Method and apparatus to perform real-time audience estimation and commercial selection suitable for targeted advertising

Abstract

A targeted advertising system selects an asset (e.g., ad) for a current user of a user equipment device (e.g., a digital set top box in a cable network). The system can first operate 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 system can process current user inputs to match a current user to one of the identified users of that user equipment device audience. Fuzzy logic and/or stochastic filtering 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.

1 . A method for use in targeting assets to users of user equipment devices in a communications network, comprising the steps of:
 operating a processor to progressively incorporate, over time, a plurality of user inputs by one or more users of a user equipment device into a model of a user composition of the one or more users, the model including a plurality of user classification parameters;   filtering the user composition model to obtain an estimate of a current user composition of the user equipment device; and   targeting one or more assets in the communications network using the estimated current user composition.   
     
     
         2 . The method of  claim 1 , wherein the filtered user composition model is free of user equipment device usage patterns for the users obtained before the progressive incorporation of the plurality of user inputs. 
     
     
         3 . The method of  claim 1 , further comprising:
 receiving first user inputs at a first time; and   receiving second user inputs at a second time after the first time, and wherein the second user inputs are incorporated into the model to a greater degree than are the first user inputs.   
     
     
         4 . The method of  claim 1 , further comprising:
 establishing a reference event, wherein the progressively incorporated inputs occurred at times after the reference event.   
     
     
         5 . (canceled) 
     
     
         6 . (canceled) 
     
     
         7 . The method of  claim 1 , wherein the targeting comprises:
 receiving one or more lists of assets for delivery at the user equipment device;   obtaining one or more targeting parameters for the one or more lists of assets; and   determining a level of correspondence between the user classification parameters of the current user composition of the user equipment device and the targeting parameters for the one or more lists of assets.   
     
     
         8 . The method of  claim 7 , wherein the determining is performed in multiple dimensions relating to multiple classification and targeting parameters. 
     
     
         9 . The method of  claim 8 , further comprising:
 voting for at least one asset of the one or more lists of assets based on the determined level of correspondence between the user classification parameters of the current user composition of the user equipment device and the targeting parameters for the one or more lists of assets.   
     
     
         10 - 13 . (canceled) 
     
     
         14 . The method of  claim 1 , further comprising:
 progressively incorporating, over time, a plurality of user inputs by users of a plurality of additional user equipment devices into a plurality of models of user composition of the users, the models including a plurality of user classification parameters;   filtering the user composition models to obtain estimates of current user compositions of the plurality of additional user equipment devices; and   aggregating the current user compositions of the user equipment device and the plurality of additional user equipment devices to obtain a current user composition of an aggregated audience, wherein the targeting comprises using the aggregated audience current user composition for use in targeting one or more assets in the communications network.   
     
     
         15 . (canceled) 
     
     
         16 . The method of  claim 1 , wherein the progressively incorporating comprises:
 associating the user inputs with respective user classification parameters;   treating the values as points in fuzzy sets; and   aggregating the fuzzy sets into a multi-dimensional feature terrain.   
     
     
         17 . The method of  claim 16 , wherein the filtering comprises:
 reducing noise in the feature terrain.   
     
     
         18 . The method of  claim 17 , wherein the reducing comprises:
 processing evidence in relation to reference values.   
     
     
         19 . The method of  claim 17 , wherein the reducing comprises:
 reducing a set of gradients across the feature terrain by iteratively removing low-level gradients to progressively identify candidate centers.   
     
     
         20 . The method of  claim 1 , wherein the progressively incorporating comprises:
 developing an observation model based on first inputs by one or more users with respect to one or more user equipment devices; and   developing a signal model reflective of the possible states and dynamics of a user composition of one or more users of a first user equipment device with respect to time, wherein said observation model probabilistically relates measurement data related to said first inputs to the possible states and dynamics.   
     
     
         21 . The method of  claim 20 , wherein the filtering the user composition model comprises:
 employing a stochastic filter to estimate said user composition at a time of interest through an approximate conditional distribution of a signal given the signal and observation models and second inputs by one or more users.   
     
     
         22 . The method as set forth in  claim 21 , wherein said inputs are a click stream of user inputs over time and said observation model models said click stream as a Markov chain. 
     
     
         23 . The method as set forth in  claim 22 , wherein said observation model takes into account programming related information for network content indicated by at least some of said inputs. 
     
     
         24 . The method as set forth in  claim 23 , further comprising the step of processing said Markov chain using a mathematical model wherein observations of said Markov chain may only transition to a subset of a full set of states, where said subset depends on a current state of said Markov chain. 
     
     
         25 . The method as set forth in  claim 21 , wherein said step of developing an observation model comprises modeling said observation model as a Markov chain or a k step Markov chain. 
     
     
         26 . The method as set forth in  claim 25 , wherein the transition function for the observation Markov chain depends upon a position of the signal to estimate. 
     
     
         27 . The method as set forth in  claim 21 , wherein said signal is established as representing said user composition and a separate factor affecting said user inputs. 
     
     
         28 . The method as set forth in  claim 21 , wherein a model of said signal allows for representation of said user composition as including two or more users. 
     
     
         29 . The method as set forth in  claim 21 , wherein a model of said signal allows for representation of a change in said user composition. 
     
     
         30 . The method as set forth in  claim 29 , wherein said change is a change in a number of users associated with said user equipment device. 
     
     
         31 . The method as set forth in  claim 21 , wherein said step of employing a stochastic filter comprises obtaining probabilistic estimates of said signal based on said observation model and measurement data. 
     
     
         32 . The method as set forth in  claim 31 , wherein said step of employing a stochastic filter comprises defining a nonlinear filter to obtain probabilistic estimates of said signal based on said observation model and measurement data. 
     
     
         33 . The method as set forth in  claim 32 , wherein said step of employing a stochastic filter further comprises establishing an approximation filter for approximating operation of said nonlinear filter. 
     
     
         34 . The method as set forth in  claim 33 , wherein said approximation filter is a particle filter. 
     
     
         35 . The method as set forth in  claim 33 , wherein said approximation filter is a discrete space filter. 
     
     
         36 . The method as set forth in  claim 21 , wherein said step of using comprises providing information based on said user composition to a network platform operative to insert assets into a content stream of said network. 
     
     
         37 . The method as set forth in  claim 36 , wherein said information identifies demographics of one or more users of said user equipment device. 
     
     
         38 . The method as set forth in  claim 37 , wherein said platform is operative to aggregate user composition information associated with multiple user equipment devices and to select one or more assets for insertion based on said aggregated information. 
     
     
         39 . The method as set forth in  claim 38 , wherein said platform is operative to process information from multiple user equipment devices as an observation model and to apply a filter with respect to said observation model to estimate an aggregate composition of a network audience at said time of interest. 
     
     
         40 . The method as set forth in  claim 37 , wherein said platform is operative to select assets for insertion based on said aggregate composition and additional information affecting a delivery value of particular assets. 
     
     
         41 . The method as set forth in  claim 36 , wherein said information identifies one or more appropriate assets for delivery to said user equipment device based on said user composition. 
     
     
         42 . The method as set forth in  claim 21 , wherein said step of using comprises selecting, at said user equipment device, an asset for delivery to said one or more users. 
     
     
         43 . The method as set forth in  claim 21 , wherein said step of using comprises reporting a goodness of fit of an asset delivered at said user equipment device with respect to said one or more users. 
     
     
         44 . An apparatus for use in targeting assets to users of user equipment devices in a communications network, comprising:
 a port operative for receiving input information regarding first inputs by one or more users with respect to a user equipment device; and   a processor operative for providing an observation model based on said first inputs, modeling the observation model as dependent upon a signal model reflective of at least a user composition of one or more users of said user equipment device with respect to time, where said observation model probabilistically relates measurement data related to said first inputs to said user composition, employing a stochastic filter to estimate the user composition at a time of interest as a state of a signal through an approximate conditional distribution of the signal given the signal and observation models and second inputs by one or more users, and using the estimated user composition in targeting an asset with respect to the user equipment device.   
     
     
         45 . The apparatus as set forth in  claim 44 , wherein said processor is operative for defining a nonlinear filter to obtain estimates of said signal based on said observation model and measurement data. 
     
     
         46 . The apparatus as set forth in  claim 45 , wherein said processor is operative for establishing an approximation filter for approximating operation of said nonlinear filter. 
     
     
         47 . The apparatus as set forth in  claim 46 , wherein said nonlinear filter is one of a particle filter and a discrete space filter. 
     
     
         48 . The apparatus as set forth in  claim 44 , further comprising a port for transmitting information for use in targeting assets to a separate network platform, wherein said information is based on said estimated user composition. 
     
     
         49 . A method for use in targeting assets to users of user equipment devices in a broadcast network, comprising the steps of:
 collectively analyzing a stream of data corresponding to a series of first user inputs with respect to one or more user equipment devices, wherein said step of collectively analyzing comprises establishing an observation model; and   applying logic for matching a pattern described by a stream corresponding to a series of second user inputs to a characteristic associated with an audience classification of a user, wherein said step of applying logic comprises employing a stochastic filter to approximately estimate the conditional distribution of a signal given the observation model and second inputs and extract signal estimates from said series of second user inputs to estimate said audience classification at a time of interest.   
     
     
         50 . The method as set forth in  claim 49 , wherein said series of user inputs are modeled as a Markov chain. 
     
     
         51 . The method as set forth in  claim 49 , wherein said step of applying logic comprises using a nonlinear filter model. 
     
     
         52 . The method as set forth in  claim 51 , wherein said step of applying logic comprises executing an approximation filter to approximate operation of said nonlinear filter.