Methods and apparatus to impute media consumption behavior

Patent metadata

Field	Value
Publication number	US-12445685-B2
Application number	US-202418748794-A
Country	US
Kind code	B2
Filing date	Jun 20, 2024
Priority date	Jun 7, 2016
Publication date	Oct 14, 2025
Grant date	Oct 14, 2025

Abstract

Methods, apparatus, systems and articles of manufacture to impute media consumption behavior are disclosed. An example system includes one or more media meters to obtain tuning data, one or more people meters to obtain viewing data, and one or more servers to, in response to a determination that a difference satisfies a first threshold, determine that a first subset of the tuning data associated with first panelist households having tuned to first media in a first area exhibits local bias, determine that a second subset of the viewing data associated with second panelist households having viewed the first media in the second area represents heavy viewing, and impute the second subset of the viewing data for the first subset of the tuning data in response to the second subset of the viewing data representing heavy viewing.

First claim

What is claimed is:

1. An audience measurement computing system for performing viewership assignment, the audience measurement computing system comprising:
a network interface;
a processor; and
memory having stored thereon machine-readable instructions that, when executed by the processor, cause performance of operations comprising:
obtaining, via the network interface and from media meters in a first area, first tuning data associated with first panelists exposed to first media, wherein the media meters correspond to first households, and wherein the media meters do not identify, within the first tuning data, respective ones of the first panelists that are exposed to the first media;
classifying a subset of the first tuning data as heavy tuning data based on one or more of a total number of the first households or a total number of exposure minutes of the first media;
determining that the heavy tuning data represents a local bias in the first area based on a comparison of exposure minutes of second media viewed by second panelists in a second area to exposure minutes of the second media viewed by third panelists in the first area, wherein the second media is related to the first media;
obtaining, via the network interface and from people meters in the second area, viewing data associated with the second panelists, wherein the people meters identify, within the viewing data, respective ones of the second panelists that are exposed to the second media; and
based on determining that the heavy tuning data represents the local bias, imputing the viewing data associated with the second panelists to at least one of the first panelists.

2. The audience measurement computing system of claim 1 , wherein the viewing data comprises demographic data for the second panelists.

3. The audience measurement computing system of claim 2 , wherein the viewing data further comprises media consumption behavior data for the second panelists.

4. The audience measurement computing system of claim 1 , wherein determining that the heavy tuning data represents the local bias in the first area based on the comparison of the exposure minutes of the second media viewed by the second panelists in the second area to the exposure minutes of the second media viewed by the third panelists in the first area comprises determining that a difference between the exposure minutes of the second media viewed by the second panelists in the second area to the exposure minutes of the second media viewed by the third panelists in the first area satisfies a threshold.

5. The audience measurement computing system of claim 1 , wherein the second media and the first media are from the same media source.

6. The audience measurement computing system of claim 1 , wherein the second media and the first media have the same media genre.

7. The audience measurement computing system of claim 1 , wherein classifying the subset of the first tuning data as the heavy tuning data based on one or more of the total number of the first households or the total number of exposure minutes of the first media comprises:
classifying the subset of the first tuning data as the heavy tuning data based on one or more of (i) a first determination that the total number of the first households satisfies a household number threshold or (ii) a second determination that the total number of exposure minutes of the first media relative to a total number of exposure minutes of a plurality of media, including the first media, satisfies an exposure percentage threshold.

8. A non-transitory computer readable storage medium comprising instructions that, when executed, cause a processor of an audience measurement computing system to perform operations comprising:
obtaining, via a network interface of the audience measurement computing system and from media meters in a first area, first tuning data associated with first panelists exposed to first media, wherein the media meters correspond to first households, and wherein the media meters do not identify, within the first tuning data, respective ones of the first panelists that are exposed to the first media;
classifying a subset of the first tuning data as heavy tuning data based on one or more of a total number of the first households or a total number of exposure minutes of the first media;
determining that the heavy tuning data represents a local bias in the first area based on a comparison of exposure minutes of second media viewed by second panelists in a second area to exposure minutes of the second media viewed by third panelists in the first area, wherein the second media is related to the first media;
obtaining, via the network interface and from people meters in the second area, viewing data associated with the second panelists, wherein the people meters identify, within the viewing data, respective ones of the second panelists that are exposed to the second media; and
based on determining that the heavy tuning data represents the local bias, imputing the viewing data associated with the second panelists to at least one of the first panelists.

9. The non-transitory computer readable storage medium of claim 8 , wherein the viewing data comprises demographic data for the second panelists.

10. The non-transitory computer readable storage medium of claim 9 , wherein the viewing data further comprises media consumption behavior data for the second panelists.

11. The non-transitory computer readable storage medium of claim 8 , wherein determining that the heavy tuning data represents the local bias in the first area based on the comparison of the exposure minutes of the second media viewed by the second panelists in the second area to the exposure minutes of the second media viewed by the third panelists in the first area comprises determining that a difference between the exposure minutes of the second media viewed by the second panelists in the second area to the exposure minutes of the second media viewed by the third panelists in the first area satisfies a threshold.

12. The non-transitory computer readable storage medium of claim 8 , wherein the second media and the first media are from the same media source.

13. The non-transitory computer readable storage medium of claim 8 , wherein the second media and the first media have the same media genre.

14. The non-transitory computer readable storage medium of claim 8 , wherein classifying the subset of the first tuning data as the heavy tuning data based on one or more of the total number of the first households or the total number of exposure minutes of the first media comprises:
classifying the subset of the first tuning data as the heavy tuning data based on one or more of (i) a first determination that the total number of the first households satisfies a household number threshold or (ii) a second determination that the total number of exposure minutes of the first media relative to a total number of exposure minutes of a plurality of media, including the first media, satisfies an exposure percentage threshold.

15. A method performed by an audience measurement computing system comprising a network interface, a processor, and a memory, the method comprising:
obtaining, via the network interface and from media meters in a first area, first tuning data associated with first panelists exposed to first media, wherein the media meters correspond to first households, and wherein the media meters do not identify, within the first tuning data, respective ones of the first panelists that are exposed to the first media;
classifying a subset of the first tuning data as heavy tuning data based on one or mo

Assignees

• Nielsen Co Us Llc

Inventors

• Kurzynski David J
• Shankar Balachander
• Peters Richard
• Sullivan Jonathan
• Poppie Molly

Classifications

• H04N21/84

  Generation or processing of descriptive data, e.g. content descriptors {(systems specially adapted for using meta-information in broadcast systems H04H60/73)} · CPC title

• G06Q30/0255

  based on user history · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• H04N21/44224

  Monitoring of user activity on external systems, e.g. Internet browsing · CPC title

• G06N20/00

  Machine learning · CPC title