Granted Patent B2

US 12,443,155 · App. 18/142,536 · Granted Oct 14, 2025

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

Assignee: The Strategic Coach Inc.

G05B13/042G06N3/08G06N3/09G06N20/00

Abstract

Claims (62)

1. An apparatus for actualizing future process outputs using artificial intelligence, the apparatus comprising:

at least a processor; and

a memory communicatively coupled to the at least a processor, the memory containing instructions configuring the at least a processor to:

receive input data comprising cluster data, wherein the cluster data comprises data associated with each user of a clustered group of users, wherein the clustered group comprises an organized body, wherein the cluster data comprises user role data comprising data associated with a current role of each user within the organized body;

identify at least one future process output as a function of the input data, wherein identifying the at least one future process output as a function of the input data comprises:

receiving user training data;

training a future process output machine learning model as a function of the user training data; and

generating a plurality of future process outputs using the trained future process output machine learning model;

determine a highest priority future process output of the plurality of future process outputs using a linear program configured to optimize a linear objective function performed by the at least a processor, given at least a user constraint;

classify the highest priority future process output into one or more objective groups as a function of an objective group classifier;

determine at least an actualization item as a function of the one or more objective groups and the highest priority future process output;

determine at least a process parameter as a function of the highest priority future process output; and

generate an objective output as a function of the at least a process parameter.

2. The apparatus of claim 1 , wherein determining the highest priority future process output further comprises ranking the plurality of future process outputs.

3. The apparatus of claim 1 , wherein determining the at least an actualization item as a function of the one or more objective groups and highest priority future process output comprises:

generating an actualization item machine learning model;

training the actualization item machine learning model as a function of actualization item training data; and

generating the at least an actualization item using the trained actualization item machine learning model.

4. The apparatus of claim 3 , wherein determining the at least an actualization item as a function of the one or more objective groups and highest priority future process output comprises:

determining an actualization item score for each of the at least an actualization item;

comparing each actualization item score to a threshold actualization item score; and

identifying the at least an actualization item based on the comparison of each actualization item score to the threshold actualization item score.

5. The apparatus of claim 4 , wherein determining the at least an actualization item as a function of the one or more objective groups and the highest priority future process output comprises generating a ranked list of the at least an actualization item as a function of the actualization item score.

6. The apparatus of claim 1 , wherein determining the at least a process parameter as a function of the highest priority future process output comprises:

generating a process parameter machine learning model;

training the process parameter machine learning model as a function of process parameter training data; and

generating the at least a process parameter using the trained process parameter machine learning model.

7. The apparatus of claim 1 , wherein the memory further comprises instructions configuring the at least a processor to determine a success expectation for the at least one user objective as a function of the user data, wherein determining the success expectation for the at least one user objective as a function of the user data comprises:

generating a success machine learning model;

training the success machine learning model as a function of success training data; and

generating the success expectation using the trained success machine learning model.

8. The apparatus of claim 1 , wherein the memory further comprises instructions configuring the at least a processor to receive a user response from at least one user for the at least an actualization item, wherein the user response from the at least one user comprises a modification to the at least a process parameter.

9. A method for actualizing future process outputs using artificial intelligence, the method comprising:

receiving, by at least a processor, input data comprising cluster data, wherein the cluster data comprises data associated with each user of a clustered group of users, wherein the clustered group comprises an organized body, wherein the cluster data comprises user role data comprising data associated with a current role of each user within the organized body;

identifying, by the at least a processor, at least one future process output as a function of the input data, wherein identifying the at least one future process output as a function of the input data comprises:

receiving user training data;

training a future process output machine learning model as a function of the user training data; and

generating a plurality of future process outputs using the trained future process output machine learning model;

determining, by the at least a processor, a highest priority future process output of the plurality of future process outputs using a linear program configured to optimize a linear objective function performed by the at least a processor, given at least a user constraint;

classifying, by the at least a processor, the highest priority future process output into one or more objective groups as a function of an objective group classifier;

determining, by the at least a processor, at least an actualization item as a function of the one or more objective groups and the highest priority future process output;

determining, by the at least a processor, at least a process parameter as a function of the highest priority future process output; and

generating, by the at least a processor, an objective output as a function of the at least a process parameter.

10. The method of claim 9 , wherein determining the highest priority future process output further comprises ranking the plurality of future process outputs.

11. The method of claim 9 , wherein determining the at least an actualization item as a function of the one or more objective groups and the highest priority future process output comprises:

generating, by the at least a processor, an actualization item machine learning model;

training, by the at least a processor, the actualization item machine learning model as a function of actualization item training data; and

generating, by the at least a processor, the at least a actualization item using the trained actualization item machine learning model.

12. The method of claim 11 , wherein determining the at least an actualization item as a function of the one or more objective groups and the highest priority future process output comprises:

determining, by the at least a processor, an actualization item score for each of the at least an actualization item;

comparing, by the at least a processor, each actualization item score to a threshold actualization item score; and

identifying, by the at least a processor, the at least an actualization item based on the comparison of each actualization item score to the threshold actualization item score.

13. The method of claim 12 , wherein determining the at least an actualization item as a function of the one or more objective groups and the highest priority future process output further comprises generating a ranked list of the at least an actualization item as a function of the actualization item score.

14. The method of claim 9 , wherein determining the at least a process parameter as a function of highest priority future process output comprises:

generating, by the at least a processor, a process parameter machine learning model;

training, by the at least a processor, the process parameter machine learning model as a function of process parameter training data; and

generating, by the at least a processor, the at least a process parameter using the trained process parameter machine learning model.

15. The method of claim 9 , further comprising determining, by the at least a processor, a success expectation for the at least one user objective as a function of the user data, wherein determining the success expectation for the at least one user objective as a function of the user data comprises:

generating, by the at least a processor, a success machine learning model;

training, by the at least a processor, the success machine learning model as a function of success training data; and

generating, by the at least a processor, the success expectation using the trained success machine learning model.

16. The method of claim 9 , further comprising receiving, by the at least a processor, a user response from at least one user for the at least an actualization item, wherein the user response from the at least one user comprises a modification to the at least a process parameter.

Assignments (1)

Continuity (1)

References Cited (14)