Targeting TV advertising slots based on consumer online behavior

• US 10,085,073 B2
• Filed: 07/25/2016
• Issued: 09/25/2018
• 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:
  US15/219,259
• Publication Number:
  US 20170034591A1
• Time in Patent Office: 792 Days
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0275 Auctions
• CPC Class Codes: H04L 65/611 for multicast or broadcast systems for broadcast or conference H04L12/18; arrangements for broadcast or distribution combined with broadcast H04H20/00; arrangements for broadcast applications with a direct linkage to broadcast information or to broadcast space-time H04H60/00; selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS], H04W4/06
• CPC Class Codes: H04L 65/612 for unicast
• 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 21/47202 for requesting content on demand, e.g. video on demand
• CPC Class Codes: H04N 21/64322 IP
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 21/8456 by decomposing the content in the time domain, e.g. in time segments

Abstract

Within an advertising buying platform, advertising buyers are able to direct advertising content to TV/OTT/VOD inventory that matches targeted market segments. The advertising buyers select from a list of available market segments, and the system pairs the market segments to an anonymous dataset of media consumers (using a database of hashed online IDs). The dataset of hashed online IDs is then mapped to external OEM IDs (such as from a Smart TV) via the functions of a hashed identity mapping method. This permits the advertiser to target advertising content to a population of TV viewers. Also described herein are methods of bidding on programmatic TV content.

Claims

1. A method for targeting a population of TV viewers to which to deliver advertising content, the method being performed by at least one computer system containing at least one hardware 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 hardware processor, TV data for a first population of consumers, wherein the TV data comprises at least one IP address and a list of TV viewing data for each TV accessible to a consumer in the first population of consumers;

de-identifying, by the at least one hardware processor, the IP address for each TV, thereby creating a hash-ID for each TV that is associated with each consumer in the first population of consumers;

associating, by the at least one hardware processor, the hash-ID for each TV with a consumer identifier in a graph of consumer data, the graph being of a set of nodes 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, wherein the graph of consumer data is constructed from online behavioral and demographic data on a second population of consumers;

identifying, by the at least one hardware processor, one or more slots of TV advertising inventory based on one or more demographic factors of consumers in the second population of consumers and the list of TV viewing data associated with those consumers;

providing, by the at least one hardware processor, a way for an advertiser to purchase a slot of TV advertising inventory relevant to the second population of consumers; and

, after an advertiser purchases a slot of TV advertising inventory,

instructing, by the at least one hardware processor, a media conduit to deliver the item of advertising content to the second population of consumers in the one or more slots of inventory.

2. The method of claim 1, wherein the TV data comprises programmatic TV data.

3. The method of claim 1, wherein the purchasing comprises bidding on inventory, wherein the bidding is by real-time bidding or by bidding on programmatic TV content.

4. The method of claim 1, wherein the providing a way for an advertiser to purchase, comprises:

presenting, via a browser or web-enabled interface, a market segment selection form permitting an advertiser to select from a variety of market segment categories.

5. The method of claim 1, wherein the consumer data comprises transmutable and non-transmutable data.

6. The method of claim 1, wherein the identifying comprises:

predicting, via a machine learning process, one or more inventory slots for a particular market segment.

7. The method of claim 1, wherein the de-identifying uses a one-way hash function.

8. The method of claim 1, wherein the one or more hardware processes are physically distributed amongst multiple datacenters.