Sensing, storing, indexing, and retrieving data leveraging measures of user activity, attention, and interest

• US 20070016553A1
• Filed: 06/29/2005
• Published: 01/18/2007
• Est. Priority Date: 06/29/2005
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Horvitz, Eric, Dumais, Susan
• Granted Patent: US 7,693,817 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: H04L 51/234 for tracking messages
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing

Abstract

Various components and processes are provided to enable data processing on multiple data types where aspects of the history of user activity, attention, interest, location, or other interaction with data is determined and employed to enhance information storage and access. In one particular aspect, a data manipulation system is provided. The system includes one or more data items that are associated with one or more tags and indicate at least one user'"'"'s interaction or activity with the data items. A manipulation tool that processes the data items to determine a subset of data items based at least in part on the user'"'"'s interaction with the data items. Methods are described for using the manipulation tool to weight terms in an index, to compress indexes, to influence the rank of items returned in a search, to generate additional queries for data items either automatically or with user direction, or for improved presentation of data items.

Claims

1. A data manipulation system, comprising:

one or more data items that are associated with one or more tags that indicate at least one user'"'"'s interaction with or attention to the data items; and

a manipulation tool that processes the data items to determine a subset of data items based at least in part on at least one user'"'"'s interaction with the data items.

2. The system of claim 1, the data items include documents, files, email messages, calendar appointment, web pages, sub-sections within the data items, or cross-item abstractions.

3. The system of claim 1, where the tags represent a location that a user last accessed an item, or represent a location history of times that a user has accessed or interacted with an item.

4. The system of claim 1, where the tag represents a represents a time a user last accessed an item, a total number of times that an item has been accessed, represents a frequency that an item has been accessed, within a period of time extending into the past, or represents a frequency that an item has been accessed, within one or more arbitrarily specified periods of time.

5. The system of claim 1, further comprising a component that encodes higher-order statistics of frequency of access over time.

6. The system of claim 1, further comprising a viewer that allows a user to retrieve items based on functions of one or more tags, a viewer that allows a user to sort or filter retrieved items based on functions of one or more tags, or a viewer that presents retrieved items based on functions of one or more tags.

7. The system of claim 1, further comprising tags that are probabilistic indications of activity or interest.

8. A computer readable medium having computer executable instructions stored thereon for executing the components of claim 1.

9. A method for analysis of user activities with data items, comprising:

automatically tagging a quantity or nature of interaction that data items have received from computer users; and

employing the tags to further process the data items in accordance with future data activities.

10. The method of claim 9, storing data within an attentional annotation associated with the data items in a separate database or within a data structure embedded in the data items.

11. The method of claim 10, the data items compose a computer readable storage medium.

12. The method of claim 11, the storage medium is a data item that includes text, graphics, and related data components.

13. The method of claim 9, further comprising providing indexing procedures that weight subcomponents of data items differently for retrieval depending on a status of annotations indicating attention or interaction with data items.

14. The method of claim 13, the indexing procedures overlook or delete information in data items depending on a status of annotations indicating attention or interaction with data items.

15. The method of claim 13, further comprising an index that is compressed by removing components that have not been attended to or interacted with by computer users.

16. The method of claim 15, further comprising an index that is compressed by removing components that are lesser attended to or interacted with components of data items.

17. The method of claim 15, further comprising providing a ranking score used for retrieval, to yield more weight to terms or objects that appear in sections of a data item that a user has attended to or has interacted with.

18. The method of claim 15, further comprising employing attentional annotations to automatically or semi-automatically generate queries based on regions that have been attended to or interacted within the past and/or present, or employing attentional annotations to provide differential access to items or differential display of items that have been attended to or interacted within the past and/or present.

19. A system for analyzing user activities with data items, comprising:

means for determining user activities with respect to one or more data items;

means for tagging the data items based at least in part on the user activities; and

means for storing or retrieving data based in part on the tagged data items.

20. The system of claim 19, further comprising means for encoding attentional annotations for the data items and means for encoding attentional annotations for subcomponents within the data items, where the annotations capture pointers or indications of each of the subcomponents and resulting user attention that has been received for the subcomponents.