Training, inference and user interface for guiding the caching of media content on local stores

• US 20050210530A1
• Filed: 05/09/2005
• Published: 09/22/2005
• Est. Priority Date: 04/04/2001
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Wong, Curtis G., Horvitz, Eric J., Ozer, Stuart, Kadie, Carl M.
• Granted Patent: US 7,451,151 B2
• Time in Patent Office: Days
• US Class Current: 725/134
• CPC Class Codes: H04N 21/4147 PVR [Personal Video Recorder] H04N5/76 takes precedence ; arrangements for broadcast with accumulation-type receivers H04H20/40
• CPC Class Codes: H04N 21/4331 Caching operations, e.g. of an advertisement for later insertion during playback
• CPC Class Codes: H04N 21/4335 Housekeeping operations, e.g. prioritizing content for deletion because of storage space restrictions storage management, e.g. defragmentation G06F3/0604; unloading stored programs G06F9/445; storage management in file systems G06F16/10; buffering arrangements in a network node or in an end terminal in packet networks H04L49/90
• CPC Class Codes: H04N 21/44008 involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/466 Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/47 End-user applications
• CPC Class Codes: H04N 21/4755 for defining user preferences, e.g. favourite actors or genre retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/482 End-user interface for program selection broadcast systems using EPGs H04H60/72
• CPC Class Codes: H04N 5/76 Television signal recording
• CPC Class Codes: H04N 5/765 Interface circuits between an apparatus for recording and another apparatus
• CPC Class Codes: H04N 5/775 between a recording apparatus and a television receiver
• CPC Class Codes: H04N 7/163 by receiver means only
• CPC Class Codes: Y10S 707/99931 Database or file accessing
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99937 Sorting
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

The present invention is related to a system and method of caching data employing probabilistic predictive techniques. The system and method has particular application to multimedia systems for providing local storage of a subset of available viewing selections by assigning a value to a selection and retaining selections in the cache depending on the value and size of the selection. The value assigned to an item can represent the time-dependent likelihood that a user will review an item at some time in the future. An initial value of an item can be based on the user'"'"'s viewing habits, the user'"'"'s viewing habit over particular time segment (e.g., early morning, late morning, early afternoon, late afternoon, primetime, late night) and/or viewing habits of a group of user'"'"'s during a particular time segment. A value assigned to a selection dynamically changes according to a set of cache retention policies, where the value can be time-dependent functions that decay based on the class of the item, as determined by inference about the class or via a label associated with the item. A selections value may be reduced as the selection ages because a user is less likely to view the selection over time. Additionally, a value of a selection may change based on changes on a user'"'"'s viewing habits, changes in time segments or a user'"'"'s modification of the cache retention policies.

Claims

1. A system that facilitates utilization of local storage space, comprising:

a component that logs information relating to historical access of items; and

an inference component that employs the log information in a utility-based analysis in connection with selectively storing to and/or deleting items from a local storage space.

2. The system of claim 1, the inference component dynamically computes changing value densities across a subset of the items to at least one of:

repack, recompress, and replace old items with new items.

3. The system of claim 2, the utility analysis considers decay rates per respective volatility of content relating to the subset of items.

4. The system of claim 1, the utility analysis computes a likelihood that an item will be accessed given certain evidence.

5. The system of claim 1, the items comprise media.

6. The system of claim 1, the items comprise electronic documents.

7. The system of claim 1, the utility analysis comprising employment of parametric functions to assess value of a respective item.

8. The system of claim 1, further comprising a model of the probability an item will be accessed.

9. The system of claim 8, the model is a statistical model.

10. The system of claim 1, the inference component employs collaborative filtering techniques on the historical access of items to assign values to selections in a recommended list from a higher probability to a lower probability that a user will access an item.

11. A system that facilitates memory utilization, comprising:

means for storing logging information relating to historical access of items;

means for appointing respective values to a subset of the items based on access thereof; and

means for applying the values in a value density-based utility analysis in connection with at least one of;

replacement, retention, and compression of the subset of items to a memory.

12. A method that facilitates utilization of local storage space, comprising:

employing log information relating to historical access of items; and

utilizing an inference component that uses the log information in a utility-based analysis in connection with selectively performing one or more of storing to and deleting items from a local storage space.

13. The method of claim 12, utilizing the inference component to dynamically compute changing value densities across a subset of the items to at least one of:

repack, recompress, and replace old items with new items.

14. The method of claim 12, further comprising computing tenability that an item will be accessed given certain evidence.

15. The method of claim 12, further comprising scrutinizing decay rates per respective volatility of content relating to a subset of items.

16. The method of claim 12, further comprising employing at least one parametric function to assess value of a respective item.

17. The method of claims 12, further comprising utilizing a model of a probability an item will be accessed.

18. The method of claim 12, the items comprise media.

19. The method of claim 12, the items comprise electronic documents.

20. The method of claim 12, employing a collaborative filtering model based on the historical access of items to train the inference component.