Television system with aided user program searching

• US 9,788,030 B2
• Filed: 03/28/2014
• Issued: 10/10/2017
• Est. Priority Date: 07/17/1998
• Status: Expired due to Fees

• Current Assignee: UV Corporation (Adeia Inc.)
• Original Assignee: Rovi Guides, Inc. (Adeia Inc.)
• Inventors: Boyer, Franklin E., Demers, Timothy B.
• Primary Examiner(s): Kumar, Pankaj
• Assistant Examiner(s): Brown, Reuben M
• Application Number:
  US14/229,395
• Publication Number:
  US 20140298387A1
• Time in Patent Office: 1,292 Days
• Field of Search: 725 45, 725 46
• CPC Class Codes: H04N 21/251 Learning process for intelligent management, e.g. learning user preferences for recommending movies details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08
• CPC Class Codes: H04N 21/4312 involving specific graphical features, e.g. screen layout, special fonts or colors, blinking icons, highlights or animations
• CPC Class Codes: H04N 21/4314 for fitting data in a restricted space on the screen, e.g. EPG data in a rectangular grid
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• 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 21/454 Content or additional data filtering, e.g. blocking advertisements
• 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 21/4666 using neural networks, e.g. processing the feedback provided by the user
• 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 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/47 End-user applications
• CPC Class Codes: H04N 21/482 End-user interface for program selection broadcast systems using EPGs H04H60/72
• CPC Class Codes: H04N 21/4826 using recommendation lists, e.g. of programs or channels sorted out according to their score

Abstract

A system having an adaptive browse feature and an adaptive flip feature is provided. The adaptive browse and flip features may be selected to receive program viewing suggestions. The system may provide a suggestion by displaying an adaptive browse region or adaptive flip region including a program suggestion. The system identifies programs to suggest based on a user=s viewing activity. The system uses different algorithms that are user-selectable and user-adjustable to identify program suggestions. The system may query a program guide database to build a list of programs having attributes similar to the attributes of the current program or the last viewed program. The system may use an adaptive learning algorithm such as a neural network. The neural network may be trained by the program guide by monitoring user-viewing activity. Each algorithm may be personalized for multiple users.

Claims

1. A method for providing an adaptive flip function on user equipment, the method comprising:

receiving a first user selection selecting an attribute category of a plurality of attribute categories as a criterion for the adaptive flip function, the attribute category including one or more attribute values;

identifying a currently-accessed media asset of a user in a database of program listings comprising a plurality of media assets, each of the plurality of media assets being associated with respective one or more attribute values and an attribute category;

automatically identifying from the database of program listings, using processing circuitry, an attribute value of the currently-accessed media asset, the identified attribute value belonging to the selected attribute category;

determining that a subset of media assets of the plurality of media assets in the database of program listings have the identified attribute value belonging to the selected attribute category;

receiving a second user selection to flip to a next program;

selecting a first media asset of the subset of media assets;

tuning, using a tuner, to the first media asset of the subset of media assets; and

generating for display, using the processing circuitry, the first media asset of the subset of media assets based on receiving the second user selection.

2. The method of claim 1, wherein automatically identifying the attribute value of the selected attribute category comprises determining that the currently-accessed media asset of the user has been accessed by the user for a substantial portion of the media asset'"'"'s length.

3. The method of claim 1 further comprising allowing the user to set a minimum access duration, wherein automatically identifying the attribute value of the selected attribute category comprises determining that the currently-accessed media asset has been accessed by the user for at least the minimum access duration.

4. The method of claim 1, further comprising determining whether the user has selected at least one attribute category of title, duration, genre, channel, scheduled duration, rating, and content rating as a criterion for recommending media assets.

5. The method of claim 1, wherein the selected attribute category is associated with a weight, and wherein generating for display the identifier of the first media asset comprises recommending the first media asset of the subset of media assets, having one or more attribute values that best match the identified attribute value of the currently-accessed media asset, determined to be included within the selected attribute category based on the weight.

6. The method of claim 5 further comprising allowing the user to assign the weight associated with the selected attribute category.

7. The method of claim 1, wherein selecting the first media asset further comprises:

training a neural network using as training stimuli at least attribute values of the currently-accessed media asset belonging to the selected attribute category; and

applying attribute values associated with available media assets to the trained neural network to select the first media asset.

8. The method of claim 7 further comprising:

determining a time of access associated with the currently-accessed media asset; and

determining a length of access associated with the currently-accessed media asset, wherein the training stimuli includes the determined time of access and the determined length of access.

9. The method of claim 1, wherein determining that the subset of media assets have the identified attribute value comprises building a list of media asset identifiers associated with the subset of media assets from a media asset identifiers database stored at the processing circuitry.

10. The method of claim 1 further comprising generating for display a listing of identifiers of the subset of media assets.

11. A system for providing an adaptive flip function on user equipment, the system comprising:

user input interface circuitry configured to;

receive a first user selection selecting an attribute category of a plurality of attribute categories as a criterion for the adaptive flip function, the attribute category including one or more attribute values; and

processing circuitry configured to;

identify a currently-accessed media asset of a user in a database of program listings comprising a plurality of media assets, each of the plurality of media assets being associated with respective one or more attribute values and an attribute category;

automatically identify, from the database of program listings, an attribute value of the currently-accessed media asset, the identified attribute value belonging to the selected attribute category;

determine that a subset of media assets of the plurality of media assets in the database of program listings have the identified attribute value belonging to the selected attribute category;

receive a second user selection to flip to a next program;

select a first media asset of the subset of media assets;

tune, using a tuner, to the first media asset of the subset of media assets; and

generate for display the first media asset of the subset of media assets based on receiving the second user selection.

12. The system of claim 11, wherein the processing circuitry is further configured to determine that the currently-accessed media asset has been accessed by the user for a substantial portion of the media asset'"'"'s length.

13. The system of claim 11, wherein the processing circuitry is further configured to:

allow the user to set a minimum access duration, and

determine that the currently-accessed media asset has been accessed by the user for the minimum access duration.

14. The system of claim 11, wherein the user input interface circuitry is further configured to determine whether the user has selected at least one attribute category of title, duration, genre, channel, scheduled duration, rating, and content rating as a criterion for recommending media assets.

15. The system of claim 11, wherein the selected attribute category is associated with a weight, and wherein the processing circuitry is further configured to recommend the first media asset of the subset of media assets, having one or more attribute values that best match the identified attribute value of the currently-accessed media asset, determined to be included within the selected attribute category based on the weight.

16. The system of claim 15, wherein the processing circuitry is further configured to allow the user to assign the weight associated with the selected attribute category.

17. The system of claim 11, wherein the user processing circuitry is further configured to:

train a neural network using as training stimuli at least attribute values of the currently-accessed media asset belonging to the selected attribute category; and

apply attribute values associated with available media assets to the trained neural network to select the first media asset.

18. The system of claim 17, wherein the processing circuitry is further configured to:

determine a time of access associated with the currently-accessed media asset; and

determine a length of access associated with the currently-accessed media asset, wherein the training stimuli includes the determined time of access and the determined length of access.

19. The system of claim 11, wherein the processing circuitry is further configured to determine that the subset of media assets have the identified attribute value by building a list of media asset identifiers associated with the subset of media assets from a media asset identifiers database stored at the processing circuitry.

20. The system of claim 11, wherein the processing circuitry is further configured to generate for display a listing of identifiers of the subset of media assets.