Granted Patent B2

US 12,555,669 · App. 17/164,412 · Granted Feb 17, 2026

Inventor: Kenneth Neumann (Lakewood, CO)

Assignee: KPN INNOVATIONS, LLC.

G16H20/60G06N20/00

Abstract

Claims (59)

1 . A system for generating an integumentary dysfunction nourishment program, the system comprising:

a computing device, the computing device configured to:

obtain an integumentary bundle element, wherein the integumentary bundle element comprises at least a skin sample and;

identify a physiological group as a function of the integumentary bundle element, wherein the physiological group comprises a group of integumentary bundle elements associated with regulation of body temperature;

produce an integumentary profile as a function of the physiological group, wherein producing the integumentary profile further comprises:

ascertaining an integumentary functional divergence as a function of the physiological group and an integumentary recommendation, wherein the integumentary recommendation comprises an epidermal layer thickness guideline, wherein the integumentary functional divergence comprises a transgression parameter wherein the transgression parameter identifies one or more integumentary functional divergences that exceed a variance limit;

classifying the integumentary functional divergence to at least one category of integumentary functional divergence based on a degree of divergence, wherein classifying the integumentary functional divergence comprises:

generating a classifier as a function of unfiltered training data using a classification algorithm;

filtering elements of the training data using the classifier to generate a plurality of training data sets each containing a plurality of data entries correlating integumentary functional divergences to categories of integumentary functional divergences based on degrees of divergence; and

classifying the integumentary functional divergence by selecting at least one filtered training data set of the plurality of training data sets; and

producing the integumentary profile as a function of the classified integumentary functional divergence and the selected at least one filtered training data set using an integumentary machine-learning model;

determine an edible as a function of the integumentary profile, wherein determining the edible further comprises:

receiving a nourishment composition from an edible directory;

producing a nourishment demand as a function of the integumentary profile; and

determining the edible as a function of the nourishment composition and the nourishment demand using an edible machine-learning model, wherein using the edible machine-learning model comprises:

training the edible machine-learning model using an edible training set, wherein the edible training set comprises at least a nourishment composition and at least a nourishment demand correlated to at least an edible from a previous iteration of the edible machine-learning model; and

generating the edible using the edible machine-learning model; and

generate a nourishment program as a function of the edible.

2 . The system of claim 1 , wherein the integumentary bundle element includes a biomarker.

3 . The system of claim 1 , wherein obtaining the integumentary bundle element includes receiving an input from a user and obtaining the integumentary bundle element as a function of the input.

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

generating a likelihood parameter, wherein the likelihood parameter relates a user taste profile to an edible profile; and

determining the edible as a function of the likelihood parameter.

5 . The system of claim 4 , wherein determining the edible profile further comprises receiving a flavor variable from a flavor directory and determining the edible profile as a function of the flavor variable.

6 . The system of claim 1 , wherein generating the nourishment program further comprises:

receiving an integumentary outcome; and

generating the nourishment program as a function of the integumentary outcome using a nourishment machine-learning model.

7 . The system of claim 6 , wherein the integumentary outcome includes a treatment outcome.

8 . The system of claim 6 , wherein the integumentary outcome includes a prevention outcome.

9 . The system of claim 1 , wherein the integumentary recommendation further comprises a skin hydration recommendation.

10 . A method for generating an integumentary dysfunction nourishment program, the method comprising:

obtaining, by a computing device, an integumentary bundle element, wherein the integumentary bundle element comprises a skin sample;

identifying, by the computing device, a physiological group as a function of the integumentary bundle element, wherein the physiological group comprises a group of integumentary bundle elements associated with regulation of body temperature;

producing, by the computing device, an integumentary profile as a function of the physiological group, wherein producing the integumentary profile further comprises:

ascertaining an integumentary functional divergence as a function of the physiological group and an integumentary recommendation, wherein the integumentary recommendation comprises an epidermal layer thickness guideline, wherein the integumentary functional divergence comprises a transgression parameter wherein the transgression parameter identifies one or more integumentary functional divergences that exceed a variance limit;

classifying the integumentary functional divergence to at least one category of integumentary functional divergence based on a degree of divergence, wherein classifying the integumentary functional divergence comprises:

generating a classifier as a function of unfiltered training data using a classification algorithm;

filtering elements of the training data using the classifier to generate a plurality of training data sets each containing a plurality of data entries correlating integumentary functional divergences to categories of integumentary functional divergences based on degrees of divergence; and

classifying the integumentary functional divergence by selecting at least one filtered training data set of the plurality of training data sets; and

producing the integumentary profile as a function of the classified integumentary functional divergence and the selected at least one filtered training data set using an integumentary machine-learning model;

determining, by the computing device, an edible as a function of the integumentary profile, wherein determining the edible further comprises:

receiving a nourishment composition from an edible directory;

producing a nourishment demand as a function of the integumentary profile; and

determining the edible as a function of the nourishment composition and the nourishment demand using an edible machine-learning model, wherein using the edible machine-learning model comprises:

training the edible machine-learning model using an edible training set, wherein the edible training set comprises at least a nourishment composition and at least a nourishment demand correlated to at least an edible from a previous iteration of the edible machine-learning model; and

generating the edible using the edible machine-learning model; and

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

11 . The method of claim 10 , wherein the integumentary bundle element includes a biomarker.

12 . The method of claim 10 , wherein obtaining the integumentary bundle element includes receiving an input from a user and obtaining the integumentary bundle element as a function of the input.

13 . The method of claim 10 , wherein determining the edible further comprises:

generating a likelihood parameter, wherein the likelihood parameter relates a user taste profile to an edible profile; and

determining the edible as a function of the likelihood parameter.

14 . The method of claim 13 , wherein determining the edible profile further comprises receiving a flavor variable from a flavor directory and determining the edible profile as a function of the flavor variable.

15 . The method of claim 10 , wherein generating the nourishment program further comprises:

receiving an integumentary outcome; and

generating the nourishment program as a function of the integumentary outcome using a nourishment machine-learning model.

16 . The method of claim 15 , wherein the integumentary outcome includes a treatment outcome.

17 . The method of claim 15 , wherein the integumentary outcome includes a prevention outcome.

18 . The method of claim 10 , wherein the integumentary recommendation further comprises a skin hydration recommendation.

Assignments (1)

Continuity (1)

References Cited (18)