Abstract: A system and method for providing supervised learning to associate profiles in video audiences is provided. The method includes: receiving data providing an association of consumer profiles and video decoders to households within a network; recording zapping events (patterns) created by consumers; and associating zapping patterns of consumers with households. The step of associating further includes: collecting external data and converting a format of the external data into an internal format; converting zapping logs into different data models that can be used to provide set top box signatures; providing the set top box signatures; using the set top box signatures with a list of set top boxes and profiles to provide an association rule; and applying the association rule to the set top box signatures to determine a list of profiles of the consumer profiles associated with a specific set top box of the set top boxes.

Abstract: A method of auctioning targeted advertisement placement for video audiences, contains the steps of: receiving one or more advertising campaign, where each advertising campaign is defined by one or more advertisement effect criteria, and where each advertising campaign is for providing one or more advertisement to one or more viewer profile; receiving at least one bid for at least one of the one or more advertisement campaign; determining an optimal placement of a requested advertisement based upon the advertisement effect criteria and bid prices provided, so as to optimize revenue; and billing an advertiser according to a level at which a request of the advertiser, as defined by an associated advertising campaign, was supplied and actual viewership of the one or more advertisement associated with the advertising campaign.

Abstract: A system and method is provided for providing real time targeted rating to enable content placement for video audiences. The method includes determining if at least one set top box, located within a network having at least one set top box, is on or off, wherein being on is defined as a set top box having a zapping event occur within a predefined time period; determining what one or more viewer profiles are currently consuming content provided by a set top box within the network, wherein currently consuming refers to consuming within the predefined period; and determining targeted rating per a viewer profile that had been identified as currently consuming content via at least one of the set top boxes within the network.

Abstract: A system and method for associating content to one or more viewer profile in video audiences is provided. The method includes obtaining a first set of targeted ratings, wherein the targeted ratings provide a percentage of viewer profiles that consumed content; obtaining a second input set, wherein the second input set contains data showing which of one or more of the viewer profiles are associated with which of one or more set top boxes within the network; and performing operations to identify which of the viewer profiles associated with one or more of the set top boxes from the second input set consumed one or more content that is included in the first set.

Abstract: A system and method for providing unsupervised learning to associate profiles in video audiences is provided. The method includes: receiving a zapping log and a broadcast schedule, wherein the zapping log includes records of set top box zapping signatures for at least a portion of the set top boxes of the network; deriving set top box signatures from the zapping log and broadcast schedule; clustering viewer profiles into groups of viewer profiles using the set top box signatures; and associating at least one set top box within the network with at least one viewer profile, wherein the method of performing unsupervised learning does not use data associating demographic or psychographic profiles to the at least a portion of the set top boxes of the network for which the zapping log contains records of set top box zapping signatures.

Abstract: A system and method for providing supervised learning to associate profiles in video audiences is provided. The method includes: receiving data providing an association of consumer profiles and video decoders to households within a network; recording zapping events (patterns) created by consumers; and associating zapping patterns of consumers with households. The step of associating further includes: collecting external data and converting a format of the external data into an internal format; converting zapping logs into different data models that can be used to provide set top box signatures; providing the set top box signatures; using the set top box signatures with a list of set top boxes and profiles to provide an association rule; and applying the association rule to the set top box signatures to determine a list of profiles of the consumer profiles associated with a specific set top box of the set top boxes.

Abstract: A system and method for providing targeted rating of profiles in video audiences is provided. The method includes the steps of deriving a first input set, wherein the first input set contains data showing which viewer profiles are associated with which set top boxes within the network, wherein the data may also include an association between a single viewer profile and a single set top box within the network; deriving a second input set containing data of at least one set top box signature, wherein the data of the at least one set top box signature further comprises a processed zapping log containing information summarizing viewing habits of at least one set top box within the network; and processing the first input set and the second input set assuming that the second input set can be derived by operations, wherein the operations involve data associating the viewer profiles to set top boxes within the network and to the targeted rating of profiles.