The Nielsen Comapny (us), LLC

Michael Evan Anderson

Samantha M Mowrer

David J Kurzynski

Fatemehossadat Miri

Robert Gottesman

Rachel Worth Olson

Michael Grotelueschen

Astha Jain

Molly Poppie

Demetrios Fassois

Rishi Sriram

Margaret M Orton

Tushar Chandra

Balachander Shankar

Arijit Jha

Zachary Zofrea

Peter Schnatz

Methods, apparatus, systems and articles of manufacture to determine whether a smart device is in-tab are disclosed. An example apparatus includes memory; instructions in the apparatus; and processor circuitry to execute the instructions to: provide smart television data from a smart television as an input to a model to generate an output, the smart television data being included in population data from a population of smart televisions; determine a tab status of the smart television based on the output; in response to the tab status of the smart television being out-of-tab, remove the smart television data from the population data; and credit media based on the population data.

Methods and apparatus to determine when a smart device is out-of-tab

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to deduplicate common devices across multiple data sources are disclosed. An example apparatus includes instructions to identify a first device in a first data source and a second device in a second data source as a possible common device, calculate at least one of a station duration metric, a time match metric or a station path metric, the station duration metric, the time match metric based times of day that the first device tuned to a second set of stations and times of day that the second device tuned to the second set of stations, determine a score based on the at least one of the station duration metric, the time match metric, or the station path metric, and determine when the first device and the second device are a common device based on the score.

Deduplication across multiple different data sources to identify common devices

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.

Media device on/off detection using return path data

Methods, apparatus, systems, and articles of manufacture are disclosed to determine total audience ratings. An example apparatus includes metrics generator circuitry to generate a first audience size for media accessed by first devices of a first media platform at a first level of aggregation, the first level of aggregation corresponding to the media accessed on a first television network and on a first website, generate a second audience size for the media accessed by the first devices of the first media platform at a second level of aggregation, the second level of aggregation corresponding to the media accessed on the first television network and accessed on the first website and a second website, comparator circuitry to compare the first audience size to the second audience size, adjustor circuitry to reduce the first audience size based on the second audience size, and audience determination circuitry to determine a total audience size.

Methods and apparatus to structure processor systems to determine total audience ratings

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.

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to deduplicate common devices across multiple data sources are disclosed. An example system includes a comparison controller to identify a first device in a first data source and a second device in a second data source as a possible common device. The example system also includes a first metric calculator to calculate a station duration metric based on first station duration data and second station duration data, the first station duration data associated with a first set of durations of time that the first device tuned to a first set of stations, the second station duration data associated with a second set of durations of time that the second device tuned to the first set of stations; a second metric calculator to calculate a time match metric based on first time data and second time data, the first time data associated with a first set of times of day that the first device tuned to a second set of stations, the second time data associated with a second set of times of day that the second device tuned to the second set of stations, the first set of times of day overlapping with the second set of times of day; and a third metric calculator to calculate a station path metric based on a first sequence of stations tuned to by the first device and a second sequence of stations tuned to by the second device. The example system also includes a comparison scorer to determine a joint score based on the station duration metric, the time match metric, and the station path metric; and a common device selector to determine when the first device and the second device are a common device based on the joint score.

Methods, apparatus, systems and articles of manufacture to determine whether a smart device is in-tab are disclosed. An example apparatus includes a labelled data generator to identify a first smart television that corresponds to a first panelist by comparing first data from the first smart television to second data from the first panelist; and generate training data by labelling the first smart television as in-tab or out-of-tab based on the second data from the first panelist; and a model trainer to train a model using the training data, the model trainer to train the model to determine a tab status for a second smart television.

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Chicago, IL, United States of America

Michael Evan Anderson