Methods and apparatus to estimate demographics of a household

Patent metadata

Field	Value
Publication number	US-9848224-B2
Application number	US-201514838096-A
Country	US
Kind code	B2
Filing date	Aug 27, 2015
Priority date	Aug 27, 2015
Publication date	Dec 19, 2017
Grant date	Dec 19, 2017

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 non-panelist households, and reduce a quantity of panelist data retrieved from a computer memory, the method comprising:
reducing, by executing an instruction with a processor, the quantity of panelist data retrieved from the computer memory by calculating a first demographic constraint average and a second demographic constraint average in connection with tuning events of non-panelist households, the first and second demographic constraint averages based on (a) acquiring a first quantity of temporally-mapped panelist demographic distribution data occurring during and associated with a first tuning event of a set-top box of a respective one of the non-panelist households and (b) acquiring a second quantity of temporally-mapped panelist demographic distribution data occurring during and associated with a second tuning event of the set-top box of the respective one of the non-panelist households, the first and second tuning events of the non-panelist household reducing a quantity of the first and second temporally-mapped panelist demographic distribution data retrieved from the computer memory, the first quantity of temporally-mapped panelist subset of demographic distribution data including (a) a first probability that the first tuning event is associated with a first demographic constraint and (b) a second probability that the first tuning event is associated with a second demographic constraint, the second quantity of temporally-mapped panelist demographic distribution data including (a) a third probability that the second tuning event is associated with the first demographic constraint and (b) a fourth probability that the second tuning event is associated with the second demographic constraint;
based on the first demographic constraint average and the first probability that the first tuning event is associated with the first demographic constraint, determining, by executing an instruction with the processor, a first likelihood score of the non-panelist household being associated with the first demographic constraint;
based on the second demographic constraint average and the second probability that the first tuning event is associated with the second demographic constraint, determining, by executing an instruction with the processor, a second likelihood score of the non-panelist household being associated with the second demographic constraint;
estimating, by executing an instruction with the processor, a household characteristic of the non-panelist household based on the first likelihood score and the second likelihood score, the household characteristic including a number of members of the non-panelist household; and
producing, by executing an instruction with the processor, ratings of presented media for a mixed audience of non-panelist and panelist households based on the household characteristic.

2. The method as defined in claim 1 , further including estimating a number of televisions within respective ones of the non-panelist households and locations of the televisions within the respective ones of the non-panelist households.

3. The method as defined in claim 1 , further including estimating a demographic of a non-panelist household member.

4. The method as defined in claim 3 , wherein the estimating of the demographic of the non-panelist household member includes determining a marginal of a demographic dimension for the non-panelist household member, the demographic dimension including the first demographic constraint and the second demographic constraint.

5. The method as defined in claim 1 , further including:
dividing the first demographic constraint average of a first one of the non-panelist households by a first constraint probability of an average demographic distribution for consumption events of panelist households; and
dividing the second demographic constraint average of a second one of the non-panelist households by a second constraint probability of the average demographic distribution of panelist households.

6. The method as defined in claim 1 , wherein the estimating of the household characteristic of the non-panelist households is based on at least one of the first and second demographic constraint, a total number of minutes consumed by respective ones of the non-panelist households, or a number of minutes consumed by respective ones of the non-panelist households per predetermined time-period segments.

7. The method as defined in claim 1 , further including calculating a score vector based on the first likelihood score and the second likelihood score, the score vector to be applied to a machine learning classifier to estimate the household characteristic.

8. The method as defined in claim 7 , wherein the machine learning classifier is a decision tree ensemble.

9. The method as defined in claim 8 , further including:
training the decision tree ensemble on consumption data of a first panelist household; and
testing the decision tree ensemble on consumption data of a second panelist household, the second panelist household being different than the first panelist household.

10. The method as defined in claim 1 , further including identifying whether at least one of the first likelihood score or the second likelihood score is equal to or greater than a threshold value.

11. An apparatus to determine demographics for non-panelist households, and to reduce a quantity of panelist data retrieved from a computer memory, the apparatus comprising:
a score calculator to:
reduce the quantity of panelist data retrieved from the computer memory by calculating a first demographic constraint average and a second demographic constraint average in connection with tuning events of non-panelist households, the first and second demographic constraint averages based on (a) acquiring a first quantity of temporally-mapped panelist demographic distribution data occurring during and associated with a first tuning event of a set-top box of a respective one of the non-panelist households and (b) acquiring a second quantity of temporally-mapped panelist demographic distribution data occurring during and associated with a second tuning event of the set-top box of the respective one of the non-panelist households, the first and second tuning events of the non-panelist households reducing a quantity of the first and second temporally-mapped panelist demographic distribution data retrieved from the computer memory, the first quantity of temporally-mapped panelist demographic distribution data including (a) a first probability that the first tuning event is associated with a first demographic constraint and (b) a second probability that the first tuning event is associated with a second demographic constraint, the second quantity of temporally-mapped panelist demographic distribution data including (a) a third probability that the second tuning event is associated with the first demographic constraint and (b) a fourth probability that the second tuning event is associated with the second demographic constraint;
based on the first demographic constraint average and the first probability that the first tuning event is associated with the first demographic constraint, determine a first likelihood score of the non-panelist household being associated with the first demographic constraint;
based on the second demographic constraint average and the second probability that the second tuning event is associated with the second demographic constraint, determine a second likelihood score of the non-panelist household being associated with the second demographic constraint;

a household estimator to estimate a household characteristic of the non-panelist house

Assignees

• Nielsen Co Us Llc
• The Nielsen Company(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

• H04N21/4665

  involving classification methods, e.g. Decision trees · 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