Abstract: An example apparatus to perform media device on/off detection using return path data includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to classify view segments associated with common homes data based on whether first return path data in respective ones of the view segments has matching panel meter data to determine labeled view segments, train a machine learning algorithm, based on a first set of features, to output media device on/off determinations to produce a machine learning algorithm, the first set of features generated from the labeled view segments, and apply second return path data to the machine learning algorithm trained to output a first on/off determination associated with a media device represented in the second return path data.

Abstract: Example methods disclosed herein include accessing common homes data for a group of common homes, the common homes data including return path data and panel meter data. Disclosed example methods also include accessing common homes data for a group of common homes, the common homes data including first return path data and corresponding panel meter data associated with respective ones of the common homes, grouping the common homes data into view segments, classifying the view segments based on whether the return path data in respective ones of the view segments has matching panel meter data to determine labeled view segments, generating features from the labeled view segments, training a machine learning algorithm based on the features, and applying second return path data to the trained machine learning algorithm to determine whether a media device associated with the second return path data is on or off.

Abstract: Techniques are disclosed that enable the generation of a content agent based on content parameter(s) determined from an initial user request for content as well as a dialog session to further refine the request for content. Various implementations include using the content agent to render additional content responsive to an additional user request. Additional or alternatively implementations include using the content agent to proactively render content to the user.

Abstract: Methods, apparatus, and articles of manufacture to deduplicate audiences across media platforms are disclosed.

Abstract: Example methods disclosed herein include accessing common homes data for a group of common homes, the common homes data including return path data and panel meter data. Disclosed example methods also include accessing common homes data for a group of common homes, the common homes data including first return path data and corresponding panel meter data associated with respective ones of the common homes, grouping the common homes data into view segments, classifying the view segments based on whether the return path data in respective ones of the view segments has matching panel meter data to determine labeled view segments, generating features from the labeled view segments, training a machine learning algorithm based on the features, and applying second return path data to the trained machine learning algorithm to determine whether a media device associated with the second return path data is on or off.

Abstract: Parallel processing of data may include a set of map processes and a set of reduce processes. Each map process may include at least one map thread. Map threads may access distinct input data blocks assigned to the map process, and may apply an application specific map operation to the input data blocks to produce key-value pairs. Each map process may include a multiblock combiner configured to apply a combining operation to values associated with common keys in the key-value pairs to produce combined values, and to output intermediate data including pairs of keys and combined values. Each reduce process may be configured to access the intermediate data output by the multiblock combiners. For each key, an application specific reduce operation may be applied to the combined values associated with the key to produce output data.

Abstract: A system and method for generating a model based on the user's interests and activities by receiving with a logging unit user activities from heterogeneous data sources, generating a log of user activities for a content item by joining the user activities for the content item, expanding attributes of the log by at least one of content and by the user to form an expanded log and generating a user model based on the expanded log. A feature extractor extracts features from content items and assigns weights to the features. A scoring engine receives the model and the content items with their associated weighted features and scores the content items based on the user model. The scoring engine generates a stream of content based on the scored content items.

Abstract: Servers and storage in a Storage Area Network (SAN) are dynamically allocated to a system user, such as a customer or an application, by identifying at least one master storage image that is in the storage and that will be associated with a system user when a server is allocated to the system user is identified. A plurality of replicas of each identified master storage image is generated prior to at least one server being allocated to the system user. A selected replica of the plurality of replicas of the master storage image is allocated to each server allocated to the system user. Each time a server is de-allocated, the corresponding replica is de-allocated and assigned to a pool of de-allocated replicas. The pool of de-allocated replicas is configured to automatically scrub all replicas, such as by reformatting, when the number of de-allocated replicas equals a predetermined number.