SYSTEM, METHOD, AND APPARATUS FOR IMPLEMENTING TARGETED ADVERTISING IN COMMUNICATION NETWORKS

• US 20080276270A1
• Filed: 07/07/2008
• Published: 11/06/2008
• Est. Priority Date: 06/16/2008
• Status: Abandoned Application

• Current Assignee: Chandra Shekar Kotaru, Divya Murthy Komati, Paratieshwara Rao, Prakasa Rao Ragala
• Original Assignee: Chandra Shekar Kotaru, Divya Murthy Komati, Paratieshwara Rao, Prakasa Rao Ragala
• Inventors: Ragala, Prakasa Rao, Kotaru, Chandra Shekar, Rao, Paratieshwara, Komati, Divya Murthy
• Application Number:
  US12/168,212
• Publication Number:
  US 20080276270A1
• Time in Patent Office: Days
• US Class Current: 725/34
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25875 involving end-user authentication restricting access to computer systems by authenticating users using a predetermined code G06F21/33; arrangements for secret or secure communication including means for verifying the identity or authority of a user of the system H04L9/32; networks authentication protocols H04L63/08; authentication in wireless network security H04W12/06
• CPC Class Codes: H04N 21/25883 being end-user demographical data, e.g. age, family status or address arrangements for identifying locations of users in broadcast systems H04H60/52
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; 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 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/441 Acquiring end-user identification , e.g. using personal code sent by the remote control or by inserting a card
• CPC Class Codes: H04N 21/44218 Detecting physical presence or behaviour of the user, e.g. using sensors to detect if the user is leaving the room or changes his face expression during a TV program methods or arrangements for recognising human body or animal bodies or body parts G06V40/10; methods or arrangements for acquiring or recognising human faces, facial parts, facial sketches, facial expressions G06V40/16; methods or arrangements for recognising movements or behaviour G06V40/20; arrangements for identifying users in broadcast systems H04H60/45
• 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 21/6125 involving transmission via Internet transmission by internet of broadcast information H04H60/82
• CPC Class Codes: H04N 21/6175 involving transmission via Internet broadcast-related systems characterised by the transmission system being the Internet H04H60/82
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02

Abstract

The present technique detects at least one active user utilizing a set of communication devices over a communication network. The method includes receiving behavior data, fulfillment data and feedback data for the at least one active user of the set of communication devices accessing content over the communication network using an intelligent agent module. The method includes creating a database of a set of demographic profiles based on the received data using a dynamic group and rules editor module. The method further includes grouping a set of the at least one active user of the set of communication devices into their corresponding dynamic group using group creation service module.

Claims

1. A method for detecting at least one active user utilizing a set of communication devices over a communication network, the active user detection method comprising:

receiving behavior data, fulfillment data and feedback data for the at least one active user of the set of communication devices accessing content over the communication network using an intelligent agent module;

creating a database of a set of demographic profiles based on the received data using a dynamic group and rules editor module;

grouping a set of the at least one active user of the set of communication devices into their corresponding dynamic group using group creation service module;

receiving a request from the set of the at least one active user to present a targeted advertisement to the at least one active user of the set of communication devices using a business parameters module;

identifying one of the set of demographic profile in the created database that satisfies criteria set forth in the business parameters module; and

transmitting the targeted advertisement to the set of communication devices associated with the demographic profile satisfying the criteria set forth in the business parameters module.

2. The method of claim 1, wherein delivering the targeted advertisement for the at least one active user of the set of communication devices over the communication network that is using either context specific or time specific or demographic profile specific or the like or a combination thereof.

3. The method of claim 2, wherein delivering the targeted advertisement via a set of content delivery mechanisms including live television or video on demand advertisements or the like or the combination thereof.

4. The method of claim 3, wherein the delivering the targeted advertisement via the content delivery mechanism comprises launching the targeted advertisement through either banner advertisements or video advertisements or scrolling advertisements or the like or the combination thereof.

5. The method of claim 1, further comprising categorizing the at least one active user into the dynamic group based on a plurality of user actions and the set of demographic profiles.

6. The method of claim 1, wherein the set of demographic profile includes user-selected preferences with respect to programming content sources.

7. The method of claim 1, wherein the behavior data includes a prior collection of activities conducted via the set of communication devices, comprising at least one of:

program content viewed;

a time frame that the program content was viewed;

an amount of time the at least one active user spent viewing the program content; and

purchasing activities conducted via the set of communication devices.

8. The method of claim 7, wherein the time frame for presenting the advertisement is determined by prioritizing and scheduling the advertisement for the at least one active user based on an average viewing time and advertisement opportunity using current success potentials of one of the targeted advertisement.

9. The method of claim 1, wherein the external data includes at least one of:

income range of the at least one active user of the set of communication devices;

family structure including martial status and number of dependents;

residential location of the at least one active user;

gender of the at least one active user;

age range of the at least one active user; and

credit worthiness of the at least one active user.

10. The method of claim 9, wherein the criteria of the business parameters module include at least one of:

a number of times the targeted advertisement is presented;

a time frame for presenting the targeted advertisement;

a program during which the targeted advertisement is presented;

a target audience to which the targeted advertisement is presented; and

a geographic area in which the targeted advertisement, is presented.

11. The method of claim 1, further comprising determining whether the at least one active user of the set of communication devices to which the targeted advertisement was transmitted have perceived the targeted advertisement by sampling a content data stream distributed to the set of communication devices of the at least one active user during presentation of the targeted advertisement of the at least one active user.

12. The method of claim 1, further comprising mapping the targeted advertisement to the dynamic group defined groups using seed success and the current success potentials.

13. A method for detecting personality of at least one active user of a set of communication device over a communication network, the personality detection method comprising:

identifying current personality of the at least one active user watching the set of the communication devices over the communication network;

detecting present viewing personality by comparing current user behavior data with predefined default user behavior data for the at least one active user of the set of the communication devices over the communication network using an inference engine module; and

detecting the at least one active user of the communication device by polled metric data using an intelligent agent module.

14. The method of claim 13, further comprising tagging an accessed content over the communication network for producing the delivery of the targeted advertisement into a plurality of pieces associated with meta data.

15. The method of claim 13, further comprising detecting digital program insertion or splice point in a main stream of a channel and replacing a dynamic targeted advertisement using a plurality of secondary streams and returning back to the main stream at end of the splice point.

16. The method of claim 13, wherein receiving the targeted advertisement and composing a real advertisement.

17. The method of claim 13, further comprising computing custom offers at custom prices to enable an inference engine module by defining a set of rules for enabling a authoring language and a rules grammar to service operators or content owners or product merchants or combination thereof.

18. The method of claim 13, further comprising generating a dynamic banner and a scrolling advertisement using a dynamic up selling text module.

19. The method of claim 13, further comprising providing product information and fulfillment to one of the specific targeted advertising.

20. The method of claim 13, further comprising providing bookmark on the targeted advertisement for lateral fulfillment without obstructing the current program of the communication device over the communication network.

21. The method of claim 13, further comprising providing the targeted advertisement to the at least one active user based on a behavior data or a fulfillment data or a feedback data or the like or the combination thereof.

22. The method of claim 13, further comprising delivering the targeted advertisement in a machine readable format by authoring and customizing using the inference engine module.

23. The method of claim 13, further comprising authoring at least one language for defining a set of rules to compute custom offers at custom prices using a dynamic group and rules editor module.

24. The method of claim 13, wherein the set of rules of the dynamic group and rules editor module enables a plurality of services to a set of clients including at least one service operator or at least one owner or at least one product merchant or the like thereof.

25. A method for detecting at least one best fit product to deliver a targeted advertisement to a set of communication devices over a communication network, the best fit detection method comprising:

producing optimal revenue from the targeted advertisement using autonomous closed loop feedback module; and

managing an advertisement campaign by selling one of the at least one best fit product using autonomous campaign management module.

26. The method of claim 25, further comprising conceptualizing and identifying for designing the autonomous campaign management module using a brick module.

27. The method of claim 25, further comprising specifying automatically a set of goals for producing optimal revenue from the targeted advertisement.

28. The method of claim 25, further comprising making a set of scrolling advertisements of the targeted advertising using a plurality of scripting constructs and key variables of a scripting module.

29. The method of claim 28, further comprising identifying the best fit product to the at least one active user and at least one user group.

30. The method of claim 29, wherein identification of the best fit product includes identifying a best fit targeted advertisement to the best fit product of the at least one user group.

31. The method of claim 25, further comprising computing a time frame for delivering the best fit targeted advertisement to the at least one user group.

32. The method of claim 25, further comprising propagating for switching to the targeted advertisement on a live television channel during a specific commercial break.

33. The method of claim 25, further comprising a self tuning for creating the targeted advertisement based on a plurality of user preferences and a plurality of user reactions using the autonomous closed loop feedback module.

34. The method of claim 25, further comprising detecting the at least one active user is interacting with one of the set of communication devices.

35. The method of claim 34, wherein the detection of the at least one active user is interacting with one of the set of communication devices comprises:

if the live television channel is being displayed to the at least one active user either paying attention or watching the live television channel; and

if the live television channel is being displayed to an empty room or to at least, one passive user not paying attention or watching the live television channel.

36. A system for detecting at least one active user utilizing a set of communication devices over a communication network, the active user detection system comprising:

an intelligent agent module adapted to receive behavior data, fulfillment data and feedback data for the at least one active user of the set of communication devices accessing content over the communication network;

a dynamic group and rules editor module adapted to create a database of a set of demographic profiles based on the received data;

a group creation service module adapted to group a set of the at least one active user of the set of communication devices into their corresponding dynamic group;

a business parameters module adapted to receive a request from the set of the at least one active user to present a targeted advertisement to the at least one active user of the set of communication devices;

the database adapted to identify one of the set of demographic profile in the created database that satisfies criteria set forth in the business parameters module; and

the set of communication devices adapted to transmit the targeted advertisement to the associated demographic profile satisfying the criteria set forth in the business parameters module.

37. The system of claim 36, wherein delivering the targeted advertisement for the at least one active user of the set of communication devices over the communication network that is using either context specific or time specific or demographic profile specific or the like or a combination thereof.

38. The system of claim 37, wherein delivering the targeted advertisement via a set of content delivery mechanisms including live television or video on demand or video advertisements or the like or the combination thereof.

39. The system of claim 38, wherein the delivering the targeted advertisement via the content delivery mechanism comprises launching the targeted advertisement through either banner advertisements or video advertisements or scrolling advertisements or the like or the combination thereof.

40. The system of claim 36, further comprising categorizing the at least one active user into the dynamic group based on a plurality of user actions and the set of demographic profiles.

41. The system of claim 36, wherein the set of demographic profile includes user-selected preferences with respect to programming content sources.

42. The system of claim 36, wherein the behavior data includes a prior collection of activities conducted via the set of communication devices, comprising at least one of:

program content viewed;

a time frame that the program content was viewed;

an amount of time the at least one active user spent viewing the program content; and

purchasing activities conducted via the set of communication devices.

43. The system of claim 42, wherein the time frame for presenting the advertisement is determined by prioritizing and scheduling the advertisement for the at least one active user based on an average viewing time and advertisement opportunity using current success potentials of one of the targeted advertisement.

44. The system of claim 43, wherein the external data includes at least one of:

income range of the at least one active user of the set of communication devices;

family structure including martial status and number of dependents;

residential location of the at least one active user;

gender of the at least one active user;

age range of the at least one active user; and

credit worthiness of the at least one active user.

45. The system of claim 44, wherein the criteria of the business parameters module include at least one of:

a number of times the targeted advertisement is presented;

a time frame for presenting the targeted advertisement;

a program during which the targeted advertisement is presented;

a target audience to which the targeted advertisement is presented; and

a geographic area in which the targeted advertisement is presented.

46. The system of claim 36, further comprising determining whether the at least one active user of the set of communication devices to which the targeted advertisement was transmitted have perceived the targeted advertisement by sampling a content data stream distributed to the set of communication devices of the at least one active user during presentation of the targeted advertisement of the at least one active user.

47. The system of claim 36, further comprising mapping the targeted advertisement to the dynamic group defined groups using seed success and the current success potentials.

48. The system of claim 36, further comprising detecting personality of the at least one active user of the set of communication device over the communication network comprising:

identifying current personality of the at least one active user watching the set of the communication devices over the communication network;

detecting present viewing personality by comparing current user behavior data with predefined default user behavior data for the at least one active user of the set of the communication devices over the communication network using an inference engine module; and

detecting the at least one active user of the communication device by polled metric data using an intelligent agent module.

49. The system of claim 48, further comprising tagging an accessed content over the communication network for producing the delivery of the targeted advertisement into a plurality of pieces associated with meta data.

50. The system of claim 36, further comprising detecting digital program insertion or splice point in a main stream of a channel and replacing a dynamic targeted advertisement using a plurality of secondary streams and returning back to the main stream at end of the splice point.

51. The system of claim 36, wherein receiving the targeted advertisement and composing a real advertisement.

52. The system of claim 36, further comprising computing custom offers at custom prices to enable an interference engine module by defining a set of rules for enabling a authoring language and a rules grammar to service operators or content owners or product merchants or combination thereof.

53. The system of claim 36, further comprising generating a dynamic banner and a scrolling advertisement using a dynamic up selling text module.

54. The system of claim 36, further comprising providing product information and fulfillment to one of the specific targeted advertising.

55. The system of claim 36, further comprising providing bookmark on the targeted advertisement for lateral fulfillment without obstructing the current program of the communication device over the communication network.

56. The system of claim 36, further comprising providing the targeted advertisement to the at least one active user based on a behavior data or a fulfillment data or a feedback data or the like or the combination thereof.

57. The system of claim 36, further comprising delivering the targeted advertisement in a machine readable format by authoring and customizing using the inference engine module.

58. The system of claim 36, further comprising authoring at least one language for defining a set of rules to compute custom offers at custom prices using a dynamic group and rules editor module.

59. The system of claim 58, wherein the set of rules of the dynamic group and rules editor module enables a plurality of services to a set of clients including at least one service operator or at least one owner or at least one product merchant or the like thereof.

60. The system of claim 36, further comprising detecting at least one best fit product to deliver the targeted advertisement to the set of communication devices over the communication network comprising:

producing optimal revenue from the targeted advertisement using autonomous closed loop feedback module; and

managing an advertisement campaign by selling one of the at least one best fit product using autonomous campaign management module.

61. The system of claim 60, further comprising conceptualizing and identifying for designing the autonomous campaign management module using a brick module.

62. The system of claim 36, further comprising specifying automatically a set of goals for producing optimal revenue from the targeted advertisement.

63. The system of claim 36, further comprising making a set of scrolling advertisements of the targeted advertising using a plurality of scripting constructs and key variables of a scripting module.

64. The system of claim 36, further comprising identifying the best fit product to the at least one active user and at least one user group.

65. The system of claim 64, wherein identification of the best fit product includes identifying a best fit targeted advertisement to the best fit product of the at least one user group.

66. The system of claim 36, further comprising computing the time frame for delivering the best fit targeted advertisement to the at least one user group.

67. The system of claim 36, further comprising propagating for switching to the targeted advertisement on a live television channel during a specific commercial break.

68. The system of claim 36, further comprising a self tuning for creating the targeted advertisement based on a plurality of user preferences and a plurality of user reactions using the autonomous closed loop feedback module.

69. The system of claim 36, further comprising detecting the at least one active user is interacting with one of the set of communication devices.

70. The system of claim 69, wherein the detection of the at least one active user is interacting with one of the set of communication devices comprises:

if the live television channel is being displayed to the at least one active user either paying attention or watching the live television channel; and

if the live television channel is being displayed to an empty room or to at least one passive user not paying attention or watching the live television channel.

71. A tangible computer-readable medium having stored thereon computer executable instructions for detecting at least one active user utilizing a set of communication devices over a communication network, the computer-readable medium comprising:

program code adapted for receiving behavior data, fulfillment data and feedback data for the at least one active user of the set of communication devices accessing content over the communication network using an intelligent agent module;

program code adapted for creating a database of a set of demographic profiles based on the received data using a dynamic group and rules editor module;

program code adapted for grouping a set of the at least one active user of the set of communication devices into their corresponding dynamic group using group creation service module;

program code adapted for receiving a request from the set of the at least one active user to present a targeted advertisement to the at least one active user of the set of communication devices using a business parameters module;

program code adapted for identifying one of the set of demographic profile in the created database that satisfies criteria set forth in the business parameters module; and

program code adapted for transmitting the targeted advertisement to the set of communication devices associated with the demographic profile satisfying the criteria set forth in the business parameters module.

72. The computer-readable medium of claim 71, wherein delivering the targeted advertisement for the at least one active user of the set of communication devices over the communication network that is using either context specific or time specific or demographic profile specific or the like or a combination thereof.

73. The computer-readable medium of claim 71, wherein delivering the targeted advertisement via a set of content delivery mechanisms including live television or video on demand or video advertisements or the like or the combination thereof.

74. The computer-readable medium of claim 73, wherein the delivering the targeted advertisement via the content delivery mechanism comprises launching the targeted advertisement through either banner advertisements or video advertisements or scrolling advertisements or the like or the combination thereof.

75. The computer-readable medium of claim 71, further comprising categorizing the at least one active user into the dynamic group based on a plurality of user actions and the set of demographic profiles.

76. The computer-readable medium of claim 71, wherein the set of demographic profile includes user-selected preferences with respect to programming content sources.

77. The computer-readable medium of claim 71, wherein the behavior data includes a prior collection of activities conducted via the set of communication devices, comprising at least one of:

program content viewed;

a time frame that the program content was viewed;

an amount of time the at least one active user spent viewing the program content; and

purchasing activities conducted via the set of communication devices.

78. The computer-readable medium of claim 77, wherein the time frame for presenting the advertisement is determined by prioritizing and scheduling the advertisement for the at least one active user based on an average viewing time and advertisement opportunity using current success potentials of one of the targeted advertisement.

79. The computer-readable medium of claim 78, wherein the external data includes at least one of:

income range of the at least one active user of the set of communication devices;

family structure including martial status and number of dependents;

residential location of the at least one active user;

gender of the at least one active user;

age range of the at least one active user; and

credit worthiness of the at least one active user.

80. The computer-readable medium of claim 79, wherein the criteria of the business parameters module include at least one of:

a number of times the targeted advertisement is presented;

a time frame for presenting the targeted advertisement;

a program during which the targeted advertisement is presented;

a target audience to which the targeted advertisement is presented; and

a geographic area in which the targeted advertisement is presented.

81. The computer-readable medium of claim 71, further comprising determining whether the at least one active user of the set of communication devices to which the targeted advertisement was transmitted have perceived the targeted advertisement by sampling a content data stream distributed to the set of communication devices of the at least one active user during presentation of the targeted advertisement of the at least one active user.

82. The computer-readable medium of claim 71, further comprising mapping the targeted advertisement to the dynamic group defined groups using seed success and the current success potentials.

83. The computer-readable medium of claim 71, further comprising detecting personality of the at least one active user of the set of communication device over the communication network comprising:

program code adapted for identifying current personality of the at least one active user watching the set of the communication devices over the communication network;

program code adapted for detecting present viewing personality by comparing current user behavior data with predefined default user behavior data for the at least one active user of the set of the communication devices over the communication network using an inference engine module; and

program code adapted for detecting the at least one active user of the communication device by polled metric data using an intelligent agent module.

84. The computer-readable of claim 83, further comprising tagging an accessed content over the communication network for producing the delivery of the targeted advertisement into a plurality of pieces associated with meta data.

85. The computer-readable medium of claim 71, further comprising detecting digital program insertion or splice point in a main stream of a channel and replacing a dynamic targeted advertisement using a plurality of secondary streams and returning back to the main stream at end of the splice point.

86. The computer-readable medium of claim 71, wherein receiving the targeted advertisement and composing a real advertisement.

87. The computer-readable medium of claim 71, further comprising computing custom offers at custom prices to enable an interference engine module by defining a set of rules for enabling a authoring language and a rules grammar to service operators or content owners or product merchants or combination thereof.

88. The computer-readable medium of claim 71, further comprising generating a dynamic banner and a scrolling advertisement using a dynamic up selling text module.

89. The computer-readable medium of claim 71, further comprising providing product information and fulfillment to one of the specific targeted advertising.

90. The computer-readable medium of claim 71, further comprising providing bookmark on the targeted advertisement for lateral fulfillment without obstructing the current program of the communication device over the communication network.

91. The computer-readable medium of claim 71, further comprising providing the targeted advertisement to the at least one active user based on a behavior data or a fulfillment data or a feedback data or the like or the combination thereof.

92. The computer-readable medium of claim 71, further comprising delivering the targeted advertisement in a machine readable format by authoring and customizing using the inference engine module.

93. The computer-readable medium of claim 71, further comprising authoring at least one language for defining a set of rules to compute custom offers at custom prices using a dynamic group and rules editor module.

94. The computer-readable medium of claim 93, wherein the set of rules of the dynamic group and rules editor module enables a plurality of services to a set of clients including at least one service operator or at least one owner or at least one product merchant or the like thereof.

100. The computer-readable medium of claim 71, further comprising detecting at least one best fit product to deliver the targeted advertisement to the set of communication devices over the communication network comprising:

program code adapted for producing optimal revenue from the targeted advertisement using autonomous closed loop feedback module; and

program code adapted for managing an advertisement campaign by selling one of the at least one best fit product using autonomous campaign management module.

101. The computer-readable medium of claim 100, further comprising conceptualizing and identifying for designing the autonomous campaign management module using a brick module.

102. The computer-readable medium of claim 71, further comprising specifying automatically a set of goals for producing optimal revenue from the targeted advertisement.

103. The computer-readable medium of claim 71, further comprising making a set of scrolling advertisements of the targeted advertising using a plurality of scripting constructs and key variables of a scripting module.

104. The computer-readable medium of claim 71, further comprising identifying the best fit product to the at least one active user and at least one user group.

106. The computer-readable medium of claim 71, further comprising computing the time frame for delivering the best fit targeted advertisement to the at least one user group.

107. The computer-readable medium of claim 71, further comprising propagating for switching to the targeted advertisement on a live television channel during a specific commercial break.

108. The computer-readable medium of claim 71, further comprising a self tuning for creating the targeted advertisement based on a plurality of user preferences and a plurality of user reactions using the autonomous closed loop feedback module.

109. The computer-readable medium of claim 71, further comprising detecting the at least one active user is interacting with one of the set of communication devices.

110. The computer-readable medium of claim 109, wherein the detection of the at least one active user is interacting with one of the set of communication devices comprises:

if the live television channel is being displayed to the at least one active user either paying attention or watching the live television channel; and

if the live television channel is being displayed to an empty room or to at least one passive user not paying attention or watching the live television channel.

105. The computer-readable medium of claim 105, wherein identification of the best fit product includes identifying a best fit targeted advertisement to the best fit product of the at least one user group.