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