IP Library Granted Patent US 11,604,815
Granted Patent B2
US 11,604,815 · App. 16/792,122 · Granted Mar 14, 2023

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 11,604,815
App. No.
16/792,122
Granted
Mar 14, 2023
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 (71)

1. A computer-implemented method comprising:

obtaining, by a computing system, a set of videos;

determining, by the computing system, a list of characters that are depicted in videos of the set of videos;

determining, by the computing system, values of at least one character attribute for respective characters of the list of characters;

determining, by the computing system, a first salience value for a first character of the list of characters, wherein the first character is depicted in a first video of the set of videos, and wherein the first salience value is a salience of the first character relative to other characters in the first video;

determining, by the computing system, a first attribute-level value for the first video based on the first salience value and a first value of a first character attribute for the first character;

determining, by the computing system, a second salience value for a second character of the list of characters, wherein the second character is depicted in a second video of the set of videos, and wherein the second salience value is a salience of the second character relative to other characters in the second video;

determining, by the computing system, a second attribute-level value for the second video based on the second salience value and a second value of a second character attribute for the second character;

indexing, by the computing system, the first video and the second video using the first attribute-level value and the second attribute-level value so as to obtain an index of videos;

storing, by the computing system, the index of videos in a database; and

retrieving, by the computing system, information for at least one video of the set of videos using the index of videos.

2. The computer-implemented method of claim 1 , further comprising:

indexing, by the computing system, the respective characters using the values of the at least one character attribute for the respective characters; and

retrieving, by the computing system, information for at least one character depicted in a video of the set of videos using the index of characters.

3. The computer-implemented method of claim 2 , wherein retrieving the information for the at least one character comprises:

determining a character identifier for the at least one character; and

retrieving a value of a character attribute for the at least one character using the character identifier and the index of characters.

4. The computer-implemented method of claim 2 , wherein determining the first attribute-level value comprises:

aggregating the first value of the character attribute for the first character and a third value of the first character attribute for a third character of the first video.

5. The computer-implemented method of claim 4 , wherein aggregating the first value of the first character attribute and the third value of the first character attribute comprises aggregating the first value of the first character attribute and the third value of the first character attribute using a capture function.

6. The computer-implemented method of claim 5 , wherein the capture function comprises a maximum of the first value of the first character attribute and the third value of the first character attribute.

7. The computer-implemented method of claim 5 , wherein the capture function comprises a salience weighted average of the first value of the first character attribute and the third value of the first character attribute.

8. The computer-implemented method of claim 2 , further comprising altering a ranking of the video within a list of videos based on the information for the at least one character depicted in the video.

9. The computer-implemented method of claim 2 , further comprising:

obtaining a query that specifies a value of a third character attribute and a value of a fourth character attribute; and

identifying, using the index of characters, one or more characters having the value of the third character attribute and the value of the fourth character attribute.

10. The computer-implemented method of claim 9 , further comprising:

identifying a video that depicts a character of the one or more characters; and

providing an indication of the video for display.

11. The computer-implemented method of claim 1 , wherein determining the values of the at least one character attribute for the respective characters of the list of characters comprises:

accessing a database that stores character models for the respective characters; and

retrieving the values of the at least one character attribute from the character models.

12. The computer-implemented method of claim 1 , wherein the at least one character attribute comprises a social trait, a physical trait, or an intellectual trait.

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

obtaining a set of videos;

determining a list of characters that are depicted in videos of the set of videos;

determining values of at least one character attribute for respective characters of the list of characters;

determining a first salience value for a first character of the list of characters, wherein the first character is depicted in a first video of the set of videos, and wherein the first salience value is a salience of the first character relative to other characters in the first video;

determining a first attribute-level value for the first video based on the first salience value and a first value of a first character attribute for the first character;

determining a second salience value for a second character of the list of characters, wherein the second character is depicted in a second video of the set of videos, and wherein the second salience value is a salience of the second character relative to other characters in the second video;

determining a second attribute-level value for the second video based on the second salience value and a second value of a second character attribute for the second character;

indexing first video and the second video using the first attribute-level value and the second attribute-level value so as to obtain an index of videos; and

storing the index of videos in a database; and

retrieving information for at least one video of the set of videos using the index of videos.

14. The computing system of claim 13 , wherein the set of acts further comprises:

indexing the respective characters using the values of the at least one character attribute for the respective characters; and

retrieving information for at least one character depicted in a video of the set of videos using the index of characters.

15. The computing system of claim 14 , wherein retrieving the information for the at least one character comprises:

determining a character identifier for the at least one character; and

retrieving a value of a character attribute for the at least one character using the character identifier and the index of characters.

16. The computing system of claim 14 , wherein the set of acts further comprises altering a ranking of the video within a list of videos based on the information for the at least one character depicted in the video.

17. The computing system of claim 14 , wherein the set of acts further comprises:

obtaining a query that specifies a value of a third character attribute and a value of a fourth character attribute; and

identifying, using the index of characters, one or more characters having the value of the third character attribute and the value of the fourth character attribute.

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

obtaining a set of videos;

determining a list of characters that are depicted in videos of the set of videos;

determining values of at least one character attribute for respective characters of the list of characters;

determining a first salience value for a first character of the list of characters, wherein the first character is depicted in a first video of the set of videos, and wherein the first salience value is a salience of the first character relative to other characters in the first video;

determining a first attribute-level value for the first video based on the first salience value and a first value of a first character attribute for the first character;

determining a second salience value for a second character of the list of characters, wherein the second character is depicted in a second video of the set of videos, and wherein the second salience value is a salience of the second character relative to other characters in the second video;

determining a second attribute-level value for the second video based on the second salience value and a second value of a second character attribute for the second character;

indexing first video and the second video using the first attribute-level value and the second attribute-level value so as to obtain an index of videos;

storing the index of videos in a database; and

retrieving information for at least one video in the set of videos using the index of videos.

19. The non-transitory computer-readable medium of claim 18 , wherein the set of acts further comprises:

indexing the respective characters using the values of the at least one character attribute for the respective characters; and

retrieving information for at least one character depicted in a video of the set of videos using the index of characters.

20. The non-transitory computer-readable medium of claim 19 , wherein the set of acts further comprises:

obtaining a query that specifies a value of a third character attribute and a value of a fourth character attribute; and

identifying, using the index of characters, one or more characters having the value of the third character attribute and the value of the fourth character attribute.

Assignments (9)
RELEASE (REEL 054066 / FRAME 0064) Recorded May 11, 2023
From: CITIBANK, N.A.
To: GRACENOTE, INC.; A. C. NIELSEN COMPANY, LLC; EXELATE, 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 Feb 14, 2020
From: BHATT, MEGHANA; PAYNE, RACHEL; MOHANTY, NATASHA
To: FEM, INC.
Reel/Frame 051827/0213 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 14, 2020
From: FEM, INC.
To: THE NIELSEN COMPANY (US), LLC
Reel/Frame 051827/0228 →