Granted Patent B2

US 12,652,118 · App. 18/766,528 · Granted Jun 9, 2026

Inventors: Daniel Zink (Redmond, WA); Jane Huang (Bellevue, WA); Hao Chen (Redmond, WA); Ian Porteous (Mercer Island, WA); Surbhi Maheshwari (Sunnyvale, CA)

Assignee: Google LLC

H04H60/66G06Q30/0202G06Q30/0251

Abstract

Claims (69)

1 . A method comprising:

providing, for display to a content provider, a user interface (UI) indicating one or more characteristics of existing audiences including a first group of users currently designated to receive content of the content provider, the UI comprising a new audiences UI element selectable to view new audiences to be added to the existing audiences;

receiving a user selection of the new audiences UI element; and

in response to the user selection of the new audiences UI element,

identifying, based on the first group of users, a second group of users not currently designated to receive the content of the content provider;

estimating content related metrics for the second group of users not currently designated to receive the content of the content provider;

predicting, using a machine learning model and based on the estimated content related metrics for the second group of users, an increase in a number of user actions related to the content of the content provider in response to an addition of the new audiences to the existing audiences, the new audiences comprising at least a sub-group of the second group;

responsive to the predicted increase satisfying a threshold condition, adding the second group of users to the new audiences; and

causing display of information identifying the new audiences, and an indication of the predicted increased number of user actions related to the content of the content provider in response to the addition of the new audiences to the existing audiences.

2 . The method of claim 1 , wherein the information identifying the new audiences comprises, for each new audience, an audience identifier, an indication of an estimated number of user actions related to the content of the content provider, and an option to request that a corresponding audience be added to the first group of users currently designated to receive the content of the content provider.

3 . The method of claim 1 , wherein the content of the content provider comprises one or more secondary media items to be presented to the first group of users in association with a primary media item on the UI.

4 . The method of claim 1 , wherein predicting, using the machine learning model and based on the estimated content related metrics for the second group of users, the increase in the number of user actions related to the content of the content provider in response to the addition of the new audiences to the existing audiences further comprises:

collecting data identifying a number of presentations of the content of the content provider to the first group of users and a number of user actions related to the content of the content provider by the first group of users;

identifying, in the first group of users, a sub-group of users that also belongs to the second group of users not currently designated to receive the content of the content provider;

determining content related metrics for the identified sub-group of users; and

estimating, based on the content related metrics for the identified sub-group of users, content related metrics for the second group of users,

wherein the predicted increase in the number of user actions related to the content of the content provider is based on the estimated content related metrics for the second group of users.

5 . The method of claim 4 , wherein estimating the content related metrics for the second group of users further comprises:

determining a number of presentations of the content of the content provider to the identified sub-group of users;

determining a number of user actions related to the content of the content provider by the identified sub-group of users; and

determining a conversion rate for the second group of users based on the number of presentations of the content of the content provider to the identified sub-group of users and the number of user actions related to the content of the content provider by the identified sub-group of users.

6 . The method of claim 5 , wherein determining the conversion rate for the second group of users further comprises:

comparing a set of characteristics associated with the identified sub-group of users with a set of characteristics associated with the second group of users, wherein the conversion rate for the second group of users is determined based on a comparison of the set of characteristics associated with the identified sub-group of users with the set of characteristics associated with the second group of users.

7 . The method of claim 1 , wherein the increase in the number of user actions related to the content of the content provider is predicted using an estimation function.

8 . The method of claim 7 , wherein the estimation function comprises one or more formulas that use a plurality of parameters and a plurality of corresponding weights, wherein the plurality of parameters comprises a number of users in the second group that are not currently designated to receive the content of the content provider, an estimated number of presentations of the content of the content provider to the second group of users, and an estimated number of user actions related to the content of the content provider by the second group of users.

9 . The method of claim 8 , further comprising:

determining content related metrics of the first group of users and content related metrics of a third group of users, wherein the first group of users and the third group of users were previously designated to receive the content of the content provider in the past;

using the determined content related metrics of the first group of users and the estimation function, obtaining estimated content related metrics of the third group of users; and

modifying the plurality of weights of the estimation function based on a comparison of the estimated content related metrics of the third group of users and the determined content related metrics of the third group of users.

10 . A system comprising:

a memory; and

a processing device, coupled to the memory, to perform operations comprising:

providing, for display to a content provider, a user interface (UI) indicating one or more characteristics of existing audiences including a first group of users currently designated to receive content of the content provider, the UI comprising a new audiences UI element selectable to view new audiences to be added to the existing audiences;

receiving a user selection of the new audiences UI element; and

in response to the user selection of the new audiences UI element,

identifying, based on the first group of users, a second group of users not currently designated to receive the content of the content provider;

estimating content related metrics for the second group of users not currently designated to receive the content of the content provider;

predicting, using a machine learning model and based on the estimated content related metrics for the second group of users, an increase in a number of user actions related to the content of the content provider in response to an addition of the new audiences to the existing audiences, the new audiences comprising at least a sub-group of the second group;

responsive to the predicted increase satisfying a threshold condition, adding the second group of users to the new audiences; and

causing display of information identifying the new audiences, and an indication of the predicted increased number of user actions related to the content of the content provider in response to the addition of the new audiences to the existing audiences.

11 . The system of claim 10 , wherein the information identifying the new audiences comprises, for each new audience, an audience identifier, an indication of an estimated number of user actions related to the content of the content provider, and an option to request that a corresponding audience be added to the first group of users currently designated to receive the content of the content provider.

12 . The system of claim 10 , wherein predicting, using the machine learning model and based on the estimated content related metrics for the second group of users, the increase in the number of user actions related to the content of the content provider in response to the addition of the new audiences to the existing audiences further comprises:

collecting data identifying a number of presentations of the content of the content provider to the first group of users and a number of user actions related to the content of the content provider by the first group of users;

identifying, in the first group of users, a sub-group of users that also belongs to the second group of users not currently designated to receive the content of the content provider;

determining content related metrics for the identified sub-group of users; and

estimating, based on the content related metrics for the identified sub-group of users, content related metrics for the second group of users,

wherein the predicted increase in the number of user actions related to the content of the content provider is based on the estimated content related metrics for the second group of users.

13 . The system of claim 12 , wherein estimating the content related metrics for the second group of users further comprises:

determining a number of presentations of the content of the content provider to the identified sub-group of users;

determining a number of user actions related to the content of the content provider by the identified sub-group of users; and

determining a conversion rate for the second group of users based on the number of presentations of the content of the content provider to the identified sub-group of users and the number of user actions related to the content of the content provider by the identified sub-group of users.

14 . The system of claim 13 , wherein determining the conversion rate for the second group of users further comprises:

comparing a set of characteristics associated with the identified sub-group of users with a set of characteristics associated with the second group of users, wherein the conversion rate for the second group of users is determined based on a comparison of the set of characteristics associated with the identified sub-group of users with the set of characteristics associated with the second group of users.

15 . A non-transitory computer-readable medium comprising instructions, which when executed by a processing device cause the processing device to perform operations comprising:

providing, for display to a content provider, a user interface (UI) indicating one or more characteristics of existing audiences including a first group of users currently designated to receive content of the content provider, the UI comprising a new audiences UI element selectable to view new audiences to be added to the existing audiences;

receiving a user selection of the new audiences UI element; and

in response to the user selection of the new audiences UI element,

identifying, based on the first group of users, a second group of users not currently designated to receive the content of the content provider;

estimating content related metrics for the second group of users not currently designated to receive the content of the content provider;

predicting, using a machine learning model and based on the estimated content related metrics for the second group of users, an increase in a number of user actions related to the content of the content provider in response to an addition of the new audiences to the existing audiences, the new audiences comprising at least a sub-group of the second group;

responsive to the predicted increase satisfying a threshold condition, adding the second group of users to the new audiences; and

causing display of information identifying the new audiences, and an indication of the predicted increased number of user actions related to the content of the content provider in response to the addition of the new audiences to the existing audiences.

16 . The non-transitory computer-readable medium of claim 15 , wherein the information identifying the new audiences comprises, for each new audience, an audience identifier, an indication of an estimated number of user actions related to the content of the content provider, and an option to request that a corresponding audience be added to the first group of users currently designated to receive the content of the content provider.

17 . The non-transitory computer-readable medium of claim 15 , wherein the increase in the number of user actions related to the content of the content provider is predicted using an estimation function.

18 . The non-transitory computer-readable medium of claim 17 , wherein the estimation function comprises one or more formulas that use a plurality of parameters and a plurality of corresponding weights, wherein the plurality of parameters comprises a number of users in the second group that are not currently designated to receive the content of the content provider, an estimated number of presentations of the content of the content provider to the second group of users, and an estimated number of user actions related to the content of the content provider by the second group of users.

19 . The non-transitory computer-readable medium of claim 18 , the operations further comprising:

determining content related metrics of the first group of users and content related metrics of a third group of users, wherein the first group of users and the third group of users were previously designated to receive the content of the content provider in the past;

using the determined content related metrics of the first group of users and the estimation function, obtaining estimated content related metrics of the third group of users; and

modifying the plurality of weights of the estimation function based on a comparison of the estimated content related metrics of the third group of users and the determined content related metrics of the third group of users.

Assignments (1)

Continuity (2)

References Cited (29)