Granted Patent B2

US 12,640,253 · App. 17/514,376 · Granted May 26, 2026

Inventor: Kenneth Neumann (Lakewood, CO)

Assignee: KPN INNOVATIONS, LLC

G16H20/60A61B5/41A61B5/4842A61B5/7275G16H10/60G16H50/20G16H50/70G16H70/60G16H10/40

Abstract

Claims (81)

1 . A system for representing an arranged list of provider aliment possibilities, the system comprising:

a computing device, the computing device designed and configured to:

receive an input representing an autoimmune disorder, wherein receiving the input regarding the autoimmune disorder comprises:

displaying a graphical user interface to a user, wherein the graphical user interface comprises one or more display fields;

displaying, to the user, a first autoimmune disorder to the user through the one or more display fields;

receiving, through the graphical user interface a user input representing a selection related to the first autoimmune disorder; and

displaying, to the user and through the one or more display fields, one or more additional autoimmune disorders, wherein the one or more additional autoimmune disorders correlated to the first autoimmune disorder as a function of the user input;

identify a marker of a user, wherein identifying the marker of the user comprises:

comparing the represented autoimmune disorder to a list of autoimmune disorders stored with an autoimmune data bank, wherein each autoimmune disorder in the list of autoimmune disorders is associated with a respective marker; and

identifying the marker of the user as a function of the comparison of the represented autoimmune disorder to the list of autoimmune disorders;

generate a marker classifier, wherein generating the marker classifier comprises:

training the marker classifier according to a first training set correlating each of a plurality of markers to a respective disorder state label such that the trained marker classifier is configured to receive the identified marker of the user as an input and output a disorder state label as a function of the identified marker of the user and correlations of each of the plurality of marker to a respective disorder state label in the first training data;

detect a trigger pattern as a function of the marker;

determine, as a function of the trigger pattern and disorder state label, an aliment instruction set, wherein determining further comprises:

identifying at least a probable event as a function of the trigger pattern and a physiological exacerbation likelihood which is determined using a physiological machine-learning model which comprises:

receiving training data, wherein the training data correlates a plurality of physiological exacerbation likelihood data to a plurality of probable event data,

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

identifying the at least a probable event using the trained physiological machine-learning model; and

determining the aliment instruction set as a function of the at least a probable event; and

represent the aliment instruction set through the graphical user interface on a display.

2 . The system of claim 1 , wherein detecting the trigger pattern further comprises identifying a flare frequency marker.

3 . The system of claim 1 , wherein detecting the trigger pattern further comprises:

receiving an environmental parameter; and

detecting the trigger pattern as a function of the environmental parameter.

4 . The system of claim 3 , wherein the environmental parameter includes irritant data.

5 . The system of claim 1 , wherein detecting the trigger pattern further comprises:

identifying a medical history; and

detecting the trigger pattern as a function of the medical history.

6 . The system of claim 1 , wherein the determining the aliment instruction set further comprises:

receiving a treatment program;

identifying a treatment edible as a function of the treatment program; and

determining the aliment instruction set as a function of the treatment edible.

7 . The system of claim 1 , wherein the determining the aliment instruction set further comprises:

receiving a preventative program;

identifying a preventative edible as a function of the preventative program; and

determining the aliment instruction set as a function of the preventative edible.

8 . The system of claim 1 , wherein the at least a probable event includes an autoimmune symptom.

9 . The system of claim 1 , wherein the aliment instruction set identifies a functional program.

10 . The system of claim 1 , wherein the computing device is further configured to:

determine an impact value for each aliment provider possibility of the aliment instruction set;

generate an arranged list of provider aliment possibilities from a plurality of aliment possibilities as a function of the aliment instruction set and the impact value for each alimentary provider possibility; and

display the arranged list through the graphical user interface.

11 . A method of representing an arranged list of provider aliment possibilities, the method comprising:

receiving by a computing device, an input representing an autoimmune disorder, wherein receiving the input regarding the autoimmune disorder comprises:

displaying a graphical user interface to a user, wherein the graphical user interface comprises one or more display fields;

displaying, to the user, a first autoimmune disorder to the user through the one or more display fields;

receiving, through the graphical user interface a user input representing a selection related to the first autoimmune disorder; and

displaying, to the user and through the one or more display fields, one or more additional autoimmune disorders, wherein the one or more additional autoimmune disorders correlated to the first autoimmune disorder as a function of the user input;

identifying by the computing device, a marker of a user re, wherein identifying the marker of the user comprises:

comparing the represented autoimmune disorder to a list of autoimmune disorders stored with an autoimmune data bank, wherein each autoimmune disorder in the list of autoimmune disorders is associated with a respective marker; and

identifying the marker of the user as a function of the comparison of the represented autoimmune disorder to the list of autoimmune disorders;

generating by the computing device a marker classifier, wherein generating the marker classifier comprises:

training the marker classifier according to a first training set correlating each of a plurality of markers to a respective disorder state label such that the trained marker classifier is configured to receive the identified marker of the user as an input and output a disorder state label as a function of the identified marker of the user and correlations of each of the plurality of marker to a respective disorder state label in the first training data;

detecting by the computing device, a trigger pattern as a function of the marker;

determining by the computing device, as a function of the trigger pattern and disorder state label, an aliment instruction set, wherein determining further comprises:

identifying at least a probable event as a function of the trigger pattern and a physiological exacerbation likelihood which is determined using a physiological machine-learning model which comprises:

receiving training data, wherein the training data correlates a plurality of physiological exacerbation likelihood data to a plurality of probable event data,

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

identifying the at least a probable event using the trained physiological machine-learning model; and

determining the aliment instruction set as a function of the at least a probable event; and

representing by the computing device, the aliment instruction set through the graphical user interface on a display.

12 . The method of claim 11 , wherein detecting the trigger pattern further comprises identifying a flare frequency marker.

13 . The method of claim 11 , wherein detecting the trigger pattern further comprises:

receiving an environmental parameter; and

detecting the trigger pattern as a function of the environmental parameter.

14 . The method of claim 13 , wherein the environmental parameter includes irritant data.

15 . The method of claim 11 , wherein detecting the trigger pattern further comprises identifying a medical history and detecting the trigger pattern as a function of the medical history.

16 . The method of claim 11 , wherein the determining the aliment instruction set further comprises:

receiving a treatment program;

identifying a treatment edible as a function of the treatment program; and

determining the aliment instruction set as a function of the treatment edible.

17 . The method of claim 11 , wherein the determining the aliment instruction set further comprises:

receiving a preventative program;

identifying a preventative edible as a function of the preventative program; and

determining the aliment instruction set as a function of the preventative edible.

18 . The method of claim 11 , wherein the at least a probable event includes an autoimmune symptom.

19 . The method of claim 11 , wherein the aliment instruction set identifies a functional program.

20 . The method of claim 11 , further comprising:

determining, using the computing device, an impact value for each aliment provider possibility of the aliment instruction set;

determining, using the computing device, an arranged list of provider aliment possibilities from a plurality of aliment possibilities as a function of the aliment instruction set and the impact value for each alimentary provider possibility; and

displaying the arranged list through the graphical user interface.

Assignments (1)

Continuity (2)

References Cited (34)