Method and apparatus for continuously and opportunistically driving an optimal broadcast schedule based on most recent client demand feedback from a distributed set of broadcast clients

• US 20020194598A1
• Filed: 06/15/2001
• Published: 12/19/2002
• Est. Priority Date: 06/15/2001
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Connelly, Jay H.
• Granted Patent: US 7,020,893 B2
• Time in Patent Office: Days
• US Class Current: 725/39
• CPC Class Codes: H04H 60/06 Arrangements for scheduling broadcast services or broadcast-related services
• CPC Class Codes: H04H 60/33 Arrangements for monitoring the users' behaviour or opinions
• CPC Class Codes: H04H 60/66 for using the result on distributors' side
• CPC Class Codes: H04H 60/73 using meta-information
• CPC Class Codes: H04N 21/235 Processing of additional data, e.g. scrambling of additional data or processing content descriptors
• CPC Class Codes: H04N 21/262 Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists scheduling strategies for dispatcher in multiprogramming arrangements G06F9/4881; arrangements for scheduling broadcast services or broadcast-related services H04H60/06; flow control in packet networks H04L47/10; establishing a time schedule or organising the servicing of application requests H04L67/62
• CPC Class Codes: H04N 21/26233 involving content or additional data duration or size, e.g. length of a movie, size of an executable file
• CPC Class Codes: H04N 21/26241 involving the time of distribution, e.g. the best time of the day for inserting an advertisement or airing a children program
• 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 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• 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 21/4662 characterized by learning algorithms
• CPC Class Codes: H04N 21/4756 for rating content, e.g. scoring a recommended movie
• CPC Class Codes: H04N 21/6405 Multicasting data broadcast and multicast in packet switching networks H04L12/18
• CPC Class Codes: H04N 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38
• CPC Class Codes: H04N 21/8352 involving content or source identification data, e.g. Unique Material Identifier [UMID]
• CPC Class Codes: H04N 21/84 Generation or processing of descriptive data, e.g. content descriptors systems specially adapted for using meta-information in broadcast systems H04H60/73
• CPC Class Codes: H04N 7/17318 : Direct or substantially direct transmission and handling of requests

Abstract

A broadcast method and system for continuously and opportunistically driving an optimal broadcast schedule based on most recent client demand feedback from a distributed set of broadcast clients. The broadcast system includes an operation center that broadcasts meta-data to a plurality of client systems. The meta-data describes a plurality of pieces of content that are in consideration for upcoming broadcasts by the server. Each client receives the broadcasted meta-data from and sends back a set of client demand feedback data to the operations center, wherein the user feedback data reflects a client'"'"'s interest level in at least a portion of the pieces of content. The feedback data, which typically may include ratings and/or relative rankings, may be user-generated, automatically-generated, or a combination of the two. The system then determines a most opportunistic piece of content to be broadcast based on an aggregation of the client demand feedback data.

Claims

1. A method for generating an opportunistic broadcast schedule, comprising:

broadcasting meta-data to a plurality of client systems, the meta-data including descriptions of a plurality of pieces of content that are in consideration for upcoming broadcasts;

receiving individual sets of client demand feedback data from at least a portion of said plurality of client systems, each individual set of client demand feedback data comprising data indicating a client interest level in at least a portion of the plurality of pieces of content;

determining a piece of content from among said plurality of pieces of content that is most opportunistic for a next broadcast by aggregating the individual sets of client demand feedback data; and

scheduling the piece of content that is determined to be most opportunistic to broadcast for a next broadcast.

2. The method of claim 1, wherein scheduling the piece of content that is determined to be most opportunistic for a next broadcast comprises:

maintaining a broadcast schedule queue comprising an ordered list of pieces of content that are scheduled to be broadcast in sequence derived from the aggregation of the client demand feedback data based on a relative level of client interest in each piece of content.

placing the piece of content that is determined to be most opportunistic for a next broadcast at the top of the broadcast schedule queue such that it is next to be broadcast,

wherein the determination of the piece of content that is most opportunistic for a next broadcast is performed continuously.

3. The method of claim 2, further comprising resetting the client demand feedback data for each piece of content that is determined to be most opportunistic for a next broadcast in response to a broadcast of that piece of content such that the piece of content cannot be determined again to be the piece of content that is most opportunistic for a next broadcast until new client demand feedback data corresponding to that piece of content is received.

4. The method of claim 2, wherein the individual sets of client demand feedback data are received from respective client systems on an asynchronous basis and the broadcast schedule queue is recalculated upon receiving each individual set of client demand feedback data.

5. The method of claim 2, further comprising adjusting the broadcast schedule queue in consideration of business objectives.

6. The method of claim 1, wherein the client demand feedback data comprises ratings data corresponding to respective pieces of content, and wherein the piece of content that is determined to be most opportunistic to broadcast comprises a highest rated piece of content derived from an aggregation of the ratings data.

7. The method of claim 6, wherein the aggregation of the ratings data comprises calculating an average ratings value for each piece of content and the highest rated piece of content is the piece of content with the highest average rating value.

8. The method of claim 6, wherein at least a portion of the ratings data comprise rating inputs provided by users of the client systems, each rating input indicating a level of desirability of a given user to receive a corresponding piece of content.

9. The method of claim 6, wherein at least a portion of the ratings data is automatically generated by the client systems based on data stored on the client systems that are indicative of content preferences of users of the client systems.

10. The method of claim 6, further comprises adjusting ratings data corresponding to any pieces of content that are rated by a given client system in consideration of a revenue-generating potential for those pieces of content.

11. The method of claim 6, wherein, for each individual set of client demand feedback data received from a client system, a first portion of the ratings data comprises rating inputs provided by one or more users of that client system and a second portion of the ratings data are automatically generated by that client system based on data stored on that client system that are indicative of content preferences of said one or more users of that client system.

12. The method of claim 6, wherein the meta-data is broadcast as a continuous stream and includes a content descriptor for each piece of content comprising a set of attributes and attribute values that are used to describe that piece of content, and further wherein at least a portion of the client systems provide ratings data corresponding to an individual piece of content in response to receiving the content descriptor for that piece of content.

13. The method of claim 1, wherein at least a portion of the individual sets of client demand feedback data comprise relative rankings data pertaining to relative levels of interest in at least two pieces of content, and wherein the piece of content that is determined to be most opportunistic to broadcast comprises is determined, at least in part, by aggregating the relative rankings data.

14. The method of claim 13, wherein the aggregation of the relative rankings data comprises calculating an average ranking for each piece of content among said plurality of pieces of content and the piece of content that is determined to be most opportunistic for a next broadcast is the piece of content with the highest average ranking.

15. The method of claim 13, wherein at least a portion of the relative rankings data comprise individual sets of relative ranking inputs provided by users of the client systems, each individual set of relative ranking inputs comprising a relative ranking of at least two pieces of content, wherein the relative ranking is indicative of a relative level of desirability of a given user of a respective client system to receive a broadcast of the pieces of content ranked by that user.

16. The method of claim 13, wherein at least a portion of the relative rankings data is automatically generated by the client systems based on data stored on the client systems that are indicative of content preferences of users of the client systems.

17. The method of claim 13, further comprises adjusting relative rankings data corresponding to pieces of content that are rated by a given client system in consideration of a revenue-generating potential for those pieces of content.

18. The method of claim 13, wherein, for each individual set of client demand feedback data among at least a portion of the individual sets of client demand feedback data comprising relative rankings data, a first portion of the relative rankings data comprises relative ranking inputs provided by one or more users of the client system from which that individual set of client feedback is received and a second portion of the relative rankings data are automatically generated by that client system based on data stored on that client system that are indicative of content preferences of said one or more users of that client system.

19. The method of claim 13, wherein a current set of meta-data corresponding to a set of pieces of content considered for an upcoming broadcast is broadcast as a continuous stream that is repeated and includes a respective content descriptor for each piece of content included in the set of pieces of content, and wherein at least a portion of the individual sets of client demand feedback data includes a ranked list expressing a relative interest in all of the pieces of content in the set of pieces of content.

20. The method of claim 1 further comprising broadcasting a broadcast schedule prior to broadcasting the piece of content that is determined to be the most opportunistic for the next broadcast.

21. The method of claim 1, wherein the plurality of client systems are segmented such that each client system is a member of a particular segment among multiple segments and each individual set of client feedback data includes data that identifies the segment the client system is a member of, and further wherein a most opportunistic piece of content is determined and scheduled for a next broadcast for each segment.

22. The method of claim 21, wherein the plurality of client systems are segmented based on geography such that each client is assigned to a geographical region.

23. The method of claim 21, wherein the plurality of client systems are segmented based on a network by which each client receives broadcast content.

24. The method of claim 1, further comprising broadcasting at least a portion of the piece of content that is determined to be the most opportunistic for a next broadcast using post multiplex insertion of null data packets.

25. The method of claim 1, further comprising broadcasting at least a portion of the piece of content that is determined to be the most opportunistic for a next broadcast using statistical multiplexing.

27. The apparatus of claim 26, wherein the apparatus schedules the piece of content that is determined to be most opportunistic for a next broadcast by performing the operations of:

maintaining a broadcast schedule queue comprising an ordered list of pieces of content that are scheduled to be broadcast in sequence derived from the aggregation of the client demand feedback data based on a relative level of client interest in each piece of content.

placing the piece of content that is determined to be most opportunistic for a next broadcast at the top of the broadcast schedule queue such that it is next to be broadcast,

wherein the determination of the piece of content that is most opportunistic for a next broadcast is performed continuously.

28. The apparatus of claim 27, wherein the individual sets of client demand feedback data are received from respective client systems on an asynchronous basis and the broadcast schedule queue is recalculated upon receiving each individual set of client demand feedback data.

29. The apparatus of claim 26, wherein the client demand feedback data comprises ratings data corresponding to respective pieces of content, and wherein the piece of content that is determined to be most opportunistic to broadcast comprises a highest rated piece of content derived from an aggregation of the ratings data.

30. The apparatus of claim 29, wherein, for at least a portion of the individual sets of client demand feedback data received from the client systems, a first portion of the ratings data comprises rating inputs provided by one or more users of the client system from which that individual set of client demand feedback data is received and a second portion of the ratings data are automatically generated by that client system based on data stored on that client system that are indicative of content preferences of said one or more users of that client system.

31. The apparatus of claim 29, wherein the meta-data is broadcast as a continuous stream and includes a content descriptor for each piece of content comprising a set of attributes and attribute values that are used to describe that piece of content, and further wherein at least a portion of the client systems provide ratings data corresponding to an individual piece of content in response to receiving the content descriptor for that piece of content.

32. The apparatus of claim 26, wherein at least a portion of the individual sets of client demand feedback data comprise relative rankings data pertaining to relative levels of interest in at least two pieces of content, and wherein the piece of content that is determined to be most opportunistic to broadcast comprises is determined, at least in part, by aggregating the relative rankings data.

33. The apparatus of claim 32, wherein, for each individual set of client demand feedback data among at least a portion of the individual sets of client demand feedback data comprising relative rankings data, a first portion of the relative rankings data comprises relative ranking inputs provided by one or more users of the client system from which that individual set of client feedback is received and a second portion of the relative rankings data are automatically generated by that client system based on data stored on that client system that are indicative of content preferences of said one or more users of that client system.

34. The apparatus of claim 32, wherein a current set of meta-data corresponding to a set of pieces of content considered for an upcoming broadcast is broadcast as a continuous stream that is repeated and includes a respective content descriptor for each piece of content included in the set of pieces of content, and wherein at least a portion of the individual sets of client demand feedback data includes a ranked list expressing a relative interest in all of the pieces of content in the set of pieces of content.

36. The machine-readable medium of claim 35, wherein execution of the plurality of machine instructions cause the machine to schedule the piece of content that is determined to be most opportunistic for a next broadcast by performing the operations of:

maintaining a broadcast schedule queue comprising an ordered list of pieces of content that are scheduled to be broadcast in sequence derived from the aggregation of the client demand feedback data based on a relative level of client interest in each piece of content.

placing the piece of content that is determined to be most opportunistic for a next broadcast at the top of the broadcast schedule queue such that it is next to be broadcast,

wherein the determination of the piece of content that is most opportunistic for a next broadcast is performed continuously.

37. The machine-readable media of claim 36, wherein the individual sets of client demand feedback data are received from respective client systems on an asynchronous basis and the broadcast schedule queue is recalculated upon receiving each individual set of client demand feedback data.

38. The machine-readable media of claim 35, wherein the client demand feedback data comprises ratings data corresponding to respective pieces of content, and wherein the piece of content that is determined to be most opportunistic to broadcast comprises a highest rated piece of content derived from an aggregation of the ratings data.

39. The machine-readable media of claim 38, wherein, for at least a portion of the individual sets of client demand feedback data received from the client systems, a first portion of the ratings data comprises rating inputs provided by one or more users of the client system from which that individual set of client demand feedback data is received and a second portion of the ratings data are automatically generated by that client system based on data stored on that client system that are indicative of content preferences of said one or more users of that client system.

40. The machine-readable medium of claim 38, wherein the meta-data is broadcast as a continuous stream and includes a content descriptor for each piece of content comprising a set of attributes and attribute values that are used to describe that piece of content, and further wherein at least a portion of the client systems provide ratings data corresponding to an individual piece of content in response to receiving the content descriptor for that piece of content.

41. The machine-readable medium of claim 35, wherein at least a portion of the individual sets of client demand feedback data comprise ranking data pertaining to relative levels of interest in at least two pieces of content, and wherein the piece of content that is determined to be most opportunistic to broadcast comprises is determined, at least in part, by aggregating the rankings data.

42. The machine-readable medium of claim 41, wherein, for each individual set of client demand feedback data among at least a portion of the individual sets of client demand feedback data comprising relative rankings data, a first portion of the relative rankings data comprises relative ranking inputs provided by one or more users of the client system from which that individual set of client feedback is received and a second portion of the relative rankings data are automatically generated by that client system based on data stored on that client system that are indicative of content preferences of said one or more users of that client system.

26. An apparatus, comprising:

a processor having circuitry to execute instructions;

a communications interface coupled to the processor to receive data from the one or more client systems;

a storage device coupled to the processor, having sequences of instructions stored therein, which when executed by the processor cause the apparatus to

receive individual sets of client demand feedback data from a plurality of client systems, each individual set of client demand feedback data generated in response to meta-data that is broadcast to the plurality of client systems, the meta-data including descriptions of a plurality of pieces of content that are in consideration for upcoming broadcasts, each individual set of client demand feedback data indicating a client interest level in at least a portion of the plurality of pieces of content;

determine a piece of content from among said plurality of pieces of content that is most opportunistic for a next broadcast by aggregating the individual sets of client demand feedback data; and

schedule the piece of content that is determined to be most opportunistic to broadcast for a next broadcast.

35. A machine-readable medium having a plurality of machine-executable instructions stored thereon, which when executed by a machine cause the machine to:

receive individual sets of client demand feedback data from a plurality of client systems, the individual sets of client demand feedback data generated in response to meta-data that is broadcast to the plurality of client systems, the meta-data including descriptions of a plurality of pieces of content that are in consideration for upcoming broadcasts, each individual set of client demand feedback data indicating a client interest level in at least a portion of the plurality of pieces of content;

determine a piece of content from among said plurality of pieces of content that is most opportunistic for a next broadcast by aggregating the individual sets of client demand feedback data; and

schedule the piece of content that is determined to be most opportunistic to broadcast for a next broadcast.

43. The machine-readable medium of 41, wherein a current set of metadata corresponding to a set of pieces of content considered for an upcoming broadcast is broadcast as a continuous stream that is repeated and includes a respective content descriptor for each piece of content included in the set of pieces of content, and wherein at least a portion of the individual sets of client demand feedback data includes a ranked list expressing a relative interest in all of the pieces of content in the set of pieces of content.

44. A system, comprising:

a broadcast server;

a database server, linked in communication with the broadcast server; and

a plurality of client systems linked in communication with the broadcast server via a first communications link and linked in communication with the database server via a second communication link;

wherein the broadcast server is programmed to broadcast meta-data to said plurality of client systems via the first communications link, the meta-data including descriptions of a plurality of pieces of content that are considered for upcoming broadcasts;

wherein each of said plurality of client systems is programmed to generate an individual set of client demand feedback data indicating a client interest level in at least a portion of the plurality of pieces of content based, in part, on the descriptions of such provided by the meta-data;

wherein at least a portion of the plurality of client systems send individual sets of client demand feedback data to the database server via the second communications link;

wherein the database server is programmed to determine a piece of content from among said plurality of pieces of content that is most opportunistic for a next broadcast by aggregating the individual sets of client demand feedback data;

wherein at least one of the broadcast server and database server is programmed to schedule the piece of content that is determined to be most opportunistic to broadcast for a next broadcast; and

wherein the broadcast system is further programmed to broadcast the piece of content that is determined to be most opportunistic when bandwidth becomes available on the first communications link.

45. The system of claim 44, wherein one of the database server and broadcast server schedules the piece of content that is determined to be most opportunistic for a next broadcast by performing the operations of:

maintaining a broadcast schedule queue comprising an ordered list of pieces of content that are scheduled to be broadcast in sequence derived from the aggregation of the client demand feedback data based on a relative level of client interest in each piece of content.

placing the piece of content that is determined to be most opportunistic for a next broadcast at the top of the broadcast schedule queue such that it is next to be broadcast,

wherein the determination of the piece of content that is most opportunistic for a next broadcast is performed continuously.

46. The system of claim 45, wherein the individual sets of client demand feedback data are received from respective client systems on an asynchronous basis and the broadcast schedule queue is recalculated by the database server upon receiving each individual set of client demand feedback data.

47. The system of claim 44, wherein the client demand feedback data comprises ratings data corresponding to respective pieces of content, and wherein the piece of content that is determined to be most opportunistic to broadcast comprises a highest rated piece of content derived from an aggregation of the ratings data.

48. The system of claim 47, wherein, for at least a portion of the individual sets of client demand feedback data received from the client systems, a first portion of the ratings data comprises rating inputs provided by one or more users of the client system from which that individual set of client demand feedback data is received and a second portion of the ratings data are automatically generated by that client system based on data stored on that client system that are indicative of content preferences of said one or more users of that client system.

49. The system of claim 47, wherein the meta-data is broadcast as a continuous stream and includes a content descriptor for each piece of content comprising a set of attributes and attribute values that are used to describe that piece of content, and further wherein at least a portion of the client systems provide ratings data corresponding to an individual piece of content in response to receiving the content descriptor for that piece of content.

50. The system of claim 44, wherein at least a portion of the individual sets of client demand feedback data comprise relative rankings data pertaining to relative levels of interest in at least two pieces of content, and wherein the piece of content that is determined to be most opportunistic to broadcast comprises is determined, at least in part, by aggregating the relative rankings data.

51. The system of claim 50, wherein, for each individual set of client demand feedback data among at least a portion of the individual sets of client demand feedback data comprising relative rankings data, a first portion of the relative rankings data comprises relative ranking inputs provided by one or more users of the client system from which that individual set of client feedback is received and a second portion of the relative rankings data are automatically generated by that client system based on data stored on that client system that are indicative of content preferences of said one or more users of that client system.

52. The system of claim 50, wherein a current set of meta-data corresponding to a set of pieces of content considered for an upcoming broadcast is broadcast as a continuous stream that is repeated and includes a respective content descriptor for each piece of content included in the set of pieces of content, and wherein at least a portion of the individual sets of client demand feedback data includes a ranked list expressing a relative interest in all of the pieces of content in the set of pieces of content.

53. The system of claim 44, wherein the first communication link comprises a satellite broadcast link and the second communication link comprises a telecommunications link.

54. The system of claim 44, wherein the first communication link and second communications link comprise a bi-directional cable system link.

55. The system of claim 44, wherein the first communication link comprises a satellite broadcast link and the second communication link comprises a computer network communications link

56. The system of claim 44, wherein the first communication link and the second communications link comprise computer network communications links.