Cross-screen optimization of advertising placement

• US 9,980,010 B2
• Filed: 07/25/2016
• Issued: 05/22/2018
• 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:
  US15/219,262
• Publication Number:
  US 20170099525A1
• Time in Patent Office: 666 Days
• 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 30/0275 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 21/2543 Billing , e.g. for subscription services payment schemes, architectures or protocols G06Q20/00; e-commerce G06Q30/00; arrangements for billing for the use of broadcast information or broadcast-related information H04H60/21
• 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 21/4532 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 across multiple different devices. 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 consumer across two or more devices, 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 two or more devices accessed by a consumer, wherein the devices include one or more TV'"'"'s and one or more mobile devices, 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 representing consumers and edges connecting pairs of nodes, where 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, the graph containing information about;

the 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;

wherein the information has been obtained probabilistically at least in part; 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 slots for TV and online;

identifying, by the at least one processor, one or more advertising targets, wherein each of the one or more advertising targets comprises a sequence of slots consistent with one or more of the campaign descriptions, and an overall cost consistent with the pricepoint;

allocating, by the at least one processor, the advertising content of the one or more campaign descriptions to the one or more advertising targets;

instructing, by the at least one processor, purchase of two or more slots of advertising inventory wherein one or more slots are delivered within TV content identified as likely to be viewed by the target audience based on TV viewing data for the pool of consumers, and one or more slots are delivered online as a result of a real-time decision;

the at least one processor communicating instructions to a first media conduit to deliver the item of advertising content to a consumer in the target audience on a first device; and

the at least one processor additionally communicating instructions to a second media conduit to deliver the item of advertising content to the consumer on a second device.

2. The method of claim 1, wherein the purchasing of a slot of online inventory is via real-time bidding.

3. The method of claim 1, wherein the purchasing of a slot in programmatic TV inventory is via a bidding method that utilizes TV viewing data for specific devices.

4. The method of claim 1, further comprising calculating, by the at least one processor, a deduplicated reach based on delivery of the item of advertising content to first and second devices, and adjusting, by the at least one processor, the advertising targets prior to allocating the advertising content a further time in order to improve the deduplicated reach.

5. The method of claim 1, further comprising calculating, by the at least one processor, a frequency of delivery of advertising content to the consumer, and suspending, by the at least one processor, further delivery of the advertising content to the consumer if the frequency of delivery exceeds a threshold number.

6. The method of claim 1, further comprising calculating, by the at least one processor, a cost-based efficiency based on delivery of the item of advertising content to first and second devices, and adjusting, by the at least one processor, the advertising targets prior to allocating the advertising content a further time in order to improve the cost-based efficiency.

7. A method of optimizing an advertising campaign across a plurality of devices accessible to a consumer, 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:

determining, by the at least one processor, that the consumer is a member of a target audience using a graph of consumer properties, the graph being of a set of nodes representing consumers and edges connecting pairs of nodes, where 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, the graph containing information about;

the plurality of devices used by each consumer;

demographic and online behavioral data on each consumer; and

similarities between pairs of consumers;

wherein the information has been obtained probabilistically at least in part;

identifying, by the at least one processor, a first and second device accessible to the consumer, wherein the first and second device comprise a TV and a mobile device;

receiving, by the at least one processor, instructions for purchase of slots for a first and second item of advertising content on the first and second devices, consistent with an advertising budget and the target audience;

instructing, by the at least one processor, bidding on slots for placement of the first and second items of advertising content, wherein the bidding relies on information about the likely success of a bid based on at least the consumer'"'"'s location, and the time of day;

in the event of successful bids on the first and second items of content, the at least one processor communicating instructions to a first media conduit to cause the first media conduit to deliver the first item of advertising content to the first device; and

the at least one processor additionally communicating instructions to a second media conduit to cause the second media conduit to deliver the second item of advertising content to the second device;

receiving, by the at least one processor, feedback on the consumer'"'"'s response to the first and second items of content; and

the at least one processor using the feedback to instruct purchase of further slots for the first and second items of advertising content.

8. The method of claim 7, wherein the determining that the consumer is a member of a target audience comprises matching, by the at least one processor, behavioral and demographic data on the consumer from a graph of data to parameters of the target audience.

9. The method of claim 7, wherein the bidding includes bidding on TV inventory selected from:

programmatic TV, linear TV, and video-on-demand content.