U.S. patent number RE49,120 [Application Number 16/886,724] was granted by the patent office on 2022-06-28 for methods and apparatus to determine a duration of media presentation based on tuning session duration.
This patent grant is currently assigned to The Nielsen Company (US), LLC. The grantee listed for this patent is The Nielsen Company (US), LLC. Invention is credited to Peter Lipa, Michael Sheppard, Jonathan Sullivan, Alejandro Terrazas.
United States Patent |
RE49,120 |
Sheppard , et al. |
June 28, 2022 |
Methods and apparatus to determine a duration of media presentation
based on tuning session duration
Abstract
Methods, apparatus, systems and articles of manufacture are
disclosed to monitor media presentation to determine a period of
media presentation based on tuning session period are disclosed.
Example methods disclosed herein include determining a first tuning
session based on a period of time between channel changes of a
first media presentation device, determining first presentation
session data within the determined first tuning session,
determining a model relating the first tuning session with the
first presentation session data, determine a second tuning session
for tuning data from a second media presentation device, selecting
the model for the second tuning session, based on a match of a
first duration of the second tuning session and a second duration
associated with the model, and estimating second presentation
session data for the second tuning session based on the model.
Inventors: |
Sheppard; Michael (Brooklyn,
NY), Sullivan; Jonathan (Hurricane, UT), Lipa; Peter
(Tucson, AZ), Terrazas; Alejandro (Santa Cruz, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
The Nielsen Company (US), LLC |
New York |
NY |
US |
|
|
Assignee: |
The Nielsen Company (US), LLC
(New York, NY)
|
Family
ID: |
62165937 |
Appl.
No.: |
16/886,724 |
Filed: |
May 28, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
|
62239126 |
Oct 8, 2015 |
|
|
|
Reissue of: |
15011455 |
Jan 29, 2016 |
9986272 |
May 29, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04N
21/44222 (20130101); H04N 21/4667 (20130101); H04N
21/4667 (20130101); H04N 21/252 (20130101); H04N
21/252 (20130101); H04N 21/44222 (20130101) |
Current International
Class: |
H04N
21/25 (20110101); H04N 21/442 (20110101); H04N
21/466 (20110101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
The Nielsen Company, "Average Minutes Viewed, NPOWER,"
[http://en-us-nielsen.com/sitelets/cls/], 2012, 2 pages. cited by
applicant .
United States Patent and Trademark Office, "Final Office Action,"
issued in connection with U.S. Appl. No. 15/011,455, dated Jul. 18,
2017, 12 pages. cited by applicant .
United States Patent and Trademark Office, "Notice of Allowance,"
issued in connection with U.S. Appl. No. 15/011,455, dated Jan. 10,
2018, 8 pages. cited by applicant .
United States Patent and Trademark Office, "Non-Final Office
Action," issued in connection with U.S. Appl. No. 15/990,729, dated
Feb. 1, 2019, 11 pages. cited by applicant .
United States Patent and Trademark Office, "Notice of Allowance,"
issued in connection with U.S. Appl. No. 15/990,729, dated Jun. 17,
2019, 8 pages. cited by applicant .
United States Patent and Trademark Office, "Notice of Allowance,"
issued in connection with U.S. Appl. No. 15/990,729, dated Jan. 2,
2020, 8 pages. cited by applicant .
Econometric Analysis, "Appendix B--Probability and Distribution
Theory," 2002, 52 pages. (The year of publication is sufficiently
earlier than the effective U.S. filing date and any foreign
priority date so that the particular month of publication is not in
issue.). cited by applicant .
The Nielsen Company, "Average Minutes Viewed, NPOWER," 2012, 2
pages. (The year of publication is sufficiently earlier than the
effective U.S. filing date and any foreign priority date so that
the particular month of publication is not in issue.). cited by
applicant .
Tuchman et al., "An Empirical Analysis of Complementarities between
the Consumption of Goods and Advertisements," Nov. 20, 2014, 50
pages. cited by applicant .
United States Patent and Trademark Office, "Non-Final Office
Action", issued in connection with U.S. Appl. No. 16/860,026, 18
pages. cited by applicant .
United States Patent and Trademark Office, "Final Office Action",
issued in connection with U.S. Appl. No. 16/860,026, 18 pages.
cited by applicant.
|
Primary Examiner: Worjloh; Jalatee
Attorney, Agent or Firm: Hanley, Flight & Zimmerman,
LLC
Claims
What is claimed is:
1. A method for determining media presentation sessions based on
tuning session data, the method comprising: .[.obtaining, by
executing an instruction with a processor, a first tuning session
duration indicative of an amount of time between channel changes of
a first media presentation device at a first media presentation
location; obtaining by executing an instruction with the processor,
a first presentation session duration of media presented within the
first tuning session duration;.]. storing, by executing an
instruction with the processor, a relation between .[.the.].
.Iadd.a .Iaddend.first tuning session duration and .[.the.].
.Iadd.a .Iaddend.first presentation session duration in a model
corresponding to the first tuning session duration.Iadd., the first
tuning session duration indicative of an amount of time between
channel changes of a first media presentation device at a first
media presentation location, the first presentation session
duration for media presented within the first tuning session
duration.Iaddend.; .[.obtaining, by executing an instruction with
the processor, a second tuning session duration indicative of an
amount of time between channel changes of a second media
presentation device at a second media presentation location;.].
selecting the model from storage based on a match of the first
tuning session duration and .[.the.]. .Iadd.a .Iaddend.second
tuning session duration.Iadd., the second tuning session duration
indicative of an amount of time between channel changes of a second
media presentation device at a second media presentation
location.Iaddend.; estimating a second presentation session
duration of media presented within the second tuning session
duration based on the model; and presenting, by executing an
instruction with the processor, a report including the second
presentation session duration.
2. The method of claim 1, wherein the first tuning session duration
and the first presentation session duration are received from a
metering device.
3. The method of claim 1, wherein the model is determined based on
the first tuning session duration.
4. The method of claim 1, wherein the first presentation session
duration includes data related to when the first media presentation
device is powered on.
5. The method of claim 1, wherein the first presentation session
duration includes data related to when a user is present at a
location of the first media presentation device.
6. The method of claim 1, wherein estimating the second
presentation session duration for the tuning .Iadd.session
.Iaddend.data is based on the first presentation session duration,
the second presentation session duration including at least one of
a first presentation session within the tuning .Iadd.session
.Iaddend.data, a last presentation session within the tuning
.Iadd.session .Iaddend.data, or a total presentation session within
the tuning .Iadd.session .Iaddend.data.
7. The method of claim 1, wherein the model includes at least one
of a conditional probability, an expected value, a frequency
distribution, or a cumulative distribution based on the first
presentation session duration.
8. The method of claim 1, further including combining data in the
model based on tuning session durations that are within a threshold
range.
9. The method of claim 1, wherein the second media presentation
device is a set top box.
10. An apparatus to determine media presentation sessions based on
tuning session data, the apparatus comprising: .[.a tuning session
determiner to obtain a first tuning session duration indicative of
an amount of time between channel changes of a first media
presentation device at a first media presentation location; a
presentation session determiner to obtain a first presentation
session duration of media presented within the first tuning session
duration; a modeler to.]. .Iadd.at least one memory; instructions
in the apparatus; and processor circuitry to execute the
instructions to: .Iaddend. store .Iadd.a model in the at least one
memory including .Iaddend.a relation between .[.the.]. .Iadd.a
.Iaddend.first tuning session duration and .[.the.]. .Iadd.a
.Iaddend.first presentation session duration in a model
corresponding to the first tuning session duration.Iadd., the first
tuning session duration indicative of an amount of time between
channel changes of a first media presentation device at a first
media presentation location, the first presentation session
duration for media presented within the first tuning session
duration.Iaddend.; .[.a receiver to obtain a second tuning session
duration indicative of an amount of time between channel changes of
a second media presentation device at a second media presentation
location; and a presentation session estimator to:.]. select the
model from .[.storage.]. .Iadd.the at least one memory
.Iaddend.based on a match of the first tuning session duration and
.[.the.]. .Iadd.a .Iaddend.second tuning session duration,
.[.and.]. .Iadd.the second tuning session duration indicative of an
amount of time between channel changes of a second media
presentation device at a second media presentation location;
.Iaddend. estimate a second presentation session duration of media
presented within the second tuning session duration based on the
model; and .[.a reporter to.]. present a report including the
second presentation session duration.[., at least one of the tuning
session determiner, the presentation session determiner, the
modeler, the receiver, the presentation session estimator, or the
reporter including hardware.]..
11. The apparatus of claim 10, wherein the first tuning session
duration and the first presentation session duration are received
from a metering device.
12. The apparatus of claim 10, wherein the modeler is to determine
model is based on the first tuning session duration.
13. The apparatus of claim 10, wherein the first presentation
session duration includes data related to when the first media
presentation device is powered on.
14. The apparatus of claim 10, wherein the first presentation
session duration includes data related to when a user is present at
a location of the first media presentation device.
15. The apparatus of claim 10, wherein the .[.presentation session
estimator is.]. .Iadd.processor circuitry is to execute the
instructions .Iaddend.to estimate the second presentation session
duration for the tuning .Iadd.session .Iaddend.data based on the
first presentation session duration, the second presentation
session duration including at least one of a first presentation
session within the tuning .Iadd.session .Iaddend.data, a last
presentation session within the tuning .Iadd.session .Iaddend.data,
or a total presentation session within the tuning .Iadd.session
.Iaddend.data.
16. The apparatus of claim 10, wherein the model includes at least
one of a conditional probability, an expected value, a frequency
distribution, or a cumulative distribution based on the first
presentation session duration.
17. The apparatus of claim 10, wherein the .[.modeler is.].
.Iadd.processor circuitry is to execute the instructions
.Iaddend.to combine data in the model based on tuning session
durations that are within a threshold range.
18. The apparatus of claim 10, wherein the second media
presentation device is a set top box.
19. A non-transitory computer readable medium comprising
instructions that, when executed, cause a machine to: .[.obtain a
first tuning session duration indicative of an amount of time
between channel changes of a first media presentation device at a
first media presentation location; obtain a first presentation
session duration of media presented within the first tuning session
duration;.]. store a relation between .[.the.]. .Iadd.a
.Iaddend.first tuning session duration and .[.the.]. .Iadd.a
.Iaddend.first presentation session duration in a model
corresponding to the first tuning session duration.Iadd., the first
tuning session duration indicative of an amount of time between
channel changes of a first media presentation device at a first
media presentation location, the first presentation session
duration for media presented within the first tuning session
duration.Iaddend.; .[.obtain a second tuning session duration
indicative of an amount of time between channel changes of a second
media presentation device at a second media presentation
location;.]. select the model from storage based on a match of the
first tuning session duration and .[.the.]. .Iadd.a .Iaddend.second
tuning session duration.Iadd., the second tuning session duration
indicative of an amount of time between channel changes of a second
media presentation device at a second media presentation
location.Iaddend.; estimate .Iadd.a .Iaddend.second presentation
session duration of media presented within the second tuning
session duration based on the model; and present a report including
the second presentation session duration.
20. The non-transitory computer readable medium of claim 19,
wherein the first tuning session duration and the first
presentation session duration are received from a metering
device.
21. The method of claim 1, wherein the channel change is a change
in media presentations at the first or the second media
presentation device by a media streaming service.
Description
This patent .Iadd.arises from a broadening reissue of U.S. Pat. No.
9,986,272, issued May 29, 2018, which .Iaddend.claims the benefit
of U.S. Provisional Patent Application Ser. No. 62/239,126,
entitled "METHODS AND APPARATUS TO DETERMINE A DURATION OF MEDIA
PRESENTATION BASED ON TUNING SESSION DURATION" and filed on Oct. 8,
2015.[., which is.]..Iadd.. U.S. Pat. No. 9,986,272 and U.S.
Provisional Patent Application Ser. No. 62/239,126 are
.Iaddend.hereby incorporated herein by reference in .[.its
entirety.]. .Iadd.their entireties.Iaddend..
FIELD OF THE DISCLOSURE
This disclosure relates generally to media audience measurement,
and, more particularly, to methods and apparatus to determine a
duration of media presentation based on tuning session
duration.
BACKGROUND
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 plurality of media consumers
(often called panelists) to cooperate in an audience measurement
study (often called a panel) for a predefined length of time. The
media consumption habits and demographic data associated with these
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.
The process of enlisting and retaining participants for purposes of
audience measurement is often a difficult and costly aspect of the
audience measurement process. For example, participants are
typically carefully selected and screened for particular
characteristics so that the population of participants is
representative of the overall presentation population.
Additionally, the participants are required to perform specific
tasks that enable the collection of the data, such as, for example,
periodically self-identifying while consuming media
programming.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of an example environment in which media
presentation information is collected from media presentation
locations and is analyzed by an example collection facility to
determine durations of media presentation in accordance with the
teachings of this disclosure.
FIG. 2 is a block diagram of an example implementation of the data
adjuster of FIG. 1.
FIGS. 3-5 are flowcharts illustrating example machine readable
instructions that may be executed to implement the example data
adjuster of FIGS. 1 and/or 2.
FIG. 6 is an example table of measurement data collected by the
example local people meter of FIG. 1.
FIGS. 7A-7C are tables of example tuning session data and
presentation session data collected by the example local people
meter of FIG. 1 in accordance with the teachings of this
disclosure.
FIGS. 8A-8E are tables of statistical models generated by the data
adjuster of FIGS. 1 and/or 2 based on data from the local people
meters of FIG. 1 in accordance with the teachings of this
disclosure.
FIG. 9 is an example graph illustrating expected total presentation
session durations generated by the generated by the data adjuster
of FIGS. 1 and/or 2 based on received tuning session durations in
accordance with the teachings of this disclosure.
FIG. 10 is a block diagram of an example processing system capable
of executing the example machine readable instructions of FIGS. 3-5
to implement the example data adjuster of FIGS. 1 and/or 2.
DETAILED DESCRIPTION
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 audiences. Additionally, 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 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 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.).
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 on the media presentation
device at a particular time) to an audience measurement entity
(e.g., The Nielsen Company (US), LLC.) to analyze media
presentation activity. The 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.). However, tuning data may include
extraneous data that may not accurately reflect media presentation
when, for example, the media presentation device is configured to
output media via a media output device (e.g., a television), but
the media output device is turned off, not receiving the media from
the media presentation device, etc. For example, tuning data may
include data related to a STB that outputs television media via a
television while the television is off, disconnected, turned to
input other than the STB, etc. In another example, the tuning data
collected by the media presentation device may not accurately
reflect media actually exposed to an audience when the media
presentation device is attempting to present the media but no
audience members are present (e.g., a STB and/or a television is on
and/or presenting media while no person is present to consume the
media). To develop a more accurate estimation of the actual media
presentation by the media presentation device, methods and
apparatus disclosed herein analyze measurement data (e.g., tuning
data) collected from media presentation devices (that may
inaccurately reflect the media actually presented to an
audience).
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 media consumers
as panelists. 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 panelist members via
audience measurement system(s), such as a metering device(s) and/or
a local people meter (LPM). Audience measurement typically include
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.
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 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 plurality of LPMs monitoring a plurality of panelist
households. Example disclosed herein process the collected and/or
aggregated metering data to determine model(s) based on a period of
time between channel changes (referred to herein as tuning
sessions). The metering data and/or the model(s) 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.). 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.
In an effort to transform collected tuning data from media
presentation devices (e.g., STBs) into media presentation data
(e.g., to account for data including when the media output device
is off or not used and/or when an audience member is not present),
examples disclosed herein estimate presentation data from collected
tuning data based on models determined from the metering data
received from LPMs. Examples disclosed herein include determining a
first tuning session based on a period of time between channel
changes of a first media presentation device. Such examples further
include determining first presentation session data within the
determined first tuning session. Such examples further include
determining a model relating the first tuning session with the
first presentation session data. Such examples further include
determining a second tuning session for tuning data from a second
media presentation device. Such examples further include selecting
the model for the second tuning session, based on a match of a
first duration of the second tuning session and a second duration
associated with the model. Such examples further include estimating
second presentation session data for the second tuning session
based on the model.
FIG. 1 is a block diagram of an example environment 100 in which
tuning data is collected from an example media presentation
location 110 and is analyzed by an example collection facility 114
to estimate presentation session for tuning sessions within the
tuning data. The example environment 100 includes a first example
media presentation location 102, example media output devices 104,
an example LPM 106, example media presentation devices 108, the
second media presentation location 110, an example network 112, the
example collection facility 114, an example data adjuster 120, an
example tuning storage 116, and an example metering storage 118.
According to the illustrated example, the collection facility 114
collects audience measurement (e.g., metering) data from the
example LPM 106. The example data adjuster 120 creates model(s)
based on the collected metering data. The example data adjuster 120
uses the models to estimate presentation sessions based on tuning
data from the example media presentation device 108. For example,
the data adjuster 120 of the illustrated example estimates media
presentation session(s) for tuning sessions received from the
example media presentation device 108 of the example media
presentation location 110 that does not include a device to collect
and/or send media presentation data (e.g., media presentation
locations that do not include the example LPM 106 to the collection
facility 104).
The example first media presentation location 102 is a location
that has been statistically selected to develop media ratings data
for a population/demographic of interest. According to the example
of FIG. 1, person(s) of the household have registered with a
metering device (e.g., the example local people meter 106) and
provided the demographic information. Alternatively, the first
example media presentation location 102 may be additional and/or
alternative types of environments such as, for example, a room in a
non-statistically selected household, a theater, a restaurant, a
tavern, a retail location, an arena, etc. In some examples, the
environment 100 may include a plurality of first media presentation
locations 102 for which metering data is collected.
In the illustrated example of FIG. 1, the first media presentation
location 102 includes the example media output device 104. The
example media output device 104 of FIG. 1 is a television.
Alternatively, the media output device 104 may be any other type of
device for outputting media such as, for example, a radio, a
computer monitor, a video game console, and/or any other device
capable of presenting media to a user.
The example LPM 106 is in communication with the example media
output device 104 to collect and/or capture signals emitted
externally by the media output device 104. The LPM 106 may be
coupled with the media output device 104 via wired and/or wireless
connection. The example LPM 106 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 106 may be a portable people meter, a cell phone, a
computing device, a sensor, and/or any other device capable of
metering user exposure to media. The media presentation location
102 may include a plurality of LPMs 106. In such examples, the
plurality of the LPMs 106 may be used to monitor media exposure for
multiple users and/or media output devices 104.
In some examples, the example LPM 106 includes a set of buttons
assigned to audience members to determine which of the audience
members is watching the example media output device 104. The LPM
106 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 the example first media presentation
location 102 by pressing an assigned button. To decrease the number
of prompts and, thus, the number of intrusions imposed upon the
media consumption experience of the audience members, the LPM 106
prompts only when unidentified audience members are located in the
first media presentation location 102 and/or only after the LPM 106
detects a channel change and/or a change in state of the media
output device 104. In other examples, the LPM 106 may 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 the first example media
presentation location 102. The example LPM 106 transmits metering
data to a media researcher and/or a marketing entity. The example
metering data includes the media presentation data (e.g., data
related to media presented while the media output device 104 is on
and a user is present). The metering data may further include a
household identification, a tuner key, a presentation start time, a
presentation end time, a channel key, etc., as further described in
FIG. 6.
The media presentation device 108 of the illustrated example of
FIG. 1 is installed by a service provider (e.g., cable media
service provider, a radio frequency (RF) media provider, a
satellite media service provider, etc.) to present media to an
audience member through the example media output device 104. In the
illustrated example of FIG. 1, the example media presentation
device 108 is a STB. Alternatively, the example media presentation
device 108 may be an over the top device, a video game counsel, a
digital video recorder (DVR), a digital versatile disc (DVD)
player, a receiver, a router, a server, and/or any device that
receives media from a service provider. In some examples, the media
presentation device 108 may implement a DVR and/or DVD player. The
example media presentation device 108 includes a unique serial
number 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.
Additionally, the example media presentation device 108 transmits
tuning data (e.g., data related to tuned channels while the media
presentation device 108 is on) to the example collection facility
114. Although the example media output device 104, the example LPM
106, and the example media presentation device 108 in the first
example media presentation location 102 are separate devices, one
or more of the media output device 104, the LPM 106, and/or the
media presentation device 108 may be combined.
The example second media presentation location 110 includes the
example media output device 104 and the example media presentation
device 108, but does not include the example LPM 106. Accordingly,
media presentation data is not collected at the example second
media presentation location 110. However, tuning data is collected
by the example media presentation device 108. Such tuning data
includes data collected by the media presentation device 108 (e.g.,
which channel the media presentation device 108 was tuned to) but
may not include presentation session information from the example
media presentation device 108 (e.g., information related to when
the media output device 104 is powered on and/or an audience member
is present). Therefore tuning data from the example LPM 106 may be
misleading. In some examples, the second media presentation
location 110 may include a second plurality of media presentation
locations 110.
Metering data from the example LPM 106 and/or tuning data from the
example media presentation device 108 is transmitted to the example
collection facility 114 via the example network 112. The example
network 112 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 112, the
example media presentation device 108 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.
The example collection facility 114 receives, processes, stores,
and/or reports presentation data related to metering data received
from the LPM 106 and/or tuning data from the media presentation
device 108 periodically and/or upon a request by the collection
facility 114. In some examples, the collection facility 114
receives the tuning data from a service provider associated with
the media presentation device 108 instead of and/or in addition to
obtaining the example tuning data from the example media
presentation device 108.
According to the illustrated example, the collection facility 114
is hosted by an audience measurement entity. Alternatively the
collector facility may be hosted by any other entity or may be
co-hosted by an audience measurement entity and another
entity(ies). For example, tuning data may be collected from the
example media presentation devices 108 by a media provider (e.g., a
cable television provider, a satellite television provider, etc.)
and metering data may be collected from the example LPM(s) 106 by
an audience measurement entity cooperating with the media provider
to gain access to the tuning data. The example collection facility
114 includes the example tuning storage 116 and the example
metering storage 118.
The example tuning storage 116 is a database that stores tuning
data received from the example media presentation device 108 and
the example metering storage 118 is a database that stores metering
data from the example LPM(s) 106. The example tuning storage 116
and metering storage 118 may be implemented by any one of more of a
database, a server, and/or any other data structure to store data.
According to the illustrated example, the example tuning storage
116 and the example metering storage 118 are communicatively
coupled with the first example media presentation location(s) 102
and the second example media presentation location(s) 110 via the
example network 112. Alternatively, the example tuning storage 116
and/or the example metering storage 118 may receive data in any
other manner (e.g., tuning data and/or media presentation data may
be collected by a third-party and transferred to the collection
facility 114 via the network 112 or any other path).
The example data adjuster 120 processes metering data (e.g.,
metering data received from themetering storage 118) to create a
tuning session(s) (e.g., based on a period of time between channel
changes) and a presentation session(s) (e.g., based on when the
media was presented by the media presentation device 108 on the
media output device 104). The example data adjuster 120 integrates
demographic data with the compiled presentation data to generate
demographic statistical information. The data adjuster 120 of the
illustrated example generates models to estimate presentation
session data for a received tuning session received from the
example media presentation devices 108. When the example collection
facility 114 receives tuning data from the example media
presentation devices 108 and/or from a service provider associated
with the media presentation devices 108, the example data adjuster
120 estimates and reports presentation session data based on a
comparison of the tuning data and the generated models, as further
described in FIG. 2.
In operation, there are two steps to estimating presentation
session(s) for tuning data received from the example media
presentation device 108. The first step is a model generation step
that includes generating models based on determined tuning
session(s) and presentation session(s) from metering data. The
second step is a media presentation estimation step that includes
estimating presentation sessions for received tuning data.
During the model generation step, the example LPM 106 collects
metering data at the media presentation location 102. As previously
described, the metering data includes data related to media
presented to and/or exposed to audience members of the media
presentation device 108. In some examples, the metering data
includes demographics for the users of the media output device 104,
data related to the media presented by the media presentation
device 108, timestamps for the media exposure, data related to
channel changes, data related to media output device 104 on/off
status, etc. The example LPM 106 transmits the metering data to the
example collection facility 114 via the example network 112 to be
stored in the example metering storage 118. As previously
described, the metering data is received (e.g., from the LPM 106)
periodically and/or upon a request by the collection facility 114.
Typically, multiple of the LPMs 106 associated with respective ones
of the media presentation locations 102 will send the metering data
to the example metering storage 118.
The example data adjuster 120 analyzes the metering data from the
example metering storage 118 to create tuning sessions and
presentation sessions based on the metering data. The data adjuster
120 determines tuning session(s) based on a period of time between
channel changes indicated in the metering data. The example data
adjuster 120 also determines presentation session(s) for the
determined tuning session(s) based on a time and/or date of when
the media was actually viewed by a user (e.g., the media output
device 104 was detected as being on and a user was determined to be
present to view the media output device 104). After the tuning
session(s) and the presentation session(s) are determined, the data
adjuster 120 of the illustrated example creates and/or updates a
model based on a duration(s) of the tuning session(s), as further
described in conjunction with FIG. 2.
During the media presentation estimation step, the media
presentation device 108 collects tuning data related to which
channel a media presentation device 108 is tuned to while the media
presentation device 108 is on. As previously described, the tuning
data does not include presentation session data (e.g., data related
to media presented while the media output device 104 is on and a
user is present). The example media presentation device 108
transmits the tuning data to the example tuning storage 116 of the
example collection facility 114 via the example network 112. As
previously described, the tuning data is received (e.g., from the
media presentation device 108) periodically and/or upon a request
by the collection facility 114. In some examples, the tuning data
may be collected by the service provider associated with the media
presentation device 108. In such examples, the service provider may
transmit the tuning data directly to the example collection
facility 114 to be stored in the tuning storage 116.
The example data adjuster 120 determines a duration of a tuning
session from the received tuning data. The example data adjuster
120 estimates presentation session data for the tuning session
based on the created models. For example, the data adjuster 120 may
estimate a 120-minute presentation session based on receiving a
180-minute tuning session. The example data adjuster 120 generates
reports based on the estimated presentation session data. The
reports may be generated at preset times (e.g., hourly, daily,
monthly, etc.) and/or may be initiated by user request.
Additionally, the reports may include data from one or more media
presentation locations (e.g., such as the first and second media
presentation locations 102, 110). In some examples, the reports may
include demographic and/or other statistical information, as
further described in FIGS. 8A-9.
FIG. 2 is block diagram of an example implementation of the example
data adjuster 120 of FIG. 1 to estimate presentation sessions for
tuning data based on models generated from metering data. The
example data adjuster 120 includes an example metering receiver
202, and example tuning session determiner 204, an example
presentation session determiner 206, an example modeler 208, an
example model storage 210, an example tuning data receiver 212, an
example duration determiner 214, an example presentation session
estimator 216, and an example reporter 218.
The example metering receiver 202 of FIG. 2 receives metering data
from the example LPM 106 and sends the received metering data to
the example tuning session determiner 204 for further processing.
In some examples, the metering receiver 202 receives metering data
from the example metering storage 118. In some examples, the
metering receiver 202 receives metering data from the example
LPM(s) 106. In some examples, the metering receiver 202 receives
metering data from both the example metering storage 118 and the
example LPM(s) 106. The metering receiver 202 may include a network
adapter and/or server for receiving metering data from the example
metering storage 118 and/or the example LPM(s) 106 (e.g., via the
example network 112) through a wired and/or wireless
connection.
The example tuning session determiner 204 analyzes metering data
received via the example metering receiver 202 to create a tuning
session(s) based on a period of time between channel changes.
Alternatively, the tuning session determiner 204 may generate the
created tuning session(s) based on a period of time between any
interaction with the media output device 104, the LPM 106, and/or
the media presentation device 108. According to the illustrated
example, the example tuning session determiner 204 determines a new
tuning session for each channel change identified in the metering
creates a new tuning session. In this manner, a tuning session is
representative of the period of time between each channel change.
For example, if the metering data includes a first channel change
at 4:00 PM and a second subsequent channel change at 5:30 PM on the
same day, the example tuning session determiner 204 creates a
90-minute tuning session representative of the period from 4:00 PM
to 5:30 PM. Once a tuning session(s) has been determined from the
metering data, the example tuning session determiner 204 sends data
for the determined tuning session(s) to the example presentation
session determiner 206.
The example presentation session determiner 206 receives data for a
tuning session(s) received from the example tuning session
determiner 204 and further analyzes created tuning session(s) to
determine a presentation session(s) within the tuning session(s).
The presentation sessions are determined based on when the media
presentation device 108 is actually presenting media to an audience
member (e.g., the example media output device 104 is on and/or an
audience member is present). In some examples, the metering data
may include user identifiers identifying which user is located in
the example media presentation location 102 while the example media
output device 104 is on. In such examples, the presentation session
determiner 206 may not credit a duration as a presentation session
if an audience member is not present while the media output device
104 is on. Once the presentation session(s) is determined, the
example presentation session determiner 206 transmits the created
tuning session data and the determined presentation session data to
the example modeler 208.
The example modeler 208 creates and/or updates models based on
tuning session data and presentation session data received from the
example. The example modeler 208 integrates the presentation
session data in a model with a corresponding tuning session length.
For example, if the example tuning session determiner 204
determines presentation session data from a 500-minute tuning
session, the example modeler 208 will store the corresponding
presentation session data in a 500-minute tuning session model. In
some examples, the example modeler 208 updates the model based on
the total presentation session for the tuning session. For example,
if the 500-minute tuning session includes a total presentation
session of 320 minutes, the example modeler 208 will update the
500-minute tuning session model to include the 320 minute
presentation session, as further described in FIG. 8A. In some
examples, the example modeler 208 updates the model based on
durations associated with the presentation session for the tuning
session. For example, if during the 500 minute tuning session,
there were two presentation sessions (e.g., from the 0 minute mark
to the 200 minute mark and from the 380 minute mark to the 500
minute mark), the example modeler 208 will update the 500-minute
tuning session model to include data from the periods of time
(e.g., 0-200 minutes and 380-500 minutes) associated with the
presentation sessions, as further described in FIG. 8D.
Additionally, the example modeler 208 may update the model based on
various conditional probabilities associated with the presentation
session(s), as further described in FIGS. 8B, 8C, 8E, and 9. Once
the example modeler 208 has created and/or updated a model, the
example modeler 208 stores the model in the example modal storage
210.
The example model storage 210 of FIG. 2 stores models created
and/or updated by the example modeler 208. In some examples, the
model storage 210 includes hardware, software, or firmware to store
data locally in the example data adjuster 120. Alternatively, the
model storage 210 is located outside the example data adjuster 120
(e.g., in a database and/or a cloud). The models stored in the
example model storage 210 may be updated (e.g., based on additional
metering data) and/or used to estimate presentation sessions (e.g.,
based on the tuning data).
The example tuning data receiver 212 of FIG. 2 receives tuning data
from the example media presentation device 108 and/or a service
provider and sends the received tuning data to the example duration
determiner 214 for further processing. In some examples, the tuning
data receiver 212 receives metering data from the example tuning
storage 116. Alternatively, the tuning data receiver 212 may
receive metering data from the example media presentation device(s)
108. In some examples, the tuning data receiver 212 receives
metering data from both the example tuning storage 116 and the
example media presentation device(s) 108. The tuning data receiver
212 may include a network adapter and/or server for receiving
metering data from the example tuning storage 116 and/or the
example media presentation device(s) 108 (e.g., via the example
network 112) via a wired and/or wireless connection.
The example duration determiner 214 analyzes tuning data to
determine a duration of a tuning session from the media
presentation device 108. As previously described, a tuning session
is based on a period time between channel changes of the media
presentation device 108. The tuning data does not include
presentation session data. To estimate accurate presentation
session for the tuning data, the example duration determiner 214
determines the duration of the tuning session so that an
appropriate model may be retrieved to determine the estimate. The
example duration determiner 214 transmits tuning data including the
tuning session duration to the example presentation session
estimator 216 for further processing.
The example presentation session estimator 216 estimates
presentation session for tuning data received from the example
tuning data receiver 212 via the example duration determiner 214
based on presentation session data from a model stored in the
example model storage 210. The presentation session estimator 216
retrieves, from the example model storage 210, a model with a
tuning session length that matches determined tuning session
duration determined by the example duration determiner 214. For
example, if the tuning data received from the example model storage
210 a 500 minute tuning session, the presentation session estimator
216 retrieves the 500-minute tuning session model from the example
memory 210. Since the tuning data does not differentiate between
time when the media output device 104 is on and the media output
device 104 is off and/or when an audience member is present, the
example presentation session estimator 216 estimates presentation
sessions to account for periods of time when the media presentation
device is on but the media output device 104 is off and/or an
audience member is not present. The example presentation session
estimator 216 may estimate additional presentation session data
based on, for example, an initial presentation session (e.g., the
first presentation session in the tuning session), a final
presentation session (e.g., the last presentation session in the
tuning session, and/or a total presentation session (e.g., the
total presentation minutes in the tuning session) based on the data
stored in the corresponding model. For example, the presentation
session estimator 216 may receive a 200-minute model(s) while
estimating a presentation session for a 200-minute tuning session.
The presentation session estimator 216 may estimate, based on the
200-minute model, an initial tuning session of 60 minutes, an final
presentation session of 30 minutes, and an total presentation
session estimate of 90 minutes based on user and/or administrator
settings. In some examples, the settings may be based on
statistical analysis (e.g., expected value, weighted average,
standard deviation, minimum and/or maximum percentages of
presentation sessions from the model(s) etc.).
In some examples, the example presentation session estimator 216
bins (e.g., groups) data from multiple models within a threshold
range when the number of entries in a particular model does not
satisfy a minimum threshold number of entries. For example, if a
65-minute tuning duration is determined from tuning data and the
65-minute tuning session model does not meet a threshold number (e
g, minimum) of entries, the example presentation session estimator
216 may group data from models of similar tuning session length
within a threshold range. For example, if the threshold range is 4
minutes, the data from the 63-minute tuning session model, the
64-minute tuning session model, the 66-minute tuning session model,
and the 67-minute tuning session model may be combined with the
data from the 65-minute tuning session model. In this manner, the
number of entries may be increased until the threshold number of
entries is satisfied. In some examples, the threshold number of
entries for a model and the minimum threshold range may be set
and/or adjusted by a user and/or an administrator.
The example reporter 218 of FIG. 2 generates reports of data
received, determined, and/or generated by the example data adjuster
120. The example reporter 218 generates reports including media
presentation session data, the metering data from the example LPM
106, tuning data from the media presentation device 108, data
relating models generated by the example modeler 208, presentation
session estimator 216 settings, and/or any other data relating to
the LPM 106 and/or the media presentation device 108. The reports
may include statistical analysis including conditional
distributions, cumulative distributions, expected values, etc. For
example, the reports may illustrate that 15% of 200-minute tuning
sessions from media presentation devices 108 include only 120
minutes of presentation time, that 35% of the 200-minute tuning
session was being presented at the 158.sup.th minute, that the
expected total presentation minutes for the 200-minute tuning
session is 150 minutes, etc. The reports may be preset and/or
customized by a user and/or administrator to include information
relevant to the user and/or administrator.
While an example manner of implementing the example data adjuster
120 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 metering receiver 202, the example
tuning session determiner 204, the example presentation session
determiner 206, the example modeler 208, the example model storage
210, the example tuning data receiver 212, the example duration
determiner 214, the example presentation session estimator 216, the
example reporter 218, and/or, more generally, the example the
example data adjuster 120, 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
metering receiver 202, the example tuning session determiner 204,
the example presentation session determiner 206, the example
modeler 208, the example model storage 210, the example tuning data
receiver 212, the example duration determiner 214, the example
presentation session estimator 216, the example reporter 218,
and/or, more generally, the example the example data adjuster 120,
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 the example metering receiver 202, the
example tuning session determiner 204, the example presentation
session determiner 206, the example modeler 208, the example model
storage 210, the example tuning data receiver 212, the example
duration determiner 214, the example presentation session estimator
216, the example reporter 218, and/or, more generally, the example
the example data adjuster 120, 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 data adjuster 120 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.
Flowcharts representative of example machine readable instructions
for implementing the example data adjuster 120 of FIG. 2 are shown
in FIG. 3-5. In the examples, the machine readable instructions
comprise a program for execution by a processor such as the
processor 1012 shown in the example processor platform 1000
discussed below in connection with FIG. 10. 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 1012, but the entire program and/or parts thereof
could alternatively be executed by a device other than the
processor 1012 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 data adjuster 120 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.
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. 3-5
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.
The example machine readable instructions illustrated in FIG. 3 may
be executed to cause the example data adjuster 120 of FIG. 2 to
create a model based on metering data and determine presentation
session data from tuning data of the example media presentation
device(s) 108 (e.g., the model generation step) in conjunction with
FIGS. 7-9.
At block 300, the metering receiver 202 receives metering data from
the example LPM 106. As previously described, the metering data
contains detailed media exposure data for the example media
presentation location 102 (e.g., for media from the example media
presentation device 108 output by the example media output device
104. Example metering data is illustrated and further described in
FIG. 6. At block 302, the tuning session determiner 204 creates a
tuning session based on a period of time between channel changes.
Alternatively, the example tuning session determiner 204 may create
a tuning session based on any other events at the example media
presentation location (e.g., a volume change, a detected user
presence, etc.). Once the tuning session has been created, the
example presentation session determiner 206 determines presentation
sessions within the tuning session (block 304). Alternatively,
presentation sessions may be determined prior to or in parallel
with the creation of the tuning session. An example of presentation
sessions within a tuning session is illustrated and further
described in FIGS. 7A-7C.
At block 306, the example modeler 208 adds the presentation session
data to a first set of models based on the tuning session length.
For example, based on the determined tuning sessions, the example
modeler 208 updates the example models by adding the presentation
data to the first set of models (e.g., such as frequency
distribution of total presentation time and frequency distribution
of media presented at set times, as further described in FIGS. 8A
and 8D). For example, if there are 50 minutes of total presentation
session time for a 75-minute tuning session, the example modeler
208 adds a count for the total 50 minutes presentation session to a
frequency distribution model for a 75-minute tuning session, as
further described in FIG. 8A. Additionally, if the example modeler
208 determines that the 75-minute tuning session contains two
presentation sessions (e.g., from 0-30 minutes and from 55-75
minutes), the example modeler 208 may update a 75-minute frequency
distribution model based on every minute of the two presentation
sessions (e.g., adds a count at 0 minute bucket, at a 1 minute
bucket, at a 2 minute bucket, . . . , at a 30 minute bucket, at a
55 minute bucket, . . . , at a 75 minute bucket), as further
described in FIG. 8D. Once the example modeler 208 updates the
first set of models, the example modeler 208 updates a second set
of models associated with the first set of models (block 308). For
example, there may be various models (e.g., such as models of
conditional distribution of total presentation time, models of
cumulative distribution of total presentation time, models of
conditional distribution of media presented at set times, models of
conditional expected value, etc., as further described in FIGS. 8B,
8C, 8E, and 9) that are calculated based on the first set of
models. For example, a 75-minute conditional distribution model is
based on a number of counts in one bucket divided by the total
number of counts. In such examples, the conditional probability for
a 20-minute tuning session may include 100 counts for a
presentation session totaling 15 minutes and the 20-minute tuning
session may have a total of 500 counts, therefore the conditional
probability for a 15 minute total presentation session based on a
20-minute tuning session is 20% (e.g., 100/500). However if
additional metering data is received, the conditional data is
calculated based on updates to the first set of models. For
example, if 500 more 20-minute tuning sessions are added to the
first set of models and none of the 500 20-minute tuning sessions
include 15-minute total presentation sessions, the conditional
probability for a 15-minute total presentation session would lower
to 10% (e.g., 100/1000). In such examples, the presentation session
data is first added to the first set of models and then the second
set of models may be updated (e.g., re-calculated) based on the
updated first set. At block 310, once the models have been updated,
the models are stored in the example model storage 210 to be used
by the presentation session estimator 216, as further described in
FIG. 4.
The example machine readable instructions illustrated in FIG. 4 may
be executed to cause the example data adjuster 120 of FIG. 2 to
estimate presentation sessions from tuning data from the example
media presentation device 108 (e.g., the media presentation
estimation step).
At block 400, the example tuning data receiver 212 receives tuning
data from the example media presentation device 108. As previously
described, the tuning data includes data relating to which channel
the media presentation device 108 was tuned to while the media
presentation device 108 is on. Tuning data may be inaccurate
because tuning data assumes that the media output device 104 is on
and a viewer is present whenever the media presentation device is
on. Therefore, tuning data does not provide presentation session
data (e.g., data related to when the media output device 104 is on
and a user is present) within a tuning session.
At block 402, the example duration determiner 214 determines a
duration of a tuning session based on the tuning data. Once the
duration the tuning session has been determined, the example
presentation session estimator 216 retrieves a corresponding model
from the example model storage 210 (block 404). Since the example
models are divided by tuning session durations, the presentation
session estimator 216 retrieves a model that corresponds to (e.g.,
matches with) the duration the received tuning session. At block
406, the example presentation session estimator 216 estimates
presentation session data (e.g., a total estimated presentation
session, a period for a presentation session at the beginning
and/or end of the tuning session, and/or any other data based on
the stored models as further described in FIGS. 8A-E) based on user
settings. For example, a user may create setting for an initial
presentation session based a when the total percentage of users in
a model drops below 80%. In such examples, if a 10 minute tuning
session is received from the example tuning data receiver 212, the
presentation session estimator 216 receives a 10-minute model
(e.g., such as the conditional distribution of media presented at
set times model of FIG. 8E). Since the 4.sup.th minute of the model
of FIG. 8E is the first time that the conditional percentage drops
below 80% (e.g., at the 4.sup.th minute it is 75%), the
presentation session estimator 216 estimates an initial
presentation session from the 0.sup.th minute to the 3.sup.rd
minute. As previously described, the user settings may be preset of
configured based on user and/or administartor preferences. At block
408, the example reporter 218 generates a report including the
estimated presentation session data. Additionally, the report may
include the tuning data, the metering data, demographic data, any
and/or all of the stored models, and/or any other data related to
the LPM 106 and/or the media presentation device 108. As previously
described, the data reported on the reporter may be preset of
customized.
The example machine readable instructions illustrated in FIG. 5
include alternative instructions to cause the example data adjuster
120 of FIG. 2 to estimate presentation sessions from tuning data
from the example media presentation device 108. The example machine
readable instructions cause the example data adjuster 120 of FIG. 2
to bin (e.g., group) models based on tuning session durations.
At block 500, the example tuning data receiver 212 receives tuning
data from the example media presentation device 108. As previously
described, the tuning data includes data relating to which channel
the media presentation device 108 was tuned to while the media
presentation device 108 is powered on. Tuning data may be
inaccurate because tuning data assumes that the media output device
104 is on and a viewer is present whenever the media presentation
device is on. Therefore, tuning data does not provide presentation
session data (e.g., data related to when the media output device
104 is on and a user is present) within a tuning session.
At block 502, the example duration determiner 214 determines a
duration of a tuning session based on the tuning data. Once the
duration the tuning session has been determined, the example
presentation session estimator 216 retrieves a corresponding model
from the example model storage 210 (block 504). Since example the
models are divided by tuning session durations, the presentation
session estimator 216 retrieves a model that corresponds to (e.g.,
matches with) the duration the received tuning session.
At block 506, the presentation session estimator 216 determines if
the obtained model exceeds a minimum number of entries. The minimum
number of entries may be predetermined and/or based on user and/or
administrator preferences. If a model has a limited number of
entries (e.g., small sample size), the presentation session
estimator 216 may inaccurately estimate presentation session data.
As previously described, the example presentation session estimator
216 may bin (e.g., group) similar models together to increase the
number of entries above the minimum number of entries. If the model
does exceed the minimum number of entries, the example presentation
session estimator 216 estimates presentation session data (e.g., a
total estimated presentation session, a period for a presentation
session at the beginning and/or end of the tuning session, and/or
any other data based on the stored models as further described in
FIGS. 8A-E) (block 508) based on user settings. For example, a user
may create setting for an initial presentation session based a when
the total percentage of users in a model drops below 80%. In such
examples, if a 10 minute tuning session is received from the
example tuning data receiver 212, the presentation session
estimator 216 receives a 10-minute model (e.g., such as the
conditional distribution of media presented at set times model of
FIG. 8E). Since the 4.sup.th minute of the model of FIG. 8E is the
first time that the conditional percentage drops below 80% (e.g.,
at the 4.sup.th minute it is 75%), the presentation session
estimator 216 estimates an initial presentation session from the
0.sup.th minute to the 3.sup.rd minute. As previously described,
the user settings may be preset of configured based on user and/or
administrator preferences.
If the model does not exceed the minimum number of entries, the
example presentation session estimator 216 estimates presentation
session data based on the model and data from other models within a
threshold duration range (block 510). In this manner, the example
presentation session estimator 216 can increase the number of
entries by gathering data from models with similar tuning session
durations. For example, if a threshold range is 5 minutes and a
30-minute tuning session model does not meet the minimum number of
entries, the presentations session estimator 216 may combine
entries from the 28-minute tuning session model, the 29-minute
tuning session model, the 30-minute tuning session model, the
32-minute tuning session model, and the 33-minute tuning session
model. In some examples, the presentation session estimator 216 may
add entries from one model at a time until the minimum threshold is
met. The threshold range and/or the minimum number of entries may
be preset and/or based on user and/or administrator preferences.
Once, the minimum threshold is met, the example presentation
session estimator 216 estimates presentation session data based on
the binned (e.g., grouped) models (e.g., a total estimated
presentation session, an duration for a presentation session at the
beginning and/or end of the tuning session, and/or any other data
based on the stored models as further described in FIGS. 8A-E). At
block 512, the example reporter 218 generates a report including
the estimated presentation session data. Additionally, the report
may include the tuning data, the metering data, demographic data,
any and/or all of the stored models, presentation session data
prior to binning, and/or any other data related to the LPM 106
and/or the media presentation device 108.
FIG. 6 is an illustration of example metering data 600 from the
example LPM 106. The example metering data 600 includes a household
identification (ID) 602, a tuner key 604, a start presentation time
606, an end presentation time 608, a channel key 610, a genre 612,
a presentation weight date key 614, a valid data flag 616, and a
source 618.
The example household ID 602 of FIG. 6 identifies which example
media presentation location 102 transmitted the metering data 600.
In this example, there is one household ID 602, namely `30006.`
However, there may be many household IDs from various media
presentation locations 102 within the metering data 600. The
example tuner key 604 is an identification number for the media
output device 104. Since there may be the media presentation
location 102 with multiple media presentation devices 102, the
tuner key 604 identifies which media output device 104 was being
used. The example start time 606 is a timestamp based on a start of
a presentation session (e.g., when the media presentation device
108 was actually presenting media on the media output device 104).
The example end time 608 is a timestamp based an end of a
presentation session. The example channel key 610 identifies a
channel tuned by the media presentation device 108. The example
genre 612 identifies the genre of the media tuned to on the media
presentation device 108 during the presentation session. The
example presentation weight date key 614 is a code representative
of a date of the end time 608. The example valid data flag 616 is a
Boolean value that identifies whether the metering data is valid.
The metering data may not be valid if there is an error in the
metering data (e.g., the metering data is corrupted, the metering
data is missing information, etc.). The example source 618
identifies a source (e.g., a videocassette recorder (VCR), DVD,
cable, antenna, video game counsel, etc.) of the media presentation
device 618. The source 618 may change if, for example, the user is
watching a DVD. Additionally, the metering data 600 may contain
additional columns for data related to other aspects of audience
member data. For example, the metering data may contain data
identifying whether or not a user(s) is present, demographics
relating to the user(s), and/or an identifier for the user(s)
present during a presentation session. Alternatively, the metering
data may only display data while a user is present and omit any
data while the user is not present. For example, the example LPM
106 may adjust the example metering data 600 so that the metering
data 600 does not include data from time durations when a user is
not present.
When the example metering data 600 of FIG. 6 is received by the
example collection facility 114 from the example LPM 106, the
example tuning session determiner 204 tuning sessions based on a
period time between channel changes by analyzing the metering data
600. The example columns 620 represent data from a period time
between channel changes. In this example, the tuning session
determiner 204 identifies the example columns 620 as an example
tuning session as further described in in FIGS. 7A-C.
FIGS. 7A-C illustrate an example of determining a tuning session
and presentation sessions within the tuning session based on the
example metering data 600 of FIG. 6. In the illustrated example,
the tuning session determiner 204 determines tuning session based
on a period of time between channel changes. FIG. 7A displays the
columns 620 from the example metering data 600 of FIG. 6. FIG. 7B
displays information that may be extracted from the example columns
620 to determine the tuning session and the presentation sessions.
For example, based on the information from the example columns 620,
the presentation session determiner 206 determines that at time
00:25:00 media output device `186242092` from household `50006` was
turned off after watching a channel associated with `294984.` At
time 00:34:00, the media output device `186242092` was turned on
and the channel was changed to a channel associated with a channel
key `2875552.` At time 02:26:00, the media output device
`186242092` was turned off. At time 04:52:00, the media output
device `186242092` was turned back on while remaining on the
channel associated with the channel key `2875552.` At time
05:46:00, the media output device `186242092` was turned off. At
time 20:50:00, the media output device `186242092` was turned back
on while remaining on the channel associated with the channel key
`2875552.` At time 21:35:00, the media output device `186242092`
was turned off. At time 22:41:00, the media output device
`186242092` was turned back on and the channel was changed to a
channel associated with the channel key `294984.`
FIG. 7C illustrates an example tuning session and example
presentation sessions determined for the metering data from columns
620 of FIG. 7A. Since the channel was changed at 00:34:00 and then
again at 22:41:00, the example tuning session determiner 204
generates an example tuning session of 1,327 minutes (e.g., the
period of time between channel changes). Once the tuning session is
created, the presentation session determiner 206 determines
presentation sessions based on the periods of time that media from
the media output device `186242092` was actually presented within
the tuning session (e.g., the media output device `186242092` was
on and a user was viewing the media output device `186242092`). For
example, the presentation session determiner 206 analysis the start
and end times from the metering data from columns 620 to determine
when the media output device 104 was on and when the media output
device 104 was off. The presentation sessions only include periods
of time while the media output device 104 is on. In some examples,
the metering data in columns 620 may only include data when a user
is present. In such examples, the presentation sessions are based
on when the media output device 104 is on. In some examples, the
metering data in columns 620 may include additional data such as
data related to the presence of audience members. In such examples,
the presentation session determiner 206 may need to determine if,
and/or which, audience members are present while the media output
device 104 is on. Based on the example columns 620, the
presentation session determiner 206 determines that the
presentation session periods are 00:34:00-02:56:00 (e.g., 142
minutes), 04:52:00-0:5:46:00 (e.g., 54 minutes), and
20:50:00-21:35:00 (e.g., 45 minutes). The total presentation time
for the 1,327 minute tuning session is 238 minutes (e.g.,
142+54+42=238). In this example, once the example presentation
session determiner 206 determines presentation session data based
on the created tuning session, the example modeler 208 adds the
presentation session data to a model corresponding to a tuning
session duration 1,327 minutes, as further described in FIGS.
8A-8E.
FIGS. 8A-E display example models displaying various distributions
based on presentation session data for an example 10-minute tuning
session. The example models of 8A-E are based on a total of 5963
10-minute tuning sessions collected from metering data of the
example LPM 106 in FIG. 2, as previously described in FIGS.
7A-C.
FIG. 8A displays an example model of an example frequency
distribution of total presentation time based on the gathered
10-minute tuning session. Additionally, other models may be created
for tuning sessions of varying lengths (e.g., 1-minute tuning
session, 5-minute tuning session, 60-minute tuning session,
720-minute tuning session, etc.). Alternatively, one model may be
generated with multiple rows representing multiple tuning session
lengths.
The example model of FIG. 8A includes an example frequency
distribution of presentation times 802 broken into one minute
intervals for the example 10-minute tuning session 800. In some
examples, the presentation times 802 represent a range of times.
For example, the example presentation time `0` labeled 806 may
include all time from 00:00:00 to 00:00:59, 00:00:00 to 00:00:29,
or any other range. The ranges may be predetermined and/or may be
customized by a user and/or an administrator. Alternatively, the
frequency distribution presentation times 802 may be broken into
any duration of intervals (e.g., thirty second intervals, 2 minute
intervals, 5 minute intervals, etc.).
To generate and/or update the example model of FIG. 8A, the example
collection facility 114 of FIG. 1 collects metering data from the
example LPM 106. Once the example data adjuster 120 breaks the
metering data into tuning sessions and presentation sessions, the
example data adjuster 120 populates the model(s) based on the
tuning session data and presentation session data. The example
frequency distribution of FIG. 8A is populated based on
presentation session data from tuning sessions of 10 minute length.
Each of the 5,963 collected 10-minute tuning sessions are
represented in a presentation duration bucket based on the total
presentation time of the tuning session (e.g., the amount time
within the 10-minute tuning session 800 that media was presented).
The example of FIG. 8A includes 112 instances of a total
presentation time of 0 minutes labeled 804 for a 10-minute tuning
session 800, 242 instances of a total presentation time of 1 minute
labeled 808 for a 10-minute tuning session 800, etc. As additional
metering data is processed by the example data adjuster 120, the
example model is updated to represent the additional monitored
data.
Various statistical calculations (e.g., weighted average, standard
deviation, etc.) can additionally be determined by the example
modeler 208 based on the data from the frequency distribution of
FIG. 8A. For example, an expected value (e.g., weighted average)
may be calculated using the following formula:
.times..times. ##EQU00001##
Where x is the expected value, w.sub.i is the number of instances
in presentation bucket i, and x.sub.i is the number of presentation
minutes of presentation bucket i.
The example model of FIG. 8A has an expected value of 6.58, as
shown below:
.times..times..times..times..times..times..times..times..times..times..ti-
mes. ##EQU00002##
The example expected value is the number of expected total
presentation minutes given a 10-minute tuning session. In other
words, given a received 10-minute tuning session from a media
presentation device, it is expected that a total of 6.58 minutes of
the 10 minutes were actually presented to a user. The expected
value for each tuning session length can be plotted on a graph, as
further described in FIG. 9.
FIG. 8B displays an example model of an example conditional
distribution of presentation time based on the example frequency
distribution of FIG. 8A. Additionally other models may be created
for conditional distribution of presentation time for tuning
sessions of varying lengths (e.g., 1-minute session, 5-minute
tuning session, 60-minute tuning session, 720-minute tuning
session, etc.) Alternatively, one model may be generated with
multiple rows representing varying tuning session lengths.
The example model of FIG. 8B includes an example conditional
distribution of presentation times 812 broken into one minute
intervals for an example 10-minute tuning session 800. In some
examples, the presentation times 812 represent a range of times.
For example, the example presentation time `0` labeled 814 may
include all time from 00:00:00 to 00:00:59, 00:00:00 to 00:00:29,
or any other range. The ranges may be predetermined or may be
customized by an administrator. Alternatively, the conditional
distribution 812 may be broken into any duration of intervals
(e.g., thirty second intervals, 2 minute intervals, 5 minute
intervals, etc.).
Conditional distribution buckets contain conditional percentages
based on the frequency distributions of FIG. 8A. The conditional
percentages in the example conditional distribution buckets are
calculated by dividing each frequency distribution bucket by a
total number of tuning sessions modeled for a tuning session
length. For example, the conditional distribution percentage for a
0-minute presentation session 814 within a 10-minute tuning session
800 is calculating by dividing the 112 instances of the 0-minute
presentation session labeled 806 by the total number of 10-minute
tuning sessions 800 (e.g.,
112+242+338+370+390+490+491+781+901+903+945=5963 total sessions) as
shown below:
.times..times..times..times..times..times. ##EQU00003##
2% is placed in the conditional distribution bucket for the
0-minute presentation session 816 within a 10 minute tuning session
800. Other example conditional distribution buckets are calculated
in a similar manner. For example, FIG. 8B illustrates that 4% of
the 10-minute tuning sessions contain a total presentation time of
1 minute, 8% of the 10-minute tuning sessions contain a total
presentation time of 5 minutes, 16% of the 10-minute tuning
sessions contain a total presentation time of 10 minutes, etc.
FIG. 8C is an example model of an example cumulative distribution
of presentation time based on the example conditional distribution
of FIG. 8B. Additionally other models may be created for
conditional distribution of presentation time for tuning sessions
of varying lengths (e.g., 1-minute session, 5-minute tuning
session, 60-minute tuning session, 720-minute tuning session, etc.)
Alternatively, one model may be generated with multiple rows
representing varying tuning session lengths.
The example model of FIG. 8C includes an example cumulative
distribution of presentation times 824 broken into one minute
intervals for an example 10-minute tuning session 800. In some
examples, the presentation times 824 represent a range of times.
For example, the example presentation time `0` labeled 826 may
include all time from 00:00:00 to 00:00:59, 00:00:00 to 00:00:29,
or any other range. The ranges may be predetermined or may be
customized by an administrator. Alternatively, the example
cumulative distribution 824 may be broken into any duration of
intervals (e.g., thirty second intervals, 2 minute intervals, 5
minute intervals, etc.).
Cumulative distribution buckets contain cumulative percentages
based on the conditional distribution of FIG. 8B. The cumulative
percentages in the example cumulative distribution buckets are
calculated by adding the percentage in a selected conditional
distribution bucket with the percentages in all the conditional
distribution buckets prior to the selected conditional distribution
bucket. For example, the cumulative distribution bucket for a
3-minute presentation session within a 10-minute tuning session 800
is calculated by adding the percentage in the 3 minute conditional
distribution bucket 822 for a 10-minute tuning session 800 (e.g.,
6%) with the percentage in the 2-minute (e.g., 6%) conditional
distribution bucket 720, 1-minute (e.g., 4%) conditional
distribution bucket 818, and 0-minute (e.g., 2%) conditional
distribution bucket 816 and for a 10-minute tuning session 800 as
shown below:
6%+6%+4%+2%=18%
18% is placed in the 3-minute cumulative distribution bucket 828
and the other example cumulative distribution buckets are
calculated in a similar manner. The percentages in each cumulative
distribution buckets represent the total percentage of presentation
times of up to a particular length of time. For example, 54% of the
10-minute tuning sessions contained a total presentation sessions
of 8 minutes or less. Alternatively, a cumulative distribution may
be calculated based on the frequency distribution of FIG. 8A. In
this manner, the cumulative distribution calculated using the
frequency distribution of FIG. 8A as appose to the conditional
distribution percentages of FIG. 8B. The distributions models of
FIGS. 8A, 8B, and 8C may be used to adjust tuning data from a STB
in order to determine a total presentation session for the tuning
data from the STB, as further described in FIG. 9.
FIG. 8D displays an example model of an example frequency
distribution of media output device presented at set times 830
during a 10-minute tuning session 800. Additionally, other models
may be created for tuning sessions of varying lengths (e.g.,
1-minute tuning session, 5-minute tuning session, 60-minute tuning
session, 720-minute tuning session, etc.). Alternatively, one model
may be generated with multiple rows representing the varying tuning
session lengths.
The example model of FIG. 8D includes an example frequency
distribution of media output devices presented at set times 830
broken into one minute intervals for an example 10-minute tuning
session 800. Alternatively, the frequency distribution 830 may be
broken into any duration of intervals (e.g., thirty second
intervals, 2 minute intervals, 5 minute intervals, etc.). If a user
of the media presentation device was presentation the media
presentation device at the designated time, the instance is counted
in a corresponding frequency distribution bucket. In some examples,
the blocks can represent a range of times. For example, the blocks
may be broken up so that if a user was exposed to media by the
media presentation device 108 within the 00:00:00-00:00:29 window,
the instance would be counted in the `at 0` frequency distribution
bucket 832.
To generate and/or update the example model of FIG. 8D, the example
collection facility 114 of FIG. 1 collects metering data from the
example LPM 106. Once the example data adjuster 120 breaks the
metering data into tuning sessions and presentation sessions, the
example data adjuster 120 populates the model(s) based on tuning
session data and presentation session data. The example model of
the example frequency distribution of media output devices
presenting at set times of FIG. 8D is populated based on
presentation session data from tuning sessions of 10 minute length.
Each of the 5963 gathered 10-minute tuning sessions are analyzed to
determine how many media presentation device 108 were actually
presenting media on media output device 104 at set times of the
10-minute tuning session. For example, a 10-minute tuning session
containing presentation sessions from 00:00-05:30 and from
08:45-10:00 would be entered as being watched in the at 0, at 1, at
2, at 3, at 4, at 5, at 9, and at 10 minute frequency distribution
blocks.
FIG. 8E displays an example model of an example conditional
distribution of media output devices presenting media at set times
836 during a 10-minute tuning session 800. Additionally, other
models may be created for tuning sessions of varying lengths (e.g.,
1-minute tuning session, 5-minute tuning session, 60-minute tuning
session, 720-minute tuning session, etc.). Alternatively, one model
may be generated with multiple rows representing rows representing
the varying tuning session lengths.
The example model of FIG. 8E includes an example conditional
distribution of media output devices presenting media at set times
836 broken into one minute intervals for an example 10-minute
tuning session 800. Alternatively, the conditional distribution at
set times 836 may be broken into any duration of intervals (e.g.,
thirty second intervals, 2 minute intervals, 5 minute intervals,
etc.). If a user of the media presentation device was presentation
the media presentation device at the designated time, the instance
is counted in a corresponding conditional distribution bucket. In
some examples, the blocks can represent a range of times. For
example, the blocks may be broken up so that if a user was exposed
to media by the media presentation device 108 within the
00:00:00-00:00:29 window, the instance would be counted in the `at
0` conditional distribution bucket labeled 838.
Conditional distribution buckets contain conditional percentages
based on the frequency distributions 830 of FIG. 8D. The
conditional percentages in the example conditional distribution
buckets are calculated based on dividing each frequency
distribution bucket by a total number of tuning sessions modeled
for a tuning session length. For example, the conditional
distribution percentage at the fifth minute for the example 10
minute-tuning session 800 is calculating by dividing the 4411
presentation instances at the fifth minute 834 by the total number
of 10-minute tuning sessions (e.g., 5963 total sessions) as shown
below:
.times..times..times..times..times..times..times..times.
##EQU00004##
74% is placed in the at 5 minute conditional bucket 840 for a 10
minute tuning session. Other example conditional distribution
buckets 820 are calculated in a similar manner. The conditional
distribution of FIG. 8B illustrates that 100% of the total
10-minute tuning sessions were presenting media at the zeroth
minute, 88% of the total 10-minute tuning sessions were presenting
media at the third minute, 49% of the total 10-minute tuning
sessions were presenting media at the tenth minute, etc.
Additionally, a report may be generated including any of the
example models or combination of the example models. The
distributions models of FIGS. 8D and 8E may be used to adjust
tuning data from a STB based on an initial presentation session, an
ending presentation session, and/or any other presentation session
information for the tuning data of the STB, as previously described
in FIG. 4.
FIG. 9 is an example graph of expected total presentation session
values based on various tuning sessions generated from metering
data of the LPM 106 of FIG. 1. The example data adjuster 120
determines an expected total presentation session by calculating a
weighted average of the total presentation sessions of a selected
tuning session length. For example, as previously described in FIG.
5A, the example expected value for the 10-minute tuning session of
FIG. 5A was 6.58. Therefore, the example graph of FIG. 9 will have
a coordinate (e.g., (10, 6.58)) to represent the expected value for
the 10-minute tuning session. The example graph contains a point
for every tracked tuning session (e.g., a 1 minute tuning session,
10 minute tuning session, 200 minute tuning session, etc.). A
report may be generated including the example graph.
FIG. 10 is a block diagram of an example processor platform 1000
capable of executing the instructions of FIGS. 3-5 to implement the
example data adjuster 120 of FIG. 1. The processor platform 1000
can be, for example, a server, a personal computer, 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.
The processor platform 1000 of the illustrated example includes a
processor 1012. The processor 1012 of the illustrated example is
hardware. For example, the processor 1012 can be implemented by
integrated circuits, logic circuits, microprocessors or controllers
from any desired family or manufacturer.
The processor 1012 of the illustrated example includes a local
memory 1013 (e.g., a cache). The example processor 1012 of FIG. 10
executes the instructions of FIGS. 3-5 to the example metering
receiver 202, the example tuning session determiner 204, the
example presentation session determiner 206, the example modeler
208, the example model storage 210, the example tuning data
receiver 212, the example duration determiner 214, the example
presentation session estimator 216, the example reporter 218 of
FIG. 2 to implement the example data adjuster 120. The processor
1012 of the illustrated example is in communication with a main
memory including a volatile memory 1014 and a non-volatile memory
1016 via a bus 1018. The volatile memory 1014 may be implemented by
Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random
Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)
and/or any other type of random access memory device. The
non-volatile memory 1016 may be implemented by flash memory and/or
any other desired type of memory device. Access to the main memory
1014, 1016 is controlled by a memory controller.
The processor platform 1000 of the illustrated example also
includes an interface circuit 1012. The interface circuit 1012 may
be implemented by any type of interface standard, such as an
Ethernet interface, a universal serial bus (USB), and/or a PCI
express interface.
In the illustrated example, one or more input devices 1022 are
connected to the interface circuit 1012. The input device(s) 1022
permit(s) a user to enter data and commands into the processor
1012. The input device(s) can be implemented by, for example, a
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.
One or more output devices 1024 are also connected to the interface
circuit 1012 of the illustrated example. The output devices 1024
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, a cathode ray tube display (CRT), a
touchscreen, a tactile output device, and/or speakers). The
interface circuit 1012 of the illustrated example, thus, typically
includes a graphics driver card, a graphics driver circuit or a
graphics driver processor.
The interface circuit 1012 of the illustrated example also includes
a communication device such as a transmitter, a receiver, a
transceiver, a modem and/or network interface card to facilitate
exchange of data with external machines (e.g., computing devices of
any kind) via a network 1026 (e.g., an Ethernet connection, a
digital subscriber line (DSL), a telephone line, coaxial cable, a
cellular telephone system, etc.).
The processor platform 1000 of the illustrated example also
includes one or more mass storage devices 1028 for storing software
and/or data. Examples of such mass storage devices 1028 include
floppy disk drives, hard drive disks, compact disk drives, Blu-ray
disk drives, RAID systems, and digital versatile disk (DVD)
drives.
The coded instructions 1032 of FIGS. 3-5 may be stored in the mass
storage device 1028, in the volatile memory 1014, in the
non-volatile memory 1016, and/or on a removable tangible computer
readable storage medium such as a CD or DVD.
From the foregoing, it should be appreciated that the above
disclosed methods, apparatus, and articles of manufacture estimate
presentation session from tuning data based on metering data. Media
presentation device data may have extraneous information leading to
inaccurate audience measurement data. For example, STB data does
not account for when a television is off and the television is on,
or when the television is on, but no one is watching the media
presentation device. Methods and apparatus described herein
estimate presentation sessions for tuning data to account for the
extraneous information. Since LPMs can determine more accurate
information including when a media presentation device is on and
when a user is actually watching the media presentation device,
metering data from the LPM are analyzed to create models used to
accurately adjust media presentation device data.
Using the examples disclosed herein, media presentation device data
may be more accurately analyzed based on data from a plurality of
LPMs. In some examples, models are created from metering data of
LPMs initial presentation session, a final presentation session,
and a total presentation session within a tuning session. In such
examples, presentation sessions for tuning data from media
monitoring devices may be estimated based on data in corresponding
models. In this manner, reports may be generated to include the
estimated presentation session for a tuning session of a media
presentation device.
From the foregoing, persons of ordinary skill in the art will
appreciate that the above disclosed methods and apparatus may be
realized within a single device or across two cooperating devices,
and could be implemented by software, hardware, and/or firmware to
implement the data adjuster disclosed herein.
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.
* * * * *
References