METHODS AND APPARATUS FOR IMPROVING THE ACCURACY AND REACH OF ELECTRONIC MEDIA EXPOSURE MEASUREMENT SYSTEMS

• US 20080191934A1
• Filed: 04/18/2008
• Published: 08/14/2008
• Est. Priority Date: 07/30/2004
• Status: Abandoned Application

• Current Assignee: RDPA, LLC
• Original Assignee: RDPA, LLC
• Inventors: Baker, James W., Pasco, Daniel L., Burke, Kay S., Percy, R. Cameron, Percy, Roger D.
• Application Number:
  US12/106,031
• Publication Number:
  US 20080191934A1
• Time in Patent Office: Days
• US Class Current: 342/357.52
• CPC Class Codes: G01S 19/09 providing processing capability normally carried out by the receiver
• CPC Class Codes: G01S 19/20 Integrity monitoring, fault detection or fault isolation of space segment
• CPC Class Codes: G01S 19/22 Multipath-related issues
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: H04H 60/33 Arrangements for monitoring the users' behaviour or opinions

Abstract

Methods and apparatus for crediting media exposure are disclosed. An example method includes processing data representative of locations recorded by an electronic device to enhance at least one of completeness or accuracy of the data, deriving position fixes from the processed data, and modifying via a decision tree at least one of the derived position fixes to align with a known course of travel.

Claims

1. A method comprising:

processing data representative of locations recorded by an electronic device to enhance at least one of completeness or accuracy of the data;

deriving position fixes from the processed data; and

modifying via a decision tree at least one of the derived position fixes to align with a known course of travel.

2. A method as defined in claim 1, wherein processing the data comprises filtering the data.

3. A method as defined in claim 2, wherein filtering the data comprises using a plurality of filters.

4. A method as defined in claim 1, wherein processing the data includes at least one of deriving a precise satellite position, calculating a satellite elevation, setting an initial position fix based on an estimated position fix contained in the data, or performing receiver autonomous integrity management.

5. A method as defined in claim 1, wherein deriving the position fixes from the processed data comprises filtering the processed data.

6. A method as defined in claim 1, wherein deriving the position fixes from the processed data includes at least one of using a derived precise satellite position and a received signal to derive a position fix, using a plurality of received signals and an interpolated clock drift to derive a position fix, using a known position and respondent travel velocity to derive a position fix, or removing a determined or a derived position fix if multipath interference is detected.

7. A method as defined in claim 1, wherein modifying via the decision tree the at least one of the derived position fixes to align with a known course of travel comprises using an artificial intelligence technique.

8. A method as defined in claim 1, wherein modifying via the decision tree the at least one of the derived position fixes to align with a known course of travel comprises constructing the decision tree to be representative of possible travel paths, assigning likelihood values to nodes of the decision tree, and selecting a travel path that corresponds to a branch of the decision tree having the highest combined likelihood.

9. A method as defined in claim 8, wherein the likelihood values are based on at least one of a data moment, a data line dot product, or a contextual analysis.

10. An apparatus, comprising a processor coupled to a memory and programmed to:

process data representative of locations recorded by an electronic device to enhance at least one of a completeness or an accuracy of the data;

derive position fixes from the processed data; and

modify via a decision tree at least one of derived position fixes to align with a known course of travel.

11. An apparatus as defined in claim 10, wherein the processor is programmed to process the data by filtering the data using at least one filter.

12. An apparatus as defined in claim 10, wherein the processor is programmed to process the data by using at least one of a derived precise satellite position, a calculated satellite elevation, an estimated position fix contained in the data, or a result of receiver autonomous integrity management.

13. An apparatus as defined in claim 10, wherein the processor is programmed to derive the position fixes from the processed data by using at least one of a derived precise satellite position and a received signal to derive a position fix, using a plurality of received signals and an interpolated clock drift to derive a position fix, using a known position and respondent travel velocity to derive a position fix, or removing a determined or a derived position fix if multipath interference is detected.

14. An apparatus as defined in claim 10, wherein the processor is programmed to modify via the decision tree the at least one of the derived position fixes to align with a known course of travel by using an artificial intelligence technique.

15. An apparatus as defined in claim 10, wherein the processor is programmed to modify via the decision tree the at least one of the derived position fixes to align with a known course of travel by constructing the decision tree to be representative of possible travel paths, assigning likelihood values to nodes of the decision tree, and selecting a travel path that corresponds to a branch of the decision tree having the highest combined likelihood.

16. An apparatus as defined in claim 15, wherein the likelihood values are based on at least one of a data moment, a data line dot product, or a contextual analysis.

17. A machine readable medium having instructions stored thereon that, when executed, cause a machine to:

process data representative of locations recorded by an electronic device to enhance at least one of a completeness or an accuracy of the data;

derive position fixes from the processed data; and

modify via a decision tree at least one of the derived position fixes to align with a known course of travel.

18. A machine readable medium as defined in claim 17, wherein the instructions, when executed, cause the machine to process the data by filtering the data using at least one filter.

19. A machine readable medium as defined in claim 17, wherein the instructions, when executed, cause the machine to process the data by using at least one of a derived precise satellite position, a calculated satellite elevation, an estimated position fix contained in the data, or a result of receiver autonomous integrity management.

20. A machine readable medium as defined in claim 17, wherein the instructions, when executed, cause the machine to derive the position fixes from the processed data by using at least one of a derived precise satellite position and a received signal to derive a position fix, using a plurality of received signals and an interpolated clock drift to derive a position fix, using a known position and respondent travel velocity to derive a position fix, or removing a determined or a derived position fix if multipath interference is detected.

21. A machine readable medium as defined in claim 17, wherein the instructions, when executed, cause the machine to modify via the decision tree the at least one of the derived position fixes to align with a known course of travel by using an artificial intelligence technique.

22. A machine readable medium as defined in claim 17, wherein the instructions, when executed, cause the machine to modify via the decision tree the at least one of the derived position fixes to align with a known course of travel by constructing the decision tree to be representative of possible travel paths, assigning likelihood values to nodes of the decision tree, and selecting a travel path that corresponds to a branch of the decision tree having the highest combined likelihood.

23. A machine readable medium as defined in claim 22, wherein the likelihood values are based on at least one of a data moment, a data line dot product, or a contextual analysis.

24. An apparatus comprising

a file reader configured to read recorded data representative of respondent locations;

a memory configured to store data; and

a processing engine configured to apply filters that;

process the recorded data to enhance at least one of a completeness or an accuracy of the data,

derive position fixes from the processed data, and

modify via a decision tree at least one of the derived position fixes to align with a known course of travel.