Granted Patent B1

US 9,830,607 · App. 15/193,854 · Granted Nov 28, 2017

Abstract

Claims (30)

1. A computer-implemented method comprising:

accessing a first set of data representing accesses by a first set of users, the first set of users being included in a sample of users from a larger population of users, each user in the first set of users being associated with data in the first set of data representing accesses to one or more items of media content within a first set of media content associated with one or more of multiple media entities, and each user in the first set of users having accessed the one or more items of media content within the first set of media content on one or more of multiple types of media platforms;

based on the accessed first set of data, determining, for a first of the multiple media entities, an amount of users in the first set of users who have accessed one or more items of media content within the first set of media content associated with the first media entity on more than one of the multiple types of media platforms;

accessing a second set of data representing accesses by a second set of users, wherein the second set of users includes a greater number of users than the first set of users, each user in the second set of users being associated with data in the second set of data representing accesses to one or more items of media content within the second set of media content associated with one or more of the multiple media entities, and each user in the second set of users having accessed the one or more items of media content within the second set of media content on one or more of the multiple types of media platforms;

based on (1) the accessed first set of data, (2) the determined amount of users in the first set of users who have accessed one or more items of media content within the first set of media content associated with the first media entity on more than one of the multiple types of media platforms, and (3) the accessed second set of data, deriving, by one or more processors, amount of users in the second set of users who have accessed media content associated with any one of the multiple media entities on more than one of the multiple types of media platforms; and

applying, by the one or more processors, the derived function to a third set of data associated with one or more second media entities to estimate an amount of users who have accessed media content associated with the one or more second media entities on more than one of the multiple types of media platforms.

2. The computer-implemented method of claim 1 , wherein deriving the function comprises applying a Maximum Likelihood Estimate (MLE) method to (1) the accessed first set of data, (2) the determined amount of users in the first set of users who have accessed one or more items of media content within the first set of media content associated with the first media entity on more than one of the multiple types of media platforms, and (3) the accessed second set of data.

3. The computer-implemented method of claim 1 , wherein deriving the function comprises training a quantile regression model to (1) amount of users in the second set of users who have accessed media content associated with any one of the multiple media entities on more than one of the multiple types of media platform and (2) features derived from the accessed second set of data.

4. The computer-implemented method of claim 3 , wherein the features derived from the accessed second set of data comprise platform reach, frequency on platform, relative size of platforms, and dictionary differences between related media entities on each platform.

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

the accessed first set of data represents accesses by the first set of users with three types of media platforms; and

deriving the function comprises generating a function for each unique pair of the three types of media platforms, wherein each generated function estimates an amount of users who have accessed media content associated with the one or more second media entities on both media platforms in a unique pair of the three types of media platforms.

6. The computer-implemented method of claim 5 , further comprising combining outputs from each of the generated functions for each unique pair of the three types of media platforms to estimate a de-duplicated audience for a media entity across the three types of media platforms.

7. A system comprising:

a first set of media platforms configured to access media content associated with one or more media entities, the first set of media platforms being of multiple different types and being associated with a first set of users who access the media content with the media platforms;

a second set of media platforms configured to access media content associated with one or more media entities, the second set of media platforms being of multiple different types and being associated with a second set of users who access the media content with the media platforms;

one or processors;

one or more storage devices that store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

accessing a first set of data representing accesses by a first set of users, the first set of users being included in a sample of users from a larger population of users, each user in the first set of users being associated with data in the first set of data representing accesses to one or more items of media content within a first set of media content associated with one or more of multiple media entities, and each user in the first set of users having accessed the one or more items of media content within the first set of media content on one or more of multiple types of media platforms;

based on the accessed first set of data, determining, for a first of the multiple media entities, an amount of users in the first set of users who have accessed one or more items of media content within the first set of media content associated with the first media entity on more than one of the multiple types of media platforms;

accessing a second set of data representing accesses by a second set of users, wherein the second set of users includes a greater number of users than the first set of users, each user in the second set of users being associated with data in the second set of data representing accesses to one or more items of media content within the second set of media content associated with one or more of the multiple media entities, and each user in the second set of users having accessed the one or more items of media content within the second set of media content on one or more of the multiple types of media platforms;

based on (1) the accessed first set of data, (2) the determined amount of users in the first set of users who have accessed one or more items of media content within the first set of media content associated with the first media entity on more than one of the multiple types of media platforms, and (3) the accessed second set of data, deriving, by one or more processors, a function that estimates an amount of users in the second set of users who have accessed media content associated with any one of the multiple media entities on more than one of the multiple types of media platforms; and

applying, by the one or more processors, the derived function to a third set of data associated with one or more second media entities to estimate an amount of users who have accessed media content associated with the one or more second media entities on more than one of the multiple types of media platforms.

8. The system of claim 7 , wherein deriving the function comprises applying a Maximum Likelihood Estimate (MLE) method to (1) the accessed first set of data, (2) the determined amount of users in the first set of users who have accessed one or more items of media content within the first set of media content associated with the first media entity on more than one of the multiple types of media platforms, and (3) the accessed second set of data.

9. The system of claim 7 , wherein deriving the function comprises training a quantile regression model to (1) amount of users in the second set of users who have accessed media content associated with any one of the multiple media entities on more than one of the multiple types of media platform and (2) features derived from the accessed second set of data.

10. The system of claim 8 , wherein the features derived from the accessed second set of data comprise platform reach, frequency on platform, relative size of platforms, and dictionary differences between related media entities on each platform.

11. The system of claim 7 , wherein:

the accessed first set of data represents accesses by the first set of users with three types of media platforms; and

deriving the function comprises generating a function for each unique pair of the three types of media platforms, wherein each generated function estimates an amount of users who have accessed media content associated with the one or more second media entities on both media platforms in a unique pair of the three types of media platforms.

12. The system of claim 11 , wherein the one or more storage devices further store instructions that, when executed by the one or more processors, cause the one or more processors to perform the operation of: combining outputs from each of the generated functions for each unique pair of the three types of media platforms to estimate a de-duplicated audience for a media entity across the three types of media platforms.

Assignments (8)