Granted Patent B2

US 12,263,019 · App. 18/142,508 · Granted Apr 1, 2025

Inventors: Barbara Sue Smith (Toronto, CA); Daniel J. Sullivan (Toronto, CA)

Assignee: The Strategic Coach Inc.

A61B5/7275H04L51/02H04L67/306

Abstract

Claims (50)

1. An apparatus for generation of a plurality of personal targets, wherein the apparatus comprises:

at least a processor; and

a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:

receive a user profile from a user, wherein the user profile comprises a plurality of user data, wherein:

receiving the user profile comprises extracting the user profile using at least a sensor;

the at least a sensor comprises a wearable device; and

the at least a sensor is configured to transform a property of an analyte in body liquid into a measurable signal that is proportional to a concentration of the analyte in the body liquid, wherein:

transforming the property of the analyte in the body liquid into the measurable signal comprises detecting an analyte molecule in a selective way by transforming the detection into an analytical electrical signal; and

the concentration of the analyte in the body liquid comprises a glucose concentration in the body liquid;

determine tenure data as a function of the plurality of user data, wherein the plurality of user data comprises physiological data represented as a value on a numerical score range utilizing a tenure machine-learning model and further comprising:

receiving tenure training data, wherein the tenure training data correlates the plurality of user data to a plurality of examples of physiological data;

training the tenure machine-learning model using the tenure training data; and

generating the tenure data using the tenure machine-learning model;

adjusting the numerical score range using a linguistic value according to a severity of a condition;

predict forecast data as a function of the tenure data and the plurality of user data utilizing a forecast machine-learning model which comprises:

receiving forecast training data, wherein the forecast training data correlates the plurality of user data and tenure data to a plurality of forecast data;

training the forecast machine-learning model using the forecast training data; and

generating the forecast data as a function of the plurality of user data, tenure data and forecast data;

generate a plurality of personal targets as a function of the forecast data, wherein each personal target of the plurality of personal targets is associated with a timeline of the user; and

display the plurality of personal targets and the forecast data using a display device.

2. The apparatus of claim 1 , wherein receiving the user profile comprises extracting the user profile using a web crawler.

3. The apparatus of claim 1 , wherein receiving the user profile comprises extracting the user profile using a chatbot.

4. The apparatus of claim 1 , wherein the plurality of user data comprises biological data associated with the user.

5. The apparatus of claim 1 , wherein the plurality of personal targets comprises at least a health target.

6. The apparatus of claim 1 , wherein the forecast data additionally comprises a pecuniary status.

7. The apparatus of claim 1 , wherein the forecast data additionally comprises a lifestyle status.

8. A method for generation of a plurality of personal targets, wherein the method comprises:

receiving, using at least a processor, a user profile from a user, wherein the user profile comprises a plurality of user data, wherein:

receiving the user profile comprises extracting the user profile using at least a sensor;

the at least a sensor comprises a wearable device; and

the at least a sensor is configured to transform a property of an analyte in body liquid into a measurable signal that is proportional to a concentration of the analyte in the body liquid, wherein:

transforming the property of the analyte in the body liquid into the measurable signal comprises detecting an analyte molecule in a selective way by transforming the detection into an analytical electrical signal; and

the concentration of the analyte in the body liquid comprises a glucose concentration in the body liquid;

determining, using the at least a processor, tenure data as a function of the plurality of user data, wherein the user data comprises physiological data represented as a value on a numerical score range utilizing a tenure machine-learning model and further comprising:

receiving tenure training data, wherein the tenure training data correlates the plurality of user data to a plurality of examples of physiological data;

training the tenure machine-learning model using the tenure training data; and

generating the tenure data using the tenure machine-learning model;

adjusting the numerical score range using a linguistic value according to a severity of a condition;

predicting, using the at least a processor, forecast data as a function of the tenure data utilizing a forecast machine-learning model which comprises:

receiving forecast training data, wherein the forecast training data correlates the plurality of user data and tenure data to a plurality of forecast data;

training the forecast machine-learning model using the forecast training data; and

generating the forecast data as a function of the plurality of user data, tenure data and forecast data;

generating, using the at least a processor, a plurality of personal targets as a function of the forecast data, wherein each personal target of the plurality of personal targets is associated with a timeline of the user; and

displaying, using a display device, the plurality of personal targets and the forecast data.

9. The method of claim 8 , wherein receiving the user profile comprises extracting the user profile using a web crawler.

10. The method of claim 8 , wherein receiving the user profile comprises extracting the user profile using a chatbot.

11. The method of claim 8 , wherein the plurality of user data comprises biological data associated with the user.

12. The method of claim 8 , wherein the plurality of personal targets comprises at least a health target.

13. The method of claim 8 , wherein the forecast data additionally comprises a pecuniary status.

14. The method of claim 8 , wherein the forecast data additionally comprises a lifestyle status.

Assignments (1)

Continuity (1)

References Cited (21)