Granted Patent B2

US 12,322,491 · App. 17/187,989 · Granted Jun 3, 2025

Inventor: Kenneth Neumann (Lakewood, CO)

Assignee: KPN INNOVATIONS, LLC.

G16H20/60G16H50/80C12Q2600/156G06N20/00

Abstract

Claims (70)

1. A system for generating a geographically linked nourishment program, the system comprising:

a computing device, the computing device configured to:

obtain a biochemical element of a user;

receive a geographical indicator of the user;

identify a pathogenic particle enumeration of the user;

train a progression machine-learning model, wherein the progression machine-learning model is trained using progression training set, wherein training the progression machine-learning model further comprises:

applying the progression training set to an input layer of nodes comprising at least the pathogenic particle enumeration, one or more intermediate layers of nodes, and an output layer of nodes comprising a progression phase output;

adjusting one or more connections and one or more weights between nodes in adjacent layers of the progression machine-learning model;

detecting additional correlations between the output layer of nodes and the input layer of nodes;

iteratively training the progression machine-learning model as a function of the detected additional correlations;

determine a progression phase as a function of the trained progression machine-learning model, wherein determining the progression phase comprises: determining a severity index of the user; and

identifying the progression phase as a function of the pathogenic particle enumeration and the severity index using a progression classifier

identify a vigor panel as a function of the biochemical element, the progression phase determined using the trained progression machine-learning model and the progression classifier and a geographical predictive element determined as a function of the progression phase, wherein identifying the vigor panel comprise:

identifying the vigor panel as a function of the geographical predictive element determined as a function of the progression phase determined using the trained progression machine-learning model and the progression classifier and the biochemical element using a geographical machine-learning model which further comprises: receiving a geographical training data set, wherein the

geographical training data set comprises a plurality of geographical predictive elements and biochemical elements correlated to the vigor panel;

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

identifying the vigor panel as a function of the trained geographical machine-learning model and the geographical predictive element and the biochemical element;

determine an edible for the user as a function of the vigor panel;

receive an intended outcome as a function of the determined edible; and generate a nourishment program as a function of the edible and the intended

outcome using a nourishment machine-learning model which comprises: receiving a nourishment training data set, wherein the nourishment

training data set comprises a plurality of edibles and intended outcomes correlated to a plurality of nourishment programs;

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

identifying the nourishment program as a function of the trained nourishment machine-learning model.

2. The system of claim 1 , wherein the geographical indicator includes a contact tracing element.

3. The system of claim 1 , wherein ascertaining the geographical predictive element further comprises determining a contagion factor and ascertaining the geographical predictive element as a function of the contagion factor.

4. The system of claim 1 , wherein identifying the vigor panel further comprises determining an origination element and identifying the vigor panel as a function of the origination element.

5. The system of claim 1 , wherein identifying the vigor panel includes determining a geographically linked dysfunction and identifying the vigor panel as a function of the geographically linked dysfunction.

6. The system of claim 5 , wherein determining the geographically linked dysfunction further comprises:

obtaining a dysfunction training set that correlates at least a geographical enumeration and a health system effect to the geographically linked dysfunction; and

determining the geographically linked dysfunction as a function of the biochemical element and using a dysfunction machine-learning model, wherein the dysfunction machine-learning model is trained as a function of the dysfunction training set.

7. The system of claim 1 , wherein determining the edible further comprises:

receiving a nourishment composition from an edible directory;

producing a nourishment demand as a function of the vigor panel; and

determining the edible as a function of the nourishment composition and the nourishment demand using an edible machine-learning model.

8. A method for generating a geographically linked nourishment program, the method comprising:

obtaining, by a computing device, a biochemical element of a user; receiving, by the computing device, a geographical indicator of the user;

identifying, by the computing device, a pathogenic particle enumeration of the user; training, by the computing device, a progression machine-learning model, wherein the

progression machine-learning model is trained using progression training set, wherein training the progression machine-learning model further comprises: applying the progression training set to an input layer of nodes comprising at least

the pathogenic particle enumeration, one or more intermediate layers of nodes, and an output layer of nodes comprising a progression phase output;

adjusting one or more connections and one or more weights between nodes in adjacent layers of the progression machine-learning model;

detecting additional correlations between the output layer of nodes and the input layer of nodes;

iteratively training the progression machine-learning model as a function of the detected additional correlations;

determining, by the computing device, a progression phase as a function of the trained progression machine-learning model, wherein determining the progression phase comprises:

determining a severity index of the user; and

identifying the progression phase as a function of the pathogenic particle enumeration and the severity index using a progression classifier;

identifying, by the computing device, a vigor panel as a function of the biochemical element, the progression phase determined using the trained progression machine-learning model and the progression classifier and a geographical

predictive element determined as a function of the progression phase, wherein identifying the vigor panel comprises:

identifying the vigor panel as a function of the geographical predictive element determined as a function of the progression phase determined using the trained progression machine-learning model and the progression classifier and the biochemical element using a geographical machine-learning model which further comprises:

receiving a geographical training data set, wherein the geographical training data set comprises a plurality of geographical predictive elements and biochemical elements correlated to the vigor panel;

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

identifying the vigor panel as a function of the trained geographical machine-learning model and the geographical predictive element and the biochemical element;

determining, by the computing device, an edible for the user as a function of the vigor panel;

receiving, by the computing device, an intended outcome as a function of the determined edible; and

generating, by the computing device, a nourishment program as a function of the edible and the intended outcome using a nourishment machine-learning model which comprises:

receiving a nourishment training data set, wherein the nourishment training data set comprises a plurality of edibles and intended outcomes correlated to a plurality of nourishment programs;

training, iteratively, the nourishment machine-learning model using the nourishment training data set, wherein training the nourishment machine

learning model includes retraining the nourishment machine-learning model with feedback from previous iterations of the nourishment machine-learning model; and

identifying the nourishment program as a function of the trained

nourishment machine-learning model.

9. The method of claim 8 , wherein the geographical indicator includes a contact tracing element.

10. The method of claim 8 , wherein ascertaining the geographical predictive element further comprises determining a contagion factor and ascertaining the geographical predictive element as a function of the contagion factor.

11. The method of claim 8 , wherein identifying the vigor panel further comprises determining an origination element and identifying the vigor panel as a function of the origination element.

12. The method of claim 8 , wherein identifying the vigor panel includes determining a geographically linked dysfunction and identifying the vigor panel as a function of the geographically linked dysfunction.

13. The method of claim 12 , wherein determining the geographically linked dysfunction further comprises:

obtaining a dysfunction training set correlates at least a geographical enumeration and a health system effect to the geographically linked dysfunction; and

determining the geographically linked dysfunction as a function of the biochemical element and using a dysfunction machine-learning model, wherein the dysfunction machine-learning model is trained as a function of the dysfunction training set.

14. The method of claim 11 , wherein determining the edible further comprises:

receiving a nourishment composition from an edible directory;

producing a nourishment demand as a function of the vigor panel; and

determining the edible as a function of the nourishment composition and the nourishment demand using an edible machine-learning model.

Assignments (1)

Continuity (1)

References Cited (64)

Cited By (1)

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