A system and method are disclosed for training a machine learning model using information pertaining to transmissions of one or more media items to user devices associated with a user account. Generating training data for the machine learning model includes generating first contextual information associated with a first user device and generating a first target output that identifies an indication of a preference of a user preference to cancel the first transmission. The method includes providing the training data to train the machine learning model.

Methods, apparatus and systems related to production of a movie, a TV show or a multimedia content are described. In one example aspect, a system for producing a multimedia digital content includes a pre-production subsystem configured to receive information about a storyline, cameras, cast, and other assets for the content from a user. The pre-production subsystem is configured to generate one or more machine-readable scripts that include information about one or more advertisements. The system includes a production subsystem configured to receive the one or more machine-readable scripts from the pre-production system to obtain a footage according to the storyline. The production subsystem is further configured to embed one or markers corresponding to the one or more advertisements in the footage. The system also includes a post-production editing subsystem configured to detect the one or more markers embedded in the footage and replace each of the one or more markers with a corresponding advertising target.

Methods and apparatus to detect spillover are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to: identify a quantity of first durations of loudness in an audio signal of media; calculate a ratio of the quantity of the first durations of loudness to a quantity of second durations of loudness in the audio signal of the media, the quantity of the second durations of loudness including the quantity of the first durations of loudness; and in response to a detection of the audio signal being spillover, store data denoting the media as un-usable to credit a media exposure when the ratio does not satisfy a loudness ratio threshold, the storing of the data to improve an accuracy of media exposure credits by not crediting spillover media.

A request from a user to view a video content item may be received, the requesting user being associated with a set of preferences and a context. A group of similar users may be identified based the set of preferences or the context. A number of promotional video items corresponding to the video content item may be identified. A first subset of the number of promotional video items may be determined based on the set of preferences or the context of the user. A watch time difference may be determined for each promotional video item in the first subset. A second subset may be determined based on the watch time difference associated with each promotional video items. An activity rate associated with the promotional video items in the second subset is determined. A promotional video item of the second subset that satisfies a criterion is provided to the user.

Methods and apparatus to calibrate media ratings based on return path data are disclosed. An apparatus includes a processor and memory including instructions that, when executed, cause the processor to: determine an initial rating for the media provided in a first geographic area based on return path data (RPD) tuning information obtained from RPD devices in subscriber households in the first geographic area; determine a first panelist rating for the media provided in a second geographic area based on first panel tuning information obtained from first metering devices in a first subset of panelist households in the second geographic area; determine a nonsubscriber calibration factor based on the first panelist rating; and determine a final rating for the media in the first geographic area by modifying the initial rating based on the nonsubscriber calibration factor.

A system for performing user-initiated content modification includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive a request to perform a modification to content, determine, in response to the request, whether the modification is permissible or impermissible, and when the modification is determined to be impermissible, deny the request. When the modification is determined to be permissible, the processing hardware is configured to further execute the software code to obtain the content, obtain or produce alternate content for use in modifying the content per the request, and perform the modification to the content, using the alternate content, to provide modified content.

A method implemented by one or more computing systems includes accessing content viewing data associated with a first user account, wherein the first user account is associated with one or more client devices. The content viewing data includes temporal-based content viewing data. The method further includes determining, using one or more sequence models, a set of content viewing features based on the temporal-based content viewing data, and concatenating the content viewing features into a single computational array. The method further includes providing, through one or more dense layers of a deep-learning model, the single computational array to an output layer of the deep-learning model, and calculating, based on the output layer, one or more probabilities for one or more labels for the first user account. Each label includes a predicted attribute for the first user account.

Disclosed is a system for generating personalized recommendations based on dynamic and customized content selections and modeling of the content selections. The system may receive a request with an identifier and a query, and may obtain a particular recommendation configuration based the identifier and the query. The system may retrieve a set of content that satisfies the query and that is identified with at least one content prioritization parameter specified in the particular recommendation configuration, may generate a set of models of one or more model types that model relevance between the set of content and a different event specified in the particular recommendation configuration, and may compute a score for each content in each model based on the modeled relevance. The system may present recommended content in a different order than the set of content based on aggregate scores compiled for each content from the set of models.

Both a conventional receiver and an HDR-compatible receiver well perform electro-optical conversion processing on transmission video data obtained by using an HDR opto-electronic transfer characteristic. High dynamic range opto-electronic conversion is performed on high dynamic range video data to obtain the transmission video data. Encoding processing is performed on this transmission video data to obtain a video stream. A container of a predetermined format including this video stream is transmitted. Metadata information indicating a standard dynamic range opto-electronic transfer characteristic is inserted into a layer of the video stream, and metadata information indicating a high dynamic range opto-electronic transfer characteristic is inserted into at least one of the layer of the video stream and a layer of the container.

Methods and apparatus to model on/off states of media presentation devices based on return path data are disclosed. An apparatus includes a memory and processor circuitry to execute instructions stored in the memory to: generate a first probability distribution indicative of actual durations of panel tuning segments, the panel tuning segments corresponding to time periods during which panelists were exposed to first media; generate a second probability distribution indicative of modelled durations of modelled tuning segments, the modelled tuning segments corresponding to modified lengths of the panel tuning segments; and estimate a set-on time for a media set associated with an RPD device based on RPD tuning information and the first and second probability distributions, the RPD tuning information reported from the RPD device, the RPD tuning information indicative of a reported RPD tuning segment during which the RPD device was accessing second media.