SYSTEMS AND METHODS FOR ASSISTED RESOLUTION OF SUPPORT TICKETS

1 . A system for helping resolve support tickets, the system including a non-transitory computer-readable medium storing computer-executable instructions thereon such that when the instructions are executed, the system is configured to:

receive a problem query, the problem query including searchable text;

determine, from a ticket corpus, one or more issues similar to the problem query;

provide a subset of the one or more issues to a service device;

receive an issue selection from the service device;

determine one or more resolutions associated with the issue selection;

provide a subset of the one or more resolutions to the service device, the subset determined based on one or more features of each of the one or more resolutions, the one or more features including last activity length in user activity field of the one or more resolutions; and

receive a resolution selection from the service device.

2 . The system of claim 1 , further configured to:

receive a problem area of interest; and

determine the one or more issues in the ticket corpus based at least in part on the problem area of interest.

3 . The system of claim 1 , further configured to:

determine the subset of the one or more issues based at least in part on incident similarity analysis between the problem query and each of the one or more issues, wherein the subset of the one or more issues includes the one or more issues with highest incident similarity to the problem query.

4 . The system of claim 3 , further configured to:

perform the incident similarity analysis using one or more long short term memory (LSTM) neural networks, one or more distance metrics, one or more fully connected neural networks, or any combination thereof.

5 . The system of claim 1 , wherein the ticket corpus includes tickets from diverse domains.

6 . The system of claim 5 , wherein the subset of the one or more issues is determined based at least in part on training a machine learning model on a subset of the tickets from the diverse domains.

7 . The system of claim 5 , wherein the machine learning model is trained a finite number of times including one time, two times, or three times.

8 . The system of claim 1 , further configured to determine the subset of the one or more resolutions via resolution quality scores associated with each of the one or more resolutions.

9 . The system of claim 8 , wherein the resolution quality scores are determined via weak supervision.

10 . The system of claim 8 , wherein the resolution quality scores are further determined using the one or more features from the group consisting of: (a) verb ratio, (b) action count, (c) problem count, and (d) length of user activity field.

11 . The system of claim 10 , wherein the one or more features further include a noun ratio, an adjective ratio, an acronym to activity ratio, a punctuation ratio, a number ratio, or any combination thereof.

12 . The system of claim 8 , wherein the resolution quality scores associated with each of the one or more resolutions are determined based on ensemble modeling including a classifier and a regressor.

13 . A method for helping resolve support tickets, the method performed by a computing device, the method comprising:

receiving a problem query, the problem query including searchable text;

determining from a ticket corpus, one or more issues similar to the problem query;

providing a subset of the one or more issues to a service device;

receiving an issue selection from the service device;

determining one or more resolutions associated with the issue selection;

providing a subset of the one or more resolutions to the service device, the subset determined based on one or more features of each of the one or more resolutions, the one or more features including last activity length in user activity field of the one or more resolutions; and

receiving a resolution selection from the service device.