Methods and apparatus to estimate demographics of a household

• US 10,298,982 B2
• Filed: 12/18/2017
• Issued: 05/21/2019
• Est. Priority Date: 08/27/2015
• Status: Active Grant

• Current Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Original Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Inventors: Sullivan, Jonathan, Lipa, Peter, Sheppard, Michael, Terrazas, Alejandro
• Primary Examiner(s): Castro, Alfonso
• Application Number:
  US15/845,042
• Publication Number:
  US 20180205980A1
• Time in Patent Office: 519 Days
• Field of Search: 725 18
• CPC Class Codes: 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
• CPC Class Codes: 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
• CPC Class Codes: H04N 21/44226 on social networks
• CPC Class Codes: H04N 21/4661 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 Class Codes: H04N 21/4665 involving classification methods, e.g. Decision trees
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections

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.

Claims

1. A system to determine demographics for non-panelist households, the system comprising:

first means for collecting;

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 first one of the non-panelist households, 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; and

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 first one of the non-panelist households, 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;

second means for calculating a first demographic constraint average and a second demographic constraint average in connection with the first and second tuning events, the first and second demographic constraint averages based on the first and second quantities of temporally-mapped panelist demographic distribution data, the second means to reduce a quantity of panelist data retrieved from a computer memory;

third means for determining;

a first score of the first one of the non-panelist households being associated with the first demographic constraint, the first score based on the first demographic constraint average and the first probability that the first tuning event is associated with the first demographic constraint; and

a second score of the first one of the non-panelist households being associated with the second demographic constraint, the second score based on the second demographic constraint average and the third probability that the second tuning event is associated with the second demographic constraint; and

fourth means for estimating a household characteristic of the first one of the non-panelist households based on the first score and the second score, the household characteristic including a number of members of the first one of the non-panelist households, the fourth means to produce ratings of presented media for a mixed audience of non-panelist and panelist households based on the household characteristic.

2. The system as defined in claim 1, wherein the fourth means is to estimate 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 system as defined in claim 1, wherein the fourth means is to estimate a demographic of a non-panelist household member.

4. The system as defined in claim 3, wherein the fourth means is to determine a marginal of a demographic dimension of the non-panelist household member, the demographic dimension including the first demographic constraint and the second demographic constraint.

5. The system as defined in claim 1, wherein the second means is to:

divide the first demographic constraint average of the first one of the non-panelist households by a first constraint probability of an average demographic distribution for consumption events of panelist households; and

divide 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 to determine the second score.

6. The system as defined in claim 1, wherein the fourth means is to estimate the household characteristic of the non-panelist households based on at least one of the first and second demographic constraints, 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 system as defined in claim 1, wherein the second means is to calculate a score vector based on the first score and the second score, and the fourth means is to apply the score vector to a classifier to estimate the household characteristic.

8. The system as defined in claim 7, wherein the classifier includes a decision tree ensemble.

9. The system as defined in claim 8, further including fifth means for:

training the decision tree ensemble on consumption data of a second one of the panelist households; and

testing the decision tree ensemble on consumption data of a third one of the panelist households, the second one of the panelist households being different than the third one of the panelist households.

10. The system as defined in claim 1, wherein the fourth means is to identify whether at least one of the first score or the second score satisfies a threshold value.

11. The system as defined in claim 10, wherein the fourth means is to confirm a particular demographic constraint in the respective non-panelist household when the threshold value is satisfied.