# US 2020/0401919 A1 · Media device on/off detection using return path data

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- Number: US 2020/0401919 A1
- Document ID: US20200401919A1
- Status: Published application
- Kind: A1
- Application number: 16/698,167
- Filed: 2019-11-27
- Published: 2020-12-24
- Earliest claimed: 2019-06-18
- Assignees: The Nielsen Company (US), LLC
- Applicants: The Nielsen Company (US), LLC
- Inventors: Michael Grotelueschen; Michael Evan Anderson; Fatemehossadat Miri; Demetrios Fassois; Tushar Chandra; David J. Kurzynski
- Web page: https://patentgrep.com/patent/US20200401919A1

## CPC

- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06F 16/2228: {Indexing structures}
- G06F 16/285: {Clustering or classification}
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 20/20: Ensemble learning
- G06Q 30/0201: Market modelling; Market analysis; Collecting market data
- H04N 21/25883: {being end-user demographical data, e.g. age, family status or address (arrangements for identifying locations of users in broadcast systems H04H60/52)}
- H04N 21/25891: {being end-user preferences (retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18)}
- H04N 21/44204: {Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched (monitoring of user activities for profile generation for accessing a video database G06F16/739; protecting generic digital content where the protection is independent of the precise nature of the content G06F21/10; arrangements for monitoring the use made of the broadcast services in broadcast systems H04H60/31)}
- H04N 21/44222: {Analytics of user selections, e.g. selection of programmes or purchase activity (monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33)}
- H04N 21/6582: {Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number (arrangements where receivers interact with the broadcast H04H20/38)}
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION

## 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.

## Claims

### Claim 1

A method to perform media device on/off detection using return path data, the method comprising:
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.

### Claim 2 (depends on 1)

The method of claim 1, wherein the training includes setting a classification probability threshold, the classification probability threshold used by the machine learning algorithm to predict whether a media device would have matching panel meter data, thereby classifying a media associated with the return path data as on.

### Claim 3 (depends on 2)

The method of claim 2, further including adjusting the classification probability threshold based on whether the machine learning algorithm correctly identifies a media device as on or off during the training.

### Claim 4 (depends on 1)

The method of claim 1, wherein the panel meter data is data retrieved by an audience measurement entity from the group of common homes, the panel meter data including media device viewing data.

### Claim 5 (depends on 1)

The method of claim 1, wherein the machine learning algorithm is a random forest classification algorithm.

### Claim 6 (depends on 1)

The method of claim 1, wherein respective ones of the view segments have at least a 15 minute duration.

### Claim 7 (depends on 1)

The method of claim 1, wherein the features generated from the labeled view segments include a view segment index or a view segment duration, the view segment index corresponding to where in a tuning event the view segment occurs.

### Claim 8

An apparatus to perform media device on/off detection using return path data, comprising:
an identifier to:
access 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;
group the common homes data into view segments;
a classifier to classify 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;
a generator to generate features from the labeled view segments;
a trainer to train a machine learning algorithm based on the features; and
an on/off determiner to apply 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.

### Claim 9 (depends on 8)

The apparatus of claim 8, wherein the trainer is to set a classification probability threshold, the classification probability threshold used by the machine learning algorithm to predict whether a media device would have matching panel meter data, thereby classifying a media associated with the return path data as on.

### Claim 10 (depends on 9)

The apparatus of claim 9, wherein the trainer is to adjust the classification probability threshold based on whether the machine learning algorithm correctly identifies a media device as on or off during the training.

### Claim 11 (depends on 8)

The apparatus of claim 8, wherein the panel meter data is data retrieved by an audience measurement entity from the common homes, the panel meter data including media device viewing data.

### Claim 12 (depends on 8)

The apparatus of claim 8, wherein the machine learning algorithm is a random forest classification algorithm.

### Claim 13 (depends on 8)

The apparatus of claim 8, wherein the view segments each have at least a15 minute duration.

### Claim 14 (depends on 8)

The apparatus of claim 8, wherein features generated from the labeled view segments include a view segment index or a view segment duration, the view segment index corresponding to where in a tuning event the view segment occurs.

### Claim 15

A non-transitory computer readable storage medium comprising computer readable instructions that, when executed, cause one or more processors to, at least:
access 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;
group the common homes data into view segments;
classify 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;
generate features from the labeled view segments;
train a machine learning algorithm based on the features; and
apply 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.

### Claim 16 (depends on 15)

The storage medium of claim 15, wherein the instructions, when executed, further cause the one or more processors to identify a classification probability threshold used by the machine learning algorithm to predict whether a media device would have matching panel meter data, thereby classifying a media associated with the return path data as on.

### Claim 17 (depends on 16)

The storage medium of claim 16, wherein the instructions, when executed, further cause the one or more processors to adjust the classification threshold based on whether the algorithm correctly identifies a media device as on or off during the training.

### Claim 18 (depends on 15)

The storage medium of claim 15, wherein the machine learning algorithm is a random forest classification algorithm.

### Claim 19 (depends on 15)

The storage medium of claim 15, wherein the instructions, when executed, further cause the one or more processors to set each of the view segments to at least a 15 minute duration.

### Claim 20 (depends on 15)

The storage medium of claim 15, wherein the instructions, when executed, further cause the one or more processors to generate features from the labeled view segments such that the features include a view segment index or a view segment duration, the view segment index corresponding to where in a tuning event the view segment occurs.