U.S. patent application number 12/758532 was filed with the patent office on 2011-03-17 for fuzzy logic based viewer identification for targeted asset delivery system.
This patent application is currently assigned to INVIDI TECHNOLOGIES CORPORATION. Invention is credited to EARL COX, ALDEN LLOYD PETERSON, II, PATRICK M. SHEEHAN.
Application Number | 20110067046 12/758532 |
Document ID | / |
Family ID | 38668531 |
Filed Date | 2011-03-17 |
United States Patent
Application |
20110067046 |
Kind Code |
A1 |
COX; EARL ; et al. |
March 17, 2011 |
FUZZY LOGIC BASED VIEWER IDENTIFICATION FOR TARGETED ASSET DELIVERY
SYSTEM
Abstract
A targeted advertising system uses a machine learning tool to
select an asset for a current user of a user equipment device, for
example, to select an ad for delivery to a current user of a
digital set top box in a cable network. The machine learning tool
first operates in a learning mode to receive user inputs and
develop evidence that can characterize multiple users of the user
equipment device audience. In a working mode, the machine learning
tool processes current user inputs to match a current user to one
of the identified users of that user equipment device audience.
Fuzzy logic may be used to improve development of the user
characterizations, as well as matching of the current user to those
developed characterizations. In this manner, targeting of assets
can be implemented not only based on characteristics of a household
but based on a current user within that household.
Inventors: |
COX; EARL; (EL SEGUNDO,
CA) ; SHEEHAN; PATRICK M.; (JAMISON, PA) ;
PETERSON, II; ALDEN LLOYD; (NEW PROVIDENCE, NJ) |
Assignee: |
INVIDI TECHNOLOGIES
CORPORATION
PRINCETON
NJ
|
Family ID: |
38668531 |
Appl. No.: |
12/758532 |
Filed: |
April 12, 2010 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
11743544 |
May 2, 2007 |
7698236 |
|
|
12758532 |
|
|
|
|
60746245 |
May 2, 2006 |
|
|
|
60746244 |
May 2, 2006 |
|
|
|
Current U.S.
Class: |
725/14 |
Current CPC
Class: |
H04N 21/44222 20130101;
H04H 2201/70 20130101; H04N 21/4667 20130101; H04N 21/25891
20130101; H04H 60/45 20130101; G06Q 30/02 20130101; H04N 21/4666
20130101; H04N 21/2668 20130101; H04N 21/812 20130101; H04N
21/44218 20130101; H04N 21/25883 20130101; H04N 21/6338
20130101 |
Class at
Publication: |
725/14 |
International
Class: |
H04H 60/32 20080101
H04H060/32 |
Claims
1-43. (canceled)
44. A method for in targeting assets in a broadcast network,
comprising the steps of: determining, at a user equipment device,
user information regarding a user of said user equipment device
based at least in part on user inputs to said user equipment
device; and signaling said broadcast network based on the user
information.
45. An apparatus for use in targeting assets in a broadcast
network, comprising: a processor associated with a user equipment
device operative for determining information regarding a user of
said user equipment device based at least in part on user inputs to
said user equipment device; and an interface, operatively
associated with the processor, for use in signaling said broadcast
network based on the user information.
46. A method for use in targeting assets in a broadcast network,
comprising the steps of: collectively analyzing a stream of data
corresponding to a series of user inputs; and applying logic for
matching a pattern described by that stream to a characteristic
associated with an audience classification of a user.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. patent
application Ser. No. 11/743,544, entitled, "FUZZY LOGIC BASED
VIEWER IDENTIFICATION FOR TARGETED ASSET DELIVERY SYSTEM," filed on
MAY 2, 2007, which claims priority from U.S. Provisional
Application No. 60/746,245, entitled, "A METHOD AND SYSTEM FOR
DISCOVERING THE INDIVIDUAL VIEWERS OF A TELEVISION AUDIENCE (ALONG
WITH THEIR ATTRIBUTES, AND BEHAVIOR) AND USING THIS INFORMATION TO
ACCURATELY SELECT AND INSERT CANDIDATE ADS IN REAL-TIME FOR
ACTIVELY WATCHING VIEWERS," filed on May 2, 2006, and U.S.
Provisional Application No. 60/746,244, entitled "METHOD AND
APPARATUS TO PERFORM REAL-TIME ESTIMATION AND COMMERCIAL SELECTION
SUITABLE FOR TARGETED ADVERTISING," filed on May 2, 2006. The
contents all of which are incorporated herein as if set forth in
full.
FIELD OF INVENTION
[0002] The present invention relates generally to targeted delivery
of assets, such as advertisements or other content, in a
communications network. In particular, the invention relates to
identifying a current network user and matching assets to the
user.
BACKGROUND OF THE INVENTION
[0003] Broadcast network content or programming is commonly
provided in conjunction with associated informational content or
assets. These assets include advertisements, associated
programming, public-service announcements, ad tags, trailers,
weather or emergency notifications and a variety of other content,
including paid and unpaid content. In this regard, asset providers
(e.g., advertisers) who wish to convey information (e.g.,
advertisements or "ads") regarding services and/or products to
users of the broadcast network often pay for the right to insert
their information into programming of the broadcast network. For
instance, advertisers may provide ad content to a network operator
such that the ad content may be interleaved with broadcast network
programming during one or more programming breaks. The delivery of
such paid assets often subsidizes or covers the costs of the
programming provided by the broadcast network. This may reduce or
eliminate costs borne by the users of the broadcast network
programming.
[0004] In order to achieve a better return on their investment,
asset providers often try to target their assets to a selected
audience that is deemed likely to be interested in the goods or
services of the asset provider. The case of advertisers on a cable
television network is illustrative. For instance, an advertiser or
a cable television network may target its ads to certain
demographic groups based on, for example, geographic location,
gender, age, income etc. Accordingly, once an advertiser has
created an ad that is targeted to a desired group of viewers (e.g.,
targeted group) the advertiser may attempt to procure insertion
times in the network programming when the targeted group is
expected to be among the audience of the network programming.
[0005] More recently, it has been proposed to target assets to
individual households. This would allow asset providers to better
target audience segments of interest or to tailor messages to
different audience segments. However, targeting households is
problematic. Again, the case of a cable television network is
illustrative. It is often possible to obtain audience
classification information for a household based on name or address
information. For example, information based on credit card
transactions or other financial transactions may be available from
third party databases. However, information based on an identified
household does not always ensure appropriate targeting of assets.
In the case of a family household, for example, a current network
user might be a mother, a father, a child, a babysitter, etc.
Additionally, where the matching of ads to households is performed
in the network, some mechanism is required to target the selected
ads to the appropriate households. This is difficult in broadcast
networks. Accordingly, household-based targeting, while an
improvement over untargeted asset delivery or conventional
ratings-based asset targeting in a broadcast network, still entails
significant obstacles and/or targeting. uncertainty.
SUMMARY OF THE INVENTION
[0006] It has been recognized that the effectiveness of asset
targeting can be enhanced by identifying a current network user,
e.g., determining demographic or other classification parameters of
a putative current network user or users. This would ideally allow
an asset targeting system to distinguish between different
potential users of a single household, as well as identifying
unknown users, such that appropriate targeting of assets can be
executed.
[0007] The present invention enables such functionality in the
context of asset delivery in communications networks, including
cable television networks. Moreover, such functionality can be
executed transparently, from the perspective of the network user,
based on monitoring ordinary network usage activities, for example,
as indicated by a click stream of a remote control. Moreover, the
present invention allows such functionality to be implemented in
substantially real-time, using limited processing resources. Thus,
for example, the user identification functionality can be executed
by an application running on a conventional digital set top box.
The invention also provides a mechanism for signaling the network
in relation to the user identification process, for example, to
enhance selection of assets for insertion into network content
streams or to report information for evaluating size and
composition of the audience actually reached.
[0008] In accordance with one aspect of the present invention, a
method and apparatus ("utility") is provided that uses machine
learning, e.g., fuzzy logic, to match assets to current users.
Specifically, the utility involves identifying an asset having a
target audience defined by one or more targeting parameters and
matching the identified asset to a current user of a user equipment
device using a machine learning system. The targeting parameters
may define certain demographic values of a target audience of a
television commercial. The machine learning system preferably
involves identifying classification parameters of at least one user
based on evidence aggregated from user inputs collected in a
learning mode. These inputs may, for example, be analyzed based on
correlated programming information, or based on programming
independent characteristics, e.g., volume settings or quickness of
the click process.
[0009] Fuzzy logic may be used to match assets to current users.
The fuzzy logic used to match the asset to the current user may
involve either or both of fuzzy sets and fuzzy rules. For example,
the noted matching may involve using fuzzy logic to identify a
number of discrete users in an audience (e.g., number of members of
a household) and/or to determine one or more classification
parameters of a user or users. This may be based on user inputs
such as a click stream of a remote control. Thus, user inputs may
be monitored and associated with values related to the
classification parameter(s). These values can then be treated as
points in a fuzzy set. In one implementation, the matching function
involves monitoring a number of user inputs to aggregate points in
a fuzzy set. This matching may involve multiple dimensions related
to multiple classification parameters (e.g., age, gender, income,
etc.), and the aggregated points may be used to define one or more
features of a multidimensional feature terrain. The feature terrain
may be processed to remove noise and to reduce the set of gradients
in the terrain, for example, by clustering features. The remaining
features of the processed feature terrain can then be used to
identify each user in an audience and determine one or more
classification parameters for each user. Similar processing can be
used to identify viewing patterns as a function of time
(periodicity). For example, different terrains can be developed for
different time periods, e.g., different times of day.
[0010] Additionally or alternatively, fuzzy logic may be used to
develop a characterization of a target audience of a network
programming event. For example, the target audience of an asset may
be defined by a demographic profile including a number of
demographic parameter values. These values may be associated with a
series of fuzzy numbers or fuzzy sets. An additional implementation
of fuzzy logic may be used to correlate the fuzzy numbers with
classification parameters of putative user. For example, a
congruent similarity function may be used to match the audience
characterization or targeting parameters to the classification
parameters. Similar processing can be used to match a periodicity
pattern to an identified user. Alternatively, where different
terrains are developed for different times, as noted above, such
periodicity is reflected in the terrains; that is, time becomes a
dimension of the terrain set. A match may be determined based on a
combination of the degree of correlation of the user classification
parameters to the ad targeting parameters and the likelihood that
an appropriate viewer will be watching at the time of the ad
delivery. The resulting match may be used to "vote" for assets to
be inserted into content streams of the network to select ads for
delivery and/or to report a "goodness of fit" of a user receiving
the asset to the asset targeting parameters. The noted utility may
also be operative to determine whether the user equipment device is
"on" and to determine whether any user is present at the user
equipment device. The user inputs or click stream data can be
processed at the user equipment device or at another location,
e.g., raw or preprocessed click stream data may be transmitted to a
head end for processing to determine classification parameter
information. For example, this may be done where messaging
bandwidth is sufficient and user equipment device resources are
limited.
[0011] In one implementation, the machine learning system may be a
substantially unsupervised system. That is, the system can
accumulate evidence and thereby learn a composition of a user set,
such as a viewing audience, without requiring a training process in
which the system is provided knowledge about or examples of usage
(e.g., viewing) patterns. In this manner, the system can readily
adapt to changes, e.g., changes in the viewing audience or viewing
audience demographics due to, for example, additions to or
departures from the household, changing demographics due to aging,
change of income, etc., addition of a television set (e.g., in a
child's room) that impacts viewership, etc. Moreover, the system
can operate substantially autonomously, thereby substantially
avoiding the need for any supervised set-up or retraining
process.
[0012] In accordance with another aspect of the present invention,
functionality for identifying a user can be executed at a user
equipment device. It has been recognized that a current user can be
effectively identified based on analysis of user inputs at a user
equipment device. An associated utility in accordance with the
present invention involves receiving user inputs at the user
equipment device and analyzing the inputs to associate audience
classification parameters with the user using a machine learning
system. For example, the inputs may relate to a click stream of a
remote control device reflecting program selections, volume control
inputs and the like. The machine learning system is preferably
capable of learning in a substantially unsupervised fashion. Fuzzy
logic can be used to analyze these inputs on an individual basis to
obtain evidence concerning the classification parameters of the
user. This evidence can then be aggregated and analyzed using fuzzy
logic to determine classification parameters of a user.
[0013] In accordance with a still further aspect of the present
invention, a current user of a communications network can be
identified without requiring persistent storage of user profiles.
This is advantageous in a number of respects. First, because a
persistent profile is not required, any privacy concerns are
reduced. Additionally, because a persistent profile is not required
to make identifications, the system is effective not only to
identify known users but also to identify unknown users. Moreover,
the system is adapted to quickly converge on classification
parameters based on contemporaneous user inputs such that errors
due to user changes are reduced. An associated utility involves
developing a model of a network user based on user inputs free from
persistent storage of a profile of the user and using the model of
the network user in targeting assets to the user. In this regard,
recent user inputs may be analyzed using machine learning, e.g.,
involving fuzzy logic, to determine classification parameters of a
current user.
[0014] In accordance with another aspect of the present invention,
a user equipment device is operative to signal a broadcast network
regarding a user of the device. An associated utility involves
determining, at the user equipment device, user information
regarding the user of the device based at least in part on user
inputs to the device, and signaling the broadcast network based on
user information. For example, the user information may include
classification parameters of the user. The signals transmitted to
the broadcast network may reflect the results of a matching process
whereby user classification information is compared to targeting
information for an asset. In this regard, the information
transmitted across the network need not include any classification
information regarding the user. Such signaling information may be
used, for example, to vote for assets to be inserted into network
content streams or to report information regarding assets actually
delivered at the user equipment device, e.g., for measuring the
size and/or composition of the audience.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 illustrates delivery of assets to different users
watching the same programming channel.
[0016] FIG. 2 illustrates audience aggregation across multiple
programming networks.
[0017] FIG. 3 illustrates a virtual channel in the context of
audience aggregation.
[0018] FIG. 4 illustrates targeted asset insertion being
implemented at Customer Premises Equipment (CPEs).
[0019] FIG. 5 illustrates asset options being transmitted from a
headend on separate asset channels.
[0020] FIG. 6 illustrates a messaging sequence between a CPE, a
network platform, and a traffic and billing (T&B) system.
[0021] FIG. 7A illustrates an example of CPEs that include a
television set and a Digital Set Top Box (DSTB) as used by a
plurality of users.
[0022] FIG. 7B illustrates a user classifier.
[0023] FIG. 8 is a flow chart illustrating a process for
implementing time-slot and targeted impression buys.
[0024] FIG. 9 illustrates an overview of a classifier process in
accordance with the present invention.
[0025] FIG. 10 is a stay transition graph illustrating a process
for handling click stream data in accordance with the present
invention.
[0026] FIGS. 11-21 illustrate learning mode operation of the
classifier in accordance with the present invention.
[0027] FIGS. 22-26 illustrate working mode operation of the
classifier in accordance with the present invention.
[0028] FIG. 27 is a block diagram illustrating the basic functional
components of the classifier in accordance with the present
invention.
DETAILED DESCRIPTION
[0029] The present invention relates to various structure and
functionality for delivery of targeted assets, classification of
network users, matching of asset targeting parameters to audience
classification parameters and network monitoring for use in a
communications network. The invention has particular application
with respect to networks where content is broadcast to network
users. In this regard, content may be broadcast in a variety of
networks including, for example, cable and satellite television
networks, satellite radio networks, IP networks used for
multicasting content and networks used for podcasts or telephony
broadcasts/multicasts. Content may also be broadcast over the
airwaves though, as will be understood from the description below,
certain aspects of the invention make use of bi-directional
communication channels which are not readily available, for
example, in connection with conventional airwave based televisions
or radios (i.e., such communication would involve supplemental
communication systems). In various contexts, the content may be
consumed in real time or stored for subsequent consumption. Thus,
while specific examples are provided below in the context of a
cable television network for purposes of illustration, it will be
appreciated that the invention is not limited to such contexts but,
rather, has application to a variety of networks and transmission
modes.
[0030] The targeted assets may include any type of asset that is
desired to be targeted to network users. It is noted that such
targeted assets are sometimes referred to as "addressable" assets
(though, as will be understood from the description below,
targeting can be accomplished without addressing in a
point-to-point sense). For example, these targeted assets may
include advertisements, internal marketing (e.g., information about
network promotions, scheduling or upcoming events), public service
announcements, weather or emergency information, or programming.
The targeted assets may be independent or included in a content
stream with other assets such as untargeted network programming. In
the latter case, the targeted assets may be interspersed with
untargeted programming (e.g., provided during programming breaks)
or may otherwise be combined with the programming as by being
superimposed on a screen portion in the case of video programming.
In the description below, specific examples are provided in the
context of targeted assets provided during breaks in television
programming. While this is an important commercial implementation
of the invention, it will be appreciated that the invention has
broader application. Thus, distinctions below between "programming"
and "assets" such as advertising should not be understood as
limiting the types of content that may be targeted or the contexts
in which such content may be provided.
[0031] As noted above, the present invention relates to identifying
members of an audience, determining classification information for
those users, determining which user or users may be watching at a
time of interest, and matching assets to the identified audience.
The matching related functionality is useful in a variety of
contexts in a targeted asset delivery system. Accordingly, an
overview of the targeted asset delivery system is first provided
below. Thereafter, the matching related functionality and
associated structure is described in detail.
I. An Exemplary Targeted Asset Delivery System
[0032] A. The Targeted Asset Delivery Environment
[0033] Although the matching-related subject matter of the present
invention can be used in a variety of targeted asset delivery
systems, a particularly advantageous targeted asset delivery system
is described below. The inventive system, in the embodiments
described below, allows for delivery of targeted assets such as
advertising so as to address certain shortcomings or inefficiencies
of conventional broadcast networks. Generally, such targeting
entails delivering assets to desired groups of individuals or
individuals having desired characteristics. These characteristics
or audience classification parameters may be defined based on
personal information, demographic information, psychographic
information, geographic information, or any other information that
may be relevant to an asset provider in identifying a target
audience. Preferably, such targeting is program independent in
recognition that programming is a highly imperfect mechanism for
targeting of assets. For example, even if user analysis indicates
that a particular program has an audience comprised sixty percent
of women, and women comprise the target audience for a particular
asset, airing on that program will result in a forty percent
mismatch. That is, forty percent of the users potentially reached
may not be of interest to the asset provider and pricing may be
based only on sixty percent of the total audience. Moreover,
ideally, targeted asset delivery would allow for targeting with a
range of granularities including very fine granularities. For
example, it may be desired to target a group, such as based on a
geographical grouping, a household characterization or even an
individual user characterization. The present invention
accommodates program independent targeting, targeting with a high
degree of granularity and targeting based on a variety of different
audience classifications.
[0034] FIGS. 1 and 2 illustrate two different contexts of targeted
asset delivery supported in accordance with the present invention.
Specifically, FIG. 1 illustrates the delivery of different assets,
in this case ads, to different users watching the same programming
channel, which may be referred to as spot optimization. As shown,
three different users 500-502 are depicted as watching the same
programming, in this case, denoted "Movie of the Week." At a given
break 504 the users 500-502 each receive a different asset package.
Specifically, user 500 receives a digital music player ad and a
movie promo, user 501 receives a luxury car ad and a health
insurance ad, and user 502 receives a minivan ad and a department
store ad. Alternately, a single asset provider (e.g., a motor
vehicle company) may purchase a spot and then provide different
asset options for the spot (e.g., sports car, minivans, pickup
trucks, etc.). Similarly, separate advertisers may collectively
purchase a spot and then provide ads for their respective products
(e.g., where, the target audiences of the advertisers are
complementary). It will be appreciated that these different asset
packages may be targeted to different audience demographics. In
this manner, assets are better tailored to particular viewers of a
given program who may fall into different demographic groups. Thus,
spot optimization refers to the delivery of different assets (by
one or multiple asset providers) in a given spot.
[0035] FIG. 2 illustrates a different context of the present
invention, which may be termed audience aggregation. In this case,
three different users 600-602 viewing different programs associated
with different channels may receive the same asset or asset
package. In this case, each of the users 600-602 receives a package
including a digital music player ad and a movie promo in connection
with breaks associated with their respective channels. Though the
users 600-602 are shown as receiving the same asset package for
purposes of illustration, it is likely that different users will
receive different combinations of assets due to differences in
classification parameters. In this manner, users over multiple
channels (some or all users of each channel) can be aggregated
(relative to a given asset and time window) to define a virtual
channel having significant user numbers matching a targeted
audience classification. Among other things, such audience
aggregation allows for the possibility of aggregating users over a
number of low share channels to define a significant asset delivery
opportunity, perhaps on the order of that associated with one of
the high share networks. This can be accomplished, in accordance
with the present invention, using equipment already at a user's
premises (i.e., an existing CPE). Such a virtual channel is
graphically illustrated in FIG. 3, though this illustration is not
based on actual numbers. Thus, audience aggregation refers to the
delivery of the same asset in different spots to define an
aggregated audience. These different spots may occur within a time
window corresponding to overlapping (conflicting) programs on
different channels. In this manner, it is likely that these spots,
even if at different times within the window, will not be received
by the same users.
[0036] Such targeting including both spot optimization and audience
aggregation can be implemented using a variety of architectures in
accordance with the present invention. Thus, for example, as
illustrated in FIG. 4, targeted asset insertion can be implemented
at the CPEs. This may involve a forward-and-store functionality. As
illustrated in FIG. 4, the CPE 800 receives a programming stream
802 and an asset delivery stream 804 from the headend 808. These
streams 802 and 804 may be provided via a common signal link such
as a coaxial cable or via separate communications links. For
example, the asset delivery stream 804 may be transmitted to the
CPE 800 via a designated segment, e.g., a dedicated frequency
range, of the available bandwidth or via a programming channel that
is opportunistically available for asset delivery, e.g., when it is
otherwise off air. The asset delivery stream 804 may be provided on
a continuous or intermittent basis and may be provided concurrently
with the programming stream 802. In the illustrated example, the
programming stream 802 is processed by a program-decoding unit,
such as DSTB, and programming is displayed on television set 814.
Alternatively, the programming stream 802 may be stored in
programming storage 815 for CPE insertion.
[0037] In the illustrated implementation, the asset, together with
metadata identifying, for example, any audience classification
parameters of the targeted audience, is stored in a designated
storage space 806 of the CPE 800. It will be appreciated that
substantial storage at the CPE 800 may be required in this regard.
For example, such storage may be available in connection with
certain digital video recorder (DVR) units. A selector 810 is
implemented as a processor running logic on the CPE 800. The
selector 810 functions analogously to the headend selector
described above to identify breaks 816 and insert appropriate
assets. In this case, the assets may be selected based on
classification parameters of the household or, more preferably, a
user within the household. Such information may be stored at the
CPE 800 or may be determined based on an analysis of viewing habits
such as a click stream from a remote control as will be described
in more detail below. Certain aspects of the present invention can
be implemented in such a CPE insertion environment.
[0038] In FIG. 5, a different architecture is employed.
Specifically, in FIG. 5, asset options transmitted from headend 910
synchronously with a given break on a given channel for which
targeted asset options are supported. The CPE 900 includes a
channel selector 902, which is operative to switch to an asset
channel associated with a desired asset at the beginning of a break
and to return to the programming channel at the end of the break.
The channel selector 902 may hop between channels (between asset
channels or between an asset channel and the programming channel)
during a break to select the most appropriate assets. In this
regard, logic resident on the CPE 900 controls such hopping to
avoid switching to a channel where an asset is already in progress.
As described below, this logic can be readily implemented, as the
schedule of assets on each asset channel is known. Preferably, all
of this is implemented invisibly from the perspective of the user
of set 904. The different options may be provided, at least in
part, in connection with asset channels 906 or other bandwidth
segments (separate from programming channels 908) dedicated for use
in providing such options. In addition, certain asset options may
be inserted into the current programming channel 908. Associated
functionality is described in detail below. The architecture of
FIG. 5 has the advantage of not requiring substantial storage
resources at the CPE 900 such that it can be immediately
implemented on a wide scale basis using equipment that is already
in the field.
[0039] As a further alternative, the determination of which asset
to show may be made at the headend. For example, an asset may be
selected based on voting as described below, and inserted at the
headend into the programming channel without options on other asset
channels. This would achieve a degree of targeting but without spot
optimization opportunities as described above. Still further,
options may be provided on other asset channels, but the selection
as between those channels may be determined by the headend. For
example, information about a household or user (e.g., brand of car
owned, magazines subscribed to, etc.) stored on the headend may be
used to match an asset to a household or user. That information,
which may be termed "marketing labels," may be used by the headend
to control which asset is selected by the CPE. For example, the CPE
may be instructed that it is associated with an "ACME preferred"
customer. When an asset is disseminated with ACME preferred
metadata, the CPE may be caused to select that asset, thereby
overriding (or significantly factoring with) any other audience
classification considerations. However, it will be appreciated that
such operation may entail certain concerns relating to sensitive
information or may compromise audience classification based
targeting in other respects.
[0040] A significant opportunity thus exists to better target users
whom asset providers may be willing to pay to reach and to better
reach hard-to-reach users. However, a number of challenges remain
with respect to achieving these objectives including: how to
provide asset options within network bandwidth limitations and
without requiring substantial storage requirements and new
equipment at the user's premises; how to obtain sufficient
information for effective targeting while addressing privacy
concerns; how to address a variety of business related issues, such
as pricing of asset delivery, resulting from availability of asset
options and attendant contingent delivery; and how to operate
effectively within the context of existing network structure and
systems (e.g., across node filters, using existing traffic and
billing systems, etc.).
[0041] From the foregoing it will be appreciated that various
aspects of the invention are applicable in the context of a variety
of networks, including broadcast networks. In the following
discussion, specific implementations of a targeted asset system are
discussed in the context of a cable television network. Though the
system enhances viewing for both analog and digital users, certain
functionality is conveniently implemented using existing DSTBs. It
will be appreciated that, while these represent particularly
advantageous and commercially valuable implementations, the
invention is not limited to these specific implementations or
network contexts.
[0042] B. System Architecture
[0043] In one implementation, the system of the present invention
involves the transmission of asset options in time alignment or
synchronization with other assets on a programming channel, where
the asset options are at least partially provided via separate
bandwidth segments, e.g. channels at least temporarily dedicated to
targeted asset delivery. Although such options may typically be
transmitted in alignment with a break in programming, it may be
desired to provide options opposite continuing programming (e.g.,
so that only subscribers in a specified geographic area get a
weather announcement, an emergency announcement, election results
or other local information while others get uninterrupted
programming). Selection as between the available options is
implemented at the user's premises, as by a DSTB in this
implementation. In this manner, asset options are made available
for better targeting, without the requirement for substantial
storage resources or equipment upgrades at the user's premises
(e.g., as might be required for a forward-and-store architecture).
Indeed, existing DSTBs can be configured to execute logic, for
implementing the system described below by downloading and/or
preloading appropriate logic.
[0044] Because asset options are synchronously transmitted in this
implementation, it is desirable to be efficient in identifying
available bandwidth and in using that bandwidth. Various
functionality for improved bandwidth identification, e.g.,
identifying bandwidth that is opportunistically available in
relation to a node filter, is described later in this discussion.
Efficient use of available bandwidth involves both optimizing the
duty cycle or asset density of an available bandwidth segment
(i.e., how much time, of the time a bandwidth segment is available
for use in transmitting asset options, is the segment actually used
for transmitting options) and the value of the options transmitted.
The former factor is addressed, among other things, by improved
scheduling of targeted asset delivery on the asset channels in
relation to scheduled breaks of the programming channels.
[0045] The latter factor is addressed in part by populating the
available bandwidth spots with assets that are most desired based
on current network conditions. These most desired assets can be
determined in a variety of ways including based on conventional
ratings. In the specific implementation described below, the most
desired assets are determined via a process herein termed voting.
FIG. 6 illustrates an associated messaging sequence 1000 in this
regard as between a CPE 1002 such as a DSTB, a network platform for
asset insertion such as a headend 1004 and a traffic and billing
(T&B) system 1006 used in the illustrated example for obtaining
asset delivery orders or contracts and billing for asset delivery.
It will be appreciated that the functionality of the T&B system
1006 may be split between multiple systems running on multiple
platforms and the T&B system 1006 may be operated by the
network operator or may be separately operated.
[0046] The illustrated sequence begins by loading contract
information 1008 from the T&B system 1006 onto the headend
1004. An interface associated with system 1006 allows asset
providers to execute contracts for dissemination of assets based on
traditional time-slot buys (for a given program or given time on a
given network) or based on a certain audience classification
information (e.g., desired demographics, psychographics, geography,
and/or audience size). In the latter case, the asset provider or
network may identify audience classification information associated
with a target audience. The system 1006 uses this information to
compile the contract information 1008, which identifies the asset
that is to be delivered together with delivery parameters regarding
when and to whom the asset is to be delivered.
[0047] The illustrated headend 1004 uses the contract information
together with a schedule of breaks for individual networks to
compile an asset option list 1010 on a channel-by-channel and
break-by-break basis. That is, the list 1010 lists the universe of
asset options that are available for voting purposes for a given
break on a given programming channel together with associated
metadata identifying the target audience for the asset, e.g., based
on audience classification information. The transmitted list 1010
may encompass all supported programming channels and may be
transmitted to all participating users, or the list may be limited
to one or a subset of the supported channels e.g., based on an
input indicating the current channel or the most likely or frequent
channels used by a particular user or group of users. The list 1010
is transmitted from the headend 1004 to the CPE 1002 in advance of
a break for which options are listed.
[0048] Based on the list 1010, the CPE 1002 submits a vote 1012
back to the headend 1004. More specifically, the CPE 1002 first
identifies the classification parameters for the current user(s)
and perhaps the current channel being watched, identifies the
assets that are available for an upcoming break (for the current
channel or multiple channels) as well as the target audience for
those assets and determines a "fit" of one or more of those asset
options to the current classification. In one implementation, each
of the assets is attributed a fit score for the user(s), e.g.,
based on a comparison of the audience classification parameters of
the asset to the putative audience classification parameters of the
current user(s). This may involve how well an individual user
classification parameter matches a corresponding target audience
parameter and/or how many of the target audience parameters are
matched by the user's classification parameters. Based on these fit
scores, the CPE 1002 issues the vote 1012 indicating the most
appropriate asset(s). Any suitable information can be used to
provide this indication. For example, all scores for all available
asset options (for the current channel or multiple channels) may be
included in the vote 1012. Alternatively, the vote 1012 may
identify a subset of one or more options selected or deselected by
the CPE 1002, with or without scoring information indicating a
degree of the match and may further include channel information. In
one implementation, the headend 1004 instructs CPEs (1002) to
return fit scores for the top N asset options for a given spot,
where N is dynamically configurable based on any relevant factor
such as network traffic levels and size of the audience.
Preferably, this voting occurs shortly before the break at issue
such that the voting more accurately reflects the current status of
network users. In one implementation, votes are only submitted for
the programming channel to which the CPE is set, and votes are
submitted periodically, e.g., every fifteen minutes.
[0049] The headend 1004 compiles votes 1012 from CPEs 1002 to
determine a set of selected asset options 1014 for a given break on
a supported programming channel. As will be understood from the
description below, such votes 1012 may be obtained from all
relevant and participating CPEs 1002 (who may be representative of
a larger audience including analog or otherwise non-participating
users) or a statistical sampling thereof. In addition, the headend
1004 determines the amount of bandwidth, e.g., the number of
dedicated asset option channels, that is available for transmission
of options in support of a given break for a given programming
channel.
[0050] Based on all of this information, the headend 1004 assembles
a flotilla of assets, e.g., the asset options having the highest
vote values or the highest weighted vote values where such
weighting takes into account value per user or other information
beyond classification fit. Such a flotilla may include asset
options inserted on the current programming channel as well as on
asset channels, though different insertion processes and components
may be involved for programming channel and asset channel
insertion. It will be appreciated that some assets may be assembled
independently or largely independently of voting, for example,
certain public service spots or where a certain provider has paid a
premium for guaranteed delivery. Also, in spot optimization
contexts where a single asset provider buys a spot and then
provides multiple asset options for that spot, voting may be
unnecessary (though voting may still be used to select the
options).
[0051] In one implementation, the flotilla is assembled into sets
of asset options for each dedicated asset channel, where the time
length of each set matches the length of the break, such that
channel hopping within a break is unnecessary. Alternatively, the
CPE 1002 may navigate between the asset channels to access desired
assets within a break (provided that asset starts on the relevant
asset channels are synchronized). However, it will be appreciated
that the flotilla matrix (where columns include options for a given
spot and rows correspond to channels) need not be rectangular.
Stated differently, some channels may be used to provide asset
options for only a portion of the break, i.e., may be used at the
start of the break for one or more spots but are not available for
the entire break, or may only be used after one or more spots of a
break have aired. A list of the selected assets 1014 and the
associated asset channels is then transmitted together with
metadata identifying the target audience in the illustrated
implementation. It will be appreciated that it may be unnecessary
to include the metadata at this step if the CPE 1002 has retained
the asset option list 1010. This list 1014 is preferably
transmitted shortly in advance of transmission of the asset 1016
(which includes sets of asset options for each dedicated contact
options channel used to support, at least in part, the break at
issue).
[0052] The CPE 1002 receives the list of selected, asset options
1014 and associated metadata and selects which of the available
options to deliver to the user(s). For example, this may involve a
comparison of the current audience classification parameter values
(which may or may not be the same as those used for purposes of
voting) to the metadata associated with each of the asset options.
The selected asset option is used to selectively switch the CPE
1002 to the corresponding dedicated asset options channel to
display the selected asset 1016 at the beginning of the break at
issue. One of the asset option sets, for example, the one comprised
of the asset receiving the highest vote values, may be inserted
into the programming channel so that switching is not required for
many users. Assuming that the voting CPEs are at least somewhat
representative of the universe of all users, a significant degree
of targeting is thereby achieved even for analog or otherwise
non-participating users. In this regard, the voters serve as
proxies for non-voting users. The CPE 1002 returns to the
programming channel at the conclusion of the break. Preferably, all
of this is transparent from the perspective of the user(s), i.e.,
preferably no user input is required. The system may be designed so
that any user input overrides the targeting system. For example, if
the user changes channels during a break, the change will be
implemented as if the targeting system was not in effect (e.g., a
command to advance to the next channel will set the CPE to the
channel immediately above the current programming channel, without
regard to any options currently available for that channel,
regardless of the dedicated asset channel that is currently
sourcing the television output).
[0053] In this system architecture, as in forward-and-store
architectures or any other option where selections between asset
options are implemented at the CPE, there will be some uncertainty
as to how many users or households received any particular asset
option in the absence of reporting. This may be tolerable from a
business perspective. In the absence of reporting, the audience
size may be estimated based on voting data, conventional ratings
analysis and other tools. Indeed, in the conventional asset
delivery paradigm, asset providers accept Nielsen rating estimates
and demographic information together with market analysis to gauge
return on investment. However, this uncertainty is less than
optimal in any asset delivery environment and may be particularly
problematic in the context of audience aggregation across multiple
programming networks, potentially including programming networks
that are difficult to measure by conventional means.
[0054] The system of the present invention preferably implements a
reporting system by which individual CPEs 1002 report back to the
headend 1004 what asset or assets were delivered at the CPE 1002
and, optionally, to whom (in terms of audience classification).
Additionally, the reports may indicate where (on what programming
channel) the asset was delivered and how much (if any) of the asset
was consumed. Such reports 1018 may be provided by all
participating CPEs 1002 or by a statistical sampling thereof. These
reports 1018 may be generated on a break-by-break basis,
periodically (e.g., every 15 minutes) or may be aggregated prior to
transmission to the headend 1004. Reports may be transmitted soon
after delivery of the assets at issue or may be accumulated, e.g.,
for transmission at a time of day where messaging bandwidth is more
available. Moreover, such reporting may be coordinated as between
the CPEs 1002 so as to spread the messaging load due to
reporting.
[0055] In any case, the reports 1018 can be used to provide billing
information 1020 to the T&B system 1006 for valuing the
delivery of the various asset options. For example, the billing
information 1020 can be used by the T&B system 1006 to
determine how large an audience received each option and how well
that audience matched the target audience. For example, as noted
above, a fit score may be generated for particular asset options
based on a comparison of the audience classification to the target
audience. This score may be on any scale, e.g., 1-100. Goodness of
fit may be determined based on this raw score or based on
characterization of this score such as "excellent," "good," etc.
Again, this may depend on how well an individual audience
classification parameter of a user matches a corresponding target
audience parameter and/or how many of the target audience
parameters are matched by the user's audience classification
parameters. This information may in turn be provided to the asset
provider, at least in an aggregated form. In this manner, the
network operator can bill based on guaranteed delivery of targeted
messages or scale the billing rate (or increase delivery) based on
goodness of fit as well as audience size. The reports (and/or
votes) 1018 can also provide a quick and detailed measurement of
user distribution over the network that can be used to accurately
gauge ratings, share, demographics of audiences and the like.
Moreover, this information can be used to provide future audience
estimation information 1022, for example, to estimate the total
target universe based on audience classification parameters.
[0056] It will thus be appreciated that the present invention
allows a network operator such as an MSO to sell asset delivery
under the conventional asset delivery (time-slot) buy paradigm or
under the new commercial impression paradigm or both. For example,
a particular MSO may choose to sell asset delivery space for the
major networks (or for these networks during prime time) under the
old time-slot buy paradigm while using the commercial impression
paradigm to aggregate users over multiple low market share
networks. Another MSO may choose to retain the basic time-slot buy
paradigm while accommodating asset providers who may wish to fill a
given slot with multiple options targeted to different
demographics. Another MSO may choose to retain the basic time-slot
buy paradigm during prime time across all networks while using the
targeted impression paradigm to aggregate users at other times of
the day. The targeted impression paradigm may be used by such MSOs
only for this limited purpose.
[0057] FIG. 8 is a flow chart illustrating an associated process
1200. An asset provider (or agent thereof) can initiate the
illustrated process 1200 by accessing (1202) a contracting platform
as will be described below. Alternatively, an asset provider can
work with the sales department or other personnel of a system
operator or other party who accesses such a platform. As a still
further alternative, an automated buying system may be employed to
interface with such a platform via a system-to-system interface.
This platform may provide a graphical user interface by which an
asset provider can design a dissemination strategy and enter into a
corresponding contract for dissemination of an asset. The asset
provider can then use the interface to select (1204) to execute
either a time-slot buy strategy or a targeted impression buy
strategy. In the case of a time-slot buy strategy, the asset
provider can then use the user interface to specify (1206) a
network and time-slot or other program parameter identifying the
desired air times and frequency for delivery of the asset. Thus,
for example, an asset provider may elect to air the asset in
connection with specifically identified programs believed to have
an appropriate audience. In addition, the asset provider may
specify that the asset is to appear during the first break or
during multiple breaks during the program. The asset provider may
further specify that the asset is to be, for example, aired during
the first spot within the break, the last spot within the break or
otherwise designate the specific asset delivery slot.
[0058] Once the time-slots for the asset have thus been specified,
the MSO causes the asset to be embedded (1208) into the specified
programming channel asset stream. The asset is then available to be
consumed by all users of the programming channel. The MSO then
bills (1210) the asset provider, typically based on associated
ratings information. For example, the billing rate may be
established in advance based on previous rating information for the
program in question, or the best available ratings information for
the particular airing of the program may be used to bill the asset
provider. It will thus be appreciated that the conventional
time-slot buy paradigm is limited to delivery to all users for a
particular time-slot on a particular network and does not allow for
targeting of particular users of a given network or targeting users
distributed over multiple networks in a single buy.
[0059] In the case of targeted impression buys, the asset provider
can use the user interface as described in more detail below to
specify (1212) audience classification and other dissemination
parameters. In the case of audience classification parameters, the
asset provider may specify the gender, age range, income range,
geographical location, lifestyle interest or other information of a
targeted audience. The additional dissemination parameters may
relate to delivery time, frequency, audience size, or any other
information useful to define a target audience. Combinations of
parameters may also be specified. For example, an asset provider
may specify an audience size of 100,000 in a particular demographic
group and further specify that the asset is not delivered to any
user who has already received the asset a predetermined number of
times.
[0060] Based on this information, the targeted asset system of the
present invention is operative to target appropriate users. For
example, this may involve targeting only selected users of a major
network. Additionally or alternatively, this may involve
aggregating (1214) users across multiple networks to satisfy the
audience specifications. For example, selected users from multiple
programming channels may receive the asset within a designated time
period in order to provide an audience of the desired size, where
the audience is composed of users matching the desired audience
classification. The user interface preferably estimates the target
universe based on the audience classification and dissemination
parameters such that the asset provider receives an indication of
the likely audience size.
[0061] The aggregation system may also be used to do time of day
buys. For example, an asset provider could specify audience
classification parameters for a target audience and further specify
a time and channel for airing of the asset. CPEs tuned to that
channel can then select the asset based on the voting process as
described herein. Also, asset providers may designate audience
classification parameters and a run time or time range, but not the
programming channel. In this manner, significant flexibility is
enabled for designing a dissemination strategy. It is also possible
for a network operator to disable some of these strategy options,
e.g., for business reasons.
[0062] Based on this input information, the targeted asset system
of the present invention is operative to provide the asset as an
option during one or more time-slots of one or more breaks. In the
case of spot optimization, multiple asset options may be
disseminated together with information identifying the target
audience so that the most appropriate asset can be delivered at
individual CPEs. In the case of audience aggregation, the asset may
be provided as an option in connection with multiple breaks on
multiple programming channels the system then receives and
processes (1218) reports regarding actual delivery of the asset by
CPEs and information indicating how well the actual audience fit
the classification parameters of the target audience. The asset
provider can then be billed (1220) based on guaranteed delivery and
goodness of fit based on actual report information. It will thus be
appreciated that a new asset delivery paradigm is defined by which
assets are targeted to specific users rather than being associated
with particular programs. This enables both better targeting of
individual users for a given program and improved reach to target
users on low-share networks.
[0063] From the foregoing, it will be appreciated that various
steps in the messaging sequence are directed to matching assets to
users based on classification parameters, allowing for goodness of
fit determinations based on such matching or otherwise depending on
communicating audience classification information across the
network. It is preferable to implement such messaging in a manner
that is respectful of user privacy concerns and relevant regulatory
regimes.
[0064] In the illustrated system, this is addressed by implementing
the system free from persistent storage of a user profile or other
sensitive information including, for example, personally
identifiable information (PII). Specifically, it may be desired to
protect as sensitive information subject matter extending beyond
the established definition of PM As one example in this regard, it
may be desired to protect MAC addresses even though such addresses
are not presently considered to be included within the definition
of PII in the United States. Generally, any information that may
entail privacy concerns or identify network usage information may
be considered sensitive information. More particularly, the system
learns of current network conditions prior to transmission of asset
options via votes that identify assets without any sensitive
information. Reports may also be limited to identifying assets that
have been delivered (which assets are associated with target
audience parameters) or characterization of the fit of audience
classification parameters of a user(s) to a target audience
definition. Even if it is desired to associate reports with
particular users, e.g., to account for ad skipping as discussed
below, such association may be based on an identification code or
address not including PII. In any event, identification codes or
any other information deemed sensitive can be immediately stripped
and discarded or hashed, and audience classification information
can be used only in anonymous and aggregated form to address any
privacy concerns. With regard to hashing, sensitive information
such as a MAC or IP address (which may be included in a designated
header field) can be run through a hash function and reattached to
the header, for example, to enable anonymous identification of
messages from the same origin as may be desired. Moreover, users
can be notified of the targeted asset system and allowed to opt in
or opt out such that participating users have positively assented
to participate.
[0065] Much of the discussion above has referenced audience
classification parameters as relating to individuals as opposed to
households. FIG. 7A illustrates a theoretical example of a CPE
including a television set 1100 and a DSTB 1102 that are associated
with multiple users 1103-1106. Arrow 1107 represents a user input
stream, such as a click stream from a remote control, over time. A
first user 1105, in this case a child, uses the television 1100
during a first time period--for example, in the morning. Second and
third users 1103 and 1104 (designated "father" and "mother") use
the television during time periods 1109 and 1110, which may be, for
example, in the afternoon or evening. A babysitter 1106 uses the
television during a nighttime period in this example.
[0066] This illustrates a number of challenges related to targeted
asset delivery. First, because there are multiple users 1103-1106,
targeting based on household demographics would have limited
effectiveness. For example, it may be assumed that the child 1105
and father 1103 in many cases would not be targeted by the same
asset providers. Moreover, in some cases, multiple users may watch
the same television at the same time as indicated by the overlap of
time periods 1109-1110. In addition, in some cases such as
illustrated by the babysitter 1106 an unexpected user (from the
perspective of the targeted asset system) may use the television
1100.
[0067] These noted difficulties are associated with a number of
objectives that are preferably addressed by the targeted asset
system of the present invention. First, the system should
preferably be operative to distinguish between multiple users of a
single set and, in the context of the system described above, vote
and report to the network accordingly. Second, the system should
preferably react over time to changing conditions such as the
transitions from use by father 1103 to use by both father and
mother 1103 and 1104 to use by only mother 1104. The system should
also preferably have some ability to characterize unexpected users
such as the babysitter 1106. In that case, the system may have no
other information to go on other than the click stream 1107. The
system may also identify time periods where, apparently, no user is
present, though the set 1100 may still be on. Preferably, the
system also operates free from persistent storage of any user
profile or sensitive information so that no third party has a
meaningful opportunity to misappropriate such information or
discover the private network usage patterns of any of the users
1103-1106 via the targeted asset system. Privacy concerns can
alternatively be addressed by obtaining consent from users. In this
matter, sensitive information including PII can be transmitted
across the network and persistently stored for use in targeting.
This may allow for compiling a detailed user profile, e.g., at the
headend. Assets can then be selected based on the user profile and,
in certain implementations, addressed to specific CPEs.
[0068] In certain implementations, the present invention monitors
the click stream over a time window and applies a mathematical
model to match a pattern defined by the click stream to predefined
audience classification parameters that may relate to demographic
or psychographic categories. It will be appreciated that the click
stream will indicate programs selected by users, volume and other
information that may have some correlation, at least in a
statistical sense, to the classification parameters. In addition,
factors such as the frequency of channel changes and the length of
time that the user lingers on a particular asset may be relevant to
determining a value of an audience classification parameter. The
system can also identify instances where there is apparently no
user present.
[0069] In a first implementation, as is described in detail below,
logic associated with the CPE 1101 uses probabilistic modeling,
fuzzy logic and/or machine learning to progressively estimate the
audience classification parameter values of a current user or users
based on the click stream 1107. This process may optionally be
supplemental based on stored information (preferably free of
sensitive information) concerning the household that may, for
example, affect probabilities associated with particular inputs. In
this manner, each user input event (which involves one or more
items of change of status and/or duration information) can be used
to update a current estimate of the audience classification
parameters based on associated probability values. The fuzzy logic
may involve fuzzy data sets and probabilistic algorithms that
accommodate estimations based on inputs of varying and limited
predictive value.
[0070] In a second implementation, the click stream is modeled as
an incomplete or noisy signal that can be processed to obtain
audience classification parameter information. More specifically, a
series of clicks over time or associated information can be viewed
as a time-based signal. This input signal is assumed to reflect a
desired signature or pattern that can be correlated to audience
classification parameters. However, the signal is assumed to be
incomplete or noisy--a common problem in signal processing.
Accordingly, filtering techniques are employed to estimate the
"true" signal from the input stream and associated algorithms
correlate that signal to the desired audience classification
information. For example, a nonlinear adaptive filter may be used
in this regard.
[0071] In either of these noted examples, certain preferred
characteristics apply. First, the inputs into the system are
primarily a click stream and stored aggregated or statistical data,
substantially free of any sensitive information. This addresses
privacy concerns as noted above but also provides substantial
flexibility to assess new environments such as unexpected users. In
addition, the system preferably has a forgetfulness such that
recent inputs are more important than older inputs. Either of the
noted examples accommodates this objective. It will be appreciated
that such forgetfulness allows the system to adapt to change, e.g.,
from a first user to multiple users to a second user. In addition,
such forgetfulness limits the amount of viewing information that is
available in the system at any one time, thereby further addressing
privacy concerns, and limits the time period during which such
information could conceivably be discovered. For example,
information may be deleted and settings may be reset to default
values periodically, for example, when the DSTB is unplugged.
[0072] A block diagram of a system implementing such a user
classification system is shown in FIG. 7B. The illustrated system
is implemented in a CPE 1120 including a user input module 1122 and
a classification module 1124. The user input module receives user
inputs, e.g., from a remote control or television control buttons,
that may indicate channel selections, volume settings and the like.
These inputs are used together with programming information 1132
(which allows for correlation of channel selections to programming
and/or associated audience profiles) for a number of functions. In
this regard, the presence detector 1126 determines whether it is
likely that a user is present for all or a portion of an asset that
is delivered. For example, a long time period without any user
inputs may indicate that no user is present and paying attention or
a volume setting of zero may indicate that the asset was not
effectively delivered. The classifier 1128 develops audience
classification parameters for one or more users of a household as
discussed above. The user identifier is operative to estimate which
user, of the classified users, is currently present. Together,
these modules 1126, 1128 and 1130 provide audience classification
information that can be used to vote (or elect not to vote) and/or
generate reports (or elect not to generate reports).
[0073] As noted above, one of the audience classifications that may
be used for targeting is location. Specifically, an asset provider
may wish to target only users within a defined geographic zone
(e.g., proximate to a business outlet) or may wish to target
different assets to different geographic zones (e.g., targeting
different car ads to users having different supposed income levels
based on location). In certain implementations, the present
invention determines the location of a particular CPE and uses the
location information to target assets to the particular CPE. It
will be appreciated that an indication of the location of a CPE
contains information that may be considered sensitive. The present
invention also creates, extracts and/or receives the location
information in a manner that addresses these privacy concerns. This
may also be accomplished by generalizing or otherwise filtering out
sensitive information from the location information sent across the
network. This may be accomplished by providing filtering or sorting
features at the CPE or at the headend. For example, information
that may be useful in the reporting process (i.e. to determine the
number of successful deliveries within a specified location zone)
may be sent upstream with little or no sensitive information
included. Additionally, such location information can be
generalized so as to not be personally identifiable. For example,
all users on a given block or within another geographic zone (such
as associated with a zip plus 2 area) may be associated with the
same location identifier (e.g., a centroid for the zone).
[0074] In one implementation, logic associated with the CPE sends
an identifier upstream to the headend where the identifier is
cross-referenced against a list of billing addresses. The billing
address that matches the identifier is then translated, for
example, using GIS information, into a set of coordinates (e.g.,
Cartesian geographic coordinates) and those coordinates or an
associated geographic zone identifier are sent back to the CPE for
storage as part of its location information. Alternatively, a list
may be broadcast. In this case, a list including location
information for multiple or all network users is broadcast and each
CPE selects its own information. Asset providers can also associate
target location information with an asset. For example, in
connection with a contract interface as specified below, asset
providers can define target asset delivery zones. Preferably this
can be done via a graphical interface (e.g., displaying a map), and
the defined zones can match, to a fine level of granularity,
targeted areas of interest without being limited to node areas or
other network topology. Moreover, such zones can have complex
shapes including discontiguous portions. Preferably the zones can
then be expressed in terms that allow for convenient transmission
in asset metadata and comparison to user locations e.g., in terms
of grid elements or area cells.
[0075] In another implementation, individual geographic regions are
associated with unique identifiers and new regions can be defined
based on the union of existing regions. This can be extended to a
granularity identifying individual CPEs at its most fine level.
Higher levels including numerous CPEs may be used for voting and
reporting to address privacy concerns.
[0076] Upon receipt of an asset option list or an asset delivery
request (ADR), the CPE parses the ADR and determines whether the
location of the CPE is included in the locations targeted by the
asset referenced in the ADR. For example, this may involve a point
in polygon or other point in area algorithm, a radius analysis, or
a comparison to a network of defined grid or cells such as a
quadtree data structure. The CPE may then vote for assets to be
received based on criteria including whether the location of that
particular CPE is targeted by the asset.
[0077] After displaying an asset option, the CPE may also use its
location information in the reporting process to enhance the
delivery datasent upstream. The process by which the CPE uses its
location information removes substantially all sensitive
information from the location information. For example, the CPE may
report that an asset targeted to a particular group of locations
was delivered to one of the locations in the group. The CPE in this
example would not report the location to which asset was actually
delivered.
[0078] Similarly, it is often desired to associate tags with asset
selections. Such tags are additional information that is
superimposed on or appended to such assets. For example, a tag may
provide information regarding a local store or other business
location at the conclusion of an asset that is distributed on a
broader basis. Conventionally, such tags have been appended to ads
prior to insertion at the headend and have been limited to coarse
targeting. In accordance with the present invention, tags may be
targeted to users in particular zones, locations or areas, such as
neighborhoods. Tags may also be targeted based on other audience
classification parameters such as age, gender, income level, etc.
For example, tags at the end of a department store ad may advertise
specials on particular items of interest to particular
demographics. Specifically, a tag may be included in an asset
flotilla and conditionally inserted based on logic contained within
the CPE. Thus the tags are separate units that can be targeted like
other assets, however, with conditional logic such that they are
associated with the corresponding asset.
[0079] The present invention may use information relating to the
location of a particular CPE to target a tag to a particular CPE.
For example, the CPE may contain information relating to its
location in the form of Cartesian coordinates as discussed above.
If an asset indicates that a tag may be delivered with it or
instead of it, the CPE determines whether there is, associated with
any of the potential tags, a location criterion that is met by the
location information contained in the particular CPE. For example,
a tag may include a location criterion defining a particular
neighborhood. If the CPE is located in that neighborhood, the CPE
1101 may choose to deliver the tag, assuming that other criteria
necessary for the delivery of the tag are met. Other criteria may
include the time available in the given break, other demographic
information, and information relating to the national or
non-localized asset.
[0080] As briefly note above, targeting may also be implemented
based on marketing labels. Specifically, the headend may acquire
information or marketing labels regarding a user or household from
a variety of sources. These marketing labels may indicate that a
user buys expensive cars, is a male 18-24 years old, or other
information of potential interest to an asset provider. In some
cases, this information may be similar to the audience
classification parameters, though it may optionally be static (not
varying as television users change) and based on hard data (as
opposed to being surmised based on viewing patterns or the like).
In other cases, the marketing labels may be more specific or
otherwise different than the audience classification. In any event,
the headend may inform the CPE as to what kind of user/household it
is in terms of marketing labels. An asset provider, can then target
an asset based on the marketing labels and the asset will be
delivered by CPEs where targeting matches. This can be used in
audience aggregation and spot optimization contexts.
[0081] Thus, the targeted asset system of the present invention
allows for targeting of assets in a broadcast network based on any
relevant audience classification, whether determined based on user
inputs such as a click stream, based on marketing labels or other
information pushed to the customer premises equipment, based on
demographic or other information stored or processed at the
headend, or based on combinations of the above or other
information. In this regard, it is therefore possible to use, in
the context of a broadcast network, targeting concepts that have
previously been limited to other contexts such as direct mail. For
example, such targeting may make use of financial information,
previous purchase information, periodical subscription information
and the like. Moreover, classification systems developed in other
contexts, may be leveraged to enhance the value of targeting
achieved in accordance with the present invention.
[0082] An overview of the system has thus been provided, including
introductory discussions of major components of the system, which
provides a system context for understanding the operation of the
matching related functionality and associated structure. This
matching related subject matter is described in the remainder of
this description.
II. Asset Matching
[0083] A. Overview
[0084] From the discussion above, it will be appreciated that
determining classification parameters for a user and matching the
classification parameters of the user to targeting parameters of an
asset is useful in several contexts. First, this matching-related
functionality is useful in the voting process. That is, one of the
functions of the targeting system in the system described above is
to receive ad lists (identifying a set of ads that are available
for an upcoming spot), determining the targeting parameters for the
various ads and voting for one or more ads based on how well the
targeting parameters match the classification parameters of a
current users. Thus, identifying the classification parameters of
the current user(s) and matching those parameters to the targeting
parameters is important in the voting context.
[0085] Matching related functionality is also important in the ad
selection context. Specifically, after the votes from the various
participating set top boxes have been processed, a flotilla of ads
is assembled for a commercial break. A given DSTB selects a path
through the flotilla (corresponding to a set of ads delivered by
the set top box at the commercial break) based on which ads are
appropriate for the user(s). Accordingly, identifying the current
user classification parameters and matching those parameters to the
targeting parameters is important in the ad selection context.
[0086] The matching related functionality may also be used in the
reporting context. In this regard, some or all at the DSTBs provide
reports to the network concerning the ads that were actually
delivered. This enables the targeting system and the traffic and
billing system to measure the audience for an ad so that the
advertiser can be billed appropriately. Preferably, the information
provided by these reports not only indicates the size of the
audience but how well the audience fits the target audience for the
ad. Accordingly, the system described above can provide goodness of
fit information identifying how well the classification parameters
of the user(s) who received the ad match the targeting parameters.
The matching related functionality is also useful in this
context.
[0087] In one implementation of the present invention, this
matching related functionality is performed by a classifier
resident at the DSTB. This classifier will be described in more
detail below. Generally, the classifier analyzes a click stream, or
series of remote control inputs, to determine probable
classification parameters of a current user(s). The classifier also
performs a matching function to determine a suitability of each of
multiple candidate ads (e.g., from an ad list) for the current
user(s) based on inferred classification parameters of a putative
user or users. The classifier can then provide matching related
information for use in the voting, ad selection and reporting
contexts as described above. In the description below, this
matching related information is primarily discussed in relation to
the voting context. However, it will be appreciated that
corresponding information can be used for ad selection and
reporting. In addition, while the invention can be used in
connection with targeting various types of assets, in various
networks, the following description is set forth in the context of
targeting ads in a cable television network. Accordingly, the terms
"ad" and "viewer" are used for convenience and clarity. Moreover,
for convenience, though the classifier can identify multiple users,
the description below sometimes refers to a singular user or
viewer.
[0088] The classifier generally operates in two modes: the learning
mode and the working mode. It will be appreciated, however, that
these modes are not fully separate. For example, the classifier
continues to learn and adapt during normal operation. These modes
are generally illustrated in FIG. 9. In the learning mode, the
illustrated classifier 1300 monitors behavior of viewers 1302 in
the audience of a given DSTB 1304 to deduce classification
parameters for the viewers 1302. In this regard, an audience for a
given DSTB 1304 may include a father, a mother and a child, one or
more of whom may be present during a viewing session. The
classification parameters may include any of the classification
parameters noted above, such as gender, age, income, program
preferences or the like.
[0089] As shown in FIG. 9, in the learning mode, the classifier
receives inputs including click data 1310 from the user, program
data 1312 (such as program guide data) from the network and Nielsen
data 1314 generated by the Nielsen system. This information is
processed to learn certain behaviors of the viewer, including a
viewer program selection behavior. In this regard, the Nielsen data
1314 reflects the demographic composition for particular programs.
The program data 1312 may include information regarding the genre,
rating, scheduled time, channel and other information regarding
programs. The click data 1310 reflects channels selected by a user,
dwell time (how long a user remained on a given channel) and other
information that can be correlated with the Nielsen data 1314 and
program data 1312 to obtain evidence regarding classification
parameters of the viewer. In addition, the click data 1310 may
reflect frequency of channel hopping, quickness of the click
process, volume control selections and other information from which
evidence of viewer classification parameters may be inferred
independent of any correlation to program related information. As
will be discussed in more detail below, in the learning mode, the
classifier 1300 begins to generate clusters of data around segments
of classification parameter values (e.g., associated with
conventional data groupings), thereby learning to identify viewers
1310 and classify the viewers 1302 in relation to their probable
gender, age, income and other classification parameters.
[0090] The state transition functionality is illustrated in FIG.
10. State changes are triggered by events, messages and
transactions. One of the important state transitions is the stream
of click events 1400. Each click event 1400 represents a state
transition (for example, a change from one program to another, a
change in volume setting, etc.). As shown, an absence of click
events 1400 or low frequency of click events may indicate that no
viewer is present or that any viewing is only passive 1404. If the
transition count 1406 exceeds a threshold (e.g., in terms of
frequency) and the DSTB is on or active 1402, then the classifier
may be operated to match program data 1408 and learn or store 1410
the program. Programs may be deleted 1412 in this regard so as not
to exceed a maximum stack depth or to implement a degree of
desirable forgetfulness as described above.
[0091] Referring again to FIG. 9, as the learning mode progresses,
viewer identifications are developed in relation to at least two
sets of characteristic information. First, a classification
parameter set is developed for each discovered viewer 1302 of the
DSTB audience. Second, for each discovered viewer 1302, a set of
rules is developed that defines the viewing behavior over time for
that viewer 1302. This is referred to below as the periodicity of
the viewer's viewing habits. Thus, the classification set for each
discovered viewer 1302 may identify the viewer's age, gender,
education, income and other classification parameters. This
information is coupled with the periodicity of the viewer's viewing
habits so as to allow the classifier to match an ad with a target
audience during a specific timeframe. That is, the determination by
the classifier as to who is watching at a given time may be
informed both by a substantially real-time analysis of viewing
behavior and by historical viewing patterns of a viewer 1302.
Alternatively, this process of developing classification parameter
sets for discovered users may take into consideration multiple time
frames, e.g., different times of day. Developing these
classification parameter sets for discovered viewers as a function
of time of day, e.g., on an hourly, half-hourly or other time
dependent basis, has been found effective, as viewership in many
households is significantly dependent on time of day.
[0092] It will be appreciated that the learning mode and working
mode need not be distinct. For example, the classifier may estimate
classification parameters for a current viewer 1302 even if
historical periodicity data has not been developed for that viewer
1302. Similarly, even where such information has been developed in
the learning mode, a current viewing audience may be continually
defined and redefined during the working mode. Thus, the classifier
does not require persistent storage of viewer profile information
in order to function. For example, any such stored information may
be deleted when the DSTB is turned off. In such a case, the
classifier can readily develop classification parameters for one or
more viewers when power is restored to the set top box. Moreover,
the classifier may be designed to incorporate a degree of
forgetfulness. That is, the classifier may optionally de-weight or
delete aged information from its evolving model of audience
members. In this manner, the classifier can adapt to changes in the
audience composition and identify previously unknown audience
members.
[0093] When sufficient viewer behavior information has been
collected (which may only require a small number of user inputs),
the illustrated classifier moves from the learning mode to the
working mode. In the working mode, the classifier 1306 performs a
number of related functions. First, it can receive ad lists 1308
for an upcoming commercial break, match the targeting parameters
for the ads on that list 1308 to the classification parameters of
the current viewer 1302, and vote for appropriate ads. The
classifier 1306 also selects ads available in a given ad flotilla
for delivery to the current user 1302. Moreover, the classifier
1306 can report goodness of fit information regarding ads delivered
during one or more commercial breaks. Again, in the working mode,
the classifier 1306 continues to learn through a process of
stochastic reinforcement, but the classifier 1306 is deemed to have
sufficient information to meaningfully estimate classification
parameters of a current viewer 1302 or audience.
[0094] As noted above, in the working mode, the classifier 1306
controls the voting process by effectively ranking ads from an ad
list 1308. In this regard, the illustrated working mode classifier
1306 receives information regarding available ads 1316 from an ad
repository 1318. These ads 1316 are associated with targeting
parameters, for example, in the form of audience segmentation and
viewer profile classification rules 1320. For example, an
advertiser may enter targeting parameters directly into the T &
B system via the ad interface. Typically, these targeting
parameters may be defined in relation to conventional audience
segmentation categories. However, as discussed above, the targeting
system of the present invention may accommodate different or finer
targeting parameters. The working mode classifier 1306 also
receives an ad list 1308 or a view list of candidate ads, as
described above. Specifically, the headend targeting system
component 1320 processes the inputs regarding available ads and
their targeting parameters to generate the ad list 1308 for
distribution to participating DSTBs.
[0095] The similarity and proximity analyzer 1322 uses the
targeting parameters associated with individual ads and the
classification parameters of the current user to execute matching
functionality. That is, the analyzer 1322 matches an ad with at
least one of the probable viewers 1302 currently thought to be
sitting in front of the television set 1324. As will be described
in more detail below, this is done by comparing, for example, the
target age range for an ad (which may be expressed as a slightly
fuzzified region) to the set representing the viewer's age (which
may also be a fuzzy set). The more these two sets overlap the
greater the compatibility or match. Such matching is performed in
multiple dimensions relating to multiple targeting/classification
parameters. This similarity analysis is applied across each
candidate ad of the ad list 1308, and a degree of similarity is
determined for each ad. When this process is complete, the ads in
each time period can be sorted, e.g., in descending order by
similarity, and one or more of the top ads may be selected for
voting.
[0096] The passive voting agent 1326 is operative to select ads
based on the match information. This process works in the
background generally using an out of band data stream. More
specifically, the illustrated voting agent 1326 selects a record or
ADR for each of the candidate ads and determines for each if any
viewers are likely to be present at the ad time. Additionally, the
voting agent 1326 determines if any such viewer has classification
parameters that acceptably match the targeting parameters for the
ad. In the voting context, for each match, a vote is made for the
ad. This vote is returned to the headend component 1320 where it is
combined with other votes. These aggregated votes are used to
generate the next generation of ad lists 1308.
[0097] An overview of the classifier system has thus been provided.
The learning mode, working mode and matching functionality is
described in more detail in the following sections.
[0098] B. Learning Mode Operation
[0099] As noted above, the learning mode classifier develops
classification parameter information for probable viewers as well
as periodicity information for those viewers. This process is
illustrated in more detail in FIG. 11. During learning mode, the
classifier 1500 is constructing a statistical model of the
audience. In particular, it is desirable to develop a model that
enables feature separability--the ability to reliably distinguish
between identified viewers of an audience. It is thus desired to
have a good definition of the classification parameters of each
viewer and the ability to identify the current viewer 1502, from
among the identified viewers of the audience, each time the
classifier needs to know who is watching the television. In FIG.
11, this process is illustrated with respect to two examples of the
classification parameters; namely, target gender and target
age.
[0100] As discussed above, the learning mode classifier 1500
receives inputs including programming viewing frequency (or
demographic) data from Nielsen, BBM or another ratings system (or
based on previously reported information of the targeted
advertising system) 1504, program data 1506 and click data 1508.
Based on this data, the classifier 1500 can determine which
program, if any, is being viewed at a particular time and what that
indicates regarding probable classification parameters of a viewer.
Additionally, the click data 1508, since it is an event stream, may
indicate the level of focus or concentration of the viewer 1502 at
any time and may provide a measure of the level of interest in a
particular program. The click data 1508 also allows the classifier
1500 to determine when the DSTB set is turned off and when it is
turned on.
[0101] The learning mode classifier 1500 fuses the incoming data
through a set of clustering and partitioning techniques designed to
uncover the underlying patterns in the data. The goal in this
regard is to discover the number of probable viewers, build a
classification parameter set for each viewer and determine the
viewing habits of each viewer over time (or to develop
classification parameter sets for likely viewers as a function of
time). This information allows the classifier 1500 to determine
what kind of audience probably exists at the delivery time for a
specific ad. In the illustrated example, this learning process
involves the development of two classifier modules--the age and
gender classifier module 1510 and the viewing behavior or
periodicity classifier module 1512. The periodicity classifier 1512
accumulates and reinforces the results of the age and gender
classification module across time. A sequence of age interval
classes 1510 are stored across an independent axis representing the
time of day and day of week that that evidence is collected. This
time axis is used to determine the time of day that each individual
detected in the age and gender classifier module 1510 tends to
watch television.
[0102] The gender and age classifier module 1510 gathers evidence
over time as to probable viewers. Once sufficient evidence is
collected, it is expected that the evidence will cluster in ways
that indicate a number of separate audience members. This is at
once a fairly simple and complex process. It is simple because the
core algorithms used to match viewing habits to putative age and
gender features are well understood and fairly easy to implement.
For example, it is not difficult to associate a program selection
with a probability that the viewer falls into certain demographic
categories. On the other hand, it is somewhat complex to analyze
the interplay among parameters and to handle subtle phenomena
associated with the strength or weakness of the incoming signals.
In the latter regard, two parameters that affect the learning
process are dwell time and Nielsen population size. The dwell time
relates to the length of time that a viewer remains on a given
program and is used to develop an indication of a level of
interest. Thus, dwell time functions like a filter on the click
stream events that are used in the training mechanism. For example,
one or more thresholds may be set with respect to dwell time to
attenuate or exclude data. In this regard, it may be determined
that the classifier does not benefit from learning that a viewer
watched a program if the viewer watched that program for less than
a minute or, perhaps, less than 10 seconds. Thresholds and
associated attenuation or exclusion factors may be developed
theoretically or empirically in this regard so as to enhance
identification accuracy.
[0103] Also related to dwell time is a factor termed the audience
expectation measure. This is the degree to which, at any time, it
is expected that the television (when the DSTB is turned on) will
have an active audience. That is, it is not necessarily desirable
to have the classifier learn what program was tuned in if nobody is
in fact watching. The audience expectation measure can be
determined in a variety of ways. One simple measure of this factor
is the number of continuous shows that has elapsed since the last
channel change or other click event. That is, as the length of time
between click events increases, the confidence that someone is
actively watching decreases. This audience expectation measure can
be used to exclude or attenuate data as a factor in developing a
viewer identification model.
[0104] Nielsen marketing research data is also useful as a scaling
and rate-of-learning parameter. As noted above, this Nielsen data
provides gender and age statistics in relation to particular
programs. As will be discussed in more detail below, click events
with sufficient dwell time are used to accumulate evidence with
respect to each classification parameter segment, e.g., a fuzzy age
interval. In this regard, each piece of evidence effectively
increments the developing model such that classification parameter
values are integrated over time. How much a given fuzzy parameter
set is incremented is a function of the degree of membership that a
piece of evidence possesses with respect to each such fuzzy
set.
[0105] Thus, the degree of membership in a particular age group is
treated as evidence for that age. However, when the program and
time is matched with the Nielsen data, the gender distribution may
contain a broad spectrum of viewer population frequencies. The
dwellage percentage of the audience that falls into each age group
category is also evidence for that age group. Accordingly, the
amount that a set is incremented is scaled by the degree of
membership with respect to that set. Thus, for example, if few
viewers of an age category are watching a program, this is
reflected in only a small amount of evidence that the viewer is in
this age group.
[0106] The illustrated learning mode classifier 1500 also
encompasses a periodicity classifier module 1512. As the classifier
1500 develops evidence that allows for determining the number of
viewers in an audience of a DSTB and for distinguishing between the
viewers, it is possible to develop a viewing model with respect to
time for each of these viewers. This information can then be used
to directly predict who is likely to be watching at the time of ad
delivery. There are a number of ways to build the periodicity
model, and this can be executed during the learning mode operation
and/or the working mode operation. For example, this model may
involve mapping a viewer to their pattern. Alternatively, a pattern
may be discovered and then matched to a known viewer. In the
illustrated implementation, the latter viewer-to-pattern approach
is utilized. As will be understood from the description below, this
approach works well because the properties that define the
periodicity are fuzzy numbers. The match can therefore use the same
kind of similarity function that is used to match viewers to
targeting parameters of ads.
[0107] The discussion above noted that viewers are identified based
on integrating or aggregating evidence in relation to certain
(e.g., fuzzy) sets. This process may be more fully understood by
reference to FIG. 12. In this case, which for purposes of
illustration is limited to discovery of age and gender, this
involves a 2.times.M fuzzy pattern discovery matrix. The two rows
are gender segmentation vectors. The M columns are conventional age
intervals used in ad targeting. These age intervals are overlaid
with fuzzy interval measures. In the illustrated example, the
classifier is modeled around certain age intervals (12-17, 25-35,
35-49) because these are industry standard segmentations. It will
be appreciated, however, that specific age groups are not a
required feature of the classifier.
[0108] The fuzzy intervals are represented by the trapezoidal fuzzy
set brackets that illustrate a certain amount of overlap between
neighboring age intervals. This overlap may improve discrimination
as between different age ranges. The bars shown on the matrix
reflect the accumulation of evidence based on a series of click
events. As can be seen in the matrix of FIG. 12, over time, this
evidence tends to cluster in a fashion that indicates discrete,
identifiable viewers associated with different classification
parameters.
[0109] This is further illustrated in FIG. 13. In the example of
FIG. 13, this process of accumulating evidence to identify discrete
viewers of an audience is depicted in three-dimensional graphics.
Thus, the result of the learning process is a collection of
gradients or hills or mountains in the learning matrix or
multi-dimensional (in this case, two-dimensional) feature terrain.
In this case, the higher hills or "mountains" with their higher
gradient elevations provide the best evidence that their site in
the learning matrix is the site of a viewer. The hills are
constructed from the counts in each of the cells defined by the
classification parameter segmentation (e.g., age groups). The
greater the count, the higher the hill and, consequently, the more
certain it is that the characteristics correspond to a viewer of
the audience. Viewed from above, the gradients and their elevations
form a topological map, as shown in FIG. 14. The concentration and
height of the contour lines reflect a clustering that suggests
discrete and identifiable viewers. Thus, the map of FIG. 14
reflects three probable viewers identified from a learning process.
It is noted that the fuzzy terrain mapping, which allows certain
degree of overlap between surrounding age groups, provides a more
refined estimate of the actual classification parameter values of a
viewer. That is, an interpolation of evidence in adjacent fuzzy
sets enables an estimation of an actual parameter value that is not
limited to the set definitions. This interpolation need not be
linear and may, for example, be executed as a center of gravity
interpolation. That takes into account gradient height in each
fuzzy set as well as a scaled degree of membership of the height in
the set.
[0110] The process for applying evidence to the terrain may thus
result in a fuzzified terrain. That is, rather than simply applying
a "count" to a cell of the terrain based on determined
classification parameter values, a count can effectively be added
as terrain feature (e.g., a hill) centered on a cell (or on or near
a cell boundary) but including residual values that spill over to
adjacent cells. The residual values may spill over onto adjacent
cells in multiple dimensions. In one implementation, this effect is
defined by a proximity calculation. The result of applying evidence
to the terrain in a fuzzified fashion is potentially enhanced user
definition as well as enhanced ability to distinguish as between
multiple users.
[0111] The proximity algorithm and terrain seeding noise reduction
filtering can be illustrated by reference to an example involving
three classification parameter dimensions; namely, gender, age and
time of day. The feature terrain may be viewed as being defined by
a cube composed of a number of sub-cubes or grains. For example,
the terrain cube may be composed of 7680
grains--2.times.80.times.48 grains, corresponding to two gender, 80
age and 48 half-hour time categories. Each of the grains can be
populated with a reference or noise value. For example, the noise
values can be derived from a statistical analysis of the expected
viewers at each age, gender and time measured over all available
programming channels for which Nielson, BBM or another ratings
system have observers (or based on previously reported information
of the targeted advertising system). The noise may be drawn from a
distribution of these viewers from a suitable function (e.g.,
developed empirically or theoretically) and may or may not be as
simple as an average or weighted average. In any case, it is this
noise bias that cancels out a corresponding randomness in the
evidence leaving a trace or residue of evidence only when the
evidence is repeatedly associated with an actual viewer whose
behavior matches the (age, gender, time) coordinates of the
terrain.
[0112] It will be appreciated that the grain definition and
population of the grains with reference values or noise is not
limited to the granularity of the source of the reference values,
e.g., the standard Neilson categories. Rather, the reference values
can be interpolated or estimated to match the defined grain size of
the terrain cube. Thus, for example, Neilson source data may be
provided in relation to 16 age gender groups whereas the terrain
cube, as noted above, may include 2.times.80 corresponding grains
(or columns of grains where the column axis corresponds to the time
dimension). Various mathematical techniques can be used in this
regard. For example, the age distribution can be fitted to a curve
or function, which can then be solved for each age value. The
corresponding values are then applied to seed the terrain.
[0113] Evidence is the statistical frequency of viewers of a
particular age and gender who are watching a television program
(which is playing at a particular time). The combination of Neilson
frequencies and the program time generates 16 pieces of evidence
for each time period (there are 8 male and 8 female age groups with
their viewing frequencies). Finding out which of these 16 pieces of
evidence corresponds to the actual viewer is the job of the
statistical learning model underlying the classifier. Fundamental
to making this decision is the methodology used to add evidence to
the terrain. This involves first applying the noted noise filter as
follows:
d=f(a,g,t)-r(a,g,t) (1)
where (f) is the observed evidence and (r) represents the noise for
that age, gender and time. This value (d) will be either positive
or negative. The result is added to the terrain (t) on a
grain-by-grain basis:
t(a,g,t)=t(a,g,t)+d (2)
[0114] If the evidence is being drawn randomly from the incoming
statistics, then the number of positive and negative residuals (d)
will be approximately equal, and the total sum of the terrain value
(t) at that point will also be zero. If, however, the evidence is
associated with an actual viewer, then we would expect a small but
persistent bias in the frequency statistics to accumulate around
that real viewer. Over time, this means that residual (d) will be
positive more often than negative and that the contour at the
terrain cube will begin to grow. As more evidence is added, the
contour grows as a small hill on the terrain. This small hill is an
actual viewer (actually, because of the time axis, viewers appear
as a ridge of connected hills, somewhat like a winding mountain
range).
[0115] But because this is a statistical learning model, and
because reinforcement is sporadic. (due to inconsistent viewer
behaviors), it is useful to fuzzify or spread the evidence, e.g.,
to surround each emerging contour with a small bit of probabilistic
evidence that will help us define an actual viewer's behavior in
the time dimension. To do this, we take the evidence (d) and use it
to populate adjoining grains. This is done in a series of
concentric circles out from the target terrain grain. Thus, for the
first set of adjoining grains (in all directions in age, gender and
time), we add x1=d*0.10, for the next, x2=x1*0.05, for the next,
x3=x2*0.025, etc., until the multiplier fall below some threshold.
It will be appreciated that these multipliers are simply examples
and other values, derived empirically or theoretically, can be
used. As an example, if d=100, then the proximity values would look
something like this,
TABLE-US-00001 0.0125 0.0125 0.0125 0.0125 0.0125 0.0125 0.0125
0.0125 0.5 0.5 0.5 0.5 0.5 0.0125 0.0125 0.5 10 10 10 0.5 0.0125
0.0125 0.5 10 100 10 0.5 0.0125 0.0125 0.5 10 10 10 0.5 0.0125
0.0125 0.5 0.5 0.5 0.5 0.5 0.0125 0.0125 0.0125 0.0125 0.0125
0.0125 0.0125 0.0125
[0116] As can be seen, the value at each outward layer is based on
a fraction of the previous layer (not the original value). Of
course, in a real terrain, the original value is very small, so the
values in each outward cell become very, very small very, very
quickly. Yet, over time, they provide enough additional evidence to
support the growth of a valid contour site.
[0117] The proximity algorithm that fills out the terrain, also
provides a quick and effective way of discovering who is viewing
the television associated with the DSTB. The ridge of contours or
hills associated with a viewer wanders across the three-dimensional
terrain cube. These hills are smoothed out (made "fatter," so to
speak) over the terrain by the addition of the minute bits of
partial evidence laid down during the building of the terrain (the
x1, x2, x3, etc., in the previous section). To find the viewer, the
hills over the gender/age axes at a particular time can be summed
and averaged. The viewer is the hill with the maximum average
height. Optionally, a proximity algorithm can be used in
integrating these hills. For example, when determining the height
value associated with a given grain, a fraction of the height of
adjacent grains and a small fraction of the next outward layer of
grains (etc.) may he added. This is somewhat analogous to the
mountain clustering algorithm discussed herein and may be used as
an alternative thereto.
[0118] As noted above, these terrains may be developed as a
function of time of day. For example, the data may be deposited in
"bins" that collect data from different times of day on an hourly,
half-hourly or other basis (e.g., irregular intervals matching
morning, daytime, evening news, primetime, late night, etc.).
Again, the process of applying the evidence to these bins may be
fuzzified such that evidence spills over to some extent into
adjacent bins or cells in the time and other dimensions. Evidence
may be integrated in these bins over multiple days. The resulting
terrains may be conceptualized as multiple terrains corresponding
to the separate timeframes or as a single terrain with a time
dimension. This functionality may be implemented as an alternative
to the separate periodicity analysis described below.
[0119] The previous examples have reflected relatively clean
datasets that were easy to interpret so as to identify discrete
viewers and their associated classification parameters. In reality,
real world data may be more difficult to interpret. In this regard,
FIG. 15 illustrates a more difficult dataset in this regard. In
particular, the feature terrain of FIG. 15 does not readily yield
interpretation as to the number of viewers in the audience or their
specific classification parameters. Any number of factors may
result in such complexity. For example, the remote control may be
passed between different audience members, the click stream may be
influenced by other audience members, a click event of significant
duration may reflect distraction rather than interest, a viewer may
have a range of programming interests that do not cleanly reflect
their classification parameters, etc.
[0120] In order to resolve complicated data such as illustrated in
FIG. 15, the classifier implements processes of noise removal and
renormalization of the gradient terrain. The goal is to find the
actual centers of evidence so that the number of viewers and their
classification parameters can be accurately identified. One type of
process that may be implemented for removing or attenuating noise
involves consideration of reference values, e.g., average values
for all events at that time, taken in relation to the whole
audience. For example, the terrain may be seeded with reference
values or the reference values may be considered in qualifying or
rejecting data corresponding to individual events. In one
implementation, data is compared to reference values on an
event-by-event basis to qualify data for deposit into the bins for
use in developing the terrain. This has the effect of rejecting
data deemed likely to represent noise. In effect, the reference
values are subtracted from evidence as it is applied to the
terrain, thus impeding the process of constructing terrain features
so that terrain features substantially only rise as a result of
persistent or coherent accumulation of evidence likely reflective
of an actual user, and spurious peaks are avoided. Selecting the
reference values as average values, weighted average values or some
other values related to the observation context (but substantially
without information likely to bias the user identification and
definition problem) has the effect of scaling the filter effect to
properly address noise without inhibiting meaningful terrain
construction. Additionally, data may be qualified in relation to a
presence detector. As noted above, presence may be indicated by
reference to the on/off state of the DSTB and/or by the absence or
infrequency of inputs over a period of time. Data acquired when the
presence detector indicates that no user is deemed "present" may be
excluded.
[0121] Noise removal may further involve eliminating false centers
and sporadic evidence counts without disturbing the actual centers
to the extent possible. In this regard, FIG. 16 illustrates a
possible identification of viewer sites with respect to the data of
FIG. 15. FIG. 17 illustrates an alternative, also potentially
valid, interpretation of the data of FIG. 15. The classifier
implements an algorithm designed to determine which of competing
potentially valid interpretations is most likely correct. This
algorithm generally involves gradient deconstruction. The
deconstruction process is an iterative process that finds gradient
centers by first removing low-level interference noise (thus
revealing the candidate hills) and then measuring the compactness
of the distribution of hills around the site area. In this regard,
a mountain clustering algorithm can be implemented to iteratively
identify peaks, remove peaks and revise the terrain. The affect is
to identify cluster centers and to smooth out the hilliness between
cluster centers so that the cluster centers become increasingly
distinct. The remaining sites after the feature terrain is
processed in this regard are the sites of the putative viewers.
[0122] This mountain clustering and noise reduction functionality
may be further understood by reference to FIGS. 18-21. FIG. 18
shows a learning matrix or feature terrain with a set of gradients
scattered over the surface. In the first iteration of the
algorithm, as shown in FIG. 19, gradient C1 is identified as the
maximum mountain. The distance from C1's center of gravity to the
centers of each gradient on the surface (e.g., C2, C3, etc.) is
measured. The degree of mountain deconstruction on each gradient is
inversely proportional to the square distance between C1 and each
of the other clusters. This inverse square mechanism localizes
nearly all of the mountain deconstruction to the neighborhood of
C1. Thus, FIG. 20 shows significant deconstruction of features
proximate to C1 as a result of this first iteration. This causes
the general perimeter of the emerging site at C1 to contract. The
contraction also involves the assimilation of small gradients,
hence the height of C1 is increased, and the adjacent hilliness is
reduced. At the same time, the height of C3 is barely changed and
its set of satellite gradients has not yet begun to be assimilated.
The process is then repeated with respect to C3. After a number of
iterations, the final sites begin to emerge and stabilize. FIG. 21
shows the terrain after most of the smaller gradients have been
assimilated. The resulting processed terrain or feature space has
well defined gradients at each putative viewer site. The mountain
clustering process thus essentially removes much ambiguity.
[0123] A similar process is performed with respect to the
periodicity analysis. Specifically, a Periodicity learning matrix
is created and updated in a manner analogous to construction and
updating of the viewer classification parameter matrix and its
conversion to the feature terrain. In this case, the periodicity
terrain produces a set of gradients defining, by their height and
width, the expectation that the viewer is watching television at
that point in time. A matching algorithm can then be used to match
a periodicity pattern to one of identified viewers.
[0124] C. Working Mode Operation
[0125] When the classifier has been sufficiently trained, it moves
from learning mode operation to working mode operation. As noted
above, these modes are not entirely distinct. For example, the
classifier can perform estimations of classification parameters
while still in the learning mode, and the classifier continues to
learn during the working mode. However, as described above, the
learning mode is a gradual process of collecting evidence and
measuring the degree to which the viewer sites are discernable in
the learning matrix. Thus while the classifier can operate quickly
using default values, working mode operation reflects a
determination that the viewers of an audience have been identified
and classified with a high degree of confidence.
[0126] The basic operation of the working mode classifier is
illustrated in FIG. 22. In the working mode, the classifier 2600
has access to the developed feature terrain, as discussed above, as
well as to a similarity function that is operative to match
targeting parameters to the classification parameters as indicated
by the viewer identification. More specifically, Nielsen data 2602
and program data 2604 continues to be fed into the classifier 2600
in the working mode to support its continued learning process that
essentially runs in the background. The classifier 2600 then
receives an ADR from the ad list 2608. The ADR is initially
filtered for high level suitability and then, if the ADS is still
available for matching analysis, selects each viewer and each
classification parameter for each viewer for comparison. Thus, FIG.
22 depicts the process of accessing a classification matrix 2606 of
a viewer. In this case, the matrix 2606 is a two-dimensional matrix
limited to gender and age classification parameters. In practice,
any number of classification parameters may be supported.
[0127] The retrieved classification parameter values of the user
and the targeting parameters of the ad are then provided to the
similarity and proximity analyzer 2612 where they are compared.
This function returns a degree of similarity for each attribute.
The total compatibility or relative compatibility rank (RCR) is
given as:
RCR = i = l N Si .times. Wi i = l N Wi ##EQU00001##
[0128] Where, [0129] s.sub.i is the similarity measurement for the
i.sup.th property [0130] w.sub.i is the weighting factor for the
i.sup.th property. If weighting (priority or ranking) is not used,
the default, w=1, means that weights do not affect the ranking.
[0131] Through this process, an ad may be found to be compatible
with one or more viewers of an audience. For example, such
compatibility may be determined in relation to an RCR threshold
value. Thus, when an ad is found to be compatible with one or more
of the viewers, the periodicity analyzer 2616 is called to see if
the viewer is likely to be present at the target ad insertion time.
If the viewer is unlikely to be watching, the degree of this time
constraint is used to adjust the RCR. Accordingly, the RCR is
recomputed as:
RCR = i = l N Si .times. Wi i = l N Wi .times. l ( pT )
##EQU00002##
[0132] Where, [0133] l( ) is the likelihood estimating function
from the periodicity analysis. This function returns a fuzzy degree
of estimate in the interval [0,1] (which is actually a degree of
similarity between the target time period and each of the viewer's
active time periods) [0134] p.sup.T is the target time period
[0135] Accordingly, the RCR will have a high value if there is both
a high degree of match between the classification parameters of a
user and the targeting parameters of the ad, and there is a high
probability that the user will be watching at the ad delivery time.
This formula further has the desirable quality that a low
compatibility where the viewer is not watching, even if the
viewer's classification parameters are a very good match for a
given ad.
[0136] The voting agent 2614 is closely connected to the operation
of the working mode classifier 2600. In particular, the validity of
the vote is highly dependent on the ability to correctly identify a
viewer's classification parameters and viewing habits. Thus, the
voting agent 2614 essentially works in the same way as the view
list ranking. Specifically, an ADR is sent to the voting agent
2614, the voting agent 2614 extracts the ads targeting parameters
and calls the classifier 2600, which returns the RCR for this ad.
In this manner, ads are not only voted on but also delivered based
on a matching process.
[0137] D. Matching Functionality
[0138] The similarity function used to execute the various matching
functionality as discussed above can be understood by reference to
FIGS. 23-26. Thus, as noted above, when an ADR is received at the
classifier to be ranked, the targeting parameters and any ad
constraints are embedded in the ADR. The ADR may also indicate an
"importance" of the ad. For example, such importance may be based
on the ad pricing (e.g., CPM value) or another factor (e.g., a
network operator may specify a high importance for internal
marketing, at least for a specified geographic area such as where a
competing network is, or is becoming, available). The classifier
also has access to the classification parameters of the various
viewers in the audience, as discussed above. The first step in the
matching process is applying a similarity function, e.g., a fuzzy
similarity function, to each of the classification parameters and
targeting parameters. The similarity function then determines the
degree to which a targeting parameter is similar to or compatible
with a classification parameter. The weighted average of the
aggregated similarity of values for each of the
classification/targeting parameters is the base match score.
[0139] More specifically, the illustrated classifier fuzzifies the
targeting parameter and finds the degree of membership of the
corresponding classification parameter of the user in this
fuzzified region. Thus, as shown in FIG. 23, the targeting
parameters for an ad may specify a target age range of 24-42 years
of age. As shown in FIG. 23, this targeting parameter is redefined
as a fuzzy set. Unlike rigid sets, the fuzzy set has a small but
real membership value across the entire domain of the
classification parameter (age in this case). The membership
function means that the matching process cannot automatically
identify and rank classification parameters values that lie near
but, perhaps, not inside the target range. As an example, FIG. 24
shows a putative viewer with an inferred age of 26. In this case,
the viewer sits well inside the target age range. The degree of
membership is therefore 1.0, indicating a complete compatibility
with the target age. In fact, any age that is within the target age
range will return a matching membership of 1.0. However, the
matching process can also deal with situations where retrieved
classification parameters do not match the targeting parameter
range. FIG. 25 illustrates the case where a viewer is estimated to
be 20 years old and is therefore outside of the rigid target range
boundaries. If the classifier had Boolean selection rules, this
viewer would not be selected. The nature of the fuzzy target space,
however, means that viewer 2 is assigned a similarity or
compatibility value greater than zero, in this case 0.53. The
classifier now has the option of including viewer 2, knowing that
the viewer is moderately compatible with the target range.
[0140] FIG. 26 shows a slightly different situation--that of a
viewer with an inferred age outside of the required age interval.
As illustrated by the membership function, as the viewer age moves
away from the identified targeting age interval, the membership
function drops off quickly. In this case, viewer 3 has a
compatibility of only 0.18, indicating that it would normally be a
poor candidate for an ad with a 24-42 age requirement. It is noted,
however, that the fuzzy compatibility mechanism means that the
classifier can find and rank a viewer if any viewer exists.
[0141] In summary, a matching algorithm may involve the following
steps. First, an ADR is received from matching to one or more of
the identified viewers. The similarity function is applied with
respect to each parameter, and the results are aggregated as
discussed above in order to determine a degree of match. Once the
classifier has discovered one or more viewers that match the basic
targeting parameters of the ad, the classifier determines whether
or not any of these viewers are currently watching the television
set. To do this, the periodicity analysis function is called to
match a week by day and week by time of day terrain surface to the
required time. This process returns a value reflecting the expected
degree of match between the viewing time behavior of each viewer
and the time frame of the ad. The degree of match in this regard is
used to scale the base compatibility score to produce a time
compatibility score. Once the classifier has thus identified a set
of compatible viewers and determined which of these viewers are
currently watching the television (that is, the time compatibility
value is greater than some threshold), the classifier determines
whether or not to accept this ad (vote for the ad or select the ad
for delivery) based on any frequency limitation constraints and
also subject to consideration of ad importance. That is, there may
be additional constraints associated with the ad regarding the
frequency with which the ad may be delivered to an individual
viewer or the total number of times that the ad may be delivered to
a viewer. The frequency analyzer returns a value and range of zero
to one that is used to scale the time compatibility rank. This
creates a frequency compatible rank. In the case of ad importance,
a first ad may be selected (and voted) rather than a second ad,
where the ads have a similar degree of "match" or even where the
second ad has a better match, due to differences in ad importance
(e.g., where the first ad has a higher importance).
[0142] An ad may also have constraints. For example, the ad may
have target age limitations, genre restrictions, network
restrictions, program nature restrictions, rating restrictions or
the like. The placement constraint analyzer that meets the
above-noted compatibility requirements and then searches for any
required placement constraints. The placement analysis returns a
gateway value of one or zero where one indicates no active
placement constraints and zero indicates placement constraints that
are violated. This creates the final compatibility index score.
[0143] It will be appreciated that the matching process need not be
based on a continuous, or even finely graded, value range. For
example, the result of the matching process may be a binary "match"
or "no match" determination. In this regard, a threshold, or set of
thresholds with associated decision logic, may be used to define a
match or lack thereof for an ad with respect to a current
audience.
[0144] As shown in FIG. 10, the classifier may thus be viewed as
incorporating a number of functional components including a stay
transition and link manager, a feature acquisition subsystem, a
mountain clustering subsystem, periodicity analysis subsystem and
an ad matching and ranking subsystem. The stay transition and link
manager are operative to monitor the click stream and determine
whether the DSTB is turned on as well as tracking stay changes. The
feature acquisition subsystem is operative to build the initial
feature terrain as discussed above. The mountain clustering
subsystem is operative to process the feature terrain to remove
noise and better define viewer sites as discussed above. The
periodicity analysis subsystem recognizes viewing patterns and
matches those patterns to viewers as identified from the processed
feature terrain space. Finally, the ad matching and ranking
subsystem compares viewer classification parameters to targeting
parameters of an ad and also analyzes viewing habits of the viewer
in relation to the delivery time, so as to match ads to viewers and
develop a ranking system for voting, ad selection and
reporting.
[0145] The foregoing description of the present invention has been
presented for purposes of illustration and description.
Furthermore, the description is not intended to limit the invention
to the form disclosed herein. Consequently, variations and
modifications commensurate with the above teachings, and skill and
knowledge of the relevant art, are within the scope of the present
invention. For example, although fuzzy sets and fuzzy rules are
described in connection with various processes above, aspects of
the present invention can be implemented without fuzzy data sets or
rules. The embodiments described hereinabove are further intended
to explain best modes known of practicing the invention and to
enable others skilled in the art to utilize the invention in such,
or other embodiments and with various modifications required by the
particular application(s) or use(s) of the present invention. It is
intended that the appended claims be construed to include
alternative embodiments to the extent permitted by the prior
art.
* * * * *