Granted Patent B2

US 12,299,420 · App. 18/177,752 · Granted May 13, 2025

Inventors: Jason Alexander Cox (Burbank, CA); Steven William Wagner (Burbank, CA); Kyle Everett Lanier (Burbank, CA); James H. Tatum (Burbank, CA)

Assignee: Disney Enterprises, Inc.

G06F8/36G06F8/71G06F8/77G06F11/0793G06F11/3604G06N3/08G06N20/00

Abstract

Claims (63)

1. A method comprising:

receiving, by a computing device, a change to data stored in a repository, wherein an artifact entity is generated based on the change to the data and the artifact entity failed a validation of an operation of the artifact entity when executed, and wherein the artifact entity failed the validation based on movement of the artifact entity when executed;

analyzing, by the computing device, the change to the data via a model to generate a set of adjustments to the change to the data, wherein the model comprises a machine learning model, and the machine learning model is trained to output the set of adjustments based on a training process that adjusts parameters of the machine learning model based on changes to data;

generating, by the computing device, a set of adjusted artifact entities based on the set of adjustments being applied to the change to the data, wherein the set of adjusted artifact entities operate different from the artifact entity;

determining, by the computing device, an adjusted artifact entity in the set of adjusted artifact entities that is associated with an adjustment in the set of adjustments that passes the validation of the operation of the adjusted artifact entity; and

outputting, by the computing device, the adjusted artifact entity as a validated artifact entity.

2. The method of claim 1 , further comprising:

determining a set of confidence values, wherein a confidence value is associated with a respective adjustment in the set of adjustments.

3. The method of claim 2 , wherein a confidence value rates a confidence a respective adjustment generates an adjusted artifact entity that will pass the validation.

4. The method of claim 1 , wherein receiving the change to the data comprises:

receiving an original state of the data before the change to the data; and

receiving the change to the data, wherein the original state and the change to the data is input into the model.

5. The method of claim 1 , wherein an adjustment in the set of adjustments comprises a change in a value of the data.

6. The method of claim 1 , wherein the adjustments in the set of adjustments are different than the change to the data.

7. The method of claim 1 , wherein determining the adjusted artifact entity comprises:

selecting a highest ranked adjustment in the set of adjustments that passes validation.

8. The method of claim 7 , wherein:

the model outputs confidence values for adjustments in the set of adjustments, and

the highest ranked adjustment has a highest ranked confidence value that passes validation.

9. The method of claim 1 , wherein outputting the adjusted artifact entity comprises:

storing information for the adjusted artifact entity in artifact storage, wherein the artifact storage stores information for artifact entities that are generated from the data in the repository.

10. The method of claim 9 , wherein:

the information for the adjusted artifact entity is stored with metadata that indicates the adjusted artifact entity is generated based on the model, and

information for another artifact entity is stored in the repository based on another change to the data, the another artifact entity passing validation without any adjustments.

11. The method of claim 1 , wherein outputting the adjusted artifact entity comprises:

storing information for the adjusted artifact entity in training storage; and

using the information for the adjusted artifact entity to train the model.

12. The method of claim 11 , further comprising:

receiving a verification that the adjusted artifact entity should be used in training.

13. The method of claim 1 , wherein the model is trained by:

receiving a reference input;

receiving input data;

generating an output for the input data;

comparing the reference input to the output; and

adjusting a parameter in the model based on the comparing.

14. The method of claim 1 , wherein the model is trained by:

receiving data and an error message;

analyzing a space to determine a similar target to the error message;

comparing the similar target to the error message; and

adjusting a parameter in the model based on the comparing.

15. A non-transitory computer-readable storage medium having stored thereon computer executable instructions, which when executed by a computing device, cause the computing device to be operable for:

receiving a change to data stored in a repository, wherein an artifact entity is generated based on the change to the data and the artifact entity failed a validation of an operation of the artifact entity when executed, and wherein the artifact entity failed the validation based on movement of the artifact entity when executed;

analyzing the change to the data via a model to generate a set of adjustments to the change to the data, wherein the model comprises a machine learning model, and the machine learning model is trained to output the set of adjustments based on a training process that adjusts parameters of the machine learning model based on changes to data;

generating a set of adjusted artifact entities based on the set of adjustments being applied to the change to the data, wherein the set of adjusted artifact entities operate different from the artifact entity;

determining an adjusted artifact entity in the set of adjusted artifact entities that is associated with an adjustment in the set of adjustments that passes the validation of the operation of the adjusted artifact entity; and

outputting the adjusted artifact entity as a validated artifact entity.

16. The non-transitory computer-readable storage medium of claim 15 , further operable for:

determining a set of confidence values, wherein a confidence value is associated with a respective adjustment in the set of adjustments.

17. The non-transitory computer-readable storage medium of claim 15 , wherein the adjustments in the set of adjustments are different than the change to the data.

18. An apparatus comprising:

one or more computer processors; and

a computer-readable storage medium comprising instructions for controlling the one or more computer processors to be operable for:

receiving a change to data stored in a repository, wherein an artifact entity is generated based on the change to the data and the artifact entity failed a validation of an operation of the artifact entity when executed, and wherein the artifact entity failed the validation based on movement of the artifact entity when executed;

analyzing the change to the data via a model to generate a set of adjustments to the change to the data, wherein the model comprises a machine learning model, and the machine learning model is trained to output the set of adjustments based on a training process that adjusts parameters of the machine learning model based on changes to data;

generating a set of adjusted artifact entities based on the set of adjustments being applied to the change to the data, wherein the set of adjusted artifact entities operate different from the artifact entity;

determining an adjusted artifact entity in the set of adjusted artifact entities that is associated with an adjustment in the set of adjustments that passes the validation of the operation of the adjusted artifact entity; and

outputting the adjusted artifact entity as a validated artifact entity.

19. A method comprising:

receiving a change to data stored in a repository wherein an artifact entity is generated based on the change to the data and the artifact entity failed a validation of an operation of the artifact entity when executed, and wherein the artifact entity failed the validation of an appearance of the artifact entity when executed;

analyzing the change to the data via a model to generate a set of adjustments to the change to the data, wherein the model comprises a machine learning model, and the machine learning model is trained to output the set of adjustments based on a training process that adjusts parameters of the machine learning model based on changes to data;

generating a set of adjusted artifact entities based on the set of adjustments being from the change to the data, wherein the set of adjusted artifact entities operate different from the artifact entity;

determining an adjusted artifact entity in the set of adjusted artifact entities that is associated with an adjustment in the set of adjustments that passes the validation of the operation of the adjusted artifact entity; and

outputting the adjusted artifact entity as a validated artifact entity.

Assignments (2)

Continuity (1)

References Cited (13)