Methods and apparatus to estimate demographics of a household

Patent metadata

Field	Value
Publication number	US-11700405-B2
Application number	US-202117172782-A
Country	US
Kind code	B2
Filing date	Feb 10, 2021
Priority date	Aug 27, 2015
Publication date	Jul 11, 2023
Grant date	Jul 11, 2023

Abstract

Methods and apparatus to estimate demographics of a household are disclosed. An example method to determine demographics for non-panelist households includes calculating a first demographic constraint average and a second demographic constraint average based on a first demographic distribution of a first tuning event of a household and a second demographic distribution of a second tuning event of the household. The household is a non-panelist household. The example method also includes, based on the first demographic constraint average, determining a first likelihood of the household being associated with a first demographic constraint. The example method also includes, based on the second demographic constraint average, determining a second likelihood of the household being associated with a second demographic constraint. The example method also includes estimating a household characteristic of the household based on the first likelihood and the second likelihood.

First claim

What is claimed is:

1. A method to determine demographics for a non-panelist household, the method comprising:
calculating, by executing an instruction with a processor, a first demographic constraint average and a second demographic constraint average based on a first demographic distribution of a first media event of the non-panelist household and a second demographic distribution of a second media event of the non-panelist household, the first demographic distribution includes a first probability that the first media event is associated with a first demographic constraint and a second probability that the first media event is associated with a second demographic constraint, the second demographic distribution includes a third probability that the second media event is associated with the first demographic constraint and a fourth probability that the second media event is associated with the second demographic constraint;
based on the first demographic constraint average and a first constraint probability, determining, by executing an instruction with the processor, a first likelihood of the non-panelist household being associated with the first demographic constraint, the first constraint probability based on the first probability of the first demographic distribution and the third probability of the second demographic distribution;
based on the second demographic constraint average and a second constraint probability, determining, by executing an instruction with the processor, a second likelihood of the non-panelist household being associated with the second demographic constraint, the second constraint probability based on the second probability of the first demographic distribution and the fourth probability of the second demographic distribution; and
estimating, by executing an instruction with the processor, a household characteristic of the non-panelist household based on the first likelihood and the second likelihood.

2. The method of claim 1 , further including collecting data including the first media event and the second media event from a set-top box of the non-panelist household.

3. The method of claim 1 , wherein the non-panelist household does not consent to collection of demographic data.

4. The method of claim 1 , wherein the first and second demographic constraints correspond to at least one of race, age, gender, or education level.

5. The method of claim 1 , wherein the household characteristic of the non-panelist household is representative of at least one of a member of the non-panelist household, a number of members of the non-panelist household, demographics of members of the non-panelist household, or a number of televisions in the non-panelist household.

6. The method of claim 1 , further including estimating the household characteristic of the non-panelist household based on at least one of a total number of minutes of media exposure or a number of minutes for media exposures for different time-period segments.

7. The method of claim 1 , further including:
calculating a score vector based on the first and second likelihood;
applying the score vector to a classifier to estimate the household characteristic, the classifier including a decision tree ensemble;
training the decision tree ensemble based on first consumption data of a first panelist household; and
testing the decision tree ensemble based on second consumption data of a second panelist household, the first panelist household different from the second panelist household.

8. An apparatus comprising:
at least one memory;
instructions in the apparatus; and
processor circuitry to execute the instructions to at least:
calculate a first demographic constraint average and a second demographic constraint average based on (a) a first quantity of temporally-mapped panelist demographic distribution data corresponding to a first media event of a non-panelist household and (b) a second quantity of temporally-mapped panelist demographic distribution data corresponding to a second media event of the non-panelist household;
based on the first demographic constraint average, a first probability, and a second probability, determine a first likelihood score of the non-panelist household being associated with a first demographic constraint, the first probability indicative of a likelihood that the first media event corresponds to the first demographic constraint, the second probability indicative of a likelihood that the second media event corresponds to the first demographic constraint;

based on the second demographic constraint average and a third probability that the second media event corresponds to a second demographic constraint, determine a second likelihood score of the non-panelist household being associated with the second demographic constraint; and
estimate a household characteristic of the non-panelist household based on the first likelihood score and the second likelihood score.

9. The apparatus as defined in claim 8 , wherein the processor circuitry is to execute the instructions to collect data indicative of the first media event and the second media event from a set-top box of the non-panelist household.

10. The apparatus as defined in claim 8 , wherein the first and second demographic constraints correspond to at least one of race, age, gender, or education level.

11. The apparatus as defined in claim 8 , wherein the processor circuitry is to execute the instructions to determine the first likelihood score by dividing the first demographic constraint average by a first constraint probability of an average demographic distribution of panelist households corresponding to consumption events of the panelist households.

12. The apparatus as defined in claim 8 , wherein the processor circuitry is to execute the instructions to estimate the household characteristic based on at least one of a total number of minutes of media exposure, or a number of minutes for media exposures per time-period segments.

13. The apparatus as defined in claim 8 , wherein:
the first quantity of temporally-mapped panelist demographic distribution data includes the first probability that the first media event corresponds to the first demographic constraint; and
the second quantity of temporally-mapped panelist demographic distribution data includes the third probability that the second media event corresponds to the second demographic constraint.

14. The apparatus as defined in claim 8 , wherein the processor circuitry is to execute the instructions to:
calculate a score vector based on the first and second likelihood scores;
apply the score vector to a classifier to estimate the household characteristic, the classifier including a decision tree ensemble;
train the decision tree ensemble based on consumption data of a first panelist household; and
test the decision tree ensemble on consumption data of a second panelist household, the first panelist household different from the second panelist household.

15. At least one non-transitory computer readable medium comprising instructions that, when executed, ca use programmable circuitry to at least:
calculate a first demographic distribution average and a second demographic distribution average based on (a) a first quantity of temporally-mapped panelist demographic distribution data corresponding to a first media event of a non-panelist household and (b) a second quantity of temporally-mapped panelist demographic distribution data corresponding to a second media event of the non-panelist household;
based on the first demographic distribution average, a first probability, and a second probability de

Assignees

• Nielsen Co Us Llc

Inventors

• Sullivan Jonathan
• Lipa Peter
• Sheppard Michael
• Terrazas Alejandro

Classifications

• H04N21/44204

  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) · CPC title

• H04N21/25883 Primary

  being end-user demographical data, e.g. age, family status or address (arrangements for identifying locations of users in broadcast systems H04H60/52) · CPC title

• H04N21/4667

  Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections · CPC title

• H04N21/44226

  on social networks · CPC title

• H04N21/4661

  Deriving a combined profile for a plurality of end-users of the same client, e.g. for family members within a home (user profiles in network data switching protocols H04L67/306) · CPC title