Granted Patent B1

US 12,293,265 · App. 18/409,666 · Granted May 6, 2025

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

Assignee: The Strategic Coach Inc.

G06N20/00

Abstract

Claims (42)

1. A method for identifying model optimization, the method comprising:

measuring, by a computing device, a plurality of subsystems, wherein the plurality of subsystems includes at least one remote device, and wherein measuring the plurality of subsystems produces a plurality of measurements;

comparing, by the computing device, each measurement of the plurality of measurements to a predetermined continuum range, wherein the predetermined continuum range comprises a lower threshold and an upper threshold;

identifying, by the computing device, an optimal measurement of the plurality of measurements and a suboptimal measurement of the plurality of measurements as a function of each of the comparisons;

generating, by the computing device, a positive feedback function of the optimal measurement, wherein generating the positive feedback function further comprises identifying, using an optimal machine-learning model, a first set of parameter changes to a subsystem corresponding to the optimal measurement, wherein the optimal machine-learning model is generated by updating a previous optimal machine learning model, wherein updating the previous optimal machine learning model comprises:

receiving optimal training data, wherein the optimal training data comprises a plurality of data entries containing a plurality of optimal measurements as inputs correlated to a plurality of positive feedbacks as outputs;

training the previous machine-learning model using the optimal training data;

sanitizing the optimal training data using a dedicated hardware unit comprising circuitry configured to perform signal processing operations, wherein sanitizing the optimal training data comprises:

determining by the dedicated hardware unit that a training data entry has a signal to noise ratio below a threshold value; and

removing the training data entry from the optimal training data;

retraining the previous machine-learning model using the sanitized training data; and

generating the positive feedback function as a function of the optimal measurement using the retrained optimal machine-learning model;

generating, by the computing device, a negative feedback function of the suboptimal measurement, wherein generating the negative feedback function comprises identifying, using a suboptimal machine-learning model, a second set of parameter changes to a subsystem corresponding to the suboptimal measurement; and

configuring the at least one remote device using the positive feedback function and the negative feedback function.

2. The method of claim 1 , wherein configuring the at least a remote device further comprises configuring the at least a remote device to display the first set of parameter changes and the second set of parameter changes.

3. The method of claim 1 , wherein the subsystem of the plurality of subsystems comprises a plurality of data.

4. The method of claim 1 , wherein the subsystems include sets of activity and categories of actions that are performed by individuals and business entities.

5. The method of claim 1 , wherein the first set of parameter changes is configured to move the optimal measurement further up the predetermined continuum range.

6. The method of claim 1 , wherein the second set of parameter changes is configured to move the suboptimal measurement further up the predetermined continuum range using an error function.

7. The method of claim 1 , further comprising displaying a data structure related to the configuration.

8. An apparatus for model optimization, wherein the apparatus comprises:

at least a processor; and

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

measure a plurality of subsystems, wherein the plurality of subsystems includes at least one remote device, and wherein measuring the plurality of subsystems produces a plurality of measurements;

compare each measurement of the plurality of measurements to a predetermined continuum range, wherein the predetermined continuum range comprises a lower threshold and an upper threshold;

identify an optimal measurement of the plurality of measurements and a suboptimal measurement of the plurality of measurements as a function of each of the comparisons;

generate a positive feedback function of the optimal measurement, wherein generating the positive feedback function further comprises identifying, using an optimal machine-learning model, a first set of parameter changes to a subsystem corresponding to the optimal measurement, wherein the optimal machine-learning model is generated by updating a previous optimal machine learning model, wherein updating the previous optimal machine learning model comprises:

receiving optimal training data, wherein the optimal training data comprises a plurality of data entries containing a plurality of optimal measurements as inputs correlated to a plurality of positive feedbacks as outputs;

training the previous machine-learning model using the optimal training data;

sanitizing the optimal training data using a dedicated hardware unit comprising circuitry configured to perform signal processing operations, wherein sanitizing the optimal training data comprises:

determining by the dedicated hardware unit that a training data entry has a signal to noise ratio below a threshold value; and

removing the training data entry from the optimal training data;

retraining the previous machine-learning model using the sanitized training data; and

generating the positive feedback function as a function of the optimal measurement using the retrained optimal machine-learning model;

generate a negative feedback function of the suboptimal measurement, wherein generating the negative feedback function comprises identifying, using a suboptimal machine-learning model, a second set of parameter changes to a subsystem corresponding to the suboptimal measurement; and

configure the at least one remote device using the positive feedback function and the negative feedback function.

9. The apparatus of claim 8 , further comprising configuring a remote device configured to display the first set of parameter changes and the second set of parameter changes.

10. The apparatus of claim 8 , wherein the subsystem of the plurality of subsystems comprises a plurality of data.

11. The apparatus of claim 8 , wherein the subsystems include sets of activity and categories of actions that are performed by individuals and business entities.

12. The apparatus of claim 8 , wherein the measurements comprise ratios of one parameter to another, including a rate of productivity measured in output per hour, a duration, including total time spent on a process, and total time that a person remains employed, and/or differences including gains minus losses.

13. The apparatus of claim 8 , wherein the second set of parameter changes moves the suboptimal measurement further up the predetermined continuum range using an error function.

14. The apparatus of claim 8 , wherein the first set of parameter changes and the second set of parameters comprise instructions to modify parameters of the subsystems.

Assignments (1)

References Cited (11)