Window Grouping

• US 20080005693A1
• Filed: 09/27/2006
• Published: 01/03/2008
• Est. Priority Date: 06/30/2006
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Thakkar, Chintan S., Surendran, Arungunram C., Oliver, Nuria M., Smith, Gregory R.
• Granted Patent: US 7,676,761 B2
• Time in Patent Office: Days
• US Class Current: 715/781
• CPC Class Codes: G06F 11/3003 Monitoring arrangements specially adapted to the computing system or computing system component being monitored
• CPC Class Codes: G06F 11/3414 Workload generation, e.g. scripts, playback
• CPC Class Codes: G06F 11/3447 Performance evaluation by modeling
• CPC Class Codes: G06F 11/3476 Data logging G06F11/14, G06F11/2205 take precedence
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 2201/86 Event-based monitoring
• CPC Class Codes: G06F 2209/543 Local
• CPC Class Codes: G06F 2209/545 Gui
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 3/04845 for image manipulation, e.g. dragging, rotation, expansion or change of colour
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 9/449 Object-oriented method invocation or resolution
• CPC Class Codes: G06F 9/451 Execution arrangements for user interfaces
• CPC Class Codes: G06F 9/542 Event management; Broadcasting; Multicasting; Notifications

Abstract

A framework is provided for obtaining window information. The window information can be applied to different assignment models to assign windows to different groups. A group may correspond to a task being performed by a user. The window information can be semantic or temporal information captured as window events and properties of windows whose events are captured. Temporal information can be information about switches between windows. Semantic information can be window titles. Temporal information, semantic information, or both, can be used to assign windows to groups.

Claims

1. One or more computer readable volatile or nonvolatile computer readable media storing a window analysis framework for receiving information about a group of windows and assigning the windows to groups, the task analysis framework comprising:

a collecting unit for collecting window events associated with the windows;

a window-event model representing the windows and using the window events to model states of the windows, where the window-event model represents different states of the windows according to different sets of the window events; and

an assignment unit requesting states of the windows from the window model and assigning various of the windows to various different groups by applying the states to a first association model that models how windows in general are associated.

2. One or more computer readable media according to claim 1, wherein the collecting unit can collect window events for arbitrary application programs without requiring the application programs to be modified to facilitate the collecting.

3. One or more computer readable media according to claim 1, wherein the assignment unit further comprises a second association model that models, differently than the first association model, how windows in general are associated.

4. One or more computer readable media according to claim 1, wherein the first association model models associations between windows based on switches of focus between windows.

5. One or more computer readable media according to claim 1, wherein the first association model models how windows in general are associated based on titles of windows.

6. One or more computer readable media according to claim 3, wherein the first and second association models model different time spans of window states.

7. One or more computer readable media according to claim 6, where windows are assigned to groups based on both the first and second association models.

8. A method of automatically determining groups of windows, where the windows are managed by a window managing system, where the window managing system maintains representations of respective windows, and where each representation includes a title of its window for displaying in a title bar of the window, the method comprising:

analyzing the titles of the windows to determine semantic associations between the titles; and

associating the windows based on the associations between their titles.

9. A method according to claim 8, wherein the analyzing the titles comprises determining clusters of keywords in the titles and associating the windows based on the clusters.

10. A method according to claim 8, wherein the analyzing the titles comprises preprocessing the titles, extracting features from the preprocessed titles, and clustering the features in accordance with a probabilistic latent semantic indexing (PLSI) model, and determining the associations between the titles bases on the clustered features of the titles.

11. A method according to claim 10, wherein the preprocessing comprises filtering application-specific words from the titles.

12. A method according to claim 8, further comprising determining temporal relations between windows associating the windows based further on the temporal relations between the windows.

13. A method according to claim 12, wherein the temporal relations comprise switches of focus between windows.

14. A method according to claim 12, wherein the temporal relations comprise display relations between windows.

15. A method according to claim 14, wherein the display relations comprise co-visibility of windows.

16. One or more devices configured to perform a process, the process comprising:

receiving window events associated with different windows, the window events indicating changes in how the windows are displayed on a display;

using the window events to construct a model of changing display states of windows over a period of time; and

clustering the windows based on the modeled display states.

17. One or more devices according to claim 16, wherein the changes in how the windows are displayed comprise switches between windows, and the received events indicate the switches between the windows.

18. One or more devices configured according to claim 16, wherein the window events comprise window events passed between application programs and a window management system that manages windows for arbitrary application programs.

19. One or more devices configured according to claim 16, wherein the display states comprise active window states, whereby the model reflects switches between windows over time, and where the windows are grouped based on frequencies of switches between windows.

20. One or more devices configured according to claim 16, wherein the process further comprises obtaining titles of the windows and the clustering is based further on the titles of the windows.

21. One or more devices configured according to claim 16, wherein the modeled changing display states indicate changes in displayed sizes and/or locations of windows and co-presence on the display.