Methods for assisting computer users performing multiple tasks

• US 7,565,340 B2
• Filed: 05/30/2006
• Issued: 07/21/2009
• Est. Priority Date: 01/09/2006
• Status: Active Grant

• Current Assignee: Oregon State University (Oregon University System)
• Original Assignee: Oregon State University (Oregon University System)
• Inventors: Herlocker, Jonathan L., Dietterich, Thomas G.
• Primary Examiner(s): Holmes; Michael B
• Application Number:
  US11/443,503
• Publication Number:
  US 20070162907A1
• Time in Patent Office: 1,148 Days
• Field of Search: None
• US Class Current: 706/52
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

A method for assisting multi-tasking computer users includes receiving from a user a specification of a task being performed by the user or an indication of completion of a task, collecting state changes in multiple executing programs, predicting a current task being performed by the user based on a recent state change event, a past specification of a task being performed by the user, and past events and associated tasks. Based on the predicted current task, user interface elements in multiple executing programs are adapted to facilitate performance of the task. The method may also allow a user to specify a new task based on a task template derived from a completed task to facilitate completion of the new task. The task templates also may be shared among users, and active tasks may also be team tasks shared among users.

Claims

1. A computer-implemented method for assisting a user performing multiple tasks in a computing environment, the method comprising:

collecting by a computer system event records originating from multiple executing programs on the computer system, wherein each of the event records represents a state change in one of the multiple programs;

receiving by the computer system from the user a specification of a task being performed by the user;

predicting by the computer system a current task being performed by the user based on evidence including;

i) a most recent event record, ii) a most recent specification received from the user of a task being performed by the user, and iii) past event records and associated task identifiers stored in a database; and

adapting and displaying by the computer system executing user interface elements in multiple executing programs on the computer system based on the predicted current task;

wherein the predicting comprises using machine learning to predict a most probable current task by combining current and recent evidence with past associations between events and tasks.

2. The method of claim 1 further comprising:

assigning to the most recent event record a task identifier associated with the predicted current task; and

storing the most recent event record and the task identifier in the database.

3. The method of claim 1 wherein the machine learning is a hybrid machine learning comprising applying a learned density estimator to estimate an extent to which current evidence is similar to past evidence, applying the estimate to decide whether to make a prediction, and applying a machine learning method to predict the current task.

4. The method of claim 1 wherein the collecting comprises monitoring state changes in the multiple programs, selecting a subset of the monitored state changes, and generating event records representing the selected subset.

5. The method of claim 1 wherein at least one of the events represents a state change in one of the programs resulting from user interaction.

6. The method of claim 1 wherein at least one of the events represents a state change in one of the programs resulting from automated processes.

7. The method of claim 1 wherein at least one of the events represents a state change in one of the programs resulting from network communications.

8. The method of claim 1 wherein at least one of the events represents a state change in one of the programs resulting from user feedback correcting predicted associations between tasks and resources.

9. The method of claim 1 wherein adapting executing user interface elements based on the predicted current task comprises displaying the predicted current task.

10. The method of claim 1 wherein adapting executing user interface elements based on the predicted current task comprises displaying a resource list comprising resources associated with the predicted current task.

11. The method of claim 10 wherein the resource list comprises folders.

12. The method of claim 10 wherein the resource list is ranked by a likelihood that the resources would be valuable to the user given a current task.

13. The method of claim 1 wherein adapting executing user interface elements based on the predicted current task comprises hiding user interface elements that are not associated with the current task.

14. The method of claim 1 wherein adapting executing user interface elements based on the predicted current task comprises visually emphasizing user interface elements that are associated with the current task.

15. The method of claim 1 wherein adapting executing user interface elements based on the predicted current task comprises visually de-emphasizing user interface elements that are not associated with the current task.

16. The method of claim 1 wherein the executing user interface elements comprise a menu of recently used resources.

17. The method of claim 1 wherein the executing user interface elements comprise a menu of recently used tasks.

18. The method of claim 1 further comprising displaying a data-flow view of a task selected by the user, wherein the data-flow view comprises representations of resources associated with the task and representations of movement of data between the resources.

19. The method of claim 1 further comprising receiving from the user a specification of metadata associated with the task being performed by the user.

20. The method of claim 19 wherein the metadata includes relationships between tasks.

21. The method of claim 1 further comprising receiving from the user an indication of completion of a task.

22. The method of claim 1 wherein the evidence further includes prior indications received from the user of completed tasks.

23. The method of claim 1 wherein the evidence further includes a time elapsed since the most recent specification of the task being performed by the user was received from the user.

24. The method of claim 1 wherein the evidence further includes keywords contained in a resource associated with the most recent event record.

25. The method of claim 1 wherein the evidence further includes previous tasks associated with a resource associated with the most recent event record.

26. The method of claim 1 further comprising defining a new task based on a task template derived from at least one completed task such that resources associated with the completed task are associated with the new task.

27. The method of claim 1 further comprising building a task template by analyzing a “

used as a template for”

hierarchy to identify resources likely to be reused and resources not likely to be be reused.

28. The method of claim 1 further comprising importing a task template derived from at least one task performed by another user.

29. The method of claim 1 further comprising exporting a task template derived from a task selected by the user.

30. The method of claim 1 further comprising assigning control of a task to another user.

31. The method of claim 1 wherein the current task is a team task and the past event records comprise event records associated with both the user and other users.

32. The method of claim 1 wherein adapting existing user interface elements comprises restoring user interface windows to a state recorded when the task was last worked on by the user.

33. The method of claim 32 wherein restoring user interface windows to the state includes opening resource windows.

34. The method of claim 33 wherein restoring user interface windows to the state includes setting a view of the opened resource windows to a subpart of the resource window that was most recently selected by the user.

35. The method of claim 33 wherein restoring the user interface windows to the state includes setting a size and position of a user interface window.

36. The method of claim 1 wherein at least one of the events represents an arrival of a new email message.

37. The method of claim 36 further comprising notifying the user if the new email is related to the current task.

38. The method of claim 36 further comprising using machine learning to predict, for a new email message, a ranked list of tasks that the new email message is likely associated with.

39. The method of claim 38 wherein Bayesian networks are used in the computational model of the machine learning.

40. The method of claim 1 further comprising sharing a task with another user.

41. A computer-implemented method for assisting a user performing multiple tasks in a computing environment, the method comprising:

collecting by a computer system event records originating from multiple executing programs on the computer system, wherein each of the event records represents a state change in one of the multiple programs;

receiving by the computer system from the user a specification of a task being performed by the user;

predicting by the computer system a current task being performed by the user based on evidence including;

i) a most recent event record, ii) a most recent specification received from the user of a task being performed by the user, and iii) past event records and associated task identifiers stored in a database; and

adapting and displaying by the computer system executing user interface elements in multiple executing programs on the computer system based on the predicted current task;

wherein the evidence further includes explicit instructions from the user to associate a specified resource with a specified task.

42. A computer system comprising:

a computer interface comprising input devices generating user input from interactions of a user with multiple executing programs on the computer system;

an event collector that collects event records originating from the multiple executing programs on the computer system, wherein each of the event records represents a state change in one of the multiple executing programs;

a task predictor that predicts a current task being performed by the user based on evidence including;

i) a most recent event record, ii) a most recent specification received from the user of a task being performed by the user, and iii) past event records and associated task identifiers stored in a database; and

an adaptation component that adapts executing user interface elements in the multiple executing programs based on the predicted current task such that the user interface elements assist the user in navigating to resources associated with the predicted current task;

wherein the task predictor uses machine learning to predict a most probable current task by combining current and recent evidence with past associations between events and tasks;

wherein the computer interface comprises a display that displays the adapted executing user interface elements to the user to assist the user in performing multiple tasks on the computer system.