Programmatic TV advertising placement using cross-screen consumer data

• US 10,136,174 B2
• Filed: 01/31/2017
• Issued: 11/20/2018
• Est. Priority Date: 07/24/2015
• Status: Active Grant

• Current Assignee: Videoamp, Inc.
• Original Assignee: Videoamp, Inc.
• Inventors: Ray, Debajyoti, McCray, Ross, Gullo, David, Prasad, Jay
• Primary Examiner(s): Flynn, Randy A
• Application Number:
  US15/421,243
• Publication Number:
  US 20170142465A1
• Time in Patent Office: 658 Days
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0264 based upon schedule
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0272 Period of advertisement exposure
• CPC Class Codes: G06Q 30/0275 Auctions
• CPC Class Codes: H04N 21/2385 Channel allocation H04N21/266 takes precedence; Bandwidth allocation H04N21/24 takes precedence ; allocation of channels according to the instantaneous demands of the users in time-division multiplex systems H04J3/1682; admission control, resource allocation in open networks H04L12/5692; arrangements for maintenance or administration in data switching networks involving bandwidth and capacity management H04L41/0896; negotiating bandwidth in wireless networks H04W28/16
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/2547 Third Party Billing, e.g. billing of advertiser
• 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 21/266 Channel or content management, e.g. generation and management of keys and entitlement messages in a conditional access system, merging a VOD unicast channel into a multicast channel
• 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 21/4532 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 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02

Abstract

The current invention relates to a computer-generated method for optimizing placement of advertising content to consumers'"'"' TV'"'"'s using a programmatic TV bidding model. The system can allocate advertising campaigns and plans to various inventory types based on the probability of accurate consumer matching. Consumer matching can be achieved by generation of look-alike models in a consumer'"'"'s device graph to predict future consumption behavior. The system includes an interface through which an advertiser can access relevant information about inventory and success of a given placement.

Claims

1. A method for targeting delivery of advertising content to a population of consumers across one or more slots in television programming, the method being performed by at least one computer system containing at least one processor and at least one memory storing instructions that when executed by the at least one processor, cause the at least one computer system to perform operations comprising:

receiving by the at least one processor a pricepoint and one or more campaign descriptions from an advertiser, wherein each of the campaign descriptions comprises a schedule for delivery of an item of advertising content across one or more televisions accessed by a consumer in the population of consumers, and a target audience, wherein the target audience is defined by one or more demographic factors;

defining, by the at least one processor, a pool of consumers based on a graph of consumer properties, the graph being of a set of nodes and edges connecting pairs of nodes, wherein a node represents a consumer and two nodes that are connected by an edge are similar to one another according to at least a criterion, and wherein a weight of an edge defines a strength of similarity, wherein the graph contains information about two or more TV and mobile devices used by each consumer, demographic and online behavioral data on each consumer, and similarities between pairs of consumers, and wherein the pool of consumers comprises consumers having at least a threshold similarity to a member of the target audience;

receiving by the at least one processor a list of inventory from one or more content providers, wherein the list of inventory comprises one or more TV slots;

identifying by the at least one processor one or more advertising targets, wherein each of the one or more advertising targets comprises one or more TV slots that are to be delivered within TV content identified as likely to be viewed by the pool of consumers, consistent with one or more of the campaign descriptions, and an overall cost consistent with the pricepoint;

bidding, by the at least one processor, on one or more of the TV slots in an advertising target; and

if the bidding results in a success or a hold,

instructing, by the at least one processor, a media conduit to deliver the item of advertising content within the one or more TV slots to a consumer in the population of consumers on a television.

2. The method of claim 1, wherein the bidding includes calculating a probability that the bid price will have an outcome of a win or a hold.

3. The method of claim 1, wherein the TV content identified as likely to be viewed by the pool of consumers is based on a key performance indicator selected from audience reach and direct response.

4. The method of claim 1, further comprising placing a further bid if the first bidding is either a hold or a loss.

5. The method of claim 1, wherein one or more of the advertising targets includes a gross rating point.

6. The method of claim 1, wherein one or more of the slots includes a programming time selected from:

program, and daypart.

7. The method of claim 1, wherein one or more of the slots includes a geographic indicator selected from:

DMA, national, and zip-code.

8. The method of claim 2 wherein the probability is calculated as a Bayesian distribution.

9. The method of claim 1, wherein the graph of consumer properties comprises information that has been obtained probabilistically at least in part.

10. A computing apparatus having at least one processor configured to execute instructions for implementing a method for targeting delivery of advertising content to a population of consumers across one or more slots in television programming, wherein the instructions are encoded on a tangible computer-readable medium and include instructions for:

receiving by the at least one processor a pricepoint and one or more campaign descriptions from an advertiser, wherein each of the campaign descriptions comprises a schedule for delivery of an item of advertising content across one or more televisions accessed by a consumer in the population of consumers, and a target audience, wherein the target audience is defined by one or more demographic factors;

defining, by the at least one processor, a pool of consumers based on a graph of consumer properties, the graph being of a set of nodes and edges connecting pairs of nodes, wherein a node represents a consumer and two nodes that are connected by an edge are similar to one another according to at least a criterion, and wherein a weight of an edge defines a strength of similarity, wherein the graph contains information about two or more TV and mobile devices used by each consumer, demographic and online behavioral data on each consumer, and similarities between pairs of consumers, and wherein the pool of consumers comprises consumers having at least a threshold similarity to a member of the target audience;

receiving by the at least one processor a list of inventory from one or more content providers, wherein the list of inventory comprises one or more TV slots;

identifying by the at least one processor one or more advertising targets, wherein each of the one or more advertising targets comprises one or more TV slots that are to be delivered within TV content identified as likely to be viewed by the pool of consumers, consistent with one or more of the campaign descriptions, and an overall cost consistent with the pricepoint;

bidding, by the at least one processor, on one or more of the TV slots in an advertising target; and

if the bidding results in a success or a hold,

instructing, by the at least one processor, a media conduit to deliver the item of advertising content within the one or more TV slots to a consumer in the population of consumers on a television.

11. The computing apparatus of claim 10, wherein the graph of consumer properties comprises information that has been obtained probabilistically at least in part.

12. The computing apparatus of claim 10, wherein the bidding includes calculating a probability that the bid price will have an outcome of a win or a hold.

13. The computing apparatus of claim 12 wherein the probability is calculated as a Bayesian distribution.

14. The computing apparatus of claim 10, wherein the TV content identified as likely to be viewed by the pool of consumers is based on a key performance indicator selected from audience reach and direct response.

15. The computing apparatus of claim 10, further comprising placing a further bid if the first bidding is either a hold or a loss.

16. The computing apparatus of claim 10, wherein one or more of the advertising targets includes a gross rating point.

17. The computing apparatus of claim 10, wherein one or more of the slots include a programming time selected from:

program, and daypart.

18. The computing apparatus of claim 10, wherein one or more of the slots include a geographic indicator selected from:

DMA,national, and zip-code.

19. A non-transitory computer-readable medium encoded with instructions for executing a method for targeting delivery of advertising content to apopulation of consumers across one or more slots in television programming, the instructions to be executed on one or more suitably configured computer processors, and including instructions for:

receiving a pricepoint and one or more campaign descriptions from an advertiser, wherein each of the campaign descriptions comprises a schedule for delivery of an item of advertising content across one or more televisions accessed by a consumer in the population of consumers, and a target audience, wherein the target audience is defined by one or more demographic factors;

defining a pool of consumers based on a graph of consumer properties, the graph being of a set of nodes and edges connecting pairs of nodes, wherein a node represents a consumer and two nodes that are connected by an edge are similar to one another according to at least a criterion, and wherein a weight of an edge defines a strength of similarity, wherein the graph contains information about two or more TV and mobile devices used by each consumer, demographic and online behavioral data on each consumer, and similarities between pairs of consumers, and wherein the pool of consumers comprises consumers having at least a threshold similarity to a member of the target audience;

receiving a list of inventory from one or more content providers, wherein the list of inventory comprises one or more TV slots;

identifying one or more advertising targets, wherein each of the one or more advertising targets comprises one or more TV slots that are to be delivered within TV content identified as likely to be viewed by the pool of consumers, consistent with one or more of the campaign descriptions, and an overall cost consistent with the pricepoint;

bidding on one or more of the TV slots in an advertising target; and

if the bidding results in a success or a hold,

instructing a media conduit to deliver the item of advertising content within the one or more TV slots to a consumer in the population of consumers on a television.

20. The computer readable medium of claim 19, wherein the graph of consumer properties comprises information that has been obtained probabilistically at least in part.

21. The computer readable medium of claim 19, wherein the bidding includes calculating a probability that the bid price will have an outcome of a win or a hold.

22. The computer readable medium of claim 21 wherein the probability is calculated as a Bayesian distribution.

23. The computer readable medium of claim 19, wherein the TV content identified as likely to be viewed by the pool of consumers is based on a key performance indicator selected from audience reach and direct response.

24. The computer readable medium of claim 19, further comprising placing a further bid if the first bidding is either a hold or a loss.

25. The computer readable medium of claim 19, wherein one or more of the advertising targets includes a gross rating point.

26. The computer readable medium of claim 19, wherein one of more of the slots includes a programming time selected from:

program, and daypart.

27. The computer readable medium of claim 19, wherein one of more of the slots includes a geographic indicator selected from:

DMA, national, and zip-code.