IP Library › Granted Patent US 12,282,938
Granted Patent B2
US 12,282,938 · App. 17/962,051 · Granted Apr 22, 2025

Methods and apparatus for generating information about portable device advertising

Inventors: Tamara Gaffney (Belmont, CA); Kanishka Agarwal (San Francisco, CA)
Assignee: The Nielsen Company (US), LLC
G06Q30/0269G06F9/455G06Q30/02
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Quick Facts
Patent No.
US 12,282,938
App. No.
17/962,051
Granted
Apr 22, 2025
Kind
B2
Abstract

Example methods, apparatus and articles of manufacture are disclosed herein to generate information about portable device advertising. An example apparatus includes memory and at least one processor to execute machine readable instructions to, in response to one or more commands from a simulation processor external to the at least one processor, access data via the Internet based on a usage profile, the usage profile representative of portable device usage by persons having similar usage characteristics, sample portions of the data to distinguish an advertisement from non-advertising content, determine an attribute of the advertisement; and transmit the attribute to the simulation processor.

Claims (57)

1. A computing system comprising:

a processor; and

a non-transitory computer-readable storage medium, having stored thereon program instructions that, upon execution by the processor, cause performance of a set of operations comprising:

causing a first client device to access first data via the Internet based on a first usage profile, wherein the first usage profile is representative of first client device usage by a first portion of a population, and wherein the first data comprises first advertising content and first non-advertising content;

causing a second client device to access second data via the Internet based on a second usage profile, wherein the second usage profile is representative of second client device usage by a second portion of the population, and wherein the second data comprises second advertising content and second non-advertising content;

identifying, from the first data, the first advertising content;

identifying, from the second data, the second advertising content;

generating first client device advertising information corresponding to the first portion of the population and the first advertising content accessed by the first client device; and

generating second client device advertising information corresponding to the second portion of the population and the second advertising content accessed by the second client device.

2. The computing system of claim 1 , wherein the set of operations further comprising:

identifying at least one characteristic by which to group the population; and

dividing the population into the first portion and the second portion based on the at least one characteristic.

3. The computing system of claim 1 , wherein the set of operations further comprise forming a first group of client devices to simulate the first client device usage by the first portion of the population, and wherein the first group of the client devices comprises the first client device.

4. The computing system of claim 1 , wherein the set of operations further comprise identifying a first attribute of the first advertising content, and wherein the first attribute corresponds to at least one of a product, a service, or a genre.

5. The computing system of claim 1 , the set of operations further comprising:

accessing, from the first usage profile, at least one of a first location or a first time corresponding to the first client device usage; and

causing the first client device to access the first data based on the at least one of the first location or the first time.

6. The computing system of claim 1 ,

wherein the set of operations further comprise controlling the first client device to access the first data via the Internet based on a first type of usage of the first client device represented in the first usage profile; and

wherein the first type of usage of the first client device comprises at least one of Internet-related usage, television-related usage, gaming-related usage, download-related usage, media playback related usage, or messaging-related usage.

7. A non-transitory computer-readable storage medium, having stored thereon program instructions that, upon execution by a processor, cause performance of a set of operations comprising:

causing a first client device to access first data via the Internet based on a first usage profile, wherein the first usage profile is representative of first client device usage by a first portion of a population, and wherein the first data comprises first advertising content and first non-advertising content;

causing a second client device to access second data via the Internet based on a second usage profile, wherein the second usage profile is representative of second client device usage by a second portion of the population, and wherein the second data comprises second advertising content and second non-advertising content;

identifying, from the first data, the first advertising content;

identifying, from the second data, the second advertising content;

generating first client device advertising information corresponding to the first portion of the population and the first advertising content accessed by the first client device; and

generating second client device advertising information corresponding to the second portion of the population and the second advertising content accessed by the second client device.

8. The non-transitory computer-readable storage medium of claim 7 , set of operations further comprising:

identifying at least one characteristic by which to group the population; and

dividing the population into the first portion and the second portion based on the at least one characteristic.

9. The non-transitory computer-readable storage medium of claim 7 , wherein the set of operations further comprise forming a first group of client devices to simulate the first client device usage by the first portion of the population, and wherein the first group of the client devices comprises the first client device.

10. The non-transitory computer-readable storage medium of claim 7 , wherein the set of operations further comprise identifying a first attribute of the first advertising content, the first attribute corresponding to at least one of a product, a service, or a genre.

11. The non-transitory computer-readable storage medium of claim 7 , the set of operations further comprising:

accessing, from the first usage profile, at least one of a first location or a first time corresponding to the first client device usage; and

causing the first client device to access the first data based on the at least one of the first location or the first time.

12. The non-transitory computer-readable storage medium of claim 7 , wherein the set of operations further comprise controlling the first client device to access the first data via the Internet based on a first type of usage of the first client device represented in the first usage profile, and wherein the first type of usage of the first client device comprises at least one of Internet-related usage, television-related usage, gaming-related usage, download-related usage, media playback related usage, or messaging-related usage.

13. A method comprising:

causing a first client device to access first data via the Internet based on a first usage profile, wherein the first usage profile is representative of first client device usage by a first portion of a population, and wherein the first data comprises first advertising content and first non-advertising content;

causing a second client device to access second data via the Internet based on a second usage profile, wherein the second usage profile is representative of second client device usage by a second portion of the population, and wherein the second data comprises second advertising content and second non-advertising content;

identifying, from the first data, the first advertising content;

identifying, from the second data, the second advertising content;

generating first client device advertising information corresponding to the first portion of the population and the first advertising content accessed by the first client device; and

generating second client device advertising information corresponding to the second portion of the population and the second advertising content accessed by the second client device.

14. The method of claim 13 , further comprising:

dividing the population into the first portion and the second portion based on at least one characteristic by which the population is to be grouped.

15. The method of claim 13 , further comprising:

forming a first group of client devices to simulate the first client device usage by the first portion of the population, the first group of the client devices including the first client device.

16. The method of claim 13 , further comprising:

identifying a first attribute of the first advertising content, the first attribute corresponding to at least one of a product, a service, or a genre.

17. The method of claim 13 , further comprising:

accessing, from the first usage profile, at least one of a first location or a first time corresponding to the first client device usage; and

causing the first client device to access the first data based on the at least one of the first location or the first time.

18. The method of claim 13 , further causing:

controlling the first client device to access the first data via the Internet based on a first type of usage of the first client device represented in the first usage profile, wherein the first type of usage of the first client device comprises at least one of Internet-related usage, television-related usage, gaming-related usage, download-related usage, media playback related usage, or messaging-related usage.

19. The computing system of claim 1 , wherein the identifying the first advertising content comprises comparing pixels of the first data to pixels of known advertisements to identify the first advertising content.

20. The computing system of claim 1 , wherein the identifying the first advertising content comprises comparing one or more frames of the first data to one or more frames of known advertisements to identify the first advertising content.

21. The computing system of claim 1 , wherein the first advertising content comprises a first advertisement; and wherein the identifying the first advertising content comprises comparing the first advertisement to one or more advertisements stored in a database.

Assignments (7)
SECURITY INTEREST Recorded May 8, 2023
From: GRACENOTE DIGITAL VENTURES, LLC; GRACENOTE MEDIA SERVICES, LLC; GRACENOTE, INC.; TNC (US) HOLDINGS, INC.; THE NIELSEN COMPANY (US), LLC
To: ARES CAPITAL CORPORATION
Reel/Frame 063574/0632 →
SECURITY INTEREST Recorded Apr 28, 2023
From: GRACENOTE DIGITAL VENTURES, LLC; GRACENOTE MEDIA SERVICES, LLC; GRACENOTE, INC.; TNC (US) HOLDINGS, INC.; THE NIELSEN COMPANY (US), LLC
To: CITIBANK, N.A.
Reel/Frame 063561/0381 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 4, 2023
From: NIELSEN MOBILE, LLC
To: THE NIELSEN COMPANY (US), LLC
Reel/Frame 063254/0847 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 4, 2023
From: GAFFNEY, TAMARA; AGARWAL, KANISHKA
To: TELEPHIA
Reel/Frame 063224/0179 →
CHANGE OF NAME Recorded Apr 4, 2023
From: TELEPHIA, INC.
To: NIELSEN MOBILE, INC.
Reel/Frame 063256/0761 →
CONVERSION AND NAME CHANGE Recorded Apr 4, 2023
From: NIELSEN MOBILE, INC.
To: NIELSEN MOBILE, LLC
Reel/Frame 063254/0729 →
SECURITY AGREEMENT Recorded Jan 31, 2023
From: GRACENOTE DIGITAL VENTURES, LLC; GRACENOTE MEDIA SERVICES, LLC; GRACENOTE, INC.; TNC (US) HOLDINGS, INC.; THE NIELSEN COMPANY (US), LLC
To: BANK OF AMERICA, N.A.
Reel/Frame 063560/0547 →
Continuity (5)
Continuation 17164557 · Feb 1, 2021
Continuation 16215134 · Dec 10, 2018
Continuation 11805497 · May 22, 2007
Provisional Application 60905678 · Mar 7, 2007
Related Publication 20230106415A1 · Apr 6, 2023
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Bamshad Mobasher, “Introduction,” Jul. 16, 1997, retrieved from http://maya.cs.depaul.edu/.about.mobasher/webminer/survey/node1.html, on Apr. 18, 2006, 2 pages. [cited by applicant]
Bamshad Mobasher, “A Taxonomy of Web Mining,” Jul. 16, 1997, retrieved from http://maya.cs.depaul.edu/.about.mobasher/webminer/survey/node2.html-, on Apr. 18, 2006, 1 page. [cited by applicant]
Bamshad Mobasher, “Web Content Mining,” Jul. 16, 1997, retrieved from http://maya.cs.depaul.edu/.about.mobasher/webminer/survey/node3.html, on Apr. 18, 2006, 1 page. [cited by applicant]
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Bamshad Mobasher, “Transaction Identification,” Jul. 16, 1997, retrieved from http://maya.cs.depaul.edu/.about.mobasher/webminer/survey/node12.html, on Apr. 18, 2006, 2 pages. [cited by applicant]
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Bamshad Mobasher, “Analysis of Discovered Patterns,” Jul. 16, 1997, retrieved from http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node18.html, on Apr. 18, 2006, 1 page. [cited by applicant]
Bamshad Mobasher, “Visualization Techniques,” Jul. 16, 1997, retrieved from http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node19.html, on Apr. 18, 2006, 1 page. [cited by applicant]
Bamshad Mobasher, “Data and Knowledge Querying,” Jul. 16, 1997, retrieved from http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node21.html, on Apr. 18, 2006, 1 page. [cited by applicant]
Bamshad Mobasher, “Usability Analysis,” Jul. 16, 1997, retrieved from http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node22.html, on Apr. 18, 2006, 1 page. [cited by applicant]
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Bamshad Mobasher, “Research Directions,” Jul. 16, 1997, retrieved from http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node24.html, on Apr. 18, 2006, 1 page. [cited by applicant]
Bamshad Mobasher, “Data Pre-Processing for Mining,” Jul. 16, 1997, retrieved from http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node25.html, on Apr. 18, 2006, 1 page. [cited by applicant]
Bamshad Mobasher, “The Mining Process,” Jul. 16, 1997, retrieved from http://maya.cs.depaul.edu/˜mobasher/webminer/survey/node26.html, on Apr. 18, 2006, 1 page. [cited by applicant]
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