes a mitigation goal.

15 . The method of claim 9 , wherein producing the homeopathic program further comprises:

determining a genetic locus; and

producing the homeopathic program as a function of the genetic locus.

16 . The method of claim 9 , wherein producing the homeopathic program further comprises determining an inheritance element.

Assignments (1)

Continuity (2)

References Cited (31)