EP2016766A2 - Method and apparatus to perform real-time audience estimation and commercial selection suitable for targeted advertising - Google Patents
Method and apparatus to perform real-time audience estimation and commercial selection suitable for targeted advertisingInfo
- Publication number
- EP2016766A2 EP2016766A2 EP07797322A EP07797322A EP2016766A2 EP 2016766 A2 EP2016766 A2 EP 2016766A2 EP 07797322 A EP07797322 A EP 07797322A EP 07797322 A EP07797322 A EP 07797322A EP 2016766 A2 EP2016766 A2 EP 2016766A2
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- EP
- European Patent Office
- Prior art keywords
- set forth
- filter
- signal
- user
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/16—Analogue secrecy systems; Analogue subscription systems
- H04N7/173—Analogue secrecy systems; Analogue subscription systems with two-way working, e.g. subscriber sending a programme selection signal
- H04N7/17309—Transmission or handling of upstream communications
- H04N7/17318—Direct or substantially direct transmission and handling of requests
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
- H04N21/44213—Monitoring of end-user related data
- H04N21/44222—Analytics of user selections, e.g. selection of programs or purchase activity
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs
- H04N21/23424—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs involving splicing one content stream with another content stream, e.g. for inserting or substituting an advertisement
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/24—Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth, upstream requests
- H04N21/2407—Monitoring of transmitted content, e.g. distribution time, number of downloads
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
- H04N21/44213—Monitoring of end-user related data
- H04N21/44218—Detecting physical presence or behaviour of the user, e.g. using sensors to detect if the user is leaving the room or changes his face expression during a TV program
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client
- H04N21/65—Transmission of management data between client and server
- H04N21/658—Transmission by the client directed to the server
- H04N21/6582—Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/81—Monomedia components thereof
- H04N21/812—Monomedia components thereof involving advertisement data
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04H—BROADCAST COMMUNICATION
- H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
- H04H60/61—Arrangements for services using the result of monitoring, identification or recognition covered by groups H04H60/29-H04H60/54
- H04H60/63—Arrangements for services using the result of monitoring, identification or recognition covered by groups H04H60/29-H04H60/54 for services of sales
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04H—BROADCAST COMMUNICATION
- H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
- H04H60/61—Arrangements for services using the result of monitoring, identification or recognition covered by groups H04H60/29-H04H60/54
- H04H60/64—Arrangements for services using the result of monitoring, identification or recognition covered by groups H04H60/29-H04H60/54 for providing detail information
Definitions
- the present invention relates to innovations in nonlinear filtering wherein the observation process is modeled as a Markov chain, as well as utilizing an embodiment of the invention to estimate the user composition of a user equipment device in a communications network, e.g., the number and demographics of television viewers in a digital set top box (DSTB) environment. Furthermore, the present invention provides methods to optimally determine which set of assets, e.g., commercials, to insert into available network bandwidth based on a sampling of optimal conditional estimates of the current network usage (e.g., viewership).
- a communications network e.g., the number and demographics of television viewers in a digital set top box (DSTB) environment.
- DSTB digital set top box
- the present invention relates to analyzing observations obtained from a measurement device to obtain information about a signal of interest.
- the invention relates to analyzing user inputs with respect to a user equipment device of a communications network (e.g., a user input click stream entered with respect to a digital set top box (DSTB) of a cable television network) to determine information regarding the users of the user equipment device (e.g., audience classification parameters of the user or users).
- Certain aspects of the invention relate to processing corrupted, distorted and/or partial data observations received from the measurement device to infer information about the signal and providing a filter system for yielding a substantially real time estimate of a state of the signal at a time of interest.
- a filter system can provide practical approximations of optimized non-linear filter solutions based on certain constraints on allowable states or combinations therefore inferred from the observation environment.
- a method and apparatus for developing an observation model with respect to data or measurements obtained from the device under analysis.
- the system models the input measurements as a Markov chain, whose transitions depend upon the signal.
- the observation model may take into account exogenous information or information external to (though not necessarily independent of) the input measurements.
- the input measurements reflect a click stream of DSTB.
- the click stream may reflect channel selection events and/or other inputs, e.g., related to volume control.
- the observation model may further involve programming information (e.g., downloaded from a network platform such as a Head End) associated with selected channels.
- click stream information is processed as a Markov chain.
- Desired information related to the device can then be obtained by estimating the state of the signal at a time of interest.
- the signal may represent a user composition (involving one or more users and/or associated demographics) and an additional factor affecting the click stream such as a channel changing regime as discussed in more detail below.
- a state of the signal at a past, present or future time can be determined, e.g., to provide user composition information for use in connection with an asset targeting system.
- a system generates substantially real time estimates of a signal state based on an observation model.
- a non-linear filter system can be used to provide an estimate of the signal based on the observation model.
- the non-linear filter system may involve a non-linear filter model and an approximation filter for approximating an optimal non-linear filter solution.
- the approximation filter may include a particle filter or a discrete state filter for enabling substantially real time estimates of the signal based on the observation model.
- the non-linear filter system allows for identifying user compositions including more than one viewer and adapting to changes in the potential audience, e.g., additions of previously unknown persons or departures of prior users with respect to the potential audience.
- a system uses a signal obtained by applying a filter to an observation model to obtain information of interest with respect to the observation model. Specifically, information for a past, present or future time can be obtained based on an estimated state of the signal at the time of interest.
- information for a past, present or future time can be obtained based on an estimated state of the signal at the time of interest.
- the identity and/or demographics of a user or users of the DSTB at a particular time can be determined from the signal state. This information may be used, for example, to "vote" or identify appropriate assets for an upcoming commercial or programming spot, to select an asset from among asset options for delivery at the DSTB and/or to determine or report a goodness of fit of a delivered asset with respect to the user or users who received the asset.
- a system for use in targeting assets to users of user equipment devices in a communications network, for example, a cable television network.
- the system involves: developing an observation model based on inputs by one or more users with respect to a user equipment device; modeling the observation model as a signal reflective of at least a user composition of one or more users of said user equipment device with respect to time; determining the user composition at a time of interest as a state of the signal; and using the determined user composition in targeting an asset with respect to the user equipment device.
- filtering theory is applied with respect to inputs, such as a click stream, of a user equipment device so as to yield a signal indicative of user composition.
- the inputs can be modeled as a Markov chain.
- a model of the signal allows for representation of the user composition as including two or more users. Accordingly, multiple user situations can be identified for use in targeting assets and/or better evaluating audience size and composition (e.g., to improve valuation and billing for asset delivery).
- the signal model preferably allows for representation of a change in user composition, e.g., addition or removal of a person from a user audience.
- a non-linear filter may be defined to obtain the signal based on the observation model.
- the signal may represent the user composition of a household with respect to time and audience classification parameters (e.g., demographics of one or more current users) can be determined as a function of the state of the signal at a time of interest.
- audience classification parameters e.g., demographics of one or more current users
- an approximation filter may be provided for approximating operation of the non-linear filter.
- the approximation filter may include a particle filter or a discrete space filter.
- the approximation filter may implement at least one constraint with respect to one or more signal components.
- the constraint may operate to treat one component of the signal as invariant with respect to a time period where a second component is allowed to vary. Moreover, the constraint may operate to treat at least one state of a first component as illegitimate or to treat some combination of states of different signal components as illegitimate. For example, in the case of a click stream of a DSTB, the occurrence of a click event indicates the presence of at least one person. Accordingly, only user compositions corresponding to the presence of at least one person is permissible at the time of a click event. Other permissible or impermissible combinations may relate incomes to locations.
- the constraints may be implemented in connection with a finite space approximation filter.
- values incident on an illegitimate cell may be repositioned, e.g., proportionately moved to neighboring legitimate cells.
- the approximation filter can quickly converge on a legitimate solution without requiring undue processing resources.
- the constraint operates to define at least one potential calculated state as illegitimate
- the approximation filter may redistribute one or more counts associated therewith.
- the approximation filter may be operative to inhibit convergence on an illegitimate state.
- the approximation filter is designed to avoid convergence on a user composition for a DSTB that is logically impossible or unlikely (a click event when no user is present) or deemed illegitimate by rule (an income range not permitted for a given location). In one implementation, this is accomplished by adding seed counts to legitimate cells of a discrete space filter to inhibit convergence with respect to an illegitimate cell.
- the user composition information is determined at the digital set top box. That is, user information is calculated at the digital set top box and used for voting, asset selection and/or reporting.
- click stream data may be directed to a separate platform, such as a Head End, where the user composition information can be determined, e.g., where messaging bandwidth is sufficient and DSTB processing resources are limited.
- the user composition information (as opposed to, e.g., asset vote information) may be transmitted to a Head End or other platform for use in selecting content for insertion.
- the determined user composition information may be used by an asset targeting system.
- the information may be provided to a network platform such as a Head End that is operative to insert assets into a content stream of the network.
- the platform may utilize inputs from multiple DSTBs to select assets for insertion into available network bandwidth. Additional information, such as information reflecting the per user value of asset delivery, may be utilized in this regard.
- the platform may process information from multiple user equipment devices as an observation model and apply an appropriately configured filter with respect to the observation model to estimate an overall composition of a network audience at a time of interest.
- stochastic control theory is applied to the problem of targeted asset delivery, e.g., dynamic viewer classification and/or television ad selection.
- stochastic control theory has been applied in contexts where a signal or function cannot be computed directly but only estimated based on observations that may be noisy or incomplete.
- measurements from a measurement device can be processed according to stochastic control theory to estimate a signal from which state information can be determined. For example, measurements from an input device such as a click stream from a remote control are taken as noisy observations and processed using stochastic control to estimate a signal, which represents a household or viewing audience and a behavioral regime in relation to entry of the inputs.
- Stochastic control allows tracking of the signal such that the state of the signal at a particular time reflects the viewer composition and regime at that time. This information can be used to select targeted ads for delivery, e.g., by matching classification parameters of the viewing audience to targeting parameters of available ads.
- Fig. 1 is a schematic diagram of a targeted advertising system in accordance with the present invention.
- Fig. 2 illustrates the REST structure in accordance with the present invention
- Fig. 3 illustrates a cell structure for a cell of filter in accordance with the present invention
- Fig. 4 is a flowchart illustrating a filter evolution process in accordance with the present invention.
- Fig. 5 is a block diagram illustrating a process for simulating events in accordance with the present invention.
- a targeted asset delivery (e.g., targeted advertising) system for a cable television network provides particular advantages in this context as described herein.
- various aspects of this invention are not limited to this context. Rather, the scope of the invention is defined by the claims set forth below.
- Various targeted advertising systems for cable television networks have been proposed or implemented. These systems are generally predicated on understanding the current audience composition so that commercials can be matched to the audience so as to maximize the value of the commercials. It will be appreciated that a variety of such systems could benefit from the structure and functionality of the present invention for identifying classification parameters (e.g., demographics) of current viewers. Accordingly, although a particular targeted asset delivery system is referenced below for purposes of illustration, it will be appreciated that the invention is more broadly applicable.
- a DSTB operates to invisibly (from the perspective of the viewer) switch to appropriate ad channels during a commercial break to provide targeted advertising to the current viewer(s).
- the viewer identification structure and functionality of the present invention can be used in the noted targeted asset delivery system in a variety of ways.
- an ad list including targeting parameters is sent to DSTBs in advance of a commercial break.
- the DSTB determines classification parameters for a current viewer or viewers, matches those classification parameters to the targeting parameters for each ad on the list and transmits a "vote" for one or more ads to the Head End.
- the Head End aggregates votes from multiple DSTB and assembles an optimized flotilla of ads into the available bandwidth (which may include the programming channel and multiple ad channels).
- the DSTB selects a "path" through the flotilla to deliver appropriate ads.
- the DSTB can then report what ads were delivered together with goodness of fit information indicating how well the actual audience matched the targeting parameters.
- the present invention can be directly implemented in the noted targeted asset delivery system. That is, using the technology described herein, the audience classification parameters for the current viewer(s) can be determined at the DSTB. This information can be used for voting, ad selection and/or goodness of fit determinations as described in the noted pending application. Alternatively, the description below describes a filter theory based Head End ad selection system that is an alternative to noted voting processes.
- click stream information can be provided to the Head End, or another network platform, where the audience classification parameters may be calculated.
- the audience classification parameter, ad selection and other functionality can be varied and may be distributed in various ways between the DSTBs, Head End or other platforms.
- Nonlinear filtering deals with the optimal estimation of the past, present and/or future state of some nonlinear random dynamic process (typically called 'the signal') in real-time based on corrupted, distorted or partial data observations of the signal.
- the X is regarded as Markov process defined on some probability space ( ⁇ « ⁇ * * ) and is the solution to some Martingale problem.
- the observations typically occur at discrete times t m and are dependent upon the signal in some stochastic manner using a sensor function H •* *(•* «.. v*i.
- the filter can provi optimal estimates for not only the current states of the signal but for previous and future states, as well as the entire path of the signal:
- an effective optimal recursive formula is available.
- the signal follows an Ito stochastic differential equation dXi with A being the linear and B being constant.
- the observation function takes the form of ⁇ 6 independent Gaussian random variables.
- This formula is known as the Kalman filter. While the Kalman filter is very efficient in performing its estimates, its use in applications is inherently limited due to the strict description of the signal an observation processes. In the case where the dynamics of the signal are nonlinear, or the observations have non-additive and/or correlated noise, the Kalman filter provides sub-optimal estimates. As a result, other methods are sought out to provide optimal estimates in these more common scenarios.
- Particle filtering methods involve creating independent copies of the signal (called 'particles') denoted as i& ⁇ A ' , where N t is the number of particles being used at time t. These particles are evolved over time according to the signal's stochastic law. Each particle is then assigned a weight value ***.* »( £ « ) to effectively incorporate the information form the sequence of observations (F;, ..., Y m ). This can be done in such a way that the weight after m observations is the weight after m - 1 multiplied by a factor on dependent upon the m' h observation Y m .
- Particle Tracking Algorithms which is incorporated herein by reference, particles are duplicated, destroyed or left unchanged probabilistically at each time step. Based on the weight calculated for the current time step only (W 1n (El)), particles are modified according to the following routine: 1. make copies of particle ⁇ and make one additional copy with probabilit
- a control parameter p is introduced to appropriately moderate the amount of resampling performed.
- this value can be dynamic over time in order to adapt to the current state of the filter as well as the particular application.
- This filing also included efficient systems to store and compute the quantities required in this algorithm on a computer.
- a discrete space and amplitude approximation can be used.
- a discrete space filter is described in detail in U.S. Patent No. 7,188,048, entitled “Refining Stochastic Grid Filter” (REST Filter), which is incorporated herein by reference.
- the state space D is partitioned into discrete cells n c .
- this space could be a J-dimensional Euclidean space or some counting measure space.
- Each cell yields a discretized amplitude known as a 'particle count' (denoted as » • * ⁇ '), which is used to form the conditional distribution of the discrete space filter:
- the invention utilized a dynamic interleaved binary index tree to organize the cells with data structures in order to efficiently recursively compute the filter's conditional estimate based on the real-time processing of observations. While this structure was amenable to certain applications, in scenarios where the dimensional complexity of the state space is small, the data structure's overhead can reduce the method's utility.
- Fig. 1 depicts the overall targeted advertising system.
- the system is composed of a Head End 100, which controls one or more Digital Set Top Boxes 200.
- the DSTBs 200 are attempting to estimate the conditional probability of the state of potential viewers in household 205, including the current member(s) of the household watching television, using the DSTB filter 202.
- the DSTB filter 202 uses a pair of models 201 describing the signal
- the DSTB filter 202 is initialized via the setting 302 downloaded from the Head End 100. To estimate the state of the household the DSTB filter 202 also uses program information 207 (which may be current, or in the recent past or future), which is available from a store of program information 208.
- the DSTB filter 202 passes its conditional distribution or estimates derived thereof to a commercial selection algorithm 203, which then determines which commercials 204 to display to the current viewers based on the filter's output, the downloaded commercials 301, and any rules 302 that govern what commercials are permissible given the viewer estimates.
- the commercials displayed to the viewers are recorded and stored.
- the DSTB filter 202 estimates as well as commercial delivery statistics and other information may be randomly sampled 303 and aggregated 304 to provide information to the Head End 100. This information is used by a Head End filter 102, which computes (subject to its available resources) the conditional distribution for the aggregate potential and actual viewership for the set of DSTBs with which it is associated.
- the Head End filter 101 uses an aggregate household and DSTB feedback model 101 to provide its estimates. These estimates are used by the Head End commercial selection system 103 to determine which commercials should be passed to the set of DSTBs controlled by the Head End 100.
- the commercial selection system 103 also takes into account any market information 105 available concerning the current commercial contracts and economics of those contracts.
- the resulting commercials selected 301 are subsequently downloaded to the DSTBs 100.
- the commercials selected for downloading affect the level settings 104, which provide constraints on certain commercials being shown to certain types of individuals.
- the signal of a household is modeled as a collection of individuals and a household regime. In one preferred embodiment, this household represents the people who could potentially watch a particular television that uses a DSTB. Each individual (denoted as
- X * ) at a given point in time t has a state from the state space s € S, where S represents the set of characteristics that one wishes to determine for each person within a household. For example, in one embodiment one may wish to classify the age, gender, income, and watching status of each individual. Age and income may be considered as real values, or as a discrete range.
- the state space of the household member is the where k denotes the number of individuals and 5° denotes the single state with no individuals.
- the household members Xt (X t , . , .,Xf') have a time- varying random number of members, where n t is the number of members at time t. Since the order of members within this collection is immaterial to the problem, we use the empirical measure of the members to represent the household.
- the household regime depicts a current viewing 'mindset' of the household that can materially influence the generation of click stream data.
- the household's current regime r t is a value from the state space R.
- the regimes can consist of values such as 'normal', 'channel flipping', 'status checking', and 'favorite surfing'.
- the rate functions for individual i may depend only on the given individual, the empirical measure of the signal, the current time, and some external environmental variables
- the number of individuals within the household n t varies over time via birth and death rates. Birth and death rates do not merely indicate new beings being born or existing beings dying - they can represent events that cause one or more individuals to enter and exit the household. These rates are calculated based on the current state of all individuals within the household. For example, in one embodiment of the invention a rate function describing the likelihood of a bachelor to have either a roommate or spouse enter the household may be calculated.
- these rate functions can be formulated as mathematical equations with parameters empirically determined by matching the estimated probability and expected value of state changes from available demographic, macroeconomic, and viewing behavior data.
- age can be evolved deterministically in continuous state space [0, 120].
- the observation model is comprised of click stream information that is generated by one or more individuals' interaction with a DSTB.
- only current and past channel change information is represented in the observation model. Given a universe of M channels, we have a channel change queue at time t* o channels that were watched in the past B discrete time steps. In one preferred embodiment of the invention, only the times when a channel change occurs as well as the channel that was changed to are recorded to reduce overhead.
- a viewing queue contains this current and past channels as well as such things as volume history.
- the viewing queue degenerates to the channel change queue.
- this downloadable content contains, among other things, some program information detailing a qualitative category description of the shows that are currently available, for instance, for each show, whether the show is an "Action
- ⁇ * is the i' A random outcome of drawing an element from ⁇ .
- the observation probabilities that is, the probabilities of switching between two viewing queues over the next discrete step, can be first calculated by determining the probability of switching categories of the programs and then finding the probability of switching into a particular channel within that category.
- the first step is to calculate, often in a offline manner, the relative proportion of category changes that occur due to channel changes and/or changes in programs on the same channel.
- the probabilities for the category transition from Cj to Cj that occurs at a given time step are calculated first by calculating the probability of category changes given the currently available programs:
- n t (J) is the number of channels that have shows that fall in category / at the end of the current time step.
- An alternative probability measure to use is to calculate the 'popularity' of channels instead of the transition between channels at each discrete time step. This above method can be used to provide this form by simply summing over the transition probabilities for a given category:
- n t (J) is the number of channels that have shows that fall into category / at the end of the current time step.
- h is the observation time or the k" 1 observation and is some driving noise process, or some continuous time variant.
- Y is a discrete time Markov chain whose transition probabilities depend upon the signal.
- the new state Y k can depend upon the previous state, rendering the standard theory discussed above invalid.
- a new, analogous theory and system is presented for solving problems where the observations are a Markov chain.
- Markov chain observations may only be allowed to transition to a subset of all the states that depends upon the state that it is currently in.
- D t and X t are the current states of the pertinent exogenous information and signal states at the time of the possible state change.
- the signal is composed of zero or more targets * and zero or more regimes ** .
- each target and regime have only a discrete and finite number of states, and there are a finite number of targets and regimes (and consequently a finite number of possible combinations of targets and regimes). The finite number of combinations need not be all possible combinations - only a finite number of legitimate combinations are required.
- a finite possible types of households can be derived from geography-dependent census information at relatively granular levels. Instead of having all potential combinations of individuals (up to some maximum household membership ⁇ MAX), only those combinations which can be possibly found within a given geographic region need to be considered legitimate and contained within the state space.
- some of the state of the target(s) and/or regime(s) may be invariant over short-term during which the optimal estimation is occurring. In these cases, such state information is held to be constant, while other portions of the state information remain variant.
- the age, gender, income, and education levels of each individual within the household may be considered to be constant, as these values change over longer periods of time and the DSTB estimation occurs over a period of a few weeks.
- the current watching status of the household regime information change over relatively short time frames, and as a result these states are left to vary in the estimation problem.
- the invariant portion of the signal As X and the variant portion of the signal as X.
- N possible invariant states (the i th such state donated by x ') and M,- possible variant states for the i th invariant state (the j th state denoted by X ij ).
- Fig. 2 depicts one preferred embodiment of the REST filter in a finite state space environment.
- REST is composed of a collection of invariant state cells, each of which represents one possible collection of targets and regimes for the signal along with their invariant state properties.
- Each invariant cell contains a collection of variant state cells, each representing the possible time-variant states of the given invariant cell.
- the variant cells contain the invariant state information of their parent invariant cell, meaning each variant cell represents a particular potential state of the signal.
- the invariant cells themselves represent an aggregate container object only and are used for convenience purposes.
- the collections of variant and invariant cells may be stored on a computer medium in the form of arrays, vectors, list or queues.
- each variant state sell contains a particle count n «' J .
- This particle count represents the discretized amplitude of that cell. As noted previously, this amplitude is used to calculate the conditional probability of a given state.
- Each variant state cell also contains a set of imaginary clocks These imaginary clocks represent the possible state changes from the given state cell. For each variant state cell there are ⁇ » J possible state transitions. In this environment, all valid state transactions occur within the same invariant state cell.
- temporary counter entitled particle count entitled particle count delta ⁇ n * is used to store the number of particles that will be added or removed from the give variant state cell once the sequential processing of all cells is completed. Cells which have a valid state transition from the variant state cell with stat are said to be neighbors of that cell.
- the invariant state cells are containers used to simplify the processing of information.
- Each invariant state cell's particle count n t is an aggregate to its child variant state cell particle counts.
- the invariant state cell's imaginary time clock is an aggregation of all clocks from the variant cells. This aggregation facilitates the filter's evolution, as invariant states which have no current particle count can be skipped at various stages of processing.
- Fig. 4 depicts the typical evolution of the REST filter. This evolution method updates the conditional distribution of the filter over some time period dt by transferring particles between neighboring cells using the imaginary clock values. The movement of a particle between neighboring cells is know as an event. (Well, we often replace the movement of particles with extra births and deaths to allow more rate cancellation to occur.) Such events are simulated en masse to reduce the computational overhead of the evolution. The number of events to simulate is based on the total imaginary clock sum ⁇ t for all cells.
- Fig. 5 shows the method that determines how may particles move to each neighbor. When the simulation of events is complete, the particle counts can be update and the imaginary clocks are sealed back to represent the change in the state of the filter.
- This method uses some function t0 a ⁇ particles to variant state cells based on the initial distribution v of the signal. The number of particles to add to each cell depends on time, the given cell, and the overall state of the filter. This method ensures that the filter does not converge to one or more invariant states without the ability to recover from an incorrect localization.
- the Head End signal model consists of pertinent trait information of potential and current television viewers that have DSTB boxes connected to a particular Head End.
- each DSTB state including potential household viewership, watching status, and current channel; is taken from
- V* denote the random selection at time t k in the sampling process.
- V* would be a matrix with a random number of rows, each row consisting of M entries with exactly one nonzero entry corresponding to the index of the particular DSTB, which has provided a sample.
- the number rows would be the number of DSTBs providing a sample.
- the locations of the nonzero entries are naturally distinct over the rows and would be chosen uniformly over the possible permutations to reflect the actual sampling taken.
- V k would do the random selection and the h would be a function providing the information that is chosen to be fed back.
- the second observation information from the aggregated delivery statistics would be
- j ranges back over the spot segments in the reporting periods and t k is the reporting period time.
- the signal for the Head End becomes the probability distributions from the DSTB's.
- aggregate (and possibly delayed) ad delivery statistics can also provide inferences in the estimated viewership of DSTBs, as well as any 'exposed mode' information whereby households opt to provide their state information (demographics, psychographics, etc.) in exchange for some compensation.
- state information demographics, psychographics, etc.
- commercial contract is modeled as a graph of incremental profit in terms of the contract details, available resources and future signal state. We call these graphs contract graphs which arrive with rates that depend upon the contract details, signal state and economic environments.
- the random arrival of the contract graphs is denoted as the contract graph process.
- an allotment of resources (that need not be maximum allotable to any contract) to a contract graph process is called a feasible selection if, given the state (present and future) and the environment, the allotted resources do not exceed the available resources, i.e. the available commercial spots over the various categories.
- current versus future potential profits a modeled through a utility function.
- This utility function takes the stream of contract graphs available (both presently and with future random arrivals) and returns a number indicating profit in terms of dollar or some other form to satisfaction. Due to the random future behavior of contract graphs, the utility function cannot simply provide maximum profits without taking into account deviation from the expected profit to ensure the maximization does not allow significant risk of poor profit.
- Head End signal model Head End observation model
- contract generation model contract generation model
- utility (profit) model To perform optimal commercial selection, the following models need to be defined described: Head End signal model, Head End observation model, contract generation model, and utility (profit) model.
- the commercial contracts that arise are modeled as a marked point process over the contract graphs.
- the rate of arrival for the contracts depends upon the previous contracts executed as well as external factors such as economic conditions..
- R(D S ) be the available resources, now and in the future, based upon the downloadable program information D s at time s.
- n t represents the number of contracts that have arrived of the various types up to and including time t and take
- the utility function / balances current profit with future profit and the change of obtaining very high profits on a particular contract with the risk of no or low profit. In order to ensure that we start off reasonably, we will deweight future profit in an exponential manner. Moreover, in order that we are not overly aggressive we will include a variance-like condition.
- One embodiment of the resulting utility function is for small constants. Then, the goal of the commercial selection process is to maximize over the f € ⁇ . Such a goal can be solved using one or more asymptotically optimal filters.
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PCT/US2007/068075 WO2007131068A2 (en) | 2006-05-02 | 2007-05-02 | Method and apparatus to perform real-time audience estimation and commercial selection suitable for targeted advertising |
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US8761658B2 (en) | 2011-01-31 | 2014-06-24 | FastTrack Technologies Inc. | System and method for a computerized learning system |
US8863166B2 (en) * | 2011-04-06 | 2014-10-14 | Rentrak Corporation | Method and system for detecting non-powered video playback devices |
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US8245251B2 (en) * | 2002-12-06 | 2012-08-14 | General Instrument Corporation | Method and apparatus for predictive tuning in digital content receivers |
US20040172650A1 (en) * | 2003-02-28 | 2004-09-02 | Hawkins William J. | Targeted content delivery system in an interactive television network |
US7188048B2 (en) * | 2003-06-25 | 2007-03-06 | Lockheed Martin Corporation | Refining stochastic grid filter |
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