# US 2021/0344997 A1 · Methods and apparatus to determine when a smart device is out-of-tab

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2021/0344997 A1
- Document ID: US20210344997A1
- Status: Published application
- Kind: A1
- Application number: 16/862,501
- Filed: 2020-04-29
- Published: 2021-11-04
- Earliest claimed: 2020-04-29
- Assignees: The Nielsen Company (US), LLC
- Applicants: The Nielsen Company (US), LLC
- Inventors: Michael Evan Anderson; Samantha M. Mowrer; Robert Gottesman; Astha Jain
- Web page: https://patentgrep.com/patent/US20210344997A1

## CPC

- 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)}
- H04H 60/31: Arrangements for monitoring the use made of the broadcast services
- H04H 60/37: for identifying segments of broadcast information, e.g. scenes or extracting programme ID
- H04H 60/45: for identifying users
- 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 21/6582: {Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number (arrangements where receivers interact with the broadcast H04H20/38)}
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04H: BROADCAST COMMUNICATION (multiplex communication H04J; pictorial communication aspects of broadcast systems H04N)

## Abstract

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.

## Claims

### Claim 1

An apparatus comprising:
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.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein the first data includes at least one of a media identifier, a smart television identifier, a timestamp, tuning data, or disconnect data.

### Claim 3 (depends on 1)

The apparatus of claim 1, wherein the second data is meter data indicative of media exposure of the first panelist.

### Claim 4 (depends on 1)

The apparatus of claim 1, wherein the labelled data generator is to identify that the first smart television corresponds to the first panelist when a threshold amount of tuning data of the first data is consistent with the second data.

### Claim 5 (depends on 1)

The apparatus of claim 1, wherein the labelled data generator is to label the first smart television as out-of-tab when tuning data of the first data is inconsistent with meter data of the second data.

### Claim 6 (depends on 1)

The apparatus of claim 1, further including a model implementor to implement the model to determine the tab status for the second smart television based on third data from the first smart television.

### Claim 7 (depends on 6)

The apparatus of claim 6, wherein the tab status is at least one of in-tab or out-of-tab.

### Claim 8 (depends on 6)

The apparatus of claim 6, wherein the third data is part of fourth data from a population of smart televisions, the second smart television included in the population.

### Claim 9 (depends on 8)

The apparatus of claim 8, further including a report generator to:
in response to the model determining that the second smart television is out-of-tab, remove the third data from the fourth data; and
credit media based on the fourth data.

### Claim 10

A non-transitory computer readable storage medium comprising instructions which, when executed cause one or more processors to at least:
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;
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
train a model using the training data to determine a tab status for a second smart television.

### Claim 11 (depends on 10)

The computer readable storage medium of claim 10, wherein the first data includes at least one of a media identifier, a smart television identifier, a timestamp, tuning data, or disconnect data.

### Claim 12 (depends on 10)

The computer readable storage medium of claim 10, wherein the second data is meter data indicative of media exposure of the first panelist.

### Claim 13 (depends on 10)

The computer readable storage medium of claim 10, wherein the instructions cause the one or more processors to identify that the first smart television corresponds to the first panelist when a threshold amount of tuning data of the first data is consistent with the second data.

### Claim 14 (depends on 10)

The computer readable storage medium of claim 10, wherein the instructions cause the one or more processors to label the first smart television as out-of-tab when tuning data of the first data is inconsistent with meter data of the second data.

### Claim 15 (depends on 10)

The computer readable storage medium of claim 10, wherein the instructions cause the one or more processors to implement the model to determine the tab status for the second smart television based on third data from the first smart television.

### Claim 16 (depends on 15)

The computer readable storage medium of claim 15, wherein the tab status is at least one of in-tab or out-of-tab.

### Claim 17 (depends on 15)

The computer readable storage medium of claim 15, wherein the third data is part of fourth data from a population of smart televisions, the second smart television included in the population.

### Claim 18 (depends on 17)

The computer readable storage medium of claim 17, wherein the instructions cause the one or more processors to:
in response to the model determining that the second smart television is out-of-tab, remove the third data from the fourth data; and
credit media based on the fourth data.

### Claim 19

A method to determine a status of a smart television, the method comprising:
identifying, by executing an instruction with a processor, 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;
generating, by executing an instruction with the processor, 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
training, by executing an instruction with the processor, a model using the training data to determine a tab status for a second smart television.

### Claim 20 (depends on 19)

The method of claim 19, wherein the first data includes at least one of a media identifier, a smart television identifier, a timestamp, tuning data, or disconnect data.

### Claim 21

(canceled)

### Claim 22

(canceled)

### Claim 23

(canceled)

### Claim 24

(canceled)

### Claim 25

(canceled)

### Claim 26

(canceled)

### Claim 27

(canceled)

### Claim 28

(canceled)

### Claim 29

(canceled)

### Claim 30

(canceled)

### Claim 31

(canceled)

### Claim 32

(canceled)

### Claim 33

(canceled)

### Claim 34

(canceled)

### Claim 35

(canceled)

### Claim 36

(canceled)