Comscore, Inc.

Cameron Meierhoefer

Frank Pecjak

Brian Pugh

David Pham Kapar

Michael J Vinson

Magid M Abraham

Patrick James Kemp

Josh Chasin

Patrick McCann

Alan Vaughn

John Clougherty

Michael A Brown

Ramzi Joseph Nasr

Douglas J Honnold

Joseph Lahr

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing secure and trusted access to information regarding online inventory and for providing publishers control over the type and amount of information provided regarding the online inventory are disclosed. One aspect includes receiving a request to generate a secure token related to online inventory and obtaining an identifier associated with the content associated with the online inventory based at least in part on a URL included in the received request. The obtained identifier is combined with other information associated with the content associated with the online inventory. A secure token is generated by applying one or more security features to the combination of the obtained identifier and the other information associated with the content associated with the online inventory.

Secure access to controlled inventory profiles

Techniques for projecting household-level viewing events are described herein. Population data may be accessed including classes of a plurality of demographic attributes for households in a market. Representative household units (RHUs) may be generated, and the RHUs may be assigned a class for each of the demographic attributes and a quota based on the demographic attributes of a plurality of panelist households. Each of the panelist households may be assigned to one of the RHUs based on at least one panelist classes matching the classes for respective demographic attributes of the RHU, and the number of matching panelist households assigned to each of the RHU may be based on the quota. Panelist viewing data representing viewing events associated with the panelist household may be accessed. A report may be generated with the classes of the RHUs and the panelist viewing data of the assigned panelist households.

Deterministic household assignment model

Webpage or other resource accesses by client systems may be recorded, and those accesses may be analyzed to develop audience measurement reports. At times, it may be desirable to segment those reports according to classes of client systems (e.g., work vs. home client systems). A given client system can be classed into one of the reporting classes based on one or more classes of network service providers that provide the client with access to a network. The recorded resource accesses and classes of the client systems can then be used to generate audience measurement reports that are segmented according to one or more of the client system classes.

Determining client system attributes

A first data set associated with a first group of users is accessed. The first data set includes demographic data, online behavior data, and additional user data associated with the users in the first group. A second data set associated with a second group of users is accessed. The second data set includes demographic data and online behavior data but not additional user data associated with the users in the second group. One or more sets of matched users are determined based on the demographic data and online behavior data included in the first data set and the demographic data and online behavior data included in the second data set. Each set includes a user from the first group matched with a user from the second group. Based on the one or more sets of matched users, an augmented second data set that includes additional user data associated with the users in the second group is generated. One or more reports are generated based on the augmented second data set.

Data fusion using behavioral factors

In general, systems, components, methods, and techniques for gathering, recording, and development of accurate user demographics attributed to accesses by client systems of webpage, web content, or other online resources is provided. For a given a subset of unique users having known demographics for a segment of traffic or general population, a demographic profile may be created for all users of the segment or population within a tolerable margin of error. Demographic data for unique machine identifications (IDs) may be assigned, aggregated, and derived from a single source, or multiple sources, such as panel data, beacon level surveys, cookie exchanges, and the overlay of profile cookies from other publishers.

Attribution of demographics to census data

In general, webpage or other resource accesses by client systems may be recorded, and those accesses may be analyzed to develop audience measurement reports. At times, it may be desirable to segment those reports according to classes of client systems (e.g., work vs. home client systems). A given client system can be classed into one of the reporting classes based on one or more classes of network service providers that provide the client with access to a network. The recorded resource accesses and classes of the client systems can then be used to generate audience measurement reports that are segmented according to one or more of the client system classes.

Projection weights may be configured to project usage of a first set of resources by users of a first group of clients systems to usage of the first set of resources by users in a larger group of users. Users of the first group of client systems may be a sample of a larger group of users that access resources on the network using client systems. The projection weights may be determined based on a first set of usage data and a second set of usage data. The first set of usage data may be determined based on information received from monitoring applications installed on a first group of client systems. The second set of usage data may be determined based on information received from a second group of client systems sent as a result of beacon instructions included with a second set of resources accessed by the second group of client systems.

Determining projection weights based on a census data

A system includes a plurality of client devices and a server system. Each of the client devices is configured to implement at least one web browser and a panel application. The web browser is configured to access a plurality of different web pages that each provide search capability and are each configured to display search suggestions in response to character sequences entered by a user when using the search capability. The panel application is configured to send indications of displayed search suggestions and corresponding character sequences to a server system. The server system is configured to generate, based on the received indications, a data structure that correlates character sequences to lists of phrases.

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Washington, DC, United States of America

Cameron Meierhoefer