A mobile telephone or tablet or the like (40) for viewing commercial television programmes has a display screen (41), a firewall (70), and behind the firewall a first, sealed data storage section (42) which stores personal attributes of a viewer, such as a child, supplied on a token (32) from a trusted third party (10), a second, unsealed data storage section (44) to which the personal attributes are copied, the second section having a flag (48) which is set when the data in the second section has been modified by the viewer, and a third data storage section (46) in which personal preferences can be stored by the viewer. Adverts from advertisers (50) targeted on the data in the second and third sections are sent to the device (40), headers in the adverts are checked against the data in the first section, and adverts are rejected if they are unsuitable for the viewer. Unrejected adverts are shown on the display screen, and the advertiser is informed of their showing, but the identity of the viewer is not disclosed.

Methods, apparatus, systems and articles of manufacture to assign viewers to media meter data are disclosed. An apparatus includes memory to store instructions, and a processor to execute the instructions to at least: determine first probabilities for first panelists in a media meter household during a first set of time periods, determine second probabilities for second panelists in a plurality of learning households during a second set of time periods, compare the first probabilities and the second probabilities to identify a candidate household from the plurality of learning households to associated with the media meter household, and impute ones of the first number of minutes to individual ones of the first panelists when second panelist behavior data associated with the candidate household indicates activity during one of the second set of time periods that matches activity in the media meter household during one of the first set of time periods.

Techniques for projecting person-level viewership from household-level tuning events are described. Initially, panelist viewing data are accessed and a plurality of state values based on the panelist viewing data are determined. Then, tuning data representing tuning events associated with particular households are accessed. For at least one tuning event represented by the tuning data, household member data is accessed, a portion of the panelist viewing data whose panelist information matches at least a portion of the member data is determined, a total number of watched minutes of the program by an individual member and a number of continuous series of watched states of the program by the individual member is determined, and an output representative of a probability that the particular portion of the program was watched by one or more of the individual members is generated.

Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Systems and methods for generating a content item based on a difference between a user confidence score and a confidence score are disclosed. For example, a system generates for output a first content item. While the first content item is being outputted, the system receives user data via sensors of a device. The system determines a user confidence score based on the user data and metadata of the first content item. The user confidence score indicates a user's perceived probability of an event occurring in the future. The system calculates a prediction score which estimates the likelihood of the event occurring in the future. In response to determining that the difference between the user confidence score and the prediction score exceeds a threshold, the system selects a second content item related to the event and generates for output a recommendation comprising an identifier of the second content item.

Methods, apparatus, systems and articles of manufacture to assign viewers to media meter data are disclosed. An apparatus includes processor circuitry to execute computer readable instructions to at least: identify a candidate household from a plurality of second households to associate with a first household based on an analysis of a first duration of time first media was presented by a first media presentation device and a second duration of time second media was presented by second media presentation devices; match different ones of first panelists of the first household with matching ones of second panelists of the candidate household; and impute respective portions of the first duration of time to the different ones of the first panelists based on portions of the second duration of time for which the matching ones of the second panelists of the candidate household were exposed to the second media.

The disclosure provides methods and systems for interactive video content delivery. An example method comprises receiving a video content such as live television or video streaming. The method can run one or more machine-learning classifiers on video frames of the video content to create classification metadata corresponding to the machine-learning classifiers and one or more probability scores associated with the classification metadata. Furthermore, the method can create one or more interaction triggers based on a set of predetermined rules and optionally user profiles. The method can determine that a condition for triggering at least one of the triggers is met and triggers at least one of the actions with regard to the video content based on the determination, the classification metadata, and the probability scores. For example, the action can deliver additional information, present recommendations, automatically edit the video content, or control delivery of video content.

This disclosure relates to methods, non-transitory computer readable media, and systems that determine multiple personas corresponding to a user account for digital content and train a persona classifier to predict a given persona (from among the multiple personas) for content requests associated with the user account. By using the persona classifier, the disclosed methods, non-transitory computer readable media, and systems accurately detect a given persona for a content request upon initiation of the request. Based on determining the given persona, in some implementations, the methods, non-transitory computer readable media, and systems generate a digital-content recommendation for presentation on a client device associated with the user account.

Techniques for projecting person-level viewership from household-level tuning events are described. Initially, panelist viewing data are accessed and a plurality of state values based on the panelist viewing data are determined. Then, tuning data representing tuning events associated with particular households are accessed. For at least one tuning event represented by the tuning data, household member data is accessed, a portion of the panelist viewing data whose panelist information matches at least a portion of the member data is determined, a total number of watched minutes of the program by an individual member and a number of continuous series of watched states of the program by the individual member is determined, and an output representative of a probability that the particular portion of the program was watched by one or more of the individual members is generated.