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

• US 6,947,935 B1
• Filed: 04/04/2001
• Issued: 09/20/2005
• 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.
• Primary Examiner(s): Alam, Shahid
• Assistant Examiner(s): Ehichioya, Fred I.
• Application Number:
  US09/825,800
• Time in Patent Office: 1,630 Days
• Field of Search: 707/1, 707/2, 707/7, 707/10, 707/100, 707/104.1, 707/200, 707/3, 707/5, 707/102, 705/10, 705/14, 705/27, 705/30, 706/12, 709/204, 709/217, 348/1, 348/734, 455/5, 725/50
• US Class Current: 1/1
• 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-independent 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 for storing information locally that is received from an information delivery system for viewing at a local system, comprising:

a database system that logs selections of previously viewed information at a local system received from an information delivery system;

an inference system trained by the log selections of previously viewed information and that assigns values to selections in a recommendation list based on the previously viewed information, the inference system employs collaborative filtering techniques on a temporal history of the previously viewed information to assign values to selections in the recommendation list from a higher probability to a lower probability that a user of the system would prefer to view information corresponding to an available selection; and

a local storage system that stores selection information corresponding to the assigned values of the selections.

2. The system of claim 1, the previously viewed information is time stamped by event type and the inference system is based on a single collaborative filtering model being trained according to time intervals that has been viewed.

3. The system of claim 1, the inference system being based on a single collaborative filtering model being trained according to time intervals that has been viewed.

4. The system of claim 1, the viewed information is time stamped by event occurrence and the inference system is based on a single collaborative filtering model trained according to time intervals that the information has been viewed.

5. The system of claim 1, the inference system receives further attributes of other systems and utilizes these attributes during training of the inference system.

6. The system of claim 1, the inference system receives attributes of other systems and utilizes these attributes during training of the inference system.

7. The system of claim 1, the local storage system assigns sorting values to the selections based on a set of the selection.

8. The system of claim 1, the local storage system dynamically adjusts the value of the selections based on a set of cache retention policies.

9. The system of claim 8, the local storage system receives new selections with assigned values and dynamically adjusts location of the stored information and new information based on the new selections in the local storage system, wherein information is aged out of the local storage system based on an expected value density of each selection.

10. The system of claim 9, wherein aged out information is reduced in size and quality by compressing the information and stored on the local storage system based on the likelihood that the user will view the information prior to removing the information from the local storage system.

11. The system of claim 9, the expected value density of a selection is determined based on the assigned value of the selection divided by the size of the selection.

12. The system of claim 11, wherein selections are grouped by class types with different cache retention policies for aging the assigned values of selections within different class types.

13. A method for determining user preferred content to be stored in a local storage system having definite limits, the user preferred content being received from an information delivery system for viewing at a local system, comprising:

receiving a list of available selections;

assigning a value to each of the available selections comprising employing collaborative filtering techniques on a temporal history of previously viewed information and the list of available selections to determine a higher probability to a lower probability that a system user would prefer to view information corresponding to an available selection;

storing information relating to a corresponding selection in the local storage system for the available selections based on value of the selection until the limits of the local storage system is obtained;

dynamically adjusting the values of the available selections based on at least one cache retention policy; and

removing information from the storage medium as its value causes it to fall outside the limits of the storage medium.

14. The method of claim 13, the step of assigning values to available selections further comprising considering attributes of at least one system user and utilizing these attributes in assigning values to selections in the available selection list.

15. The method of claim 13, the step of assigning values to available selections further comprising considering attributes of other systems and utilizing these attributes in providing global values to a list of available selections to a cluster of systems based on the temporal viewing history of the systems of the cluster.

16. The method of claim 13, further comprising a step of determining an expected value density of each selection by dividing the value of the selection by the size of the selection for each selection stored in the local storage system.

17. The method of claim 13, the at least one cache retention policy being a value aging rule set such that the assigned values are dynamically adjusted based on the time that the selection resides in the local storage system.

18. The method of claim 17, further comprising receiving new selections with assigned values and dynamically adjusting the location of the stored information and new information in the local storage system wherein information is aged out of the local storage system having lower values that falls outside the limits of the local storage system.

19. The method of claim 17, the at least one cache retention policy comprising a plurality of cache retention policies grouped by class types with different cache retention policies for aging the assigned values of selections within different class types.

20. The method of claim 13, the local storage system comprising a first portion for storing user defined selections to be stored, a second portion for defining live show selection to be stored and a third portion for defining general selections to be stored.

21. The method of claim 20, further comprising recording programs that are currently in progress at a given time period for selective viewing of at least one of the programs from a beginning of a program in the second portion.

22. A system for determining user preferred content to be stored in a local storage system having definite limits, the user preferred content being received from an information delivery system for viewing at a local system, comprising:

means for receiving a list of available selections;

means for assigning a value to each of the available selections adapted to employ collaborative filtering techniques on a temporal history of previously viewed information and the list of available selections to determine a higher probability to a lower probability that a system user would prefer to view information corresponding to an available selection;

means for storing information relating to a corresponding selection in the local storage system for the available selections based on the assigned value until the limits of the local storage system is obtained;

means for dynamically adjusting the values of the available selections based on a utility of the selection; and

means for removing information from the storage medium as its value causes it to fall outside the limits of the storage medium.

23. The system of claim 22, further comprising means for determining an expected density value of selections by dividing the value of the selection by the size of the selection for each selection stored in the local storage system.

24. The system of claim 22, the utility of the selection being based on the time that the selection resides in the local storage system.

25. The system of claim 22, the available selections being television programs and the local system being a television.

26. The system of claim 25, further comprising means for recording television programs that are currently in progress at a given time period for selective viewing of at least one of the programs from a beginning point.