The Nielsen Comapny (us), LLC

Rachel Worth Olson

David J Kurzynski

Samantha M Mowrer

Jonathan Lee Sullivan

Molly Poppie

Balachander Shankar

Paul J Chimenti

John Charles Coughlin

Neung Soo Ha

Joshua Ivan Friedman

Adam E Hasinski

Michael Evan Anderson

Fatemehossadat Miri

Michael R Sheppard

Ludo Daemen

Remy Spoentgen

PengFei Yi

Christine Heiss

Shuangxing Chen

Rishi Sriram

Margaret M Orton

Richard Peters

Denis Voytenko

Michael Grotelueschen

Robert Gottesman

Arijit Jha

Peter Schnatz

Zachary Zofrea

Elisabeth Weber

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

Methods, apparatus, systems, and articles of manufacture are disclosed to rectify false set top box tuning data. Disclosed examples methods include identifying, by executing an instruction with a processor, in the return path data, first tuning data corresponding to a first group of set top boxes, the first group of set top boxes classified as associated with machine events, determining, by executing an instruction with a processor, a ratio between first tuning events in the return path data and second tuning events in the return path data, the first tuning events attributed to the first group of the set top boxes, the second tuning events attributed to a second group of the set top boxes classified at not associated with machine events, and in response to the ratio satisfying a threshold during a time interval, removing second tuning data associated with the time interval from the first tuning data.

Methods and apparatus to detect and rectify false set top box tuning 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

Processor systems to estimate audience sizes and impression counts for different frequency intervals are disclosed. An example processor system includes a memory management unit (MMU) to assign requests from computing devices indicative of accesses to media to a first block of memory and to assign user-identified impression data corresponding to user-identified impressions to a second block of memory. The processor system including an arithmetic logic unit (ALU) to determine multipliers relating a first probability distribution for the user-identified impressions to a second probability distribution for census impressions. The multipliers based on probability constraints defined by weighted probabilities associated with the second probability distribution, where different ones of the probabilities weighted based on estimated populations for corresponding ones of the different demographics. The ALU to determine a plurality of census impression counts associated with the census impressions based on the multipliers.

Processor systems to estimate audience sizes and impression counts for different frequency intervals

Example apparatus disclosed herein include a return path data classifier to classify a first viewing period associated with segments of return path data received from a set top box into tuning classifications based on the segments of the return path data; calculate a total reported tuning duration for the first viewing period when the first viewing period is classified as live or playback tuning; and compare the total reported tuning duration to a duration threshold to determine whether the segments of return path data associated with the first viewing period are valid. The example apparatus also includes a return path data rectifier to rectify missing tuning data associated with a second viewing period based on tuning data included in the segments of return path data associated with the first viewing period when the segments of the return path data associated with the first viewing period are determined to be valid.

Methods and apparatus to calibrate return path data for audience measurement

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 are disclosed to rectify false set top box tuning data. Disclosed examples methods include identifying in return path data a first group of set top boxes classified as likely to exhibit machine events in tuning data of the return path data more frequently than a second group of set top boxes represented in the return path data. Additionally, in some examples, the method includes determining whether the first group of set top boxes includes a machine event based on a pattern of the tuning data in the return path data for respective ones of the first group of set top boxes and improving an accuracy of return path data by rectifying the machine event.

Methods, apparatus, systems and articles of manufacture to build viewing sessions from ping-level data are disclosed herein. An example apparatus includes a threshold calculator to determine a threshold based on reference sessions represented in panel data obtained from panel meters. The example apparatus also includes a session partitioner to determine if a first ping from a device corresponds to a start ping of the session and a second ping from the device corresponds to an end ping of the session based on (i) the threshold, (ii) first payload data from the first ping, and (iii) second payload data from the second ping. The example apparatus includes the session partitioner to also determine a duration of the session based on a difference between a first time associated with the first ping and a second time associated with the second ping.

Methods and apparatus for session building from ping-level data

Growing community of inventors

Elk Grove Village, IL, United States of America

Rachel Worth Olson