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: There is a computerized learning system and method which updates a conditional estimate of a user signal representing a characteristic of a user based on observations including observations of user behavior. The conditional estimate may be updated using a non-linear filter. Learning tools may be generated using the computerized learning system based on distributions of desired characteristics of the learning tools. The learning tools may include educational items and assessment items. The learning tools may be requested by a user or automatically generated based on estimates of the user's characteristics. Permissions may be associated with the learning tools which may only allow delegation of permissions to other users of lower levels. The learning system includes a method for annotating learning tools and publishing those annotations. In-line text editors of scientific text allow users to edit and revised previously published documents.

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: There is a computerized learning system and method which updates a conditional estimate of a user signal representing a characteristic of a user based on observations including observations of user behavior. The conditional estimate may be updated using a non-linear filter. Learning tools may be generated using the computerized learning system based on distributions of desired characteristics of the learning tools. The learning tools may include educational items and assessment items. The learning tools may be requested by a user or automatically generated based on estimates of the user's characteristics. Permissions may be associated with the learning tools which may only allow delegation of permissions to other users of lower levels. The learning system includes a method for annotating learning tools and publishing those annotations. In-line text editors of scientific text allow users to edit and revised previously published documents.

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 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.