Systems and methods for intelligent ticket management and resolution

1. A computer-implemented system for efficient ticket resolution comprising:

a memory storing instructions; and

at least one processor configured to execute the instructions to perform operations comprising:

receiving, from at least one user device, a request to resolve an issue, the request comprising language;

capturing, using an activity logging module of a ticket management server, user activity occurring at the at least one user device;

analyzing, using the ticket management server, the language in the request to determine the issue to be resolved by performing natural language processing on the language wherein performing the natural language processing comprises utilizing at least one machine learning algorithm, wherein the at least one machine learning algorithm is trained to generate an assessment based on the language and based on the user activity;

based on the natural language processing, determining whether the issue meets a condition for automated resolution;

if the condition is met:

extracting, via an application programming interface and from the at least one user device, information needed to resolve the issue; and

resolving, using the ticket management server, the issue using the extracted information; and

if the condition is not met:

inserting, into at least one database, a ticket comprising:

an identifier associated with the at least one user device; and

information associated with the issue to be resolved;

assigning, using the ticket management server and based on the information associated with the issue to be resolved, a work group to the ticket;

consulting, using the ticket management server, the at least one database to determine whether a job aid associated with the issue exists; and

forwarding, to the at least one user device, at least one of:

the job aid;

received communications from the work group; and

an estimated amount of time to resolution based on the information associated with the issue to be resolved.

2. The system of claim 1 , wherein the user activity comprises user choices relative to suitability of the job aid.

3. The system of claim 2 , wherein assigning the work group to the ticket is based on the assessment.

4. The system of claim 2 , wherein the operations further comprise training the machine-learning algorithm using historical data comprising:

previously received requests, each request comprising user-provided issue descriptions;

a previously assigned work group for each previously received request;

a recorded resolution time for each previously received request; and

documentation associated with at least one of a product, service, or application.

5. The system of claim 2 , wherein the machine learning algorithm comprises at least one of a generalized least squares regression technique, an ordinary least squares regression technique, a random forest regression technique, a gradient boosting regression technique, or a support vector machine regression technique.

6. The system of claim 1 , wherein the at least one processor is further configured to establish a communication link between the at least one user device and the work group, the communication link comprising a digital collaboration application.

7. The system of claim 1 , wherein the language in the request comprises free form text.

8. The system of claim 1 , wherein the language in the request comprises spoken language.

9. The system of claim 1 , wherein analyzing the language in the request comprises instantiating a digital dialogue session with the at least one user device.

10. The system of claim 9 , wherein extracting the information needed to resolve the issue comprises receiving the information needed to resolve the issue from the at least one user device through the digital dialogue session.

11. A computer-implemented method for efficient ticket resolution comprising:

receiving, from at least one user device, a request to resolve an issue, the request comprising language;

capturing, using an activity logging module of a ticket management server, user activity occurring at the at least one user device;

analyzing, using the ticket management server, the language in the request to determine the issue to be resolved by performing natural language processing on the language wherein performing the natural language processing comprises utilizing at least one machine learning algorithm, wherein the at least one machine learning algorithm is trained to generate an assessment based on the language and based on the user activity;

based on the natural language processing, determining whether the issue meets a condition for automated resolution;

if the condition is met:

extracting, via an application programming interface and from the at least one user device, information needed to resolve the issue; and

resolving, using the ticket management server, the issue using the extracted information; and

if the condition is not met:

inserting, into at least one database, a ticket comprising:

an identifier associated with the at least one user device; and

information associated with the issue to be resolved;

assigning, using the ticket management server and based on the information associated with the issue to be resolved, a work group to the ticket;

consulting, using the ticket management server, the at least one database to determine whether a job aid associated with the issue exists; and

forwarding, to the at least one user device, at least one of:

the job aid;

received communications from the work group; and

an estimated amount of time to resolution based on the information associated with the issued to be resolved.

12. The method of claim 11 , wherein the user activity comprises user choices relative to suitability of the job aid.

13. The method of claim 12 , wherein assigning the work group to the ticket is based on the assessment.

14. The method of claim 12 , further comprising training the machine-learning algorithm using historical data comprising:

previously received requests, each request comprising user-provided issue descriptions;

a previously assigned work group for each previously received request;

a recorded resolution time for each previously received request; and

documentation associated with at least one of a product, service, or application.

15. The method of claim 12 , wherein the machine learning algorithm comprises at least one of a generalized least squares regression technique, an ordinary least squares regression technique, a random forest regression technique, a gradient boosting regression technique, or a support vector machine regression technique.

16. The method of claim 11 , wherein the at least one processor is further configured to establish a communication link between the at least one user device and the work group, the communication link comprising a digital collaboration application.

17. The method of claim 11 , wherein the language in the request comprises free form text.

18. The method of claim 11 , wherein the language in the request comprises spoken language.

19. The method of claim 11 , wherein analyzing the language in the request comprises instantiating a digital dialogue session with the at least one user device.

20. The method of claim 19 , wherein extracting the information needed to resolve the issue comprises receiving the information needed to resolve the issue from the at least one user device through the digital dialogue session.