Method and system for obtaining feedback for a content recommendation by various algorithms

• US 10,055,746 B1
• Filed: 06/21/2012
• Issued: 08/21/2018
• Est. Priority Date: 06/24/2011
• Status: Active Grant

• Current Assignee: DIRECTV, LLC (AT&T, Inc.)
• Original Assignee: The DIRECTV Group, Inc. (AT&T, Inc.)
• Inventors: Ling, Sui-Ky Ringo, Arsenault, Robert G., Pontual, Romulo, Kahn, Raynold M.
• Primary Examiner(s): Lewis, Jonathan V
• Application Number:
  US13/529,048
• Time in Patent Office: 2,252 Days
• Field of Search: None
• CPC Class Codes: G06F 11/3457 Performance evaluation by simulation
• CPC Class Codes: G06F 9/455 Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: H04L 67/08 specially adapted for terminal emulation, e.g. Telnet
• CPC Class Codes: H04L 67/131 Protocols for games, networked simulations or virtual reality
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

A simulation engine and method of operating a simulation system includes a plurality of user devices and an interface receiving user device data and a recommendation request from at least one the plurality of user devices. The system also includes a memory storing the user device data therein. The system also includes a recommendation engine that generates a content recommendation based on the user device data and an algorithm. The interface communicates the content recommendation to the user device. The recommendation engine receives a recommendation rating from at least one of the plurality of user devices and changes the algorithm in response to the recommendation rating. The user device receives a different recommendation of the changed algorithm within the simulation engine.

Claims

1. A method comprising:

communicating from a user device data through a network to a simulation engine comprising a web interface, a processor-based recommendation engine, a recommendation processing database and an algorithm file;

communicating a recommendation request from the user device to the web interface of the simulation engine through the network;

generating, via a simulation algorithm, a content recommendation at a recommendation engine of the simulation engine based on the data and in response to the recommendation request;

communicating the content recommendation to the user device through the network;

rating the simulation algorithm at the user device based on the content recommendation;

communicating the rating to the simulation engine through the network;

comparing the rating to a plurality of ratings of a plurality of algorithms using the recommendation processing database, wherein the plurality of algorithms include a second algorithm;

based on the comparing, selecting, from the algorithm file of the simulation engine and the plurality of algorithms, the second algorithm having a better rating than other ones of the simulation algorithm and the plurality of algorithms; and

changing the simulation algorithm at the simulation engine in response to the rating.

2. A method as recited in claim 1 further comprising generating a request identifier at the simulation engine in response to the recommendation request and wherein communicating the recommendation rating comprises communicating the recommendation rating and the request identifier to the simulation engine.

3. A method as recited in claim 1 wherein communicating data comprises communicating set top box data.

4. A method as recited in claim 1 wherein communicating data comprises communicating setting and list data comprising at least one of set top box playlist data, channel subscription data and channels blocked at the user device.

5. A method as recited in claim 1 wherein communicating data comprises communicating view tracking log data comprising at least one of a time of day that a program is watched, how long a program is watched and program recording deletion data.

6. A method as recited in claim 1 wherein communicating the content recommendation comprises communicating a program object identifier, a channel identifier and a program start time and wherein generating the content recommendation comprises generating the content recommendation based on the data, the simulation algorithm and program guide data.

7. A method as recited in claim 1 wherein communicating the content recommendation comprises communicating a plurality of content recommendations regularly watched and currently available for a time slot.

8. A method as recited in claim 1 wherein communicating the content recommendation comprises communicating a plurality of new content recommendations related to regularly watched programs for a time slot for currently available or upcoming programming.

9. A method as recited in claim 1 further comprising generating a screen display on a display associated with one of a plurality of user devices, wherein the screen display includes regularly watched programs for a time slot and new recommendations currently available or upcoming programming.

16. A method as recited in claim 1 wherein the content recommendation comprises a list of recommendations for a plurality of programs respectively corresponding to a plurality of channels.

10. A simulation engine in communication with a plurality of set top boxes, wherein the simulation engine comprises:

an algorithm file;

a web interface receiving, from at least one of the plurality of set top boxes and through a network, data and a recommendation request;

a memory storing the data therein; and

a processor-based recommendation engine originating, via a simulation algorithm, a content recommendation based on the data and in response to the recommendation request,

wherein said web interface communicates the content recommendation to the plurality of set top boxes; and

wherein said recommendation engine receives, based on the content recommendation, ratings of the simulation algorithm from the plurality of set top boxes and changes the simulation algorithm to a second algorithm from the algorithm file in response to the ratings from the plurality of set top boxes and generates different recommendations for the plurality of set top boxes using a recommendation processing database without changing software at the plurality of set top boxes.

11. A simulation engine as recited in claim 10 wherein the data comprises set top box data.

12. A simulation engine as recited in claim 10 wherein the data comprises setting and list data comprising at least one of set top box playlist data, channel subscription data, channels blocked at the at least one of the plurality of set top boxes and resolution data.

13. A simulation engine as recited in claim 10 wherein communicating data comprises communicating viewer tracking log data comprising at least one of data indicating a time of day a program is watched, data indicating how long a program is watched and recording deletion data.

14. A simulation engine as recited in claim 10 wherein the content recommendation comprises a program object identifier, a channel identifier and a program start time.

15. A simulation engine as recited in claim 10 wherein the recommendation engine generates the content recommendation at the plurality of set top boxes based on the simulation algorithm and program guide data.

17. A method of operating a simulation engine having a web interface comprising:

receiving data and a recommendation request from at least one of a plurality of user devices and through a network and the web interface;

storing the data in a memory of the simulation engine;

originating, via a simulation algorithm of a processor-based recommendation engine, a content recommendation based on the data and in response to the recommendation request,

communicating, from the interface, the content recommendation to the plurality of user devices; and

receiving, at the recommendation engine, based on the content recommendation, ratings of the simulation algorithm from the plurality of user devices and changes the simulation algorithm to a second algorithm in response to the ratings from the plurality of user device and generates different recommendations for the plurality of user devices using a recommendation processing database without changing software at the plurality of user device.

18. A method as recited in claim 17 wherein communicating data comprises communicating setting and list data comprising at least one of set top box playlist data, channel subscription data, channels blocked at the user device, viewer tracking log comprising at least one of a time of day that a program is watched, how long a program is watched and program recording deletion data.

19. A method as recited in claim 17 wherein communicating data comprises communicating view tracking log data comprising at least one of a time of day that a program is watched, how long a program is watched and program recording deletion data.

20. A method as recited in claim 17 wherein communicating the content recommendation comprises communicating a program object identifier, a channel identifier and a program start time and wherein generating the content recommendation comprises generating the content recommendation based on the data, the simulation algorithm and program guide data.

21. A method as recited in claim 17 wherein the content recommendation comprises a list of recommendations for a plurality of programs respectively corresponding to a plurality of channels.

22. A system comprising:

a user device communicating data through a network to a simulation engine through a web interface;

said user device communicating a recommendation request to the simulation engine through the network;

a simulation algorithm generating a content recommendation at a processor-based recommendation engine of the simulation engine based on the data and in response to the recommendation request and communicating the content recommendation to the user device through the network;

said user device rating the simulation algorithm based on the content recommendation and communicating the rating to the simulation engine through the network;

said simulation engine comparing the rating to a plurality of ratings of a plurality of algorithms using a recommendation processing database, wherein the plurality of algorithms include a second algorithm;

said simulation engine, based on comparing, selecting, from an algorithm file of the simulation engine and the plurality of algorithms, the second algorithm having a better rating than other ones of the simulation algorithm and the plurality of algorithms; and

changing the simulation algorithm at the simulation engine in response to the rating.

23. A system as recited in claim 22 wherein the data comprises set top box data.

24. A system as recited in claim 22 wherein the data comprises setting and list data comprising at least one of set top box playlist data, channel subscription data, channels blocked at the user device and resolution data.

25. A system as recited in claim 22 wherein communicating data comprises communicating viewer tracking log data comprising at least one of data indicating a time of day a program is watched, data indicating how long a program is watched and recording deletion data.

26. A system as recited in claim 22 wherein the content recommendation comprises a program object identifier, a channel identifier and a program start time.

27. A system as recited in claim 22 wherein the recommendation engine generates the content recommendation at the user device based on the simulation algorithm and program guide data.