GENERATING CONTEXTUAL HELP

1 . A computer-implemented method comprising:

receiving an in-app event history, the in-app event history including data associated with user interactions with an application that lead up to an in-app issue;

analyzing, by one or more processors running a contextual help system, the in-app event history based on previous event histories used to train the contextual help system, the contextual help system trained to determine help content likely to resolve in-app issues;

determining, using the analysis by the contextual help system, applicable help content likely to resolve the in-app issue, the applicable help content related to a previous event history having a threshold of similarity of event data with the in-app event history; and

providing the applicable help content based on the analysis in-app event history.

2 . The computer-implemented method of claim 2 , the method further comprising:

requesting feedback regarding the accuracy of the applicable help content in resolving the in-app issue; and

updating the contextual help system based on the feedback, in-app event history, and the provided help content.

3 . The computer-implemented method of claim 1 , the method further comprising:

determining no help content is likely to resolve the in-app issue;

converting the in-app event history into a human-readable form;

providing the human readable form of the in-app event history to a live-service desk; and

updating the contextual help system based on a solution determined by the live-service desk.

4 . The computer-implemented method of claim 3 , the method further comprising:

determining a known solution exists based on the in-app event history, the known solution being preexisting help content likely to resolve the in-app issue.

5 . The computer-implemented method of claim 4 , wherein the updating of the contextual help system is based on the known solution being correlated with the in-app event history.

6 . The computer-implemented method of claim 3 , the method further comprising:

determining an unknown solution based on the in-app event history, the unknown solution being a new issue encountered by the contextual help system.

7 . The computer-implemented method of claim 1 , the method further comprising:

training the contextual help system using the previous in-app event histories and corresponding help content known to resolve in-app issues associated with the previous in-app event histories; and

updating the contextual help system based on user feedback.

8 . A non-transitory computer-readable storage medium including instructions which, when executed by a computer, cause the computer to perform operations comprising:

receiving an indication for help with an in-app issue;

determining an in-app event history to send to a contextual help system, the contextual help system trained to determine help content likely to resolve in-app issues;

sending the in-app event history to the contextual help system, wherein the contextual help system analyzes the in-app event history based on previous event histories to determine applicable help content likely to resolve the in-app issue; and

receiving the applicable help content from the contextual help system, wherein the applicable help content is related to a previous event history having a threshold of similarity of event data with the in-app event history.

9 . The non-transitory computer-readable storage medium of claim 8 , further comprising:

collecting in-app events; and

storing the in-app events, the in-app events stored for a predefined time.

10 . The non-transitory computer-readable storage medium of claim 9 , wherein the in-app event history comprises one or more stored in-app events.

11 . The non-transitory computer-readable storage medium of claim 9 , wherein the in-app events are stored based on time of occurrence.

12 . The non-transitory computer-readable storage medium of claim 8 , wherein the indication for help is automatically triggered based on an occurrence of one of a predefined list.

13 . The non-transitory computer-readable storage medium of claim 8 , wherein the indication for help is based on a user interaction with a help button.

14 . The non-transitory computer-readable storage medium of claim 8 , further comprising:

providing feedback to the contextual help system, the feedback indicating the accuracy of the applicable help content in resolving the in-app issue.

15 . A computing system comprising:

one or more processors; and

one or more non-transitory computer-readable storage media, coupled with the one or more processors, having instructions stored thereon, which, when executed by the one or more processors, cause the computing system to provide:

means for determining help content related to an in-app issue using a sequence of in-app events leading up to the occurrence of the in-app issue;

means for providing live support when no help content is likely to resolve the in-app issue, the live support provided using the sequence of in-app events converted into a human readable form;

means for training a system capable of performing the determination of the help content and providing the live support.

16 . The computing system of claim 15 , wherein the help content is determined by:

receiving the sequence of in-app events; and

analyzing the sequence of in-app events to evaluate the help content likely to resolve the in-app issue by comparing similarity between previous in-app sequences and the sequence of in-app events.

17 . The computing system of claim 16 , wherein the help content is related to a previous in-app sequence having a threshold of similarity of event data with the sequence of in-app events.

18 . The computing system of claim 15 , wherein the live support is provided by:

determining no help content is likely to resolve the in-app issue; and

providing the human readable form to a live-service desk.

19 . The computing system of claim 15 , wherein the training is performed using feedback from users regarding a provided help content.

20 . The computing system of claim 15 , wherein the training is performed using a solution determined by a live service desk.