Aol Advertising Inc.

Facebook, Inc.

Verizon Media Inc.

Robert Luenberger

John B Ferber

David Luenberger

Scott Ferber

Stein E Kretsinger

Aaron Eliasib Flores

Hans Marius Holtan

Michael Jan Grebeck

Ali Nasiri Amini

Niklas Karlsson

Jose Carlos Garcia Franco

Jaime Batiz

Alireza Darvish

Kevin C Haley

Jianlong Zhang

Li Si Yang

Scott Strickland

Yih-Shin Huang

Seyed Mohammad Ziaemohseni

Hans W Uhlig

Systems, methods, and computer-readable media are provided for group identification and content delivery. In accordance with one implementation, a computer-implemented method is provided that includes operations performed by at least one processor. The operations of the method include generating, based on identification data, an identification profile for each member of a set of members, the set of members being associated with a viewing group. The set of members may comprise all of the members of the viewing group or any subset of the members of the viewing group. The operations also include determining an individual score for each member of the set of members for each media file of a plurality of available media files based on the corresponding identification profile. Additionally, the operations include determining a group score for each of the plurality of available media files based on the corresponding individual score of each member.

Computerized systems and methods for group identification and content delivery

This invention concerns optimal ad selection for Web pages by selecting and updating an attribute set, obtaining and updating an ad-attribute profile, and optimally choosing the next ad. The present invention associates a set of attributes with each customer. The attributes reflect the customers' interests and they incorporate the characteristics that impact ad selection. Similarly, the present invention associates with each ad an ad-attribute profile in order to calculate a customer's estimated ad selection probability and measure the uncertainty in that estimate. An ad selection algorithm optimally selects which ad to show based on the click probability estimates and the uncertainties regarding these estimates.

Ad placement

Systems and methods for placing electronic advertisements

System and methods are provided for analyzing and displaying digital content and advertisements over electronic networks. In accordance with one implementation, a method is provided that calculates, using at least one processor, an engagement metric (including, for example, click probability) for a specific combination of time, promotion slot, and user segment, for an individual promotion of a set of promotions, and calculates a value of a landing page associated with the promotion. The method then can multiply the engagement metric and the value to determine the valuation of the promotion. The method can operate recursively by using previous calculations in later runs of the same method.

Systems and methods for displaying digital content and advertisements over electronic networks

Systems, methods, and computer-readable media are provided for generating and using models to select thumbnail images for videos. In one exemplary method, the method comprises extracting at least one thumbnail from a video and determining at least one feature present in the at least one thumbnail. The method further comprises sending the extracted at least one thumbnail to one of an editor or at least one viewer. The method further comprises receiving feedback information related to the at least one thumbnail. The method further comprises generating a model and storing the generated model for use in identifying thumbnails for other videos. The model can be generated based on the determined at least one feature and the received feedback information.

Computerized systems and methods for generating models for identifying thumbnail images to promote videos

A method, apparatus, and computer-readable medium estimate media performance on advertising space inventory. The method selects at least one media cell that shares one or more common attributes with a target media cell. The method subsequently estimates mean revenue per impression (RPI) of the selected media cell, and then defines an initial estimate of a RPI of the target media cell based on the estimated RPI of the selected cell. The method computes the RPI of the target media cell by combining the initial RPI estimate for the target media cell with performance data associated with the target media cell.

Method and system for media initialization via data sharing

Systems, apparatuses, and methods are provided for determining a bid value for placing an advertisement onto advertising space available through an electronic marketplace. A method is used for calculating the option value of maintaining the advertisement in the advertising space during one or more periods of time. The option value may be based on expected profits and the estimated future value of maintaining the advertisement. The option value may then be used to calculate the bid price for placing the advertisement.

Systems and methods for determining bids for placing advertisements

Systems and methods relate to frequency of advertisement viewing on the Internet, and more specifically to controlling advertisement viewings by a person during an advertising cycle. A probability of return for a customer is evaluated and an advertisement is shown to the customer based on a function of at least the probability of return and a value for the return. Once the customer has seen the advertisement a target number of times, the customer is no longer shown the advertisement.

Systems and methods for providing a reverse frequency cap in advertisement viewing

Optimal internet ad placement

Growing community of inventors

Palo Alto, CA, United States of America

Robert Luenberger