Determining metrics characterizing numbers of unique members of media audiences

Patent metadata

Field	Value
Publication number	US-10909466-B2
Application number	US-201615247483-A
Country	US
Kind code	B2
Filing date	Aug 25, 2016
Priority date	Aug 25, 2016
Publication date	Feb 2, 2021
Grant date	Feb 2, 2021

Abstract

Example methods disclosed herein include accessing a query requesting a metric associated with a number of unique members of an audience of media over an aggregate monitoring interval corresponding to a plurality of component monitoring intervals. Disclosed example methods also include determining respective aggregate interval probability distributions modeling likelihoods of respective monitored individuals being exposed to the media during the aggregate monitoring interval, a first one of the aggregate interval probability distributions for a first one of the monitored individuals being determined by combining parameters of respective component interval probability distributions modeling likelihoods of the first one of the monitored individuals being exposed to the media during respective ones of the component monitoring intervals. Disclosed example methods further include evaluating an audience-level probability distribution determined from the aggregate interval probability distributions to determine the metric to transmit to the computing device in response to the query.

First claim

What is claimed is:

1. An apparatus to process queries concerning media audiences, the apparatus comprising:
memory; and
a processor to execute computer readable instructions to:
access a query received via a network from a computing device requesting a metric associated with a number of unique members of an audience of media over an aggregate monitoring interval specified in the query, the aggregate monitoring interval corresponding to a plurality of component monitoring intervals;
determine respective aggregate interval probability distributions modeling likelihoods of respective monitored individuals being exposed to the media during the aggregate monitoring interval, a first one of the aggregate interval probability distributions for a first one of the monitored individuals being determined by combining parameters of respective component interval probability distributions modeling likelihoods of the first one of the monitored individuals being exposed to the media during respective ones of the component monitoring intervals corresponding to the aggregate monitoring interval;
estimate the parameters of the respective component interval probability distributions based on impression data collected responsive to beacon requests received from client devices that access the media;
evaluate an audience-level probability distribution determined from the aggregate interval probability distributions to determine the metric; and
transmit a message including the metric to the computing device via the network.

2. The apparatus of claim 1 , wherein the component interval probability distributions for the first one of the monitored individuals are beta probability distributions specified by respective first shape parameters and second shape parameters, the first one of the aggregate interval probability distributions is a beta probability distribution specified by a third shape parameter and a fourth shape parameter, and the processor is further to:
retrieve the first shape parameters and the second shape parameters of the component interval probability distributions from memory; and
combine the first shape parameters and the second shape parameters of the component interval probability distributions to determine the third shape parameter and the fourth parameter of the first one of the aggregate interval probability distributions.

3. The apparatus of claim 2 , wherein the processor is to combine the first shape parameters and the second shape parameters of the component interval probability distributions by at least:
combining the first shape parameters and the second shape parameters of the component interval probability distributions according to a first expression to determine the third shape parameter of the first one of the aggregate interval probability distributions; and
combining the first shape parameters and the second shape parameters of the component interval probability distributions according to a second expression different from the first expression to determine the fourth shape parameter of the first one of the aggregate interval probability distributions.

4. The apparatus of claim 2 , wherein the first shape parameter and the second shape parameter of a first one of the component interval probability distributions for the first one of the monitored individuals are different from the first shape parameter and the second shape parameter of a second one of the component interval probability distributions for the first one of the monitored individuals.

5. The apparatus of claim 1 , wherein the processor is to numerically convolve the aggregate interval probability distributions for the respective monitored individuals to determine the audience-level probability distribution.

6. The apparatus of claim 5 , wherein the processor is further to evaluate the audience-level probability distribution by at least:
accessing a query value included in the query; and
numerically integrating the audience-level probability distribution based on the query value to determine the metric.

7. A method to process queries concerning media audiences, the method comprising:
accessing, by executing an instruction with a processor, a query from a computing device requesting a metric associated with a number of unique members of an audience of media over an aggregate monitoring interval specified in the query, the aggregate monitoring interval corresponding to a plurality of component monitoring intervals;
determining, by executing an instruction with the processor, respective aggregate interval probability distributions modeling likelihoods of respective monitored individuals being exposed to the media during the aggregate monitoring interval, a first one of the aggregate interval probability distributions for a first one of the monitored individuals being determined by combining parameters of respective component interval probability distributions modeling likelihoods of the first one of the monitored individuals being exposed to the media during respective ones of the component monitoring intervals corresponding to the aggregate monitoring interval;
estimating, by executing an instruction with the processor, the parameters of the respective component interval probability distributions based on impression data collected responsive to beacon requests received from client devices that access the media; and
evaluating, by executing an instruction with the processor, an audience-level probability distribution determined from the aggregate interval probability distributions to determine the metric to transmit to the computing device in response to the query.

8. The method of claim 7 , wherein the component interval probability distributions for the first one of the monitored individuals are beta probability distributions specified by respective first shape parameters and second shape parameters, the first one of the aggregate interval probability distributions is a beta probability distribution specified by a third shape parameter and a fourth shape parameter, and further including:
retrieving the first shape parameters and the second shape parameters of the component interval probability distributions from memory; and
combining the first shape parameters and the second shape parameters of the component interval probability distributions to determine the third shape parameter and the fourth parameter of the first one of the aggregate interval probability distributions.

9. The method of claim 8 , wherein the combining of the first shape parameters and the second shape parameters of the component interval probability distributions includes:
combining the first shape parameters and the second shape parameters of the component interval probability distributions according to a first expression to determine the third shape parameter of the first one of the aggregate interval probability distributions; and
combining the first shape parameters and the second shape parameters of the component interval probability distributions according to a second expression different from the first expression to determine the fourth shape parameter of the first one of the aggregate interval probability distributions.

10. The method of claim 8 , wherein the first shape parameter and the second shape parameter of a first one of the component interval probability distributions for the first one of the monitored individuals are different from the first shape parameter and the second shape parameter of a second one of the component interval probability distributions for the first one of the monitored individuals.

11. The method of claim 7 , further including numerically convolving the aggregate interval probability distributions for th

Assignees

• Nielsen Co Us Llc

Inventors

• Sheppard Michael
• Donato Paul
• Doe Peter C

Classifications

• H04H60/31 Primary

  Arrangements for monitoring the use made of the broadcast services · CPC title

• G06N7/01 Primary

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

• H04L67/535

  Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34) · CPC title

• H04N21/44226

  on social networks · CPC title

• H04H60/45

  for identifying users · CPC title