Granted Patent B2

US 12,322,493 · App. 17/390,237 · Granted Jun 3, 2025

Inventor: Kenneth Neumann (Lakewood, CO)

G16H20/70G06F16/3331G06N3/042G06N3/08G16H10/60G16H40/67G16H50/20G16H70/60A61B5/7267

Abstract

Claims (46)

1. A system for generating a lifestyle-based disease prevention plan, the system comprising a computing device configured to:

receive at least a user biomarker input;

produce a user profile as a function of the at least a user biomarker input, wherein producing the user profile comprises:

determining a user identifier as a function of the at least a user biomarker input;

generating at least a query as a function of the user identifier, wherein generating at least a query comprises a parsing module configured to convert the at least a query into a second form of the at least a query, wherein the parsing module further comprises a language processing module configured to perform at least a dependency parsing process;

extracting at least a textual output as a function of the at least a query; and

producing the user profile as a function of the at least a textual output; and

generate a lifestyle-based disease prevention plan as a function of the user profile, wherein the lifestyle-based disease prevention plan comprises a consumption schedule for nutritional elements, wherein generating the lifestyle-based disease prevention plan comprises:

training a machine learning process with a lifestyle training data set, wherein the lifestyle training data set further comprises a plurality of inputs containing lifestyle elements correlated to a plurality of outputs containing diseases prevented, wherein training the machine learning process comprises:

updating the lifestyle training data set as a function of inputs and outputs of a previous iteration of the machine learning process; and

retraining the machine learning process using the updated lifestyle training data; and

producing the lifestyle-based disease prevention plan as a function of the user profile using the trained machine learning process.

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

receive a user input relating to the lifestyle-based disease prevention plan; and

calculate a user compliance with the lifestyle-based disease prevention plan.

3. The system of claim 2 , wherein the user input is received from a wearable device.

4. The system of claim 2 , wherein the computing device is further configured to send user reminders related to the lifestyle plan.

5. The system of claim 1 , wherein training the machine learning process with the lifestyle data training set comprises utilizing a neural network.

6. The system of claim 1 , wherein the computing device is further configured to generate a user lifestyle compliance dataset.

7. The system of claim 6 , wherein the computing device is further configured to transmit a lifestyle plan comparison set to a user device as a function of the lifestyle compliance dataset.

8. The system of claim 1 , wherein the computing device is further configured to calculate a likeability of a user to follow the lifestyle plan as a function of the machine learning process.

9. The system of claim 1 , wherein the computing device is further configured to transmit a user lifestyle plan reminder.

10. The system of claim 9 , wherein a transmission of the user lifestyle plan reminder includes attributes readable by an AI assistant software.

11. A method for generating a lifestyle-based disease prevention plan, the method comprising;

receiving, by a computing device, at least a user biomarker input;

producing, by the computing device, a user profile as a function of the at least a user biomarker input, wherein producing the user profile comprises:

determining a user identifier as a function of the at least a user biomarker input;

generating at least a query as a function of the user identifier, wherein generating at least a query comprises a parsing module configured to convert the at least a query into a second form of the at least a query, wherein the parsing module further comprises a language processing module configured to perform at least a dependency parsing process;

extracting at least a textual output as a function of the at least a query; and

producing the user profile as a function of the at least a textual output; and

generating, by the computing device, a lifestyle-based disease prevention plan as a function of the user profile, wherein the lifestyle-based disease prevention plan comprises a consumption schedule for nutritional elements, wherein generating the lifestyle-based disease prevention plan comprises:

training a machine learning process with a lifestyle training data set, wherein the lifestyle training data set further comprises a plurality of inputs containing lifestyle elements correlated to a plurality of outputs containing diseases prevented, wherein training the machine learning process comprises:

updating the lifestyle training data set as a function of inputs and outputs of a previous iteration of the machine learning process; and

retraining the machine learning process using the updated lifestyle training data; and

producing the lifestyle-based disease prevention plan as a function of the user profile and the machine learning process.

12. The method of claim 11 , wherein method further comprises:

receiving, by the computing device, a user input relating to the lifestyle-based disease prevention plan; and

calculating, by the computing device, a user compliance with the lifestyle-based disease prevention plan.

13. The method of claim 12 , wherein receiving the user input comprises utilizing a wearable device.

14. The method of claim 12 , wherein the method further comprises sending, by the computing device, user reminders related to the lifestyle plan.

15. The method of claim 11 , wherein training the machine learning process with the lifestyle training data set comprises utilizing a neural network.

16. The method of claim 11 , wherein the method further comprises generating, by the computing device, a user lifestyle compliance dataset.

17. The method of claim 16 , wherein the method further comprises transmitting, by the computing device, a lifestyle plan comparison set to a user device as a function of the lifestyle compliance dataset.

18. The method of claim 11 , wherein the method further comprises calculating, by the computing device, a likeability of a user to follow the lifestyle plan as a function of the machine learning process.

19. The method of claim 11 , wherein the method further comprises transmitting, by the computing device, a user lifestyle plan reminder.

20. The method of claim 19 , wherein transmitting the user lifestyle plan reminder comprises attributes readable by an AI assistant software.

Assignments (1)

Continuity (2)

References Cited (53)