Organizing a collection of objects

• US 20050027712A1
• Filed: 07/31/2003
• Published: 02/03/2005
• Est. Priority Date: 07/31/2003
• Status: Active Grant

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Gargi, Ullas, Tretter, Daniel R.
• Granted Patent: US 7,747,625 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/54 Browsing; Visualisation therefor
• CPC Class Codes: G06F 16/58 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually

Abstract

Systems and methods of organizing a collection of objects are described. In one aspect, a sequence of objects is segmented into object clusters based on: comparisons of successive object intervals to weighted measures of cluster extent; and comparisons of successive object intervals to weighted measures of cluster object density. In another aspect, objects from the collection are segmented into clusters. Context-related meta data associated with the objects and parsable into multiple levels of a name hierarchy is extracted. Names are assigned to clusters based on the extracted context-related meta data corresponding to a level of the name hierarchy selected to distinguish segmented clusters from one another. In another aspect, a sequence of objects that are segmented into clusters is accessed. Each cluster includes multiple objects arranged in a respective sequence in accordance with context-related meta data associated with the objects. At least two constituent objects representative of beginning and ending instances in the corresponding object sequence are selected for each object cluster. The selected representative objects of each cluster are graphically presented on a screen.

Claims

1. A method of organizing a collection of objects, comprising:

segmenting a sequence of objects into object clusters based on

comparisons of successive object intervals to weighted measures of cluster extent, and

comparisons of successive object intervals to weighted measures of cluster object density.

2. The method of claim 1, wherein measures of cluster extent correspond to spans of recorded generation times over which objects in the clusters respectively extend.

3. The method of claim 1, wherein measures of cluster extent correspond to spans of recorded generation locations over which objects in the clusters respectively extend.

4. The method of claim 1, wherein measures of cluster object density correspond to average measures of time intervals between successive objects in the clusters.

5. The method of claim 1, wherein measures of cluster object density correspond to averages of space intervals between successive objects in the clusters.

6. The method of claim 1, wherein segmenting the object sequence comprises merging consecutive objects into a given cluster until an interval between a candidate object and a preceding object in the given cluster exceeds a threshold computed based on a weighted measure of the extent of the given cluster, at which point a new cluster is initiated with the candidate object.

7. The method of claim 1, wherein segmenting the object sequence comprises merging consecutive objects into a given cluster until an interval between a candidate object and a preceding object in the given cluster exceeds a threshold computed based on a weighted measure of object density in the given cluster, at which point a new cluster is initiated with the candidate object.

8. The method of claim 1, wherein weights applied to the measures of cluster extent decrease with increasing cluster size.

9. The method of claim 1, wherein weights applied to the measures of cluster object density decrease with increasing cluster size.

10. The method of claim 1, further comprising customizing at least one of the weights applied to the measures of cluster extent based on an analysis of objects in the cluster.

11. The method of claim 10, wherein at least one weight is customized based on a fractal dimension estimate for context-related meta data associated with objects in the collection.

12. The method of claim 1, further comprising customizing at least one of the weights applied to the measures of cluster object density based on an analysis of objects in the cluster.

13. The method of claim 12, wherein at least one weight is customized based on a fractal dimension estimate for context-related meta data associated with objects in the collection.

14. The method of claim 1, wherein segmenting the sequence of objects further comprises comparing object density of a given cluster including a candidate object with a weighted measure of object density for the given cluster without the candidate object.

15. The method of claim 14, wherein measures of cluster object density correspond to averages of time intervals between successive objects in the clusters.

16. The method of claim 14, wherein measures of cluster object density correspond to averages of space intervals between successive objects in the clusters.

17. The method of claim 14, wherein the measure of object density corresponds to a moving average density of objects.

18. The method of claim 14, wherein weights applied to the measures of cluster object density decrease with increasing cluster size.

19. The method of claim 1, wherein objects are segmented beginning at a first end of the object sequence.

20. The method of claim 19, wherein objects are further segmented beginning at a second end of the object sequence.

21. The method of claim 1, wherein the sequence to be segmented includes objects of the following types:

text, audio, graphics, still images, video and business events.

22. A system of organizing a collection of objects, comprising:

a segmentation engine operable to segment a sequence of objects into object clusters based on

comparisons of successive object intervals to weighted measures of cluster extent, and

comparisons of successive object intervals to weighted measures of cluster object density.

23. A method of organizing a collection of objects, comprising:

segmenting objects from the collection into clusters;

extracting context-related meta data associated with the objects and parsable into multiple levels of a name hierarchy; and

assigning names to clusters based on the extracted context-related meta data corresponding to a level of the name hierarchy selected to distinguish segmented clusters from one another.

24. The method of claim 23, wherein names are assigned to clusters based on the extracted context-related meta data corresponding to a highest level of the name hierarchy that distinguishes clusters from each other.

25. The method of claim 23, wherein the context-related meta data corresponds to object generation times.

26. The method of claim 23, wherein the context-related meta data corresponds to object generation locations.

27. The method of claim 26, wherein the context-related meta data corresponds to recorded information relating to country, city, and state of object generation.

28. The method of claim 23, wherein the context-related meta data corresponds to both object generation times and object generation locations.

29. The method of claim 23, further comprising automatically naming objects in a given cluster based on the name assigned to the given cluster.

30. The method of claim 29, wherein the objects in the given cluster are named automatically in accordance with a chronological ordering of the objects in the given cluster.

31. The method of claim 29, further comprising storing objects in the given cluster in a tree structure organized by cluster and labeled in accordance with the assigned names.

32. A system of organizing a collection of objects, comprising:

a segmentation engine operable to segment objects from the collection into clusters; and

a naming engine operable to extract context-related meta data associated with the objects and parsable into multiple levels of a name hierarchy, and assign names to each cluster based on the extracted context-related meta data corresponding to a level of the name hierarchy selected to distinguish segmented clusters from one another.

33. A method of organizing a collection of objects, comprising:

accessing a sequence of objects segmented into clusters each including multiple objects arranged in a respective sequence in accordance with context-related meta data associated with the objects;

selecting for each object cluster at least two constituent objects representative of beginning and ending instances in the corresponding object sequence; and

graphically presenting the selected representative objects of each cluster.

34. The method of claim 33, further comprising graphically presenting a stack of partially overlapping images representative of multiple objects in a cluster in response to user input.

35. The method of claim 34, further comprising revealing an increased portion of a given representative image in the stack in response to detection of a user-controlled display icon positioned over the given representative image.

36. The method of claim 33, wherein the representative objects of any given cluster are presented closer to each other than to the representative objects of other clusters.

37. The method of claim 33, further comprising merging objects of one cluster into an adjacent cluster in response to user input.

38. The method of claim 37, wherein objects of one cluster are merged into an adjacent cluster in response to dragging and dropping of the objects to be merged.

39. The method of claim 37, wherein the objects of the one cluster are merged into the adjacent cluster in response to user selection of an icon for merging the clusters.

40. The method of claim 33, further comprising presenting a graphical representation of distributions of objects in the clusters.

41. The method of claim 40, wherein a object distribution for a given cluster is presented as object instances plotted along an axis corresponding to a scaled representation of the context-related extent spanned by the given cluster.

42. The method of claim 40, further comprising splitting a given cluster in response to user selection of a point in the representation of the object distribution presented for the given cluster.

43. The method of claim 40, further comprising automatically splitting a given cluster into two or more clusters in response to user input.

44. The method of claim 43, wherein the given cluster is automatically split into a user-selected number of sub-clusters.

45. The method of claim 43, wherein the given cluster is automatically split based on relative sizes of intervals between successive objects in the given cluster.

46. The method of claim 33, wherein the context-related meta data corresponds to object generation times.

47. The method of claim 33, wherein the context-related meta data corresponds to object generation locations.

48. The method of claim 33, wherein the segmented sequence includes objects of the following types:

text, audio, graphics, still images, video, and business events.

49. The method of claim 33, further comprising graphically presenting at least one link to an object of a cluster arranged in a sequence in accordance with time-related meta data in a calendar format.

50. The method of claim 33, further comprising graphically presenting at least one link to an object of a cluster arranged in a sequence in accordance with location-related meta data in a map format.

51. A system of organizing a collection of objects, comprising a user interface layout engine operable to:

access a sequence of objects from the collection segmented into clusters each including multiple objects arranged in a respective sequence in accordance with context-related meta data associated with the objects;

select for each object cluster at least two constituent objects representative of beginning and ending instances in the corresponding object sequence; and

graphically present the selected representative objects of each cluster on a screen with the representative objects of any given cluster presented closer to each other than to the representative objects of other clusters.