Automated method of identifying troubleshooting and system repair instructions using complementary machine learning models

Patent metadata

Field	Value
Publication number	US-11294755-B2
Application number	US-201916503819-A
Country	US
Kind code	B2
Filing date	Jul 5, 2019
Priority date	Jul 5, 2019
Publication date	Apr 5, 2022
Grant date	Apr 5, 2022

Abstract

A system, method, and computer-readable medium for performing a system failure repair operation, comprising: receiving information regarding symptoms related to a faulty device; storing the information with other historical information regarding the symptoms; receiving additional information as the faulty device is diagnosed; indicating whether a repair recommendation is provided for the faulty device; and using the stored information, historical information, and additional information to provide a repair recommendation if indicating shows no repair recommendation.

First claim

What is claimed is:

1. A computer-implementable method for performing a system failure repair operation, comprising:
receiving information regarding symptoms related to a faulty device;
storing the information with other historical information regarding the symptoms;
receiving additional information as the faulty device is diagnosed;
filtering all the information as to relevant and irrelevant information;
indicating whether a repair recommendation is provided for the faulty device; and
implementing machine learning that uses the stored information, historical information, and additional information to provide a repair recommendation if indicating shows no repair recommendation, wherein the additional information is from a big data source that includes data cleansing, text parsing and data mining.

2. The method of claim 1 wherein the information regarding symptoms is a unique product identifier.

3. The method of claim 1 , further comprising selecting one symptom from the symptoms to provide the repair recommendation.

4. The method of claim 1 , further comprising selecting an appropriate symptom tier for the repair recommendation.

5. The method of claim 1 wherein the receiving additional information is through a dynamic interactive graphical user interface.

6. The method of claim 1 , further comprising providing information as to repair and validation regarding the faulty device.

7. A system comprising:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:
receiving information regarding symptoms related to a faulty device;
storing the information with other historical information regarding the symptoms;
receiving additional information as the faulty device is diagnosed;
filtering all the information as to relevant and irrelevant information;
indicating whether a repair recommendation is provided for the faulty device; and
implementing machine learning that uses the stored information, historical information, and additional information to provide a repair recommendation if indicating shows no repair recommendation, wherein the additional information is from a big data source that includes data cleansing, text parsing and data mining.

8. The system of claim 7 wherein the information regarding symptoms is a unique product identifier.

9. The system of claim 7 , further comprising selecting one symptom from the symptoms to provide the repair recommendation.

10. The system of claim 7 , further comprising selecting an appropriate symptom tier for the repair recommendation.

11. The system of claim 7 wherein the receiving additional information is through a dynamic interactive graphical user interface.

12. The system of claim 7 , further comprising providing information as to repair and validation regarding the faulty device.

13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
receiving information regarding symptoms related to a faulty device;
storing the information with other historical information regarding the symptoms;
receiving additional information as the faulty device is diagnosed;
filtering all the information as to relevant and irrelevant information;
indicating whether a repair recommendation is provided for the faulty device; and
implementing machine learning that uses the stored information, historical information, and additional information to provide a repair recommendation if indicating shows no repair recommendation, wherein the additional information is from a big data source that includes data cleansing, text parsing and data mining.

14. The non-transitory, computer-readable storage medium of claim 13 wherein the information regarding symptoms is a unique product identifier.

15. The non-transitory, computer-readable storage medium of claim 13 , further comprising selecting one symptom from the symptoms to provide the repair recommendation.

16. The non-transitory, computer-readable storage medium of claim 13 , further comprising selecting an appropriate symptom tier for the repair recommendation.

17. The non-transitory, computer-readable storage medium of claim 13 wherein the receiving additional information is through a dynamic interactive graphical user interface.

18. The non-transitory, computer-readable storage medium of claim 13 , further comprising providing information as to repair and validation regarding the faulty device.

19. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the computer executable instructions are deployable to a client system from a server system at a remote location.

20. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the computer executable instructions are provided by a service provider to a user on an on-demand basis.

Assignees

• Dell Products Lp

Inventors

• Vah Jeffrey S
• Wiggers Jimmy H
• Shukla Ravi
• Martin Brian T
• Kambalapalli Nikhila
• Mushtaq M Najam
• Rodman Carlos F
• Adams Brock A

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06F11/0787 Primary

  Storage of error reports, e.g. persistent data storage, storage using memory protection · CPC title

• G06F11/079 Primary

  Root cause analysis, i.e. error or fault diagnosis (in a hardware test environment G06F11/22; in a software test environment G06F11/36) · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06N20/00

  Machine learning · CPC title