Action Prediction and Identification Temporal User Behavior

• US 20120123993A1
• Filed: 11/17/2010
• Published: 05/17/2012
• Est. Priority Date: 11/17/2010
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Wang, Gang, Chen, Weizhu, Chen, Zheng, Fan, Zhikai
• Granted Patent: US 8,412,665 B2
• Time in Patent Office: Days
• US Class Current: 706/52
• CPC Class Codes: G06F 16/3349 Reuse of stored results of previous queries
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/043 Distributed expert systems; Blackboards
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

User behavior modeling can include determining temporal- or time-based actions performed by various users. From the mined temporal-based user actions, future actions can be predicted. Certain implementations include providing information and/or services based on the predicted future actions. Some implementations, include providing relevant information, services, and/or goods regarding the predicted future action.

Claims

4-1. The method of claim 1, wherein the selecting from the population is based at least in part on an ensemble of models that include models for particular user behavior actions.

10. A computing device comprising:

one or more processors;

memory coupled to the processor and comprising a action-prediction module that is configured to cause the one or more processors to;

select, from a population of users, a group of users that perform particular time-based user actions; and

determine a subsequent action based at least in part upon the particular time-based user actions.

11. The computing device of claim 10, wherein the population of users is taken from a user log.

12. The computing device of claim 10, wherein the particular time-based user behavior actions is associated with a behavior model of an ensemble of behavior models.

13. The computing device of claim 10, wherein the action-prediction module is configured to select the population of users is based at least in part on online actions performed by the users of the population of users.

14. The computing device of claim 10, wherein the action-prediction module is further configured to selectively provide information regarding the group of uses to parties providing information regarding the subsequent action.

15. The computing device of claim 10, wherein the action-prediction module is further configured to determine which users of the group of users have performed the subsequent action and remove, from the group, the users that have performed the subsequent action.

16. The computing device of claim 10, wherein the action-prediction module is further configured to collect actions based at least in part on data provided by respective users on a browser toolbar.

17. A computer readable medium having computer readable instructions for performing steps comprising:

determining a population of online users performing particular time-based online and offline actions;

indexing the time-based actions of the online users;

grouping a set of users from the population of online users based at least in part on a behavior pattern of the time-based actions; and

predicting a subsequent action based at least in part on the behavior pattern.

18. The computer readable medium of claim 17, wherein the determining the population of online users includes tracking users search queries and web site visits.

19. The computer readable medium of claim 17, wherein the determining includes checking for a new user and predicting if the new user will perform the subsequent action.

20. The computer readable medium of claim 17, further comprising removing users that perform the subsequent action from the set of users.