U.S. patent application number 16/843650 was filed with the patent office on 2021-10-14 for methods and apparatus to estimate population reach from marginals.
The applicant listed for this patent is The Nielsen Company (US), LLC. Invention is credited to Ludo Daemen, Jake Dailey, Damien Forthomme, Michael Sheppard.
Application Number | 20210319474 16/843650 |
Document ID | / |
Family ID | 1000004763029 |
Filed Date | 2021-10-14 |
United States Patent
Application |
20210319474 |
Kind Code |
A1 |
Sheppard; Michael ; et
al. |
October 14, 2021 |
METHODS AND APPARATUS TO ESTIMATE POPULATION REACH FROM
MARGINALS
Abstract
Methods, apparatus, systems, and articles of manufacture are
disclosed to estimate population reach from marginals. An example
apparatus includes memory including computer readable instructions;
and a processor to execute the instructions to: iteratively
converge on an output estimate of a pseudo universe estimate of a
recorded audience of first media based on (A) a recorded reach for
the recorded audience of the first media and (B) first marginal
ratings for the recorded audience of the first media; determine a
panel representation value based on the pseudo universe estimate of
the recorded audience of the first media; and iteratively converge
on an output estimate of a final reach of second media for a
population audience based on the panel representation value and
second marginal ratings for the population audience of the second
media.
Inventors: |
Sheppard; Michael; (Holland,
MI) ; Daemen; Ludo; (Duffel, BE) ; Forthomme;
Damien; (Seattle, WA) ; Dailey; Jake; (San
Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Nielsen Company (US), LLC |
New York |
NY |
US |
|
|
Family ID: |
1000004763029 |
Appl. No.: |
16/843650 |
Filed: |
April 8, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04N 21/812 20130101;
G06Q 30/0277 20130101; G06Q 30/0246 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; H04N 21/81 20060101 H04N021/81 |
Claims
1. An apparatus to determine reach of media from marginal ratings,
the apparatus comprising: memory including computer readable
instructions; and a processor to execute the instructions to:
iteratively converge on an output estimate of a pseudo universe
estimate of a recorded audience of first media based on (A) a
recorded reach for the recorded audience of the first media and (B)
first marginal ratings for the recorded audience of the first
media; determine a panel representation value based on the pseudo
universe estimate of the recorded audience of the first media; and
iteratively converge on an output estimate of a final reach of
second media for a population audience based on the panel
representation value and second marginal ratings for the population
audience of the second media.
2. The apparatus of claim 1, wherein the processor is to output the
estimate of the reach of the second media for the population
audience and the panel representation value to an application that
is to perform an operation based on at least one of the estimate of
the reach of the second media for the population audience or the
panel representation value.
3. The apparatus of claim 1, wherein the processor is to converge
on the output estimate of the reach of the second media for the
population audience by: determining a subsequent population
audience reach estimate from a subsequent iteration, the subsequent
population audience reach estimate based on the second marginal
ratings for the population audience and the pseudo universe
estimate of the population audience of the second media; and
evaluating an error between the subsequent population audience
reach estimate determined from the subsequent iteration and an
initial population audience reach estimate determined from an
initial iteration prior the subsequent iteration.
4. The apparatus of claim 3, wherein the processor is to:
determine, during the subsequent iteration, a first pseudo universe
estimate of the population audience of the second media
corresponding to the subsequent iteration based on the panel
representation value; and execute a second subsequent iteration
when the error between the subsequent population audience reach
estimate and the initial population audience reach estimate does
not satisfy an error threshold.
5. The apparatus of claim 4, wherein the processor is to initialize
(a) the estimate of the reach for the population audience prior to
the initial iteration and (b) the first pseudo universe estimate of
the recorded audience prior to the initial iteration.
6. The apparatus of claim 5, wherein the processor is to determine
the estimate for the reach for the population audience to be at
least one of the subsequent population audience reach estimate or
the initial population audience reach estimate when the error
threshold is satisfied.
7. The apparatus of claim 4, wherein the reach for the population
audience is a reach across two or more time intervals corresponding
to the second marginal ratings for the population audience.
8. The apparatus of claim 7, wherein the processor is to execute
the subsequent iteration and the second subsequent iteration to
converge the estimate of the reach for the population audience to a
reach number corresponding to a deduplicated number of persons
exposed to the second media across the time intervals.
9. The apparatus of claim 8, wherein the time intervals are quarter
hours.
10. The apparatus of claim 4, wherein the processor is to execute a
third subsequent iteration when a second error determined during
the second subsequent iteration does not satisfy the error
threshold.
11. The apparatus of claim 1, wherein the recorded reach for the
recorded audience and the first marginal ratings for the recorded
audience is provided by a first media provider and the second
marginal ratings for the population audience of the second media
are provided by a second media provider.
12. The apparatus of claim 11, wherein at least one of (a) the
second marginal ratings are different than the first marginal
ratings, (b) the second media provider is different from the first
media provider, (c) for a second population audience is different
from the population audience, or (d) the second media is different
from the first media.
13. A non-transitory computer readable storage medium comprising
instructions which, when executed cause one or more processors to
at least: iteratively converge on an output estimate of a pseudo
universe estimate of a recorded audience of first media based on
(A) a recorded reach for the recorded audience of the first media
and (B) first marginal ratings for the recorded audience of the
first media; determine a panel representation value based on the
pseudo universe estimate of the recorded audience of the first
media; and iteratively converge on an output estimate of a final
reach of second media for a population audience based on the panel
representation value and second marginal ratings for the population
audience of the second media.
14. The computer readable storage medium of claim 1, wherein the
instructions cause the one or more processors to output the
estimate of the reach of the second media for the population
audience and the panel representation value to an application that
is to perform an operation based on at least one of the estimate of
the reach of the second media for the population audience or the
panel representation value.
15. The computer readable storage medium of claim 1, wherein the
instructions cause the one or more processors to converge on the
output estimate of the reach of the second media for the population
audience by: determining a subsequent population audience reach
estimate from a subsequent iteration, the subsequent population
audience reach estimate based on the second marginal ratings for
the population audience and the pseudo universe estimate of the
population audience of the second media; and evaluating an error
between the subsequent population audience reach estimate
determined from the subsequent iteration and an initial population
audience reach estimate determined from an initial iteration prior
the subsequent iteration.
16. The computer readable storage medium of claim 3, wherein the
instructions cause the one or more processors to: determine, during
the subsequent iteration, a first pseudo universe estimate of the
population audience of the second media corresponding to the
subsequent iteration based on the panel representation value; and
execute a second subsequent iteration when the error between the
subsequent population audience reach estimate and the initial
population audience reach estimate does not satisfy an error
threshold.
17. The computer readable storage medium of claim 4, wherein the
instructions cause the one or more processors to initialize (a) the
estimate of the reach for the population audience prior to the
initial iteration and (b) the first pseudo universe estimate of the
recorded audience prior to the initial iteration.
18. The computer readable storage medium of claim 5, wherein the
instructions cause the one or more processors to determine the
estimate for the reach for the population audience to be at least
one of the subsequent population audience reach estimate or the
initial population audience reach estimate when the error threshold
is satisfied.
19. The computer readable storage medium of claim 4, wherein the
reach for the population audience is a reach across two or more
time intervals corresponding to the second marginal ratings for the
population audience.
20. A method to determine reach of media from marginal ratings, the
method comprising: iteratively converging on an output estimate of
a pseudo universe estimate of a recorded audience of first media
based on (A) a recorded reach for the recorded audience of the
first media and (B) first marginal ratings for the recorded
audience of the first media; determining a panel representation
value based on the pseudo universe estimate of the recorded
audience of the first media; and iteratively converging on an
output estimate of a final reach of second media for a population
audience based on the panel representation value and second
marginal ratings for the population audience of the second media.
Description
FIELD OF THE DISCLOSURE
[0001] This disclosure relates generally to media audience
measurement, and, more particularly, to methods and apparatus to
estimate population reach from marginals.
BACKGROUND
[0002] Determining a size and demographics of an audience of a
media presentation helps media providers and distributors schedule
programming and determine a price for advertising presented during
the programming. In addition, accurate estimates of audience
demographics enable advertisers to target advertisements to certain
types and sizes of audiences. To collect these demographics, an
audience measurement entity enlists a group of media consumers
(often called panelists) to cooperate in an audience measurement
study (often called a panel). In some examples, the audience
measurement entity obtains (e.g., directly, or indirectly from a
media service provider) return path data from media presentation
devices (e.g., set-top boxes) that identifies tuning data from the
media presentation devices. In such examples, because the return
path data may not be associated with a known panelist, the audience
measurement entity models and/or assigns viewers to represent the
return path data. In some examples, the media consumption habits
and demographic data associated with the enlisted media consumers
are collected and used to statistically determine the size and
demographics of the entire audience of the media presentation. In
some examples, this collected data (e.g., data collected via
measurement devices) may be supplemented with survey information,
for example, recorded manually by the presentation audience
members.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 is a block diagram of an example environment in which
return path data and meter data are collected from media
presentation locations and are analyzed by an example audience
measurement system to estimate media reach for a population based
on audience marginal ratings data.
[0004] FIG. 2 is a block diagram of an example implementation of a
population reach determiner included in the example audience
measurement system of FIG. 1.
[0005] FIGS. 3-5 are flowcharts illustrating example machine
readable instructions that may be executed to implement the example
population reach determiner of FIGS. 1 and/or 2.
[0006] FIG. 6 is a block diagram of an example processor system
structured to execute the example machine readable instructions of
FIGS. 3-5 to implement the example population reach determiner of
FIGS. 1 and/or 2.
DETAILED DESCRIPTION
[0007] Audience measurement entities seek to understand the
composition and size of audiences of media, such as television
programming. Such information allows audience measurement entity
researchers to, for example, report advertising delivery and/or
targeting statistics to advertisers that target their media (e.g.,
advertisements) to particular audiences. Also, such information
helps to establish advertising prices commensurate with audience
exposure and demographic makeup (referred to herein collectively as
"audience configuration"). One way to gather media presentation
information is to gather the media presentation information from
media output devices (e.g., gathering television presentation data
from a set-top box (STB) connected to a television). As used
herein, media presentation includes media output by a media device
regardless of whether or not an audience member is present (e.g.,
media output by a media output device at which no audience is
present, media exposure to an audience member(s), etc.).
[0008] A media presentation device (e.g., STB) provided by a
service provider (e.g., a cable television service provider, a
satellite television service provider, an over the top service
provider, a music service provider, a movie service provider, a
streaming media provider, etc.) or purchased by a consumer may
contain processing capabilities to monitor, store, and transmit
tuning data (e.g., which television channels are tuned by the media
presentation device at a particular time) back to the service
provider, which can then aggregate and provide such return path
data to an audience measurement entity (e.g., The Nielsen Company
(US), LLC.) to analyze media presentation activity. Data
transmitted from a media presentation device back to the service
provider is referred to herein as return path data. Return path
data includes tuning data. Tuning data is based on data received
from the media presentation device while the media presentation
device is on (e.g., powered on, switched on, and/or tuned to a
media channel, streaming, etc.). Although return path data includes
tuning data, return path data may not include data related to the
user viewing the media corresponding to the media presentation
device. Accordingly, return path data may not be able to be
associated with specific viewers, demographics, locations, etc.
[0009] To determine aspects of media presentation data (e.g., which
household member is currently consuming a particular media and the
demographics of that household member), market researchers may
perform audience measurement by enlisting a subset of the media
consumers as panelists. Panelists or monitored panelists are
audience members (e.g., household members, users, panelists, etc.)
enlisted to be monitored, who divulge and/or otherwise share their
media activity and/or demographic data to facilitate a market
research study. An audience measurement entity typically monitors
media presentation activity (e.g., viewing, listening, etc.) of the
monitored panelists via audience measurement system(s), which
include metering device(s), such as a local people meter (LPM).
Audience measurement typically includes determining the identity of
the media being presented on a media output device (e.g., a
television, a radio, a computer, etc.), determining data related to
the media (e.g., presentation duration data, timestamps, channel
data, etc.), determining demographic information of an audience,
and/or determining which members of a household are associated with
(e.g., have been exposed to) a media presentation. For example, an
LPM in communication with an audience measurement entity
communicates audience measurement (e.g., metering) data to the
audience measurement entity. As used herein, the phrase "in
communication," including variances thereof, encompasses direct
communication and/or indirect communication through one or more
intermediary components and does not require direct physical (e.g.,
wired) communication and/or constant communication, but rather
additionally includes selective communication at periodic or
aperiodic intervals, as well as one-time events.
[0010] In some examples, metering data (e.g., including media
presentation data) collected by an LPM or other meter is stored in
a memory and transmitted via a network, such as the Internet, to a
datastore managed by the audience measurement entity. Typically,
such metering data is combined with additional metering data
collected from a group of LPMs monitoring a group of panelist
households. The metering data may include, but are not limited to,
a number of minutes a household media presentation device was tuned
to a particular channel, a number of minutes a household media
presentation device was used (e.g., consumed) by a household
panelist member and/or a visitor (e.g., a presentation session),
demographics of the audience (which may be statistically projected
based on the panelist data), information indicative of when the
media presentation device is on or off, and/or information
indicative of interactions with the media presentation device
(e.g., channel changes, station changes, volume changes, etc.),
etc. As used herein, a channel may be a tuned frequency, selected
stream, an address for media (e.g., a network address), and/or any
other identifier for a source and/or carrier of media.
[0011] In some examples, the audience measurement entity processes
the collected and/or aggregated metering data for markets where a
panel is maintained and obtains (e.g., from one or more service
provider) return path data for markets where a panel is not
maintained to generate a seed panel. A seed panel is a synthetic
panel including monitored panelists and non-panelist selected to
correspond to return path data homes (e.g., in-market return path
data) and regional panel homes (e.g., over the air only panelists)
and used as the basis for generation of synthetic respondent level
data (e.g., representative of a group synthetic/virtual panelists)
based on a similarity to the segment of the market that is not
covered by return path data. These monitored panelists are selected
from a panel (e.g., a national panel of metered users) based on a
regional proximity to a designated market area, a similarity
between demographics of the monitored panelists and demographics of
the return path data audience location, household media
characteristics (e.g., how the households receive television
signals (cable, satellite, over-the-air radio, etc.)), a similarity
between media consumption of the monitored panelists and the return
path data audience, etc. As used herein, a return path data
audience is represented by audience (e.g., viewer or listener)
assigned return path data associated with a population (e.g., a
universe or users) and/or location. As used herein, a seed panelist
is a monitored panelist that has been selected to be included in a
seed panel. As used herein, synthetic respondent level data or
respondent level data is processed viewing data at the level of
individual respondents. Synthetic respondent level data may include
complete synthesized time records (e.g., at the quarter hour level,
hour level, etc.) across each broadcasting day of all viewing
session by individual family member and guest on individual metered
media output devices in a home, and include the demographic data.
As used herein, designated market area is a geographical area that
defines a media market where synthetic respondent level data is
produced.
[0012] In some examples, the audience measurement entity adjusts
the seed panel to satisfy target ratings and/or target reach. As
used herein, a rating is an average percentage of a population
exposed to media across a given time interval. As used herein,
reach is a cumulative percentage or total of a population that has
been counted as a viewer of the media at least once during a given
time interval (e.g., hourly, daily, weekly, monthly, etc.). Ratings
and reach values may be determined for a same time interval or
respective different time intervals. Examples disclosed herein
adjust the seed panel by adjusting weights of seed panelists
corresponding to the target rating and/or reach until the target
rating and/or reach is satisfied. For example, if the target rating
is 25% of men exposed to a first program during a first duration of
time and 30% of the generated seed panel men were exposed to the
first program during the first duration of time, the audience
measurement entity may adjust the seed panel to reduce the current
rating (e.g., 30%) for that demographic, to a rating closer to the
target rating (e.g., 25%). The audience measurement entity may use
the adjusted seed panel to generate media exposure data based on
marginal data (e.g., quarter hours, hours, days, etc.). Such media
exposure data may include a total number of seed panelists exposed
to the media at the different margins, a total population of
audience members exposed to the media at the different margins, a
total number of deduplicated seed panelists exposed to the media
(e.g., an audience reach) from all of the different marginal rating
(e.g., probabilities), and a total population size (e.g., universe
estimate) of users that potentially could be exposed to the media.
As used herein, a marginal rating corresponds to a probability that
a person in the universe of people was exposed to media for a
particular duration of time. As used herein, a recorded audience
refers to monitored panelists exposed to media and a population
audience represent the total population of people (e.g., monitored
panelists and other unmonitored media consumers associated with
return path data) exposed to the media. Although examples disclosed
herein are described in conjunction with marginal data from a seed
panel, examples disclosed herein may be utilized to determine
population data based on any type of recorded data.
[0013] Examples disclosed herein receive marginal media exposure
data for different instances of media exposure (e.g., different
episodes of a television series, different quarter hour time slots
of a television program, or a radio program, etc.) and estimates a
population reach across all of the different instances of media
exposure based on the marginal media exposure data. Traditional
techniques of determining the total population based on marginal
media ratings include numerical calculations that enumerate the
marginal rating data for all combinations in which someone can
watch a program. The number of probabilities to be solved by such
traditional techniques is 2.sup.n number of probabilities, where n
is the number of marginal probabilities provided in the marginal
ratings data (e.g., the ratings for the different possible
instances of media exposure). For example, if reach is to be
calculated across 4 quarter hour intervals (e.g., for an hour-long
media exposure), which corresponds to four possible marginal
ratings, the total number of probabilities to be solved using such
a traditional technique is 16 (e.g., 2.sup.4). If the reach is to
be calculated across 96 quarter hour intervals (e.g., a day-long
media exposure), the total number of probabilities to be solved
using such a traditional technique is 8*10.sup.28 (e.g., 2.sup.96),
thereby exceeding the memory limit and/or processing power of any
existing computer. Examples disclosed herein alleviate such
memory/processing resource problems associated with such a
traditional technique by calculating the solution using a disclosed
analytical process.
[0014] To estimate the total population reach, examples disclosed
herein determine pseudo universe estimate(s) (e.g., a pseudo
universe estimate of the monitored (also referred to as the
recorded) audience and a pseudo universe estimate of the return
path data (also referred to as the population) audience). A pseudo
universe estimate corresponds to what the size of the universe of
individuals capable of being in a given audience would need to be
to achieve the ratings and reach values for that audience if the
different marginal ratings are assumed to be independent,
regardless of how much dependency actually exists among the
different audience marginal ratings. For example, when the universe
of a recorded audience is equal to the pseudo universe value, then
the total reach of the recorded audience can be calculated from the
audience marginal ratings assuming the audience marginal ratings
are independent. However, if there is a difference between the
pseudo universe of the recorded audience and the actual universe of
the recorded audience, the audience marginal ratings are dependent.
Examples disclosed herein (A) access marginal ratings for the
recorded audience, marginal ratings for the population audience,
reach for the recorded audience, and total population size (e.g.,
the actual universe estimate) and (B) determine pseudo universe
estimates to solve a system of equations resulting in the
population audience reach. The system of equations includes a link
tool (e.g., a link equation), which is based on the pseudo universe
estimates to link that dependency of the population audience
marginal ratings to the dependencies exhibited by recorded audience
marginal ratings. In this manner, examples disclosed herein
determine the population audience reach from the marginal ratings,
in a manner that takes into account the dependency among the
different marginal ratings.
[0015] Examples disclosed herein use the below system of Equations
(e.g., Equations 1a-3a), to determine the total population
reach.
1 - A d Q R = i = 1 N .times. ( 1 - A i Q R ) ( Equation .times.
.times. 1 .times. a ) Q R - A d UE - A d = Q p - X d UE - X d (
Equation .times. .times. 2 .times. a ) 1 - X d Q P = i = 1 N
.times. ( 1 - X i Q P ) ( Equation .times. .times. 3 .times. a )
##EQU00001##
[0016] In the preceding equations, UE is the actual universe
estimate (e.g., the total population size), A.sub.d is the total
recorded audience reach, Q.sub.R is the pseudo universe estimate of
the recorded audience, N is the number of marginals, A.sub.i is the
recorded audience marginal rating for the ith marginal, X.sub.d is
the total population reach, Q.sub.P is the pseudo universe estimate
of the population audience, and X.sub.i is the population marginal
rating for the ith marginal. The pseudo universe estimates of the
recorded audience and the population audience of Equations 1a and
3a represent the sizes of the respective recorded and population
audiences would need to be for the respective marginal rating to
yield to corresponding reach value if there was independence
between the different marginal ratings. Equation 2a is the link
between the independency assumption and the actual dependency
exhibited by the recorded population. In the above Equations 1a-3a,
Equation 1a defines the relationship between the recorded audience
reach and the recorded audience marginal ratings assuming
independence of the marginal ratings and the total population size
is the pseudo universe for the recorded audience, Equation 3a
defines relationship between the population audience reach and the
population audience marginal ratings assuming independence of the
marginal ratings and the total population size is the pseudo
universe for the population audience, and Equation 2a is the link
tool linking the independence associated with Equations 1a and 3a
with the actual dependence of the marginal ratings.
[0017] Additionally or alternatively, Equations 1a-3a may be
normalized across the UE, where each variable is a percentage of
the actual universe estimate (UE), as shown in the below system of
Equations (e.g., Equations 1b-3b).
1 - A d Q R = i = 1 N .times. ( 1 - A i Q R ) ( Equation .times.
.times. 1 .times. b ) Q R - A d 1 - A d = Q p - X d 1 - X d (
Equation .times. .times. 2 .times. b ) 1 - X d Q P = i = 1 N
.times. ( 1 - X i Q P ) ( Equation .times. .times. 3 .times. b )
##EQU00002##
[0018] In some examples disclosed herein, the above system of
equations may be solved using a root-finding algorithm which may
require a nonlinear root finding system. Alternatively, the above
system of equations may be adjusted to determine the total
population reach without the need of a root-finding algorithm. For
example, solving Equation 1a for Q.sub.R results in Equation 4.
Q .times. R = A d 1 - i = 1 N .times. ( 1 - A i Q R ) ( Equation
.times. .times. 4 ) ##EQU00003##
[0019] In Equation 4, Q.sub.R is a recursive function of itself,
which can be solved iteratively for the subsequent pseudo estimate,
as shown in Equation 5.
Q R k + 1 = A d 1 - i = 1 N .times. ( 1 - A i Q R k ) ( Equation
.times. .times. 5 ) ##EQU00004##
[0020] At a given iteration, because Q.sub.R.sup.k is known,
examples disclosed herein can solve Q.sub.P.sup.k using Equation 2a
and rephrase Equation 3a to solve for X.sub.d. Accordingly,
Equations 1a-3a may be rephrased and solved iteratively using the
Equations 6-8 below, thereby eliminating the need to using a
root-finding algorithm.
Q P k = X d k + ( U .times. E - X d k ) .times. ( Q R k - A d U
.times. E - A d ) ( Equation .times. .times. 6 ) X d k + 1 = Q P k
.function. ( 1 - i = 1 N .times. ( 1 - X i Q P k ) ) ( Equation
.times. .times. 7 ) Q R k + 1 = A d 1 - i = 1 N .times. ( 1 - A i Q
R k ) ( Equation .times. .times. 8 ) ##EQU00005##
[0021] In the preceding equations, Equation 6 defines the pseudo
universe estimate of the population and is the link tool linking
the independence assumption of Equations 7 and 8 with the actual
dependencies exhibited by the marginal ratings, and Equation 8
defines the pseudo universe estimate of the recorded audience. In
some examples, Equations 1b-3b may be rephrased resulting in a
system of equations similar to Equations 6-8, but that is
normalized across the UE. Examples disclosed herein let Q.sub.R be
the solution to Equation 1a and define a panel representation value
(r) using the below Equation 9.
r = Q R - A d 1 - A d ( Equation .times. .times. 9 )
##EQU00006##
[0022] Examples disclosed herein further define the system of
equations to solve the censes using the below Equations 10 and the
above Equation 3b.
r = Q R - X d 1 - X d ( Equation .times. .times. 10 )
##EQU00007##
[0023] In operation, examples disclosed herein initialize the
pseudo universe estimate for the recorded audience as 1 (e.g.,
Q.sub.R.sup.0=1) and interactively solves the above Equation 8
until the value of QR converges on a solution (e.g., the difference
between the value of Q.sub.R at any iteration is less than a
threshold distance from the value of Q.sub.R at a subsequent
iteration). Once Q.sub.R is determined, examples disclosed herein
determine the panel representation value using the above Equation
9. Examples disclosed herein store the panel representation value
for use in the current and subsequent reach determinations, thereby
eliminating the need to recalculate panel parameters for subsequent
reach determinations. In this manner, processing and memory
resources are conserved because the recalculation of the panel
presentation value not needed. Although the panel representation
value may be used for the same or different media (e.g., different
episodes of the same show, episodes of different shows, and/or the
same episode of the same show for a different audience (e.g., live
vs. streaming)) from the same or different media providers, the
panel representation value may be more accurate for similar types
of media (e.g., media with similar audiences).
[0024] After the panel representation value is determined, examples
disclosed herein initialize the pseudo universe estimate of the
population audience (e.g., with Q.sub.P.sup.0=1) and the total
population reach using the below Equation 11.
X.sub.d.sup.0=1-.PI..sub.i=1.sup.N(1-X.sub.i) (Equation 11)
[0025] After the initialized values, examples disclosed herein
iteratively solve the above Equation 7 and the below Equation 12
until the value of X.sub.d converges to a solution (e.g., the
difference between the value of X.sub.d at any iteration is less
than a threshold distance from the value of X.sub.d at a subsequent
iteration) representative of the population reach (e.g., a
de-duplicated total audience). The below Equation 12 corresponds to
the above Equation 6 with the panel representation value (r)
replacing
Q R - A d 1 - A d . ##EQU00008##
In this manner, Q.sub.R does not need to be recalculated for
subsequent reach determinations (e.g., based on different marginal
ratings (at the same time or a different time), different media
providers, and/or different media), thereby conserving memory and
processing resources.
Q.sub.P.sup.k=X.sub.d.sup.k+(1-X.sub.d.sup.k)r (Equation 12)
[0026] FIG. 1 is a block diagram of an environment in which example
return path data 100 and example meter data 102 are collected to
determine reach based on a marginal ratings data. FIG. 1 includes
the example return path data 100, the example meter data 102, an
example media provider 104, an example media presentation device
106, example media output devices 108, 110, an example local people
meter (LPM) 112, and an example audience measurement system 114
managed by an example audience measurement entity (AME). The
example audience measurement system 114 includes an example modeler
116, an example return path data (RPD) audience storage 118, an
example panelist data storage 120, an example seed panel generator
122, an example station data storage 124, an example seed panel
storage 126, an example seed panel optimizer 128, and an example
population reach determiner 130.
[0027] The example media provider 104 of FIG. 1 is a service
provider (e.g., cable media service provider, a radio frequency
(RF) media provider, a satellite media service provider, etc.) that
presents media to an audience member via the example media
presentation device 106. The media provided by the example media
provider 104 is transmitted (e.g., via a wired or wireless network
connection) to the media presentation device 106. The media
presentation device 106 is connected, via a wired or wireless
connection, to the example media output device 108 to output the
media to an audience member. The media output device 108 is a
device capable of outputting the received media. For example, the
media output device 108 may be a television, a radio, speakers, a
projector, a computer, a computing device, a tablet, a mobile
device, and/or any other device capable of outputting media.
[0028] When the example media presentation device 106 of FIG. 1 is
on, the media presentation device 106 receives media corresponding
to a station, program, website, etc., based on the tuning of the
example media presentation device 106. For example, the media
presentation device 106 may be a set-top box. Additionally or
alternatively, the example media presentation device 106 may be an
over the top device, a video game console, a digital video recorder
(DVR), a digital versatile disc (DVD) player, a receiver, a router,
a server, a computer, a mobile device, and/or any device that
receives media from a service provider. In some examples, the media
presentation device 106 may implement a DVR and/or DVD player. In
some examples, the example media presentation device 106 includes a
unique device identifier (e.g., such as a unique serial number, a
unique network address, etc.) that, when associated with subscriber
information, allows an audience measurement entity, a marketing
entity, and/or any other entity to ascertain specific subscriber
behavior information.
[0029] By way of example, the example media presentation device 106
may be tuned to channel 5. In such an example, the media
presentation device 106 outputs media (from the example media
provider 104) corresponding to the tuned channel 5. The media
presentation device 106 may gather tuning data corresponding to
which channels, stations, websites, etc., that the example media
presentation device 106 was tuned. The example media presentation
device 106 generates and transmits the example return path data 100
to the example media provider 104. The example return path data 100
includes the tuning data and/or data corresponding to the example
media provider 104. Although the illustrated example of FIG. 1
includes the example media provider 104 receiving the example
return path data 100 from one media presentation device (e.g., the
example media presentation device 106), at one location,
corresponding to one media provider (e.g., the example media
provider 104), the example media provider 104 may receive return
path data 100 from any number or type(s) of media presentation
devices, at any number of locations. The media provider 104
transmits the collected return path data 100 to the example
audience measurement system 114. Additionally or alternatively, the
audience measurement system 114 may be hosted by any other entity
or may be co-hosted by another entity(ies). For example, the
example return path data 100 may be collected from the example
media presentation devices 106 by a media provider (e.g., a cable
television provider, a satellite television provider, etc.) and the
example meter data 102 may be collected from an LPM (e.g., such as
the example LPM 112) by the example audience measurement system 114
cooperating with the media provider to gain access to the tuning
data. The example audience measurement system 114 includes the
example return path data audience storage 118 (e.g., a database)
and the example panelist data storage 120 (e.g., a database).
[0030] The example media output device 110 of FIG. 1 is a device
capable of outputting the received media. For example, the media
output device 110 may be a television, a radio, speakers, a
projector, a computer, a computing device, a tablet, a mobile
device, and/or any other device capable of outputting media. In
some examples, the media output device 110 receives media
over-the-air. In this manner, the media output device 110 receives
media via an antenna and does not correspond to a media provider
(e.g., including the example media provider 104). In the
illustrated example of FIG. 1, the media output device 110
corresponds to one or more monitored panelists. The example LPM 112
monitors the panelists exposure to media output by the example
media output device 110. For example, the example LPM 112 is in
communication with the example media output device 110 to collect
and/or capture signals emitted externally by the media output
device 110. The LPM 112 may be coupled with the media output device
110 via wired and/or wireless connection. The example LPM 112 may
be implemented in connection with additional and/or alternative
types of media presentation devices, such as, for example, a radio,
a computer monitor, a video game console, and/or any other device
capable to present media to a user. The LPM 112 may be a portable
people meter, a cell phone, a computing device, a sensor, and/or
any other device capable of metering (e.g., monitoring) user
exposure to media. In some examples, a media presentation location
may include a group of LPMs 112. In such examples, the group of the
LPMs 112 may be used to monitor media exposure for multiple users
and/or media output devices 110. Additionally, the example panelist
data storage 120 receives and stores the example meter data 102
from the example LPM 112.
[0031] In some examples, the example LPM 112 of FIG. 1 includes a
set of buttons assigned to audience members to determine which of
the audience members is watching the example media output device
110. The LPM 112 may periodically prompt the audience members via a
set of LEDs, a display screen, and/or an audible tone, to indicate
that the audience member is present at a first media presentation
location by pressing an assigned button. In some examples, to
decrease the number of prompts and, thus, the number of intrusions
imposed upon the media consumption experience of the audience
members, the LPM 112 prompts only when unidentified audience
members are located at the first media presentation location and/or
only after the LPM 112 detects a channel change and/or a change in
state of the media output device 110. In some examples, the LPM 112
may additionally or alternatively include at least one sensor
(e.g., a camera, 3-dimensional sensor, etc.) and/or be
communicatively coupled to at least one sensor that detects a
presence of the user in a first example media presentation
location. The example LPM 112 transmits the example meter data 102
to the audience measurement system 114. The example meter data 102
includes the media presentation data (e.g., data related to media
presented while the media output device 110 is on and a user is
present). Although the illustrated example illustrates the example
audience measurement system 114 collecting the example meter data
102 from one LPM 112 at one location, the example audience
measurement system 114 may collect meter data from any number or
type of meters at any number of locations.
[0032] The example return path data 100 of FIG. 1 from the example
media presentation device 106 and/or the example meter data 102
from the example LPM 112 is transmitted to the example audience
measurement system 114 via a network. The network may be
implemented using any type of public or private network, such as,
but not limited to, the Internet, a telephone network, a local area
network (LAN), a cable network, and/or a wireless network. To
enable communication via the network, the example media
presentation device 106 includes a communication interface that
enables a connection to an Ethernet, a digital subscriber line
(DSL), a telephone line, a coaxial cable, or any wireless
connection, etc.
[0033] The example modeler 116 of the example audience measurement
system 114 of FIG. 1 collects the example return path data 100
corresponding to the example media presentation device(s) 106. As
described above, the example return path data 100 includes tuning
data of the example media presentation device 106. However, the
example return path data 100 may not include specific data
identifying any information relating to the audience of the example
media output device 108. The example modeler 116 models such
audience information. For example, the modeler 116 may assign
and/or model virtual users to augment the example return path data
100, thereby generating audience assigned return path data. The
example modeler 116 outputs the audience assigned return path data
to the example return path data audience storage 118.
[0034] The example seed panel generator 122 of FIG. 1 gathers (A)
the audience assigned return path data from the example return path
data audience storage 118, (B) the example meter data 102 from the
example panelist data storage 120, (C) and station data from the
example station data storage 124 to generate a seed panel. As
explained above, a seed panel is a panel including synthetic
respondent level data from a set monitored panelists corresponding
to the LPM(s) 112 selected based on a return path data audience
and/or homes. The seed panelists are selected to represent the
return path data audience and/or homes. The example seed panel
generator 122 assigns virtual geography and income data to the
persons and/or homes corresponding to the audience assigned return
path data and the meter data 102. The example seed panel generator
122 initializes the seed panel by selecting monitored panelists to
be representative of the viewer/geography/income assigned return
path data audience. For example, a monitored panelist may be
selected based on a similarity between (A) the location of the
monitored panelist and the location of a return path data audience
member, (B) demographics corresponding to the location of the
return path data audience member and the demographics of the
monitored panelist, (C) media viewing characteristics of the return
path data audience and the monitored panelist, etc. The example
station data storage 124 stores data related to station
receivability by location (e.g., such as by county, market area,
etc.). The example seed panel generator 122 uses the station data
to calculate the station receivability for over the air homes. In
some examples, the seed panel generator 122 filters the seed
panelists to collect attributes of interest at the person level
and/or the household level. Attributes of interest at the person
level may include age, gender, ethnicity, nationality, race, etc.,
and attributes at the household level may include head of household
data, cable data, single set data, ADS data, county data, metro
data, income, zip code, number of televisions, pay service data,
etc. The example seed panel generator 122 weights the seed
panelists according to the universe estimate(s) of the designated
market area. The example seed panel generator 122 stores the final
seed panel in the example seed panel storage 126. The final seed
panel includes synthetic respondent level data associated with the
seed panel.
[0035] The example seed panel optimizer 128 of FIG. 1 adjusts the
seed panel generated by the example seed panel generator 122 to
satisfy target ratings and/or target household ratings based on
constraints. Such constraints may include audience constraints,
including quarter hour constraints, daypart constraints, daily
constraints, weekly constraints, monthly constraints, etc. Such
constraints may also include reach constraints, including daypart
reach constraints, daily reach constraints, weekly reach
constraints, monthly reach constraints, etc. In some examples, the
seed panel optimizer 128 applies a discrete optimization greedy
search to adjust the panels to satisfy the target ratings and/or
target household ratings based on the constraints. The example seed
panel optimizer 128 transmits the adjusted seed panel data with
corresponding synthetic level data (e.g., data related to the media
corresponding to the target rating) to the example population reach
determiner 130. The corresponding synthetic level data may include,
for example, recorded audience marginal ratings of the media,
population marginal ratings of the media, total reach of the
recorded audience from marginal ratings, and a universe estimate.
In some examples, parts of the synthetic level data may come from
the adjusted seed panel and other parts may come from other data
gathering devices and/or storage.
[0036] The example population reach determiner 130 of FIG. 1
receives the recorded audience marginal ratings (e.g., from the
meter data 102) for different marginals (e.g., quarter hours, half
hours, hours, days, etc.) of given media, the population marginal
ratings (e.g., from the return path data 100) for the different
marginals of the media, the total reach (e.g., from the meter data
102) for the recorded audience and a total population size (e.g.,
the actual universe estimate) from one or more devices. For
example, the obtain data corresponding to (1) how many panelists
viewed different quarter hours of a particular show on television
(e.g., recorded audience marginal ratings for different marginals),
(2) how many total users viewed different quarter hours of the
particular show on television (e.g., the population marginal
ratings for the different marginals of the media), the total number
of deduplicated panelists that viewed the particular show across
the different margins (e.g., the total reach for the recorded
audience), and a number of users in a particular area (e.g., the
total population size). Additionally or alternatively, the
marginals may correspond to different websites, different webpages,
different store visits, different programs, different stations,
different media exposures, etc. In some examples, the example
population reach determiner 130 determines a total population reach
for the given media based on the received data. The example
population reach determiner 130 determines the total population
reach for the given media analytically using Equations 7, 8, 9, 11
and/or 12, thereby reducing memory and/or processing resources
associated with the traditional numerical calculation. The example
population reach determiner 130 outputs the reach output to another
device and/or user and can reuse the panel representation value for
subsequent reach determinations, thereby reducing memory and/or
processing resources. The example population reach determiner 130
is further described in conjunction with FIG. 2.
[0037] In the illustrated example, the example audience measurement
system 114 includes one or more media monitoring applications 132
to process the media identification information and match duration
information output by the population reach determiner 130 to
perform one or more operations based on that information. For
example, the media monitoring application(s) 132 may include a
media crediting application to credit media exposure at a media
presentation environment to the reference media asset represented
by the media identification information output by the population
reach determiner 130. Additionally or alternatively, the media
monitoring application(s) 132 may include one or more dynamic
advertisement insertion applications to utilize the media
identification information output by the population reach
determiner 130 to select companion media, targeted commercials,
etc., to provide to the media presentation devices 106 for
presentation. Additionally or alternatively, the media monitoring
application(s) 132 may include one or more secondary media
selection insertion applications to utilize the media
identification information output by the population reach
determiner 130 to select secondary media to provide to secondary
media devices (e.g., such as a smartphone and/or computer
associated with one or more of panelist) for presentation in
association with the monitored media being presented by the media
presentation device 106. In some examples, the medio monitoring
application(s) 132 is implemented in and/or as part of the
population reach determiner 130.
[0038] FIG. 2 is a block diagram of an example implementation of
the example population reach determiner 130 of FIG. 1 to determine
population reach for given media based on marginal ratings data.
The example population reach determiner 130 of FIG. 2 includes an
example interface 200, an example pseudo universe determiner 202,
an example storage 204, an example reach determiner 206, and an
example iteration comparer 208. Although the example population
reach determiner 130 is described in conjunction with data
generated by the example seed panel optimizer 128 of FIG. 1, the
example population reach determiner 130 may determine reach based
on information provided by any device that is capable of outputting
audience marginal ratings, population marginal ratings, audience
reach from the different marginal ratings, and universe estimates.
For example, the example population reach determiner 130 may
determine reach from marginal ratings for different programs,
different stations different channels, different media, stores
visits for different stores, different website visits, etc. The
population reach determiner 130 may determine population reach
based on any data that relates to a recorded audience to a
population (e.g., panelist data, survey data, etc.), regardless if
the recorded audience is included in the population.
[0039] The example interface 200 of FIG. 2 receives recorded
audience marginal ratings corresponding to media for two or more
margins (e.g., quarter hours), population audience marginal ratings
corresponding to the media for the two or more margins, recorded
audience reach from two or more marginal ratings, and a universe
estimate of users. As described above, although the illustrated
example of FIGS. 1 and 2 include the example seed panel optimizer
128 providing such data, the data may come from any device and/or
may correspond to any type of data (e.g., website exposures, store
entries, etc.) related to media exposure at different marginals. In
the illustrated example, the marginals are broken up into quarter
hours. For example, the recorded and/or population audience
marginal ratings may correspond to four quarter hour marginals of
an hour media program. However, the marginals may be broken up into
any increments of time (hours, days, etc.). Additionally, the
example interface 200 may output an estimated population reach
(e.g., reach output) to another device/system and/or as a report to
a user. In some examples, the interface 200 includes a panel
representation value and/or a panelist correlation value (described
below) with the transmission of the determined reach. In some
examples, the example interface 200 may be two interfaces (e.g., a
first interface to receive the datasets and a second interface to
output the reach).
[0040] The example interface 200 of FIG. 2 is an example means for
receiving (e.g., accessing, obtaining, etc.) recorded audience
marginal ratings corresponding to media for two or more margins
(e.g., quarter hours), population audience marginal ratings
corresponding to the media for the two or more margins, recorded
audience reach from two or more marginal ratings, and a universe
estimate of users. Additionally, the interface 200 is an example
means for outputting an estimated population reach (e.g., reach
output).
[0041] The example pseudo universe determiner 202 of FIG. 2
generates and/or adjusts pseudo universe estimates as part of an
iterative process to determine population reach. As described
above, a pseudo universe corresponds to what the size of the
universe of individuals capable of being in a given audience would
need to be to achieve the ratings and reach values for that
audience if the different marginal ratings are assumed to be
independent, regardless of how much dependence actually exists. The
example pseudo universe determiner 202 generates and/or adjusts
recorded pseudo universe estimates and population pseudo universe
estimates to increase the efficiency of the reach calculation. As
further described below in conjunction with FIGS. 3-5, the example
pseudo universe determiner 202 initializes the pseudo estimate and,
in subsequent iterations, updates (e.g., adjusts) the initial
pseudo estimate until the reach converges to an appropriate
estimate.
[0042] After the example pseudo universe determiner 202 of FIG. 2
determines the recorded pseudo universe estimate via an iterative
process, the example pseudo universe determiner 202 determines the
panel representation value using the above Equation 9. As described
above, the pseudo universe determiner 202 stores the panel
representation value in the example storage 204 so that it can be
used to determine the current reach and/or a subsequent reach for
subsequent media without needing to recalculate the panel
representative value, thereby eliminating the iterative process for
subsequent reach determination to determine the panel
representative value. The example pseudo universe determiner 202
may redetermine the panel representation value periodically,
aperiodically, and/or based on changes to the panel or the total
audience. In some examples, the pseudo universe determiner 202 may
determine a panelist correlation value (r*) based on panelist
representation value. The pseudo universe determiner 202 may
determine the panelist correlation value based on the below
Equation 13.
r * = 1 - r 1 + r ( Equation .times. .times. 13 ) ##EQU00009##
[0043] The panelist correlation value represents a deviation from
independence of the datasets (e.g., the data from the panel and the
data from the total audience). For example, r* being close to 1
represents a strong correlation, r* being close to 0 represents
strong independence, and r* being close to -1 represents strongly
disjoint data sets. As described above, the interface 200 may
include the panel representation value and/or the panelist
correlation value with the transmission of the reach
determination.
[0044] The example pseudo universe determiner 202 of FIG. 2 is an
example means for determining, generating, adjusting, and/or
iteratively converging on pseudo universe estimates. Additionally,
the example pseudo universe determiner 202 is an example means for
determining, redetermining, calculating, recalculating, and/or
storing the panel representation value and/or a panelist
correlation value.
[0045] The example reach determiner 206 of FIG. 2 generates and/or
adjusts reach estimates until the reach converges to an appropriate
estimate. As described above, the reach is a deduplicated total
audience corresponding to the total audience of given media who
contributed to at least one of the different marginal ratings of
the media. The example reach determiner 206 generates and/or
adjusts total population reach to increase the efficiency of the
reach calculation. As further described below in conjunction with
FIGS. 3-5, the example reach determiner 206 initializes the reach
to a particular value and, in subsequent iterations, updates (e.g.,
adjusted) the total audience reach until the reach converges to an
appropriate estimate (e.g., within a margin of error). The example
reach determiner 206 of FIG. 2 is an example means for determining,
generating, adjusting, and/or iteratively converging on reach
estimates.
[0046] The example iteration comparer 208 of FIG. 2 compares (A) a
population audience reach determined during an iteration to a
population audience reach determined during a subsequent iteration
and/or (B) a pseudo universe of the recorded audience of determined
during a previous iteration to a pseudo universe of the recorded
audience determined during a subsequent iteration. In some
examples, the iteration comparer 208 compares the reach and/or
pseudo universe estimates by applying a mathematical difference. In
such examples, the difference corresponds to an error of the
estimate. The lower the difference, the lower the error and vice
versa. The example iteration comparer 208 determines whether or not
to continue performing iterations to further converge the
population reach estimate, thereby increasing the accuracy of the
estimated reach. For example, the iteration comparer 208 may
continue to converge the population reach estimate until the error
satisfies an error threshold. The error threshold may be based on
user and/or manufacture preferences. The example iteration comparer
208 of FIG. 2 is an example means for comparing (e.g., by applying
a mathematical difference) data across iterations. Additionally,
the example iteration comparer 208 is an example means for
determining whether or not to continue performing iterations based
on an error determination.
[0047] While an example manner of implementing the example
population reach determiner 130 of FIG. 1 is illustrated in FIG. 2,
one or more of the elements, processes and/or devices illustrated
in FIG. 2 may be combined, divided, re-arranged, omitted,
eliminated and/or implemented in any other way. Further, the
example interface 200, the example pseudo universe determiner 202,
the example storage 204, the example reach determiner 206, the
example iteration comparer 208, and/or, more generally, the example
the example population reach determiner 130, of FIG. 2 may be
implemented by hardware, software, firmware and/or any combination
of hardware, software and/or firmware. Thus, for example, any of
the example interface 200, the example pseudo universe determiner
202, the example storage 204, the example reach determiner 206, the
example iteration comparer 208, and/or, more generally, the example
the example population reach determiner 130, of FIG. 2 could be
implemented by one or more analog or digital circuit(s), logic
circuits, programmable processor(s), programmable controller(s),
graphics processing unit(s) (GPU(s)), digital signal processor(s)
(DSP(s)), application specific integrated circuit(s) (ASIC(s)),
programmable logic device(s) (PLD(s)) and/or field programmable
logic device(s) (FPLD(s)). When reading any of the apparatus or
system claims of this patent to cover a purely software and/or
firmware implementation, at least one of the example interface 200,
the example pseudo universe determiner 202, the example storage
204, the example reach determiner 206, the example iteration
comparer 208, and/or, more generally, the example the example
population reach determiner 130 of FIG. 2 is/are hereby expressly
defined to include a non-transitory computer readable storage
device or storage disk such as a memory, a digital versatile disk
(DVD), a compact disk (CD), a Blu-ray disk, etc. including the
software and/or firmware. Further still, the example population
reach determiner 130 of FIG. 2 may include one or more elements,
processes and/or devices in addition to, or instead of, those
illustrated in FIGS. 3-5, and/or may include more than one of any
or all of the illustrated elements, processes and devices. As used
herein, the phrase "in communication," including variations
thereof, encompasses direct communication and/or indirect
communication through one or more intermediary components, and does
not require direct physical (e.g., wired) communication and/or
constant communication, but rather additionally includes selective
communication at periodic intervals, scheduled intervals, aperiodic
intervals, and/or one-time events.
[0048] Flowchart representative of example hardware logic, machine
readable instructions, hardware implemented state machines, and/or
any combination thereof for implementing the example population
reach determiner 130 of FIG. 2 are shown in FIGS. 3-5. The machine
readable instructions may be one or more executable programs or
portion(s) of an executable program for execution by a computer
processor such as the processor 612 shown in the example processor
platform 600 discussed below in connection with FIG. 6. The program
may be embodied in software stored on a non-transitory computer
readable storage medium such as a CD-ROM, a floppy disk, a hard
drive, a DVD, a Blu-ray disk, or a memory associated with the
processor 612, but the entire program and/or parts thereof could
alternatively be executed by a device other than the processor 612
and/or embodied in firmware or dedicated hardware. Further,
although the example program is described with reference to the
flowchart illustrated in FIG. 4, many other methods of implementing
the example population reach determiner 130 of FIG. 2 may
alternatively be used. For example, the order of execution of the
blocks may be changed, and/or some of the blocks described may be
changed, eliminated, or combined. Additionally or alternatively,
any or all of the blocks may be implemented by one or more hardware
circuits (e.g., discrete and/or integrated analog and/or digital
circuitry, an FPGA, an ASIC, a comparator, an operational-amplifier
(op-amp), a logic circuit, etc.) structured to perform the
corresponding operation without executing software or firmware.
[0049] The machine readable instructions described herein may be
stored in one or more of a compressed format, an encrypted format,
a fragmented format, a compiled format, an executable format, a
packaged format, etc. Machine readable instructions as described
herein may be stored as data (e.g., portions of instructions, code,
representations of code, etc.) that may be utilized to create,
manufacture, and/or produce machine executable instructions. For
example, the machine readable instructions may be fragmented and
stored on one or more storage devices and/or computing devices
(e.g., servers). The machine readable instructions may require one
or more of installation, modification, adaptation, updating,
combining, supplementing, configuring, decryption, decompression,
unpacking, distribution, reassignment, compilation, etc. in order
to make them directly readable, interpretable, and/or executable by
a computing device and/or other machine. For example, the machine
readable instructions may be stored in multiple parts, which are
individually compressed, encrypted, and stored on separate
computing devices, wherein the parts when decrypted, decompressed,
and combined form a set of executable instructions that implement a
program such as that described herein.
[0050] In another example, the machine readable instructions may be
stored in a state in which they may be read by a computer, but
require addition of a library (e.g., a dynamic link library (DLL)),
a software development kit (SDK), an application programming
interface (API), etc. in order to execute the instructions on a
particular computing device or other device. In another example,
the machine readable instructions may need to be configured (e.g.,
settings stored, data input, network addresses recorded, etc.)
before the machine readable instructions and/or the corresponding
program(s) can be executed in whole or in part. Thus, the disclosed
machine readable instructions and/or corresponding program(s) are
intended to encompass such machine readable instructions and/or
program(s) regardless of the particular format or state of the
machine readable instructions and/or program(s) when stored or
otherwise at rest or in transit.
[0051] The machine readable instructions described herein can be
represented by any past, present, or future instruction language,
scripting language, programming language, etc. For example, the
machine readable instructions may be represented using any of the
following languages: C, C++, Java, C#, Perl, Python, JavaScript,
HyperText Markup Language (HTML), Structured Query Language (SQL),
Swift, etc.
[0052] As mentioned above, the example processes of FIGS. 3-5 may
be implemented using executable instructions (e.g., computer and/or
machine readable instructions) stored on a non-transitory computer
and/or machine readable medium such as a hard disk drive, a flash
memory, a read-only memory, a compact disk, a digital versatile
disk, a cache, a random-access memory and/or any other storage
device or storage disk in which information is stored for any
duration (e.g., for extended time periods, permanently, for brief
instances, for temporarily buffering, and/or for caching of the
information). As used herein, the term non-transitory computer
readable medium is expressly defined to include any type of
computer readable storage device and/or storage disk and to exclude
propagating signals and to exclude transmission media.
[0053] "Including" and "comprising" (and all forms and tenses
thereof) are used herein to be open ended terms. Thus, whenever a
claim employs any form of "include" or "comprise" (e.g., comprises,
includes, comprising, including, having, etc.) as a preamble or
within a claim recitation of any kind, it is to be understood that
additional elements, terms, etc. may be present without falling
outside the scope of the corresponding claim or recitation. As used
herein, when the phrase "at least" is used as the transition term
in, for example, a preamble of a claim, it is open-ended in the
same manner as the term "comprising" and "including" are open
ended. The term "and/or" when used, for example, in a form such as
A, B, and/or C refers to any combination or subset of A, B, C such
as (1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with
C, (6) B with C, and (7) A with B and with C. As used herein in the
context of describing structures, components, items, objects and/or
things, the phrase "at least one of A and B" is intended to refer
to implementations including any of (1) at least one A, (2) at
least one B, and (3) at least one A and at least one B. Similarly,
as used herein in the context of describing structures, components,
items, objects and/or things, the phrase "at least one of A or B"
is intended to refer to implementations including any of (1) at
least one A, (2) at least one B, and (3) at least one A and at
least one B. As used herein in the context of describing the
performance or execution of processes, instructions, actions,
activities and/or steps, the phrase "at least one of A and B" is
intended to refer to implementations including any of (1) at least
one A, (2) at least one B, and (3) at least one A and at least one
B. Similarly, as used herein in the context of describing the
performance or execution of processes, instructions, actions,
activities and/or steps, the phrase "at least one of A or B" is
intended to refer to implementations including any of (1) at least
one A, (2) at least one B, and (3) at least one A and at least one
B.
[0054] As used herein, singular references (e.g., "a", "an",
"first", "second", etc.) do not exclude a plurality. The term "a"
or "an" entity, as used herein, refers to one or more of that
entity. The terms "a" (or "an"), "one or more", and "at least one"
can be used interchangeably herein. Furthermore, although
individually listed, a plurality of means, elements or method
actions may be implemented by, e.g., a single unit or processor.
Additionally, although individual features may be included in
different examples or claims, these may possibly be combined, and
the inclusion in different examples or claims does not imply that a
combination of features is not feasible and/or advantageous.
[0055] While an example manner of implementing the example
population reach determiner 130 of FIG. 1 is illustrated in FIG. 2,
one or more elements, processes and/or devices illustrated in FIG.
2 may be combined, divided, re-arranged, omitted, eliminated and/or
implemented in any other way. Further, the example interface 200,
the example pseudo universe determiner 202, the example storage
204, the example reach determiner 206, the example iteration
comparer 208, and/or, more generally, the example the example
population reach determiner 130, of FIG. 2 may be implemented by
hardware, machine readable instructions, software, firmware and/or
any combination of hardware, machine readable instructions,
software and/or firmware. Thus, for example, any of the example
interface 200, the example pseudo universe determiner 202, the
example storage 204, the example reach determiner 206, the example
iteration comparer 208, and/or, more generally, the example the
example population reach determiner 130, of FIG. 2 could be
implemented by one or more analog or digital circuit(s), logic
circuit(s), programmable processor(s), application specific
integrated circuit(s) (ASIC(s)), programmable logic device(s)
(PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When
reading any of the apparatus or system claims of this patent to
cover a purely software and/or firmware implementation, at least
one of the example interface 200, the example pseudo universe
determiner 202, the example storage 204, the example reach
determiner 206, the example iteration comparer 208, and/or, more
generally, the example the example population reach determiner 130,
of FIG. 2 is/are hereby expressly defined to include a tangible
computer readable storage device or storage disk such as a memory,
a digital versatile disk (DVD), a compact disk (CD), a Blu-ray
disk, etc., storing the software and/or firmware. Further still,
the example population reach determiner 130 of FIG. 2 may include
one or more elements, processes and/or devices in addition to, or
instead of, those illustrated in FIG. 2, and/or may include more
than one of any or all of the illustrated elements, processes and
devices.
[0056] Flowcharts representative of example machine readable
instructions for implementing the example population reach
determiner 130 of FIG. 2 are shown in FIGS. 3-5. In the examples,
the machine readable instructions comprise a program for execution
by a processor such as the processor 612 shown in the example
processor platform 600 discussed below in connection with FIG. 6.
The program may be embodied in software stored on a tangible
computer readable storage medium such as a CD-ROM, a floppy disk, a
hard drive, a digital versatile disk (DVD), a Blu-ray disk, or a
memory associated with the processor 612, but the entire program
and/or parts thereof could alternatively be executed by a device
other than the processor 612 and/or embodied in firmware or
dedicated hardware. Further, although the example program is
described with reference to the flowcharts illustrated in FIGS.
3-5, many other methods of implementing the example population
reach determiner 130 of FIG. 2 may alternatively be used. For
example, the order of execution of the blocks may be changed,
and/or some of the blocks described may be changed, eliminated, or
combined.
[0057] As mentioned above, the example processes of FIGS. 3-5 may
be implemented using coded instructions (e.g., computer and/or
machine readable instructions) stored on a tangible computer
readable storage medium such as a hard disk drive, a flash memory,
a read-only memory (ROM), a compact disk (CD), a digital versatile
disk (DVD), a cache, a random-access memory (RAM) and/or any other
storage device or storage disk in which information is stored for
any period (e.g., for extended time periods, permanently, for brief
instances, for temporarily buffering, and/or for caching of the
information). As used herein, the term tangible computer readable
storage medium is expressly defined to include any type of computer
readable storage device and/or storage disk and to exclude
propagating signals and to exclude transmission media. As used
herein, "tangible computer readable storage medium" and "tangible
machine readable storage medium" are used interchangeably.
Additionally or alternatively, the example processes of FIGS. 3 and
4 may be implemented using coded instructions (e.g., computer
and/or machine readable instructions) stored on a non-transitory
computer and/or machine readable medium such as a hard disk drive,
a flash memory, a read-only memory, a compact disk, a digital
versatile disk, a cache, a random-access memory and/or any other
storage device or storage disk in which information is stored for
any period (e.g., for extended time periods, permanently, for brief
instances, for temporarily buffering, and/or for caching of the
information). As used herein, the term non-transitory computer
readable medium is expressly defined to include any type of
computer readable storage device and/or storage disk and to exclude
propagating signals and to exclude transmission media. As used
herein, when the phrase "at least" is used as the transition term
in a preamble of a claim, it is open-ended in the same manner as
the term "comprising" is open ended.
[0058] FIG. 3 is an example flowchart representative of example
machine readable instructions 300 that may be executed by the
example population reach determiner 130 of FIGS. 1 and 2 to
generate a total audience reach from different marginals (e.g.,
quarter hours). Although the instructions of FIG. 3 are described
in conjunction with population reach estimation based on quarter
hour marginals of media exposure performed by the example
population reach determiner 130 of FIGS. 1 and 2, the example
instructions may be utilized to determine reach based on any type
of media and/or marginal from any type of rating data. Although the
below calculations are based on percentages (e.g., where each total
is a percentage of the universe estimate), the below calculations
may be based on total audience numbers by slightly modifying the
above Equations 1-13 (e.g., replacing totals with percentage of the
UE and replacing UE with 1 or 100%).
[0059] At block 302, the example interface 200 accesses marginal
ratings for the recorded audience (e.g., based on the meter data
102), marginal ratings for the population audience (e.g., based on
the return path data 100), reach for the recorded audience from
increments of time (e.g., based on the meter data 102), and a total
population size (e.g. universe estimate). For example, the below
example table (Table 1) includes marginal ratings of a recorded
audience and corresponding reach of the recorded audience for a
1-hour program broken up into 4 quarter hour margins (e.g.,
represented as percentages of the UE), where the entries include
percentages of people exposed to media relative to a total universe
estimate represented by 100% (e.g., or 1). For example, 10%
reflects 100 person out of the 1000 universal estimate.
TABLE-US-00001 TABLE 1 Example Data UE = 100% (e.g., Recorded
Population corresponding to audience Audience 1,000 persons)
(percent) (percent) A 6% 7.5%.sup. B 8% 7% C 4% 6% D 5% 5% Total
10% Xd?
[0060] In Table 1, UE is the universe estimate, A is the marginal
for the first quarter hour, B is the marginal for the second
quarter hour, C is the marginal for the third quarter house, D is
the marginal for the fourth quarter hour, Total is the deduplicated
total recorded audience corresponding to any of the marginals A, B,
C, and D (e.g., the recorded reach), and Xd is the population
reach, which is to be determined. Alternatively, as explained
above, A, B, C, and D may represent hours, days, stores, websites,
etc.
[0061] At block 304, the example pseudo universe determiner 202
performs processing operations to iteratively determine the output
pseudo universe estimate (Q.sub.R) of the recorded audience, as
further described below in conjunction with FIG. 4. At block 306,
the example pseudo universe determiner 202 determines the panel
representation value based on the output pseudo universe estimate
and stores the panel representation value in the example storage
204. For example, the pseudo universe determiner 202 determines the
panel representation value using Equation 9. Using the information
of the example of Table 1, if the total recorded audience reach Ad
is 10% and the example pseudo universe determiner 202 determines
Q.sub.R=0.103028, the example pseudo universe determiner 202
determines that the panel representation value to be 0.00336
( r = 0 . 1 .times. 0 .times. 3 .times. 0 .times. 2 .times. 8 - 0 .
1 1 - 0 . 1 ) . ##EQU00010##
At block 308, the example pseudo universe determiner 202 determines
a panelist correlation based on the panel representation value,
using Equation 13. For example, the pseudo universe determiner 202
determines the panelist correlation value to be 0.993293
( e . g . , .times. r * = 1 - 0.003 .times. 3 .times. 6 1 + 0.003
.times. 3 .times. 6 ) . ##EQU00011##
[0062] At block 310, the example population reach determiner 130
performs processing operations to iteratively determine an
estimated (e.g., output) reach of the population audience based on
the panel representation value and audience parameters (e.g., the
marginal ratings for the population audience, the record for the
recorded audience and/or the total population size). The iterative
estimation performed at block 310 is further described below in
conjunction with FIG. 5. At block 312, the example interface 200
outputs the estimated population reach, the panel representation
value, and/or the panelist correlation. The example interface 200
may output the estimated population reach, the panel representation
value, and/or the panelist correlation as a signal for further
processing and/or as a report to a user (e.g., customer). At block
314, the example reach determiner 206 determines if there are
additional audience parameters for reach determination (e.g.,
subsequently obtained population totals, data for a different total
audience, data from different media, data from a different media
provider, etc.). For example, the interface 200 may obtain second
marginal data from a second population for second media
corresponding to the below Table 2 different than the first
marginal data from the first population for the first media from
Table 1 (e.g., for a different show), from a second media provider
different than the first media provider from Table 1 (e.g., if the
data from Table 1 is from a first media provider and the data from
the below Table 2 is from a second media provider), from a second
total audience different from a first total audience (e.g., if the
data from Table 1 is for a first audience at a first region and the
data from the below Table 2 is for a second audience at a second
region), etc., as shown below in Table 2.
TABLE-US-00002 TABLE 2 Example Data UE = 100% (e.g., Population
corresponding to Audience 1,000 persons) (percent) A 12% B 10% C 7%
D 13% Total Xd?
[0063] In such an example, control can return to block 310 and the
population reach determiner 130 can determine a final output
estimate population reach for the different at least one of media,
media provider, marginals, etc., based on the marginal data from
Table 2 and the panel representation value determined at block 306
without performing the iterative process of block 304, thereby
conserving processor resources and time. For example, base on the
iterative process of block 310 (further described below in
conjunction with the instructions of FIG. 5) and the determined
panel representative value (r) from block 306, the Xd of the above
Table 2 converges to 16.9%.
[0064] If the example reach determiner 206 determines that
additional audience parameters are available for an additional
reach determination (block 314: YES), control returns to block 310
to estimate the reach for the additional audience parameters. For
example, if there is a subsequent population data for the same
media or different media (e.g., from the same or a different media
provider) and/or a different number for the total audience, control
would return to block 310 to perform the reach determination for
the subsequent population data. As described above, for subsequent
audience parameters, the reach can be determined using the panel
representation value without re-determining the pseudo universe
estimate for the recorded audience because the panel representation
value (r) has been stored and can be used to determine the reach.
If the example reach determiner 206 determines that additional
audience parameters are not available (block 314: NO), the
flowchart of FIG. 3 ends.
[0065] FIG. 4 is an example flowchart representative of example
machine readable instructions 304 that may be executed by the
example population reach determiner 130 of FIGS. 1 and 2 to perform
processing operations to iteratively determine the pseudo universe
estimate of the recorded audience, as described above in
conjunction with block 304 of FIG. 3. Although the instructions of
FIG. 4 are described in conjunction with a reach estimation based
on quarter hour margins of media exposure from the example
population reach determiner 130 of FIGS. 1 and 2, the example
instructions may be utilized to determine reach based on any type
of media and/or marginals from any type of ratings data. The
example flowchart of FIG. 4 is described in conjunction with the
above Table 1.
[0066] At block 402, the example pseudo universe determiner 202
sets the initial pseudo universe estimate of the recorded audience
(Q.sub.R.sup.k) to be representative of the total population size
(UE) (block 402). Using the example of Table 1, the example pseudo
universe determiner 202 determines the initial pseudo universe
estimate of recorded audience to be 1 or 100% (e.g.,
Q.sub.R.sup.0=1).
[0067] At block 404, the example pseudo universe determiner 202
iteratively updates the estimate of the pseudo universe of recorded
audience (Q.sub.R.sup.k+1), as shown in Equation 8. Using the
example of Table 1, the example pseudo universe determiner 202
determines the subsequent pseudo universe of recorded audience
(Q.sub.R.sup.1) to be 0.473255
( e . g . , .times. 0 . 1 1 - ( 1 - 0.06 1 ) .times. ( 1 - 0.08 1 )
.times. ( 1 - 0.04 1 ) .times. ( 1 - 0.05 1 ) ) . ##EQU00012##
[0068] At block 406, the example iteration comparer 208 determines
if an error corresponding to a comparison of the subsequent pseudo
universe of recorded audience and current pseudo universe of
recorded audience satisfies a second error threshold. For example,
the iteration comparer 208 may calculate an error by computing the
mathematical difference between the subsequent pseudo universe of
the recorded audience and the current pseudo universe of the
recorded audience. Using the example of Table 1, the iteration
comparer 208 calculates the error to be 0.526745 (e.g.,
|0.473255-1|, because Q.sub.R.sup.1=0.473255 and Q.sub.R.sup.0=1).
The first threshold error corresponds to the granularity of the
population audience reach and may be set or adjusted based on user
and/or manufacture preferences. In some examples, the second error
threshold is the same as the first error threshold.
[0069] If the example iteration comparer 208 determines that the
second error corresponding to the comparison of the subsequent
pseudo universe of the recorded audience and the current pseudo
universe of the recorded audience does not satisfy the second error
threshold (block 406: NO), the example pseudo universe determiner
202 replaces the current pseudo universe of the recorded audience
with the subsequent pseudo universe of the recorded audience (block
408) and the process returns to block 404 to perform a subsequent
iteration until the error is reduced to satisfy the second error
threshold. If the example iteration comparer 208 determines that
the second error corresponding to the comparison of the subsequent
pseudo universe of the recorded audience and the current pseudo
universe of the recorded audience satisfies the second error
threshold (block 406: YES), the example pseudo universe determiner
202 determines the output pseudo universe of the recorded audience
(Q.sub.R) based on the current pseudo universe of the recorded
audience (block 410). For example, after performing the iterative
process using the example data of Table 1, the example pseudo
universe determiner 202 determines the pseudo universe of the
recorded audience to be Q.sub.R=0.103028. Additionally or
alternatively, the example reach determiner 206 may determine the
population audience reach based on the subsequent determined
population reach.
[0070] FIG. 5 is an example flowchart representative of example
machine readable instructions 310 that may be executed by the
example population reach determiner 130 of FIGS. 1 and 2 to perform
processing operations to iteratively estimate reach of the
population audience, as described above in conjunction with block
310 of FIG. 3. Although the instructions of FIG. 5 are described in
conjunction with a reach estimation based on quarter hour margins
of media exposure from the example population reach determiner 130
of FIGS. 1 and 2, the example instructions may be utilized to
determine reach based on any type of media and/or marginals from
any type of ratings data. The example flowchart of FIG. 5 is
described in conjunction with the above Table 1.
[0071] At block 502, the example pseudo universe determiner 202
sets the initial pseudo universe estimate of the recorded audience
(Q.sub.R.sup.k=0) to correspond to the total population size (UE).
Using the example of Table 1, the example pseudo universe
determiner 202 determines the initial pseudo universe estimate of
recorded audience to be 1 or 100% (e.g., Q.sub.R.sup.0=1).
[0072] At block 504, the example reach determiner 206 estimates an
initial population audience reach (X.sub.d.sup.k=0) assuming
independence among the marginal ratings for the population using
Equation 11. For example, using Equation 11 and the above Table 1,
the reach determiner 206 determines the initial population audience
reach (X.sub.d.sup.0) to be 0.231797 (e.g.,
(1-(1-0.075)(1-0.07)(1-0.06)(1-0.05))).
[0073] At block 506, the example pseudo universe determiner 202
iteratively estimates the pseudo universe of population audience
(Q.sub.P.sup.k) based on the panel representation value (r) and the
population audience reach according to Equation 12 (block 510).
Accordingly, using the example of Table 1 and Equation 12, the
pseudo universe determiner 202 estimates the current pseudo
universe population audience for the first processing iteration
(Q.sub.P.sup.1) to be 0.234382 (e.g.,
0.231797+((1-0.231797)(0.00336)).
[0074] At block 508, the example reach determiner 206 iteratively
updates the estimate of the population audience reach
(X.sub.d.sup.k+1) based on the current pseudo universe of the
population audience and the marginal ratings for the population
audience, as shown in Equation 7. Using the example of Table 1, the
example reach determiner 206 determines the current population
audience reach estimate for the first processing iteration
(X.sub.d.sup.1) to be 0.1689957
( e . g . , 0.234382 .times. .times. ( 1 - ( 1 - 0.075 0.234
.times. 3 .times. 8 .times. 2 ) .times. ( 1 - 0.07 0.234 .times. 3
.times. 8 .times. 2 ) .times. ( 1 - 0 . 0 .times. 6 0 . 2 .times. 3
.times. 4 .times. 3 .times. 8 .times. 2 ) .times. ( 1 - 0.05 0
.times. 2 .times. 3 .times. 4 .times. 3 .times. 8 .times. 2 ) ) ) .
##EQU00013##
Although the first subsequent population audience reach is the same
as the current population audience reach for the first iteration,
subsequent iterations will result in different population
reaches.
[0075] At block 510, the example iteration comparer 208 determines
if an error corresponding to a comparison of the subsequent
population audience reach and the current population audience reach
satisfies a first error threshold. For example, the iteration
comparer 208 may calculate an error by computing the absolute value
of the mathematical difference between the subsequent population
audience reach and the current population audience. Using the
example of Table 1, the iteration comparer 208 calculates the error
to be 0.76820 (e.g., |0.231797-1.0|).
[0076] If the example iteration comparer 208 determines that the
error corresponding to the comparison of the subsequent population
audience reach and the current population audience reach does not
satisfy the first error threshold (block 510: NO), the example
reach determiner 206 replaces the value of the current population
audience reach (e.g., X.sub.D.sup.k) with (e.g., sets the
X.sub.D.sup.k equal to) the value of the subsequent population
audience reach (e.g., X.sub.D.sup.k+1) (block 512) and control
returns to block 506 for a subsequent iteration (e.g., to update
the pseudo universe of the population audience and the population
audience reach estimate based on the updated population audience
reach). Using the example of Table 1, the example pseudo universe
determiner 202 replaces the value of 0.231797 with the value of
0.168957 (e.g., sets X.sub.D.sup.k equal to 0.168957). Although the
first subsequent population audience reach is the same as the
current population audience reach for the first iteration,
subsequent iterations will result in different population reaches.
In this manner, the pseudo universe of the recorded audience and
the population reach are updated for an additional iteration to
decrease the error(s) corresponding to the previous iteration,
thereby increasing the accuracy of the population reach
estimate.
[0077] If the example iteration comparer 208 determines that the
error corresponding to the comparison of the subsequent population
audience reach and the current population audience reach does
satisfy the first error threshold (block 510: YES), the example
reach determiner 206 determines the output population audience
reach based on the currently determined population audience reach
(block 514). Additionally or alternatively, the example reach
determiner 206 may determine the population audience reach based on
the subsequent determined population reach.
[0078] FIG. 6 is a block diagram of an example processor platform
600 structured to execute the instructions of FIGS. 3-5 to
implement the population reach determiner 130 of FIG. 2. The
processor platform 600 can be, for example, a server, a personal
computer, a workstation, a self-learning machine (e.g., a neural
network), a mobile device (e.g., a cell phone, a smart phone, a
tablet such as an iPad.TM.), a personal digital assistant (PDA), an
Internet appliance, or any other type of computing device.
[0079] The processor platform 600 of the illustrated example
includes a processor 612. The processor 612 of the illustrated
example is hardware. For example, the processor 612 can be
implemented by one or more integrated circuits, logic circuits,
microprocessors, GPUs, DSPs, or controllers from any desired family
or manufacturer. The hardware processor may be a semiconductor
based (e.g., silicon based) device. In this example, the processor
implements the example interface 200, the example pseudo universe
determiner 202, the example reach determiner 206, the example
iteration comparer 208, and/or the example media monitoring
application(s) 132.
[0080] The processor 612 of the illustrated example includes a
local memory 613 (e.g., a cache). In this example, the local memory
implements the example storage 204. The processor 612 of the
illustrated example is in communication with a main memory
including a volatile memory 614 and a non-volatile memory 616 via a
bus 618. The volatile memory 614 may be implemented by Synchronous
Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory
(DRAM), RAMBUS.RTM. Dynamic Random Access Memory (RDRAM.RTM.)
and/or any other type of random access memory device. The
non-volatile memory 616 may be implemented by flash memory and/or
any other desired type of memory device. Access to the main memory
614, 616 is controlled by a memory controller.
[0081] The processor platform 600 of the illustrated example also
includes an interface circuit 620. The interface circuit 620 may be
implemented by any type of interface standard, such as an Ethernet
interface, a universal serial bus (USB), a Bluetooth.RTM.
interface, a near field communication (NFC) interface, and/or a PCI
express interface.
[0082] In the illustrated example, one or more input devices 622
are connected to the interface circuit 620. The input device(s) 622
permit(s) a user to enter data and/or commands into the processor
612. The input device(s) can be implemented by, for example, an
audio sensor, a microphone, a camera (still or video), a keyboard,
a button, a mouse, a touchscreen, a track-pad, a trackball,
isopoint and/or a voice recognition system.
[0083] One or more output devices 624 are also connected to the
interface circuit 620 of the illustrated example. The output
devices 624 can be implemented, for example, by display devices
(e.g., a light emitting diode (LED), an organic light emitting
diode (OLED), a liquid crystal display (LCD), a cathode ray tube
display (CRT), an in-place switching (IPS) display, a touchscreen,
etc.), a tactile output device, a printer and/or speaker. The
interface circuit 620 of the illustrated example, thus, typically
includes a graphics driver card, a graphics driver chip and/or a
graphics driver processor.
[0084] The interface circuit 620 of the illustrated example also
includes a communication device such as a transmitter, a receiver,
a transceiver, a modem, a residential gateway, a wireless access
point, and/or a network interface to facilitate exchange of data
with external machines (e.g., computing devices of any kind) via a
network 626. The communication can be via, for example, an Ethernet
connection, a digital subscriber line (DSL) connection, a telephone
line connection, a coaxial cable system, a satellite system, a
line-of-site wireless system, a cellular telephone system, etc.
[0085] The processor platform 600 of the illustrated example also
includes one or more mass storage devices 628 for storing software
and/or data. Examples of such mass storage devices 628 include
floppy disk drives, hard drive disks, compact disk drives, Blu-ray
disk drives, redundant array of independent disks (RAID) systems,
and digital versatile disk (DVD) drives.
[0086] The machine executable instructions 632 of FIGS. 3-5 may be
stored in the mass storage device 628, in the volatile memory 614,
in the non-volatile memory 616, and/or on a removable
non-transitory computer readable storage medium such as a CD or
DVD.
[0087] From the foregoing, it should be appreciated that the above
disclosed methods, apparatus, and articles of manufacture estimate
population reach from marginals. Example disclosed herein determine
the reach analytically using the above Equations 7-9 and 11-12.
Traditional techniques for determining reach from different margins
include determining the reach numerically. However, such
traditional techniques are unsolvable for a large number of margins
dues to memory and/or processing constraints. Examples disclosed
herein alleviate the problems associated with such traditional
techniques by demining the reach analytically (e.g., via solving
the disclosed Equations). Using examples disclosed herein reach can
be determined from a near infinity number of margins. The disclosed
methods, apparatus and articles of manufacture are accordingly
directed to one or more improvement(s) in the functioning of a
computer.
[0088] Although certain example methods, apparatus and articles of
manufacture have been described herein, the scope of coverage of
this patent is not limited thereto. On the contrary, this patent
covers all methods, apparatus and articles of manufacture fairly
falling within the scope of the appended claims either literally or
under the doctrine of equivalents.
[0089] The following claims are hereby incorporated into this
Detailed Description by this reference, with each claim standing on
its own as a separate embodiment of the present disclosure.
* * * * *