Cross-screen optimization of advertising placement

A technology for advertising and advertising content, used in advertising, data processing applications, marketing, etc.

Active Publication Date: 2018-08-31
VIDEOAMP INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0018] The present disclosure seeks to address consumer and ad inventory handling issues related to the optimization of serving ad content across display devices

Method used

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  • Cross-screen optimization of advertising placement
  • Cross-screen optimization of advertising placement
  • Cross-screen optimization of advertising placement

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0323] Example 1: Improving a multi-screen campaign

[0324] One example has been developed using the equipment and procedures described elsewhere in this specification.

[0325] Figure 6 is an application diagram of methods for targeting specific audiences and improving audiences based on feedback. The consumer map is used in the planning phase, while other methods shown in this specification (eg, bidding for programmatic TV content, etc.) are used in the buying phase. Figure 6 The section shown in is for optimizing the match between audience and ad inventory. The target audience is a fixed audience. The system collects data related to advertising inventory requirements. In this example, the first demand 101 comes from an advertiser requesting to find 1000 audience members, and these audience members are male audience members aged between 25 and 35 who "want to buy a BMW car". The system runs a machine learning process on audience data to find suitable ad inventory.

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example 2

[0330] Example 2: Modeling similar groups of people

[0331] Model the consumer group into consumers with changeable and non-changeable characteristics. Both mutable and immutable traits are treated separately as macro classes. In other words, the relevant data representing each set of features is separated into two separate packets of information. Associate each consumer with relevant packets of device behavior, and classify each consumer as either a mutable feature or an immutable feature. Based on these characteristics, each data packet is further divided into sub-packets by known consumption behavior. For example, a woman who is known to have children is represented in the variable feature group as likely to need to buy diapers. This variation profile is known to vary over time, which means that the woman's child's purchase trend indicator needs to be adjusted as she ages (eg, within two years).

[0332] Similar groups of people can also be modeled by making other assu...

example 3

[0335] Example 3: Interface

[0336] In an exemplary embodiment, the systems and methods herein are implemented through a fully self-service user interface that allows advertisers to upload ad content, select ad inventory, conduct ad inventory bids, and monitor ad campaign success. An exemplary interface such as Figure 8A and Figure 8B shown in . This interface allows advertisers to select ad inventory based on bid prices. An interface for serving ads to online / web content such as Figure 8A shown. Interface for serving ads to programmatic TV content ( Figure 8B ) shows a list of TV showtimes for a specific region and key data such as expected impressions. Other similar interfaces that allow advertisers to place content into applications and VOD environments can be devised.

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PUM

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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 plansto 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 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.

Description

[0001] priority statement [0002] This application is based on U.S. Provisional Patent Application No. 62 / 196,592, filed July 24, 2015, and U.S. Provisional Patent Application No. 62 / 264,764, filed December 8, 2015, and pursuant to The entire contents of both or all of the aforementioned applications are hereby incorporated by reference into this application in their entireties to which priority is claimed under 35 USC § 119(e). [0003] Cross References to Related Applications [0004] This application is related to the U.S. Patent Application No. 15 / ____ filed on July 25, 2016 (title: "TARGETING TV ADVERTISING SLOTS BASED ONCONSUMER ONLINE BEHAVIOR)"; agent Case No.: 2792-00-003U01), U.S. Patent Application No. 15 / ____ filed on July 25, 2016 (Title: "Cross-Screen Measurement Accuracy of Advertising Performance (CROSS-SCREENMEASUREMENT ACCURACY IN ADVERTISING PERFORMANCE) "; Attorney Case No.: 2792-00-005U01), the U.S. Patent Application No. 15 / ____ filed on July 25, 2016 (n...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N21/2543H04N21/266H04N21/2668
CPCH04N21/252H04N21/2543H04N21/25883H04N21/25891H04N21/2668H04N21/4532H04N21/812G06Q30/0264G06Q30/0269G06Q30/0275H04N21/2385H04N21/4667
Inventor D·雷R·麦克雷D·古洛J·普拉萨德
Owner VIDEOAMP INC
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