IP Library Granted Patent US 11,188,573
Granted Patent B2
US 11,188,573 · App. 16/132,268 · Granted Nov 30, 2021

Character based media analytics

Inventors: Meghana Bhatt (Aliso Viejo, CA); Rachel Payne (Aliso Viejo, CA); Natasha Mohanty (Aliso Viejo, CA)
Assignee: The Nielsen Company (US), LLC
G06F16/288G06F16/43G06F16/435G06F16/44G06F16/7867G06F16/951
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Quick Facts
Patent No.
US 11,188,573
App. No.
16/132,268
Granted
Nov 30, 2021
Kind
B2
Abstract

Techniques for analyzing media content are described. One technique generally comprises performing a regression analysis for characters in a plurality of media content based on user demographics, content outcome measure, and character models. The technique determines an attribute of significance. In some embodiments, the technique selects media content for display that depicts a character having at least a threshold value of the attribute of significance. In some embodiments, the technique displays media analytics for the attribute of significance determined based on a value of the attribute of significance exceeding a threshold significance value.

Claims (59)

1. A computer-implemented method comprising:

determining a first outcome measure value of a desired outcome measure for a first sample media content;

determining a second outcome measure value of the desired outcome measure for a second sample media content;

accessing first respective character models of one or more characters represented in the first sample media content;

determining a first attribute value of a first attribute of the one or more characters represented in the first sample media content based on the first respective character models;

determining a second attribute value of a second attribute of the one or more characters represented in the first sample media content based on the first respective character models;

accessing second respective character models of one or more characters represented in the second sample media content;

determining a third attribute value of the first attribute of the one or more characters represented in the second sample media content based on the second respective character models;

determining a fourth attribute value of the second attribute of the one or more characters represented in the second sample media content based on the second respective character models;

performing a regression using the first outcome measure value, the second outcome measure value, the first attribute value, the second attribute value, the third attribute value, and the fourth attribute value, wherein performing the regression comprises estimating a statistical relationship between the desired outcome measure and the first attribute and a statistical relationship between the desired outcome measure and the second attribute;

determining, based on the regression, that the first attribute or the second attribute is an attribute of significance for the desired outcome measure;

determining that a character in a media content has at least a threshold value of the attribute of significance;

based on the character in the media content having at least the threshold value of the attribute of significance, selecting the media content from among a plurality of media content; and

providing the selected media content for display.

2. The computer-implemented method of claim 1 , further comprising determining a target demographic,

wherein determining that the first attribute or the second attribute is an attribute of significance comprises determining that the first attribute is an attribute of significance corresponding to the desired outcome measure and the target demographic, and

wherein providing the selected media content for display comprises providing the selected media content for display to a user based on the user having the target demographic.

3. The computer-implemented method of claim 1 , wherein determining that the first attribute or the second attribute is the attribute of significance comprises determining that the first attribute is the attribute of significance based on a regression value of the first attribute exceeding a threshold significance value.

4. The computer-implemented method of claim 1 , wherein the desired outcome measure is a click-through rate.

5. The computer-implemented method of claim 1 , wherein the desired outcome measure is a playback completion rate indicative of whether an interaction lasted for at least a threshold period of time.

6. The computer-implemented method of claim 1 , wherein the attribute of significance refers to a character quality, and wherein the character quality is a career, a demographic, a location, a social trait, a physical trait, or an intellectual trait.

7. The computer-implemented method of claim 1 , wherein the plurality of media content is a plurality of advertisements.

8. A non-transitory computer-readable medium having stored therein instructions that upon execution by a processor, cause performance of a set of acts comprising:

determining a first outcome measure value of a desired outcome measure for a first sample media content;

determining a second outcome measure value of the desired outcome measure for a second sample media content;

accessing first respective character models of one or more characters represented in the first sample media content;

determining a first attribute value of a first attribute of the one or more characters represented in the first sample media content based on the first respective character models;

determining a second attribute value of a second attribute of the one or more characters represented in the first sample media content based on the first respective character models;

accessing second respective character models of one or more characters represented in the second sample media content;

determining a third attribute value of the first attribute of the one or more characters represented in the second sample media content based on the second respective character models;

determining a fourth attribute value of the second attribute of the one or more characters represented in the second sample media content based on the second respective character models;

performing a regression using the first outcome measure value, the second outcome measure value, the first attribute value, the second attribute value, the third attribute value, and the fourth attribute value, wherein performing the regression comprises estimating a statistical relationship between the desired outcome measure and the first attribute and a statistical relationship between the desired outcome measure and the second attribute;

determining, based on the regression, that the first attribute or the second attribute is an attribute of significance for the desired outcome measure;

determining that a character in a media content has at least a threshold value of the attribute of significance;

based on the character in the media content having at least the threshold value of the attribute of significance, selecting the media content from among a plurality of media content; and

providing the selected media content for display.

9. The non-transitory computer-readable medium of claim 8 , wherein the set of acts further comprises determining a target demographic,

wherein determining that the first attribute or the second attribute is an attribute of significance comprises determining that the first attribute is an attribute of significance corresponding to the desired outcome measure and the target demographic, and

wherein providing the selected media content for display comprises providing the selected media content for display to a user based on the user having the target demographic.

10. The non-transitory computer-readable medium of claim 8 , wherein determining that the first attribute or the second attribute is the attribute of significance comprises determining that the first attribute is the attribute of significance based on a regression value of the first attribute exceeding a threshold significance value.

11. The non-transitory computer-readable medium of claim 8 , wherein the plurality of media content is a plurality of advertisements.

12. A computing system comprising a processor coupled to a memory, the computing system configured for performing a set of acts comprising:

determining a first outcome measure value of a desired outcome measure for a first sample media content;

determining a second outcome measure value of the desired outcome measure for a second sample media content;

accessing first respective character models of one or more characters represented in the first sample media content;

determining a first attribute value of a first attribute of the one or more characters represented in the first sample media content based on the first respective character models;

determining a second attribute value of a second attribute of the one or more characters represented in the first sample media content based on the first respective character models;

accessing second respective character models of one or more characters represented in the second sample media content;

determining a third attribute value of the first attribute of the one or more characters represented in the second sample media content based on the first respective character models;

determining a fourth attribute value of the second attribute of the one or more characters represented in the second sample media content based on the first respective character models;

performing a regression using the first outcome measure value, the second outcome measure value, the first attribute value, the second attribute value, the third attribute value, and the fourth attribute value, wherein performing the regression comprises estimating a statistical relationship between the desired outcome measure and the first attribute and a statistical relationship between the desired outcome measure and the second attribute;

determining, based on the regression, that the first attribute or the second attribute is an attribute of significance for the desired outcome measure;

determining that a character in a media content has at least a threshold value of the attribute of significance;

based on the character in the media content having at least the threshold value of the attribute of significance, selecting the media content from among a plurality of media content; and

providing the selected media content for display.

13. The computing system of claim 12 , wherein the set of acts further comprises determining a target demographic,

wherein determining that the first attribute or the second attribute is an attribute of significance comprises determining that the first attribute is an attribute of significance corresponding to the desired outcome measure and the target demographic, and

wherein providing the selected media content for display comprises providing the selected media content for display to a user based on the user having the target demographic.

14. The computing system of claim 12 , wherein determining that the first attribute or the second attribute is the attribute of significance comprises determining that the first attribute is the attribute of significance based on a regression value of the first attribute exceeding a threshold significance value.

Assignments (9)
RELEASE (REEL 054066 / FRAME 0064) Recorded May 11, 2023
From: CITIBANK, N.A.
To: A. C. NIELSEN COMPANY, LLC; EXELATE, INC.; GRACENOTE, INC.; GRACENOTE MEDIA SERVICES, LLC; THE NIELSEN COMPANY (US), LLC; NETRATINGS, LLC
Reel/Frame 063605/0001 →
RELEASE (REEL 053473 / FRAME 0001) Recorded May 11, 2023
From: CITIBANK, N.A.
To: A. C. NIELSEN COMPANY, LLC; EXELATE, INC.; GRACENOTE, INC.; GRACENOTE MEDIA SERVICES, LLC; THE NIELSEN COMPANY (US), LLC; NETRATINGS, LLC
Reel/Frame 063603/0001 →
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 →
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 →
CORRECTIVE ASSIGNMENT TO CORRECT THE PATENTS LISTED ON SCHEDULE 1 RECORDED ON 6-9-2020 PREVIOUSLY RECORDED ON REEL 053473 FRAME 0001. ASSIGNOR(S) HEREBY CONFIRMS THE SUPPLEMENTAL IP SECURITY AGREEMENT. Recorded Oct 7, 2020
From: A.C. NIELSEN (ARGENTINA) S.A.; A.C. NIELSEN COMPANY, LLC; ACN HOLDINGS INC.; ACNIELSEN CORPORATION; ACNIELSEN ERATINGS.COM; AFFINNOVA, INC.; ART HOLDING, L.L.C.; ATHENIAN LEASING CORPORATION; CZT/ACN TRADEMARKS, L.L.C.; EXELATE, INC.; GRACENOTE, INC.; GRACENOTE DIGITAL VENTURES, LLC; GRACENOTE MEDIA SERVICES, LLC; NETRATINGS, LLC; NIELSEN AUDIO, INC.; NIELSEN CONSUMER INSIGHTS, INC.; NIELSEN CONSUMER NEUROSCIENCE, INC.; NIELSEN FINANCE CO.; NIELSEN FINANCE LLC; NIELSEN INTERNATIONAL HOLDINGS, INC.; NIELSEN MOBILE, LLC; NMR INVESTING I, INC.; TCG DIVESTITURE INC.; TNC (US) HOLDINGS, INC.; THE NIELSEN COMPANY (US), LLC; VIZU CORPORATION; VNU MARKETING INFORMATION, INC.; NMR LICENSING ASSOCIATES, L.P.; NIELSEN HOLDING AND FINANCE B.V.; THE NIELSEN COMPANY B.V.; VNU INTERNATIONAL B.V.
To: CITIBANK, N.A
Reel/Frame 054066/0064 →
SUPPLEMENTAL SECURITY AGREEMENT Recorded Jun 9, 2020
From: A. C. NIELSEN COMPANY, LLC; ACN HOLDINGS INC.; ACNIELSEN CORPORATION; ACNIELSEN ERATINGS.COM; AFFINNOVA, INC.; ART HOLDING, L.L.C.; ATHENIAN LEASING CORPORATION; CZT/ACN TRADEMARKS, L.L.C.; EXELATE, INC.; GRACENOTE, INC.; GRACENOTE DIGITAL VENTURES, LLC; GRACENOTE MEDIA SERVICES, LLC; NETRATINGS, LLC; NIELSEN AUDIO, INC.; NIELSEN CONSUMER INSIGHTS, INC.; NIELSEN CONSUMER NEUROSCIENCE, INC.; NIELSEN FINANCE CO.; NIELSEN FINANCE LLC; NIELSEN INTERNATIONAL HOLDINGS, INC.; NIELSEN MOBILE, LLC; NIELSEN UK FINANCE I, LLC; NMR INVESTING I, INC.; TCG DIVESTITURE INC.; TNC (US) HOLDINGS, INC.; THE NIELSEN COMPANY (US), LLC; VIZU CORPORATION; VNU MARKETING INFORMATION, INC.; NMR LICENSING ASSOCIATES, L.P.; NIELSEN HOLDING AND FINANCE B.V.; THE NIELSEN COMPANY B.V.; VNU INTERNATIONAL B.V.
To: CITIBANK, N.A.
Reel/Frame 053473/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 14, 2018
From: BHATT, MEGHANA; PAYNE, RACHEL; MOHANTY, NATASHA
To: FEM, INC.
Reel/Frame 046883/0074 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 14, 2018
From: FEM, INC.
To: THE NIELSEN COMPANY (US), LLC
Reel/Frame 046883/0081 →