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.

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.

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.

Abstract: Input measurements from a measurement device are processed as a Markov chain whose transitions depend upon the signal. The desired information related to the device can then be obtained by estimating the state of the signal at a time of interest. A nonlinear filter system can be used to provide an estimate of the signal based on the observation model. The nonlinear filter system may involve a nonlinear filter model and an approximation filter for approximating an optimal nonlinear filter solution. The approximation filter may be a particle filter or a discrete state filter for enabling substantially real-time estimates of the signal based on the observation model. In one application, a click stream entered with respect to a digital set top box of a cable television network is analyzed to determine information regarding users of the digital set top box so that ads can be targeted to the users.

Abstract: Input measurements from a measurement device are processed as a Markov chain whose transitions depend upon the signal. The desired information related to the device can then be obtained by estimating the state of the signal at a time of interest. A nonlinear filter system can be used to provide an estimate of the signal based on the observation model. The nonlinear filter system may involve a nonlinear filter model and an approximation filter for approximating an optimal nonlinear filter solution. The approximation filter may be a particle filter or a discrete state filter for enabling substantially real-time estimates of the signal based on the observation model. In one applications a click stream entered with respect to a digital set top box of a cable television network is analyzed to determine information regarding users of the digital set top box so that ads can be targeted to the users.

Abstract: A method, and program for implementing such method, for use in estimating a conditional probability distribution of a current signal state and/or a future signal state for a non-linear random dynamic signal process includes providing sensor measurement data associated with the non-linear random dynamic signal process. A filter operating on the sensor measurement data by directly discretizing both amplitude and signal state domain for an unnormalized or normalized conditional distribution evolution equation is defined. The discretization of the signal state domain results in creation of a grid comprising a plurality of cells and the discretization in amplitude results in a distribution of particles among the cells via a particle count for each cell.

Abstract: A method, and program for implementing such method, for use in estimating a conditional probability distribution for past signal states, current signal states, future signal states, and/or complete pathspace of a non-linear random dynamic signal process, includes providing sensor measurement data associated therewith and state data including at least location and weight information associated with each of a plurality of particles as a function thereof. An estimate of the conditional probability distribution is compared for the signal state based on the state data for particles under consideration and such particles are resampled upon receipt of sensor measurement data. The resampling includes comparing weight information associated with a first particle (e.g., the highest weighted particle) with weight information associated with a second particle (e.g., the lowest weighted particle) to determine if the state data of the first and second particles is to be adjusted.

Abstract: A method, and program for implementing such method, for use in estimating a conditional probability distribution of a current signal state and/or a future signal state for a non-linear random dynamic signal process includes providing sensor measurement data associated with the non-linear random dynamic signal process. A filter operating on the sensor measurement data by directly discretizing both amplitude and signal state domain for an unnormalized or normalized conditional distribution evolution equation is defined. The discretization of the signal state domain results in creation of a grid comprising a plurality of cells and the discretization in amplitude results in a distribution of particles among the cells via a particle count for each cell.

Abstract: A method, and program for implementing such method, for use in estimating a conditional probability distribution for past signal states, current signal states, future signal states, and/or complete pathspace of a non-linear random dynamic signal process, includes providing sensor measurement data associated with the non-linear random dynamic signal process. State data including at least location and weight information associated with each of a plurality of particles is provided as a function of the sensor measurement data that collectively probabilistically represents the state of the non-linear random dynamic signal process at time t. An estimate of the conditional probability distribution is computed for the signal state based on the state data for particles under consideration. The particles under consideration are resampled upon receipt of sensor measurement data.

Abstract: A particle filter is employed so that particle locations provide signal information to construct an approximated conditional distribution of probabilistic signal state. For an optimal tracking filter, current particles are used with weight value of one for each. To construct an optimal predicting filter, a copy of the current particles are evolved forward to the time for which the prediction is to occur. A new branching particle method allows the construction of optimal smoothing filters. Ancestor particles retain probabilistic data about the likely historical path of the signal. Then these particles, weighted by their associated ancestor particle weights, provide the approximate asymptotically optimal conditional distribution of the signal state at the collection of previous times. The branching particle filter operates recursively on the observation data, allowing real-time operation of the system.