System and method for predicting availability status changes of instant messenger users

• US 10,454,855 B2
• Filed: 04/03/2017
• Issued: 10/22/2019
• Est. Priority Date: 04/01/2016
• Status: Active Grant

• Current Assignee: Genesys Cloud Services Incorporated
• Original Assignee: Interactive Intelligence Group Incorporated (Genesys Cloud Services Incorporated)
• Inventors: Rife, Randolph
• Primary Examiner(s): Wasel, Mohamed A.
• Application Number:
  US15/477,698
• Publication Number:
  US 20170289076A1
• Time in Patent Office: 932 Days
• CPC Class Codes: H04L 51/043 using or handling presence information
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/53 using third party service providers
• CPC Class Codes: H04L 67/54 Presence management, e.g. monitoring or registration for receipt of user log-on information, or the connection status of the users

Abstract

A method for predicting availability status changes includes: retrieving, via a provider computing device, present availability status information for a user of an instant messenger (IM) application managed by the provider computing device, wherein the present availability status information includes at least a present availability status and a timestamp useable to identify a present date and time the present availability status was retrieved; identifying, by the provider computing device, a behavioral pattern of a plurality of behavioral patterns for the user as a function of the present availability status; determining, by the provider computing device, a predicted availability status change event; and creating, by the provider computing device, a predicted availability status string based on the identified behavioral pattern.

Claims

1. A method for predicting availability status changes, the method comprising:

retrieving, via a provider computing device, present availability status information for a user of an instant messenger (IM) application managed by the provider computing device, wherein the present availability status information includes at least a present availability status and a timestamp usable to identify a present date and time the present availability status was retrieved;

identifying, by the provider computing device, a behavioral pattern of a plurality of behavioral patterns for the user as a function of the present availability status;

determining, by the provider computing device, a predicted next availability status information based on the identified behavioral pattern for the user, wherein the predicted next availability status information includes at least a predicted availability status change event and a timestamp useable to identify a next date and time of the availability status; and

creating, by the provider computing device, a predicted availability status string based on the predicted next availability status information.

2. The method of claim 1, further comprising retrieving, by the provider computing device, one or more present data references, wherein identifying the behavioral pattern comprises comparing the present availability status and one or more of the present data elements to a corresponding availability status and data elements of the behavioral pattern.

3. The method of claim 1, further comprising updating, by the provider computing device, the present availability status in the IM application to the predicted availability status string.

4. The method of claim 1, wherein creating the predicted availability status string comprises (i) determining a predicted availability status, (ii) determining a predicted availability status update time, and (iii) concatenating text Corresponding to the predicted availability status and the predicted availability status update time to form the predicted availability status string.

5. The method of claim 1, further comprising:

receiving, by the provider computing device, an availability status update, wherein the availability status update indicates an availability status change by the user and includes availability status information; and

creating, by the provider computing device, a behavioral pattern as a function of at least a portion of the availability status information, wherein the behavioral pattern includes at least a portion of the availability status information.

6. The method of claim 5, further comprising assigning, by the provider computing device, a confidence level of the behavioral pattern.

7. The method of claim 6, wherein assigning the confidence level comprises assigning the confidence level as a function of a weighted value associated with one or more data elements of the availability status information.

8. The method of claim 5, wherein the availability status information includes a present availability status, a timestamp usable to identify a date and time the availability status update was performed by the user.

9. The method of claim 5, further comprising retrieving, by the provider computing device, one or more 3rd party references, wherein creating the behavioral pattern further comprises creating the behavioral pattern as a function of the one or more 3rd party references.

10. The method of claim 9, wherein retrieving the one or more 3rd party references comprises retrieving the one or more 3rd party references from at least one of a 3rd party data aggregator, a local hardware resource of the provider computing device, or a software application of the provider computing device.

11. The method of claim 1, further comprising retrieving, by the provider computing device, one or more 3rd party references, wherein identifying the behavioral pattern of the plurality of behavioral patterns for the user further comprises identifying the behavioral pattern as a function of at least one of the one or more 3rd party references.

12. The method of claim 11, wherein retrieving the one or more 3rd party references comprises retrieving the one or more 3rd party references from at least one of a 3rd party data aggregator, a local hardware resource of the provider computing device, or a software application of the provider computing device.

13. A provider computing device for predicting availability status changes, the provider computing device comprising:

one or more computer-readable media comprising instructions; and

one or more processors coupled with the one or more computer-readable media and configured to execute the instructions to;

retrieve a present availability status information for a user of an instant messenger (IM) application managed by the provider computing device, wherein the present availability status information includes at least a present availability status and a timestamp usable to identify a present date and time the present availability status was retrieved;

identify a behavioral pattern of a plurality of behavioral patterns for the user as a function of the present availability status;

determine a predicted next availability status information based on the identified behavioral pattern for the user, wherein the predicted next availability status information includes at least a predicted availability status change event and a timestamp useable to identify a next date and time of the availability status; and

create a predicted availability status string based on the predicted next availability status information.

14. The provider computing device of claim 13, wherein the one or more processors are further configured to execute the instructions to retrieve one or more present data references, wherein to identify the behavioral pattern comprises comparing the present availability status and one or more of the present data elements to a corresponding availability status and data elements of the behavioral pattern.

15. The provider computing device of claim 13, wherein the one or more processors are further configured to execute the instructions to update the present availability status in the IM application to the predicted availability status string.

16. The provider computing device of claim 13, wherein to create the predicted availability status string comprises to (i) determine a predicted availability status, (ii) determine a predicted availability status update time, and (iii) concatenate text corresponding to the predicted availability status and the predicted availability status update time to form the predicted availability status string.

17. The provider computing device of claim 13, wherein the one or more processors are further configured to execute the instructions to:

receive an availability status update, wherein the availability status update indicates an availability status change by the user and includes availability status information; and

create a behavioral pattern as a function of at least a portion of the availability status information, wherein the behavioral pattern includes at least a portion of the availability status information.

18. The provider computing device of claim 17, wherein the one or more processors are further configured to execute the instructions to assign a confidence level of the behavioral pattern.

19. The provider computing device of claim 18, wherein to assign the confidence level comprises to assign the confidence level as a function of a weighted value associated with one or more data elements of the availability status information.

20. The provider computing device of claim 16, wherein the availability status information includes a present availability status, a timestamp usable to identify a date and time the availability status update was performed by the user.

21. The provider computing device of claim 16, wherein the one or more processors are further configured to execute the instructions to retrieve one or more 3rd party references, wherein to create the behavioral pattern further comprises creating the behavioral pattern as a function of the one or more 3rd party references.

22. The provider computing device of claim 21, wherein to retrieve the one or more 3rd party references comprises retrieving the one or more 3rd party references from at least one of a 3rd party data aggregator, a local hardware resource of the provider computing device, or a software application of the provider computing device.

23. The provider computing device of claim 13, wherein the one or more processors are further configured to retrieve one or more 3rd party references, wherein to identify the behavioral pattern of the plurality of behavioral patterns for the user further comprises to identify the behavioral pattern as a function of at least one of the one or more 3rd party references.

24. The provider computing device of claim 23, wherein to retrieve the one or more 3rd party references comprises to retrieve the one or more 3rd party references from at least one of a 3rd party data aggregator, a local hardware resource of the provider computing device, or a software application of the provider computing device.