Methods and systems for generating electronic program guides

• US 7,885,971 B2
• Filed: 02/25/2008
• Issued: 02/08/2011
• Est. Priority Date: 06/06/2002
• Status: Expired due to Term

• Current Assignee: Rovi Technologies Corporation (Adeia Inc.)
• Original Assignee: Microsoft Corporation
• Inventors: Marsh, David J.
• Primary Examiner(s): Fleurantin; Jean B
• Application Number:
  US12/036,988
• Publication Number:
  US 20080147650A1
• Time in Patent Office: 1,079 Days
• Field of Search: 707600-831, 725/9, 725/37, 725139-142, 725/46, 725/47, 725/153
• US Class Current: 707/770
• CPC Class Codes: G06F 16/48 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Various embodiments provide ways to collect, organize, and process metadata associated with particular instances of media content. Metadata can be collected from a variety of metadata providers and organized in individual content folders that can be provided to various client devices. The client devices can process the content folders to provide an electronic program guide for its users. Methods and systems can also make use of user-specific data to evaluate various media content and then make recommendations as to which content a user would most likely wish to experience. Such user-specific data can comprise one or more user preference files that contain information associated with individual users, and one or more significance files that hold values, for individual users, that can be used to determine the significance of any matches found between the user preference files and media content.

Claims

1. A system comprising:

a processor;

memory communicatively coupled to the processor; and

a recommendation engine, stored in the memory and executed on the processor, configured to;

receive data associated with and describing media content that is to be broadcast, the data associated with the media content being updated as additional information pertaining to the media content becomes available, the data associated with the media content including part of a content folder that contains metadata that describes the media content and one or more attributes associated with the media content that defines an individual associated with the media content;

receive data that defines user media content preferences;

provide, into individual content folders, one or more user content preference files that describe how well a particular piece of media content matches with the user media content preferences;

process the received data to calculate a score for individual pieces of media content that is to be broadcast; and

make recommendations to one or more users based upon the calculated scores for the individual pieces of media content.

2. The system of claim 1, wherein the recommendation engine is further configured to receive one or more significance files, each of the one or more significance files defining a relative importance of a plurality of attributes for which a match can be determined between the data associated with media content to be broadcast and the data that defines user media content preferences.

3. The system of claim 2, wherein the one or more significance files define a relative importance for different types of matches, at least one type of match comprising a partial match, and at least another type of match comprising a full match.

4. The system of claim 1, wherein:

the data associated with media content that is to be broadcast includes one or more content description files;

the data that defines user media content preferences includes one or more user preference files; and

the one or more content description files and the one or more user preference files each conform to a common schema.

5. The system of claim 1, wherein the data that defines user media content preferences comprises:

a first attribute of the one or more attributes that includes a character string that defines the individual associated with the media content; and

one or more attribute values, each particular attribute value corresponding to a particular one of the one or more attributes, each of the one or more attribute values defining a user'"'"'s preference for the particular one of the one or more attributes to which the particular attribute value corresponds, wherein a first attribute value of the one or more attribute values corresponds to the first attribute and includes a numerical value that indicates an extent to which a user prefers the individual.

6. The system of claim 1, wherein the data that defines user media content preferences includes one or more user preference files, each each user preference file including data that defines a particular user'"'"'s media content preferences, the one or more user preference files includes files that are:

global;

hierarchical;

tag-based; and

protected.

7. A method comprising:

receiving descriptive data associated with media content that is to be broadcast to a client device;

receiving data that defines user preferences that pertain to media content that is to be broadcast, the data that defines the user preferences comprising (1) at least one user preference file, the user preference file including a hierarchical, tag-based file that contains data that defines the user preferences, and (2) at least one significance file, the significance file including a hierarchical, tag-based file that contains data that defines a relative importance of a corresponding attribute for which a match can be determined between the media content and the user preferences, the user preference file being based at least in part on a user viewing log created by monitoring media content watched by the user, and the user preference file including one or more tag-encapsulated attributes that are associated with the media content and attribute values that define the user preferences, the attribute values including character strings that define individuals associated with the media content and character strings that define contexts that pertain to the individuals associated with the media content;

processing the received data to calculate a score for individual pieces of media content that are to be broadcast; and

based upon one or more calculated scores, making media content recommendations to one or more users of the client device.

8. The method of claim 7, wherein the user preference file comprises one or more tag-encapsulated attributes that are associated with the media content and attribute values that define the user preferences, the attribute values including positive and negative numerical values that indicate an extent to which a user prefers a particular attribute.

9. A system comprising:

a processor;

memory communicatively coupled to the processor; and

a recommendation engine, stored in the memory and executed on the processor, that is configured to;

process an XML content description file containing data associated with and describing media content that is to be broadcast to a client device;

process one or more XML user preference files containing data that defines user preferences for one or more users; and

process one or more XML significance files that define(s), for individual users, significance values that are used to calculate, along with the data in the XML user preference file(s), scores for individual pieces of media content that is to be broadcast, the recommendation engine further being configured to generate one or more reports that describe how the scores were calculated;

calculate, for each program or movie that is to be broadcast and each user, a score based upon the data contained in the XML content description file, the XML user preference file and the XML significance file, and make recommendations to one or more of the users based upon the calculated scores for the individual pieces of media content; and

calculate a relative score for each piece of media content, the relative score reflecting the media content'"'"'s score relative to scores for other media content, the relative score being computed based on each media content'"'"'s score and a high score attained for the individual pieces of media content.

10. The system of claim 9, wherein the recommendation engine is configured to enable an electronic program guide to be generated that contains individual user recommendations associated with individual pieces of media content.

11. The system of claim 9, wherein the recommendation engine is configured to provide data, for each piece of media content, into a user content preference file that describes, for that piece of the media content, how well the piece of media content matches up with an associated user preference file for an associated user.

12. The system of claim 9, wherein the recommendation engine is configured to calculate scores as the content description files are downloaded from a server.

13. The system of claim 9, wherein the recommendation engine is configured to calculate scores as the content description files are downloaded from a server, the recommendation engine being configured to calculate scores responsive to a user making a change to an associated user preference file.

14. The system of claim 9, wherein the recommendation engine is configured to sort calculated scores for each user and provide the sorted scores into a separate scores file for each user.

15. The system of claim 9, wherein the recommendation engine is configured to further calculate a relative score for each piece of media content, the relative score reflecting the media content'"'"'s score relative to scores for other media content.

16. The system of claim 9, wherein the recommendation engine is configured to further calculate a relative score for each piece of media content, the relative score reflecting the media content'"'"'s score relative to scores for other media content, the recommendation engine being configured to enable a visual display of the relative score for individual pieces of media content to be provided in an electronic program guide that is rendered.

17. The system of claim 9, wherein the recommendation engine is configured to further calculate a relative score for each piece of media content, the relative score reflecting the media content'"'"'s score relative to scores for other media content, the recommendation engine being configured to enable a threshold-based, visual display of the relative score for individual pieces of media content to be provided in an electronic program guide that is rendered.