Granted Patent B2

US 12,380,409 · App. 17/886,343 · Granted Aug 5, 2025

Inventor: Joseph Janiczek (Denver, CO)

G06Q10/1097G06N20/00

Abstract

Claims (56)

1. A method of exploiting value within a certain domain, the method comprising:

interrogating, by a computing device, a remote device associated with a user for:

scheduling data;

at least a domain, wherein a quantity of domains in the at least a domain is between one and a predetermined maximum number of domains selected by the user; and

domain-specific data, wherein the domain-specific data is a function of the at least a domain;

receiving, by the computing device and from the remote device, the scheduling data, the at least a domain, and the domain-specific data;

generating, by the computing device, at least a domain target for the at least a domain as a function of the domain-specific data and a target-setting machine learning model, wherein generating the at least a domain target for the at least a domain comprises:

receiving target-setting training data, wherein the target-setting training data correlates a plurality of domain-specific data to a domain target, wherein the target-setting training data is filtered into categories by a target-setting training data classifier;

applying the target-setting training data to an input layer of nodes comprising the at least domain-specific data input, one or more intermediate layers of nodes, and an output layer of nodes comprising update data and a user schedule correlating to at least a domain target;

training the target-setting machine learning model as a function of the target-setting training data;

generating the at least domain target for the at least the domain as a function of the target-setting machine learning model;

updating the target-setting training data as a function of the domain-specific data, domain target data and the at least a domain target; and

iteratively training the target-setting machine learning model as a function of updating the target-setting training data;

training, by the computing device, a schedule machine learning model using scheduling training data configured to correlate the at least a domain target generated as a function of the target-setting machine learning model to at least a user schedule;

generating, by the computing device, the at least a user schedule as a function of the schedule machine learning model, wherein generating the at least a user schedule comprises:

receiving a status of the at least a domain;

assigning one or more state variables to the at least a domain, wherein the one or more state variables represent the status of the at least a domain;

generating the at least a user schedule as a function of the one or more state variables;

and

displaying, by the computing device and at the remote device, the at least a user schedule and the at least a domain target to the user.

2. The method of claim 1 , wherein assigning one or more state variables to the at least a domain further comprises determining an initial state of the at least a domain.

3. The method of claim 2 , wherein the initial state of the at least a domain corresponds to a specific problem.

4. The method of claim 2 , wherein generating the at least a user schedule further comprises comparing the at least a domain target to the initial state of the at least a domain.

5. The method of claim 1 , further comprising receiving, by the computing device, update data.

6. The method of claim 5 , wherein the update data includes one or more changes to the one or more state variables.

7. The method of claim 1 , wherein the one or more state variables forms a set of state variables.

8. The method of claim 7 , wherein the set of state variables limits a quantity of domains.

9. The method of claim 1 , wherein the one or more state variables are a function of subjective data.

10. The method of claim 1 , wherein the one or more state variables are a function of objective data.

11. A system for exploiting value within a certain domain, the system comprising a computing device configured to:

interrogate a remote device associated with a user for:

scheduling data;

at least a domain, wherein a quantity of domains in the at least a domain is between one and a predetermined maximum number of domains selected by the user; and

domain-specific data, wherein the domain-specific data is a function of the at least a domain;

receive, from the remote device, the scheduling data, the at least a domain, and the domain-specific data and a target-setting machine learning model, wherein generating the at least a domain target for the at least a domain comprises:

receiving target-setting training data, wherein the target-setting training data correlates a plurality of domain-specific data to a domain target, wherein the target-setting training data is filtered into categories by a target-setting training data classifier;

applying target-setting training data to an input layer of nodes comprising the at least domain-specific data input, one or more intermediate layers of nodes, and an output layer of nodes comprising update data and a user schedule correlating to at least a domain target;

training the target-setting machine learning model as a function of the target-setting training data;

generating the at least domain target for the at least the domain as a function of the target-setting machine learning model;

updating the target-setting training data as a function of the domain-specific data, domain target data and the at least a domain target; and

iteratively training the target-setting machine learning model as a function of updating the target-setting training data;

training, by the computing device, a schedule machine learning model using scheduling training data configured to correlate the at least a domain target generated as a function of the target-setting machine learning model to at least a user schedule;

generate, by the computing device, the at least a user schedule as a function of the schedule machine learning model, wherein generating the at least a user schedule comprises:

receiving a status of the at least a domain;

assigning one or more state variables to the at least a domain, wherein the one or more state variables represent the status of the at least a domain;

generating the at least a user schedule as a function of the one or more state variables; and

display, at the remote device, the at least a user schedule and the at least a domain target to the user.

12. The system of claim 11 , wherein assigning one or more state variables to the at least a domain further comprises determining an initial state of the at least a domain.

13. The system of claim 12 , wherein the initial state of the at least a domain corresponds to a specific problem.

14. The system of claim 12 , wherein generating the at least a user schedule further comprises comparing the at least a domain target to the initial state of the at least a domain.

15. The system of claim 11 , wherein the computing device is further configured to receive update data.

16. The system of claim 15 , wherein the update data includes one or more changes to the one or more state variables.

17. The system of claim 11 , wherein the one or more state variables forms a set of state variables.

18. The system of claim 17 , wherein the set of state variables limits a quantity of domains.

19. The system of claim 11 , wherein the one or more state variables are a function of objective data.

20. The system of claim 11 , wherein the one or more state variables are a function of subjective data.

Assignments (1)

Continuity (2)

References Cited (35)