COMBINING MEASUREMENTS BASED ON BEACON DATA

• US 20140122703A1
• Filed: 03/13/2013
• Published: 05/01/2014
• Est. Priority Date: 10/26/2012
• Status: Active Grant

• Current Assignee: comScore, Inc.
• Original Assignee: comScore, Inc.
• Inventors: Pugh, Brian, Nair, Balakrishnan Vinayak, Spring, Hilary
• Granted Patent: US 9,621,446 B2
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/146 Markers for unambiguous identification of a particular session, e.g. session cookie or URL-encoding
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18

Abstract

Usage data representing resource accesses on a network by client devices in a plurality of households is accessed. Based on the accessed usage data, a set of the households that the usage data indicates are associated with a specified number and type of client devices is determined. A subset of the usage data is extracted, where the subset corresponds to resource accesses by client devices associated with the subset of households. Usage information is determined for the specified number and type of client devices based on the extracted subset of the usage data.

Claims

1. A computer-implemented method, comprising:

accessing usage data representing resource accesses on a network by client devices in a plurality of households;

determining, based on the accessed usage data, a set of the households that the usage data indicates are associated with a specified number and type of client devices;

extracting a subset of the usage data, the subset corresponding to resource accesses by client devices associated with the set of households; and

determining usage information for the specified number and type of client devices based on the extracted subset of the usage data.

2. The computer-implemented method of claim 1 wherein the specified number and type of client devices is exactly one client device of a first client device type and exactly one client device of a second client device type.

3. The computer-implemented method of claim 2 wherein the first client device type is a personal computer type and the second client device type is a mobile device type.

4. The computer-implemented method of claim 2 wherein determining usage information includes determining a measure of overlap between the first client device type and the second client device type.

5. The computer-implemented method of claim 1 wherein determining usage information includes determining a measure of overlap between the specified number and type of client devices based on the extracted subset of the usage data.

6. The computer-implemented method of claim 1, wherein:

determining, based on the accessed usage data, the set of the households that the usage data indicates are associated with a specified number and type of client devices comprises;

determining a base set of beacon cookies that correspond to resource accesses by devices of a particular type;

determining a base set of usage data comprising data indicating resource accesses associated with the determined base set of beacon cookies; and

determining, based on the base set of usage data, a set of reference beacon cookies that are persistent for at least a majority of a first time period and are associated with resource accesses from a single network address during the first time period.

7. The computer-implemented method of claim 6, wherein determining the base set of beacon cookies comprises determining a base set of beacon cookies for which each of the beacon cookies in the set is associated with one or more resource accesses by a personal computer located in a particular geographic region within the defined period of time.

8. The computer-implemented method of claim 6, wherein determining the set of households comprises:

determining a set of reference network addresses by selecting one or more network addresses associated with the set of reference beacon cookies; and

determining a set of candidate network addresses by selecting, from the reference network addresses, network addresses for which all associated cookies are persistent during a majority of the first time period.

9. The computer-implemented method of claim 8, wherein determining, based on the accessed usage data, the set of the households that the usage data indicates are associated with a specified number and type of client devices comprises selecting network addresses, from among the set of candidate network addresses, that the usage data indicates are associated with exactly one personal computer and exactly one mobile device during the first time period.

10. The computer-implemented method of claim 8, wherein determining the set of households comprises:

determining, for each of the candidate network addresses, a number of client devices of a first type that are associated with the candidate network address; and

determining, for each of the candidate network addresses, a number of client devices of a second type that are associated with the candidate network address.

11. The computer-implemented method of claim 10, wherein the first type is personal computers and determining the number of client devices of the first type that are associated with the candidate network address comprises:

determining a portion of the usage data that represents resource accesses associated with the candidate network address;

determining a number of operating systems for devices of the first type that are indicated by the portion of the usage data;

determining, for each of one or more user agent types for devices of the first type, a number of different cookies associated with the user agent type in the determined portion of the usage data; and

estimating a number of personal computers associated with the candidate network address based on the number of operating systems and the numbers of different cookies associated with the respective one or more user agent types.

12. The computer-implemented method of claim 10, wherein the first type is mobile devices and determining the number of client devices of the first type that are associated with the candidate network address comprises:

determining a portion of the usage data that represents resource accesses associated with the candidate network address;

determining a number of user agents for devices of the first type that are indicated by the portion of the usage data;

determining, for each of one or more mobile application publishers, a number of different identifier values associated with the mobile application publisher in the portion of the usage data; and

estimating a number of mobile devices associated with the network address based on the number of user agents and the numbers of different identifier values associated with the respective mobile application publishers.

13. The computer-implemented method of claim 1, wherein extracting the subset of the usage data comprises, for each of one or more households in the set of households:

extracting, from the usage data, in-home data for the household indicating resource accesses associated with a home network address corresponding to the household;

determining a set of household cookies that are associated with the in-home data; and

extracting, from the usage data, out-of-home data for the household indicating resource accesses that are (i) associated with one of the household cookies and (ii) associated with a network address different from the home network address.

14. A computer-readable storage medium storing instructions that, when executed by one or more processing devices, cause the one or more processing devices to perform the following operations:

accessing usage data representing resource accesses on a network by client devices in a plurality of households;

determining, based on the accessed usage data, a set of the households that the usage data indicates are associated with a specified number and type of client devices;

extracting a subset of the usage data, the subset corresponding to resource accesses by client devices associated with the set of households; and

determining usage information for the specified number and type of client devices based on the extracted subset of the usage data.

15. The computer-readable storage medium of claim 14 wherein the specified number and type of client devices is exactly one client device of a first client device type and exactly one client device of a second client device type.

16. The computer-readable storage medium of claim 15 wherein the first client device type is a personal computer type and the second client device type is a mobile device type.

17. The computer-readable storage medium of claim 15 wherein determining usage information includes determining a measure of overlap between the first client device type and the second client device type.

18. The computer-readable storage medium of claim 14 wherein determining usage information includes determining a measure of overlap between the specified number and type of client devices based on the extracted subset of the usage data.

19. The computer-readable storage medium of claim 14, wherein:

determining, based on the accessed usage data, the set of the households that the usage data indicates are associated with a specified number and type of client devices comprises;

determining a base set of beacon cookies that correspond to resource accesses by devices of a particular type;

determining a base set of usage data comprising data indicating resource accesses associated with the determined base set of beacon cookies; and

determining, based on the base set of usage data, a set of reference beacon cookies that are persistent for at least a majority of a first time period and are associated with resource accesses from a single network address during the first time period.

20. The computer-readable storage medium of claim 19, wherein determining the base set of beacon cookies comprises determining a base set of beacon cookies for which each of the beacon cookies in the set is associated with one or more resource accesses by a personal computer located in a particular geographic region within the defined period of time.

21. The computer-readable storage medium of claim 19, wherein determining the set of households comprises:

determining a set of reference network addresses by selecting one or more network addresses associated with the set of reference beacon cookies; and

determining a set of candidate network addresses by selecting, from the reference network addresses, network addresses for which all associated cookies are persistent during a majority of the first time period.

22. The computer-readable storage medium of claim 21, wherein determining, based on the accessed usage data, the set of the households that the usage data indicates are associated with a specified number and type of client devices comprises selecting network addresses, from among the set of candidate network addresses, that the usage data indicates are associated with exactly one personal computer and exactly one mobile device during the first time period.

23. The computer-readable storage medium of claim 21, wherein determining the set of households comprises:

determining, for each of the candidate network addresses, a number of client devices of a first type that are associated with the candidate network address; and

determining, for each of the candidate network addresses, a number of client devices of a second type that are associated with the candidate network address.

24. The computer-readable storage medium of claim 23, wherein the first type is personal computers and determining the number of client devices of the first type that are associated with the candidate network address comprises:

determining a portion of the usage data that represents resource accesses associated with the candidate network address;

determining a number of operating systems for devices of the first type that are indicated by the portion of the usage data;

determining, for each of one or more user agent types for devices of the first type, a number of different cookies associated with the user agent type in the determined portion of the usage data; and

estimating a number of personal computers associated with the candidate network address based on the number of operating systems and the numbers of different cookies associated with the respective one or more user agent types.

25. The computer-readable storage medium of claim 23, wherein the first type is mobile devices and determining the number of client devices of the first type that are associated with the candidate network address comprises:

determining a portion of the usage data that represents resource accesses associated with the candidate network address;

determining a number of user agents for devices of the first type that are indicated by the portion of the usage data;

determining, for each of one or more mobile application publishers, a number of different identifier values associated with the mobile application publisher in the portion of the usage data; and

estimating a number of mobile devices associated with the network address based on the number of user agents and the numbers of different identifier values associated with the respective mobile application publishers.

26. The computer-readable storage medium of claim 14, wherein extracting the subset of the usage data comprises, for each of one or more households in the set of households:

extracting, from the usage data, in-home data for the household indicating resource accesses associated with a home network address corresponding to the household;

determining a set of household cookies that are associated with the in-home data; and

extracting, from the usage data, out-of-home data for the household indicating resource accesses that are (i) associated with one of the household cookies and (ii) associated with a network address different from the home network address.