METHOD AND APPARATUS TO PERFORM REAL-TIME AUDIENCE ESTIMATION AND COMMERCIAL SELECTION SUITABLE FOR TARGETED ADVERTISING

• US 20090133058A1
• Filed: 11/21/2007
• Published: 05/21/2009
• Est. Priority Date: 11/21/2007
• Status: Abandoned Application

• Current Assignee: Invidi Technologies Corporation
• Original Assignee: Invidi Technologies Corporation
• Inventors: Hailes, Jarett, Kim, Surrey, Kouritzin, Michael
• Application Number:
  US11/944,078
• Publication Number:
  US 20090133058A1
• Time in Patent Office: Days
• US Class Current: 725/34
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04H 20/103 Transmitter-side switching
• CPC Class Codes: H04H 60/45 for identifying users
• CPC Class Codes: H04H 60/63 for services of sales data processing systems or methods specially adapted for electronic commerce G06Q30/00
• CPC Class Codes: H04H 60/66 for using the result on distributors' side

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.

Claims

1. A method for use in targeting assets to users of user equipment devices in a communications network, comprising the steps of:

developing an observation model based on inputs by one or more users with respect to a user equipment device;

developing a signal model reflective of the possible states and dynamics at a user composition of one or more users of said user equipment device with respect to time;

estimating said user composition at a time of interest through an approximate conditional distribution of said signal given the signal and observation models and the measurement data; and

using said estimated user composition in targeting an asset with respect to said user equipment device.

2. The method as set forth in claim 1, wherein said inputs are a click stream of user inputs over time and said observation model models said click stream as a Markov chain.

3. The method as set forth in claim 2, wherein said observation model takes into account programming related information for network content indicated by at least some of said inputs.

4. The method as set forth in claim 3, 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.

5. The method as set forth in claim 1, wherein said step of modeling comprises modeling said observation model as a Markov chain or a k step Markov chain.

6. The method as set forth in claim 5, wherein the transition function for the observation Markov chain depends upon a position of the signal to estimate.

7. The method as set forth in claim 1, wherein said signal is established as representing said user composition and a separate factor affecting said user inputs.

8. The method as set forth in claim 1, wherein a model of said signal allows for representation of said user composition as including two or more users.

9. The method as set forth in claim 1, wherein a model of said signal allows for representation of a change in said user composition.

10. The method as set forth in claim 9, wherein said change is a change in a number of users associated with said user equipment device.

11. The method as set forth in claim 1, wherein said step of modeling comprises defining a filter to obtain probabilistic estimates of said signal based on said observation model and measurement data.

12. The method as set forth in claim 11, wherein said step of modeling comprises defining a nonlinear filter to obtain probabilistic estimates of said signal based on said observation model and measurement data.

13. The method as set forth in claim 12, wherein said step of modeling further comprises establishing an approximation filter for approximating operation of said nonlinear filter.

14. The method as set forth in claim 13, wherein said approximation filter is a particle filter.

15. The method as set forth in claim 13, wherein said approximation filter is a discrete space filter.

16. The method as set forth in claim 1, 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.

17. The method as set forth in claim 16, wherein said information identifies demographics of one or more users of said user equipment device.

18. The method as set forth in claim 17, 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.

19. The method as set forth in claim 16, 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.

20. The method as set forth in claim 17, 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.

21. The method as set forth in claim 16, wherein said information identifies one or more appropriate assets for delivery to said user equipment device based on said user composition.

22. The method as set forth in claim 1, wherein said step of using comprises selecting, at said user equipment device, an asset for delivery to said one or more users.

23. The method as set forth in claim 1, 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.

24. 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 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 inputs, modeling the observation model as dependent upon a signal reflective of at least a user composition of one or more users of said user equipment device with respect to time, estimating the user composition at a time of interest, given observed measurement data, as a state of the signal, and using the estimated user composition in targeting an asset with respect to the user equipment device.

25. The apparatus as set forth in claim 24, wherein said processor is operative for defining a nonlinear filter to obtain estimates of said signal based on said observation model and measurement data.

26. The apparatus as set forth in claim 25, wherein said processor is operative for establishing an approximation filter for approximating operation of said nonlinear filter.

27. The apparatus as set forth in claim 26, wherein said nonlinear filter is one of a particle filter and a discrete space filter.

28. The apparatus as set forth in claim 24, 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.

29. 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.

30. The method as set forth in claim 29, wherein said step of collectively analyzing comprises establishing an observation model wherein said series of user inputs are modeled as a Markov chain.

31. The method as set forth in claim 29, wherein said step of applying logic comprises using a nonlinear filter model to extract signal estimates and distributions from said series of user inputs ?? estimates of the signal state to mach for said characteristic.

32. The method as set forth in claim 29, wherein said step of applying logic comprises executing an approximation filter to approximate operation of said nonlinear filter.