Quantcast Corporation

Other

Konrad Simeon Feldman

Paul G Sutter

Daniel Vogel Fernandez De Castro

Michael R Blum

Damian John Reeves

Michael Recce

Charles Sugnet

Joseph Harlan Shoop

Michael James Heller

Leonard Laufer

Nikolas Sulkowski

Patrick James Craig

Protected audience selection system. Media consumption histories of browsers which have converted are received at a modeling system where targeting of browsers is prohibited. A model is built by determining a frequency of each respective media consumption event among the histories and comparing each determined frequency of a respective media consumption event to a frequency of the respective media consumption event among a population of browsers without the conversion event. The model is sent to a targeting system which excludes conversion events. A description of the conversion event is received at the targeting system. A history of a targetable browser is received at the targeting system. The model is applied to the history of the targetable browser at the targeting system, where conversion events have been excluded from the history. Advertising content is sent to the targetable browser according to a result of applying the model.

Protected audience selection

Method and system for assessing the suitability of an entity using a proxy. A description of a behavior associated with a desirable audience is received. A proxy behavior estimated to be characteristic of the desirable audience is selected. The proxy behavior comprises the performance of proxy events related to the consumption of media received by an entity over a network, which can be found in an entity's consumption history. An entity can be assessed for inclusion in a proxy audience, by examining the entity's consumption history for proxy behaviors. A behavioral model is built using a training set comprising the proxy audience. By applying the behavioral model to the consumption history of a specified entity, the specified entity's suitability for selection can be determined. Advantageously, in an embodiment, the invention enables the use of behavioral modeling techniques even when the complete behavior of the desirable audience is not available.

Using proxy behaviors for audience selection

Protected audience selection system. Media consumption histories of browsers which have converted are received at a modeling system where targeting of browsers is prohibity. A model is built by determining a frequency of each respective media consumption event among the histories and comparing each determined frequency of a respective media consumption event to a frequency of the respective media consumption event among a population of browsers without the conversion event. The model is sent to a targeting system which excludes conversion events. A description of the conversion event is received at the targeting system. A history of a targetable browser is received at the targeting system. The model is applied to the history of the targetable browser at the targeting system, where conversion events have been excluded from the history. Advertising content is sent to the targetable browser according to a result of applying the model.

Privacy centric feature analysis. A secure set of multiple mapped features is selected and provided to a mobile device. Each mapped feature maps a sharable feature to a matching criterion for an item of protected information and no combination of mapped features for a secure set are unique to an individual item of protected information. Privacy compliance instructions enable the mobile device to select a mapped feature from a received set of mapped features by identifying an item of protected information available to the mobile device which corresponds to a matching criterion found in the received set of mapped features. The sharable feature of the selected mapped feature is provided. Advantageously, the analysis system protects the privacy of the mobile device user because it does not require the mobile device to relay protected information for the selection of customized content or relevant advertisements.

Privacy centric feature analysis for mobile devices

Privacy centric feature analysis. A secure set of multiple mapped features is selected and provided to a mobile device. Each mapped feature maps a sharable feature to a matching criterion for an item of protected information and no combination of mapped features for a secure set are unique to an individual item of protected information. Privacy compliance instructions enable the mobile device to select a mapped feature from a received set of mapped features by identifying an item of protected information available to the mobile device which corresponds to a matching criterion found in the received set of mapped features. The sharable feature of the selected mapped feature is identified and sent to a privacy compliant destination. Advantageously, the analysis system protects the privacy of the mobile device user because it does not require the mobile device to relay protected information for the selection of customized content or relevant advertisements.

Protected audience selection system. Media consumption histories of browsers which have converted are received at a modeling system where targeting of browsers has been disabled by excluding targeting labels. A model is built by determining a frequency of each respective media consumption event among the histories and comparing each determined frequency of a respective media consumption event to a frequency of the respective media consumption event among a population of browsers without the conversion event. The model is sent to a targeting system which excludes conversion events. A description of the conversion event is received at the targeting system. A history of a targetable browser is received at the targeting system. The model is applied to the history of the targetable browser at the targeting system in the absence of records of conversion events. Advertising content is sent to the targetable browser according to a result of applying the model.

An audience selection system for the selection of an entity, based on an entity's consumption history without requiring the storage of a content descriptor for identifying content previously accessed by the entity. By directly and/or indirectly observing the usage of words used to locate content through a search engine over time for a population, a list of depersonalized keywords can be discovered, creating the ability to characterize content based on depersonalized keywords. A protected consumption history can be recorded for an entity using depersonalized keywords instead of recording a content descriptor for identifying the content. Depersonalized keywords do not uniquely identify content. Associating depersonalized keywords with an entity does not mean that the entity has used those depersonalized keywords; it only means that the entity has accessed content which has been accessed in the past by other entities in a population using the depersonalized keywords.

Growing community of inventors

San Francisco, CA, United States of America

Konrad Simeon Feldman