IP Library › Granted Patent US 10,140,372
Granted Patent B2
US 10,140,372 · App. 13/611,740 · Granted Nov 27, 2018

User profile based on clustering tiered descriptors

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Quick Facts
Patent No.
US 10,140,372
App. No.
13/611,740
Granted
Nov 27, 2018
Kind
B2
Abstract

A user of a network-based system may correspond to a user profile that describes the user. The user profile may describe the user using one or more descriptors of items that correspond to the user (e.g., items owned by the user, items liked by the user, or items rated by the user). In some situations, such a user profile may be characterized as a “taste profile” that describes an array or distribution of one or more tastes, preferences, or habits of the user. Accordingly, the user profile machine within the network-based system may generate the user profile by accessing descriptors of items that correspond to the user, clustering one or more of the descriptors, and generating the user profile based on one or more clusters of the descriptors.

Claims (166)

1. A method comprising:

accessing, by executing an instruction with a processor, descriptors in metadata that is descriptive of a first item and of a second item, the descriptors and metadata corresponding to a metadata model that organizes the descriptors into multiple tiers of the metadata model, the descriptors including a first descriptor of the first item and a second descriptor of the second item;

accessing, from a database communicatively coupled to the processor, the metadata model that organizes the descriptors into the multiple tiers;

creating, by executing an instruction with the processor, a group of descriptors by grouping the accessed first and second descriptors into the group of descriptors based on the accessed first and second descriptors being both represented in a same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors;

accessing, via a device of a user communicatively coupled to the processor via a network, biometric data including a heart rate of the user;

determining, by executing an instruction with the processor, a first activity in which the user is engaged based on contextual data that correlates the first item and the second item with multiple locations of the user and the biometric data of the user received from the device of the user via the network;

generating, by executing an instruction with the processor, a user profile based on the first activity of the user and the created group of descriptors into which the first and second descriptors were grouped based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors;

storing the group within the user profile as corresponding to the first activity determined based on the multiple locations and the biometric data of the user; and

recommending, by executing an instruction with the processor and in response to a second activity of the user matching the first activity associated with the group within the user profile, a third item based on the user profile, the user profile generated based on the created group of descriptors into which the first and second descriptors were grouped, the grouping performed based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model.

2. The method of claim 1 , wherein:

the first item and the second item are specimens of a collection of items that belong to the user.

3. The method of claim 2 , wherein:

the collection of items is a media library of the user;

the first item is a first media file in the media library of the user; and

the second item is a second media file in the media library of the user.

4. The method of claim 1 , wherein the metadata model organizes the descriptors into a hierarchy of descriptors that includes the multiple tiers of the metadata model.

5. The method of claim 1 , wherein the grouping of the first descriptor and the second descriptor into the group of descriptors includes determining that the first descriptor and the second descriptor match each other.

6. The method of claim 5 , further including determining a name of the group based on the first descriptor being determined to match the second descriptor, and wherein the generating of the user profile includes storing the name of the group within the user profile as a taste descriptor that describes a taste of the user that corresponds to the user profile.

7. The method of claim 1 , wherein the grouping of the first descriptor and the second descriptor into the group of descriptors includes determining that the first descriptor and the second descriptor are similar to each other.

8. The method of claim 7 , further including:

determining a name of the group based on a third descriptor of the first item being determined to match a fourth descriptor of the second item; and wherein

the generating of the user profile includes storing the name of the group within the user profile as a taste descriptor that describes a taste of the user that corresponds to the user profile.

9. The method of claim 1 , wherein:

the metadata model indicates that the tier within which the first descriptor and the second descriptor are represented has a weight that exceeds a further weight of a further tier among the multiple tiers of the metadata model; and

the grouping of the first descriptor and the second descriptor into the group of descriptors is based on the weight of the tier exceeding the further weight of the further tier.

10. The method of claim 9 , wherein the metadata model includes a hierarchy of the multiple tiers in which a top tier is weighted lower than the weight of the tier in which the first descriptor and the second descriptor are represented.

11. The method of claim 1 , further including:

determining the first activity of the user based on contextual data that correlates the first item and the second item with a day of week and a time of day; and wherein

the generating of the user profile includes storing a name of the group within the user profile as corresponding to the first activity determined based on the day of week and the time of day.

12. The method of claim 1 , further including:

determining an anomalous phase of the user based on contextual data that correlates the first item and the second item with a time period that has a duration shorter than a threshold duration; and wherein

the generating of the user profile includes omitting a name of the group from the user profile.

13. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to at least:

access descriptors in metadata that is descriptive of a first item and of a second item, the descriptors and metadata corresponding to a metadata model that organizes the descriptors into multiple tiers of the metadata model, the descriptors including a first descriptor of the first item and a second descriptor of the second item;

access, from a database communicatively coupled to the machine, the metadata model that organizes the descriptors into the multiple tiers;

create a group of descriptors by grouping the accessed first and second descriptors into the group of descriptors based on the accessed first and second descriptors being both represented in a same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors, the grouping being performed by the one or more processors of the machine;

access, via a device of a user communicatively coupled to the machine via a network, biometric data including a heart rate of the user;

determine a first activity in which the user is engaged based on contextual data that correlates the first item and the second item with multiple locations of the user and the biometric data of the user received from the device of the user via the network;

generate a user profile based on the first activity of the user and the created group of descriptors into which the first and second descriptors were grouped based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors;

store the group within the user profile as corresponding to the first activity determined based on the multiple locations and the biometric data of the user; and

recommend, in response to a second activity of the user matching the first activity associated with the group within the user profile, a third item based on the user profile, the user profile generated based on the created group of descriptors into which the first and second descriptors were grouped, the grouping performed based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model.

14. The non-transitory machine-readable storage medium of claim 13 , wherein:

the metadata model indicates that the tier within which the first descriptor and the second descriptor are represented has a weight that exceeds a further weight of a further tier among the multiple tiers of the metadata model; and

the grouping of the first descriptor and the second descriptor into the group of descriptors is based on the weight of the tier exceeding the further weight of the further tier.

15. A system comprising:

an access module to:

access descriptors in metadata that is descriptive of a first item and of a second item, the descriptors and metadata corresponding to a metadata model that organizes the descriptors into multiple tiers of the metadata model, the descriptors including a first descriptor of the first item and a second descriptor of the second item; and

access, from a database communicatively coupled to the access module, the metadata model that organizes the descriptors into the multiple tiers;

a cluster module to create a group of descriptors by grouping the accessed first and second descriptors into the group of descriptors based on the first descriptor and the second descriptor being both represented in a same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors;

a context module to access, via a device of a user communicatively coupled to the context module via a network, biometric data including a heart rate of the user;

a correlation module to determine a first activity in which the user is engaged based on contextual data that correlates the first item and the second item with multiple locations of the user and the biometric data of the user received from the device of the user via the network;

a profile module to:

generate a user profile based on the first activity of the user and the created group of descriptors into which the first and second descriptors were grouped based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors; and

store the group within the user profile as corresponding to the first activity determined based on the multiple locations and the biometric data of the user; and

a recommender to, in response to a second activity of the user matching the first activity associated with the group within the user profile, recommend a third item based on the user profile, the user profile generated based on the created group of descriptors into which the first and second descriptors were grouped, the grouping performed based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model, at least one of the access module, the cluster module, the context module, the correlation module, or the recommender is implemented by one or more hardware processors.

16. The system of claim 15 , wherein:

the metadata model indicates that the tier within which the first descriptor and the second descriptor are represented has a weight that exceeds a further weight of a further tier among the multiple tiers of the metadata model; and

the cluster module configures the processor to group the first descriptor and the second descriptor into the group of descriptors based on the weight of the tier exceeding the further weight of the further tier.

17. A method comprising:

accessing, by executing an instruction with a processor, descriptors in metadata that is descriptive of a first item and of a second item, the descriptors and metadata corresponding to a metadata model that organizes the descriptors into multiple tiers of the metadata model, the descriptors including a first descriptor of the first item and a second descriptor of the second item;

accessing, from a database communicatively coupled to the processor, the metadata model that organizes the descriptors into the multiple tiers;

creating, by executing an instruction with the processor, a group of descriptors by grouping the accessed first and second descriptors into the group of descriptors based on the accessed first and second descriptors being both represented in a same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors, the grouping being performed by a processor of a machine;

accessing, via a device of a user communicatively coupled to the processor via a network, biometric data including a heart rate of the user;

determining, by executing an instruction with the processor, a first activity in which the user is engaged based on contextual data that correlates the first item and the second item with a day of week and a time of day and the biometric data of the user received from the device of the user via the network;

generating, by executing an instruction with the processor, a user profile based on the first activity of the user and the created group of descriptors into which the first and second descriptors were grouped based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors;

storing the group within the user profile as corresponding to the first activity determined based on the day of week and the time of day and the biometric data of the user; and

recommending, by executing an instruction with the processor and in response to a second activity of the user matching the first activity associated with the group within the user profile, a third item based on the user profile, the user profile generated based on the created group of descriptors into which the first and second descriptors were grouped, the grouping performed based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model.

18. The method of claim 17 , wherein the first item and the second item are specimens of a collection of items that belong to the user.

19. The method of claim 18 , wherein:

the collection of items is a media library of the user;

the first item is a first media file in the media library of the user; and

the second item is a second media file in the media library of the user.

20. The method of claim 17 , wherein the metadata model organizes the descriptors into a hierarchy of descriptors that includes the multiple tiers of the metadata model.

21. The method of claim 17 , wherein the grouping of the first descriptor and the second descriptor into the group of descriptors includes determining that the first descriptor and the second descriptor match each other.

22. The method of claim 21 , further including:

determining a name of the group based on the first descriptor being determined to match the second descriptor, and wherein

the generating of the user profile includes storing the name of the group within the user profile as a taste descriptor that describes a taste of the user that corresponds to the user profile.

23. The method of claim 17 , wherein the grouping of the first descriptor and the second descriptor into the group of descriptors includes determining that the first descriptor and the second descriptor are similar to each other.

24. The method of claim 23 , further including:

determining a name of the group based on a third descriptor of the first item being determined to match a fourth descriptor of the second item; and wherein

the generating of the user profile includes storing the name of the group within the user profile as a taste descriptor that describes a taste of the user that corresponds to the user profile.

25. The method of claim 17 , wherein:

the metadata model indicates that the tier within which the first descriptor and the second descriptor are represented has a weight that exceeds a further weight of a further tier among the multiple tiers of the metadata model; and

the grouping of the first descriptor and the second descriptor into the group of descriptors is based on the weight of the tier exceeding the further weight of the further tier.

26. The method of claim 25 , wherein the metadata model includes a hierarchy of the multiple tiers in which a top tier is weighted lower than the weight of the tier in which the first descriptor and the second descriptor are represented.

27. The method of claim 17 , further including:

determining an anomalous phase of the user based on contextual data that correlates the first item and the second item with a time period that has a duration shorter than a threshold duration; and wherein

the generating of the user profile includes omitting a name of the group from the user profile.

28. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to at least:

access descriptors in metadata that is descriptive of a first item and of a second item, the descriptors and metadata corresponding to a metadata model that organizes the descriptors into multiple tiers of the metadata model, the descriptors including a first descriptor of the first item and a second descriptor of the second item;

access, from a database communicatively coupled to the machine, the metadata model that organizes the descriptors into the multiple tiers;

create a group of descriptors by grouping the accessed first and second descriptors into the group of descriptors based on the accessed first and second descriptors being both represented in a same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors, the grouping being performed by the one or more processors of the machine;

access, via a device of a user communicatively coupled to the machine via a network, biometric data including a heart rate of the user;

determine a first activity in which the user is engaged based on contextual data correlates the first item and the second item with a day of week and a time of day and the biometric data of the user received from the device of the user via the network;

generate a user profile based on the first activity of the user and the created group of descriptors into which the first and second descriptors were grouped based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors;

store the group within the user profile as corresponding to the first activity determined based on the day of week and the time of day and the biometric data of the user; and

recommend, in response to a second activity of the user matching the first activity associated with the group within the user profile, a third item based on the user profile, the user profile generated based on the created group of descriptors into which the first and second descriptors were grouped, the grouping performed based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model.

29. The non-transitory machine-readable storage medium of claim 28 , wherein:

the metadata model indicates that the tier within which the first descriptor and the second descriptor are represented has a weight that exceeds a further weight of a further tier among the multiple tiers of the metadata model; and

the grouping of the first descriptor and the second descriptor into the group of descriptors is based on the weight of the tier exceeding the further weight of the further tier.

30. A system comprising:

an access module to:

access descriptors in metadata that is descriptive of a first item and of a second item, the descriptors and metadata corresponding to a metadata model that organizes the descriptors into multiple tiers of the metadata model, the descriptors including a first descriptor of the first item and a second descriptor of the second item; and

access, from a database communicatively coupled to the access module, the metadata model that organizes the descriptors into the multiple tiers;

a cluster module to create a group of descriptors by grouping the accessed first and second descriptors into the group of descriptors based on the first descriptor and the second descriptor being both represented in a same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors;

a context module to access, via a device of a user communicatively coupled to the context module via a network, biometric data including a heart rate of the user;

a correlation module to determine a first activity in which the user is engaged based on contextual data that correlates the first item and the second item with a day of week and a time of day and the biometric data of the user received from the device of the user via the network;

a profile module to:

generate a user profile based on the first activity of the user and the created group of descriptors into which the first and second descriptors were grouped based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors; and

store the group within the user profile as corresponding to the activity determined based on the day of week and the time of day and the biometric data of the user; and

a recommender to, in response to a second activity of the user matching the first activity associated with the group within the user profile, recommend a third item based on the user profile, the user profile generated based on the created group of descriptors into which the first and second descriptors were grouped, the grouping performed based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model, at least one of the access module, the cluster module, the context module, the correlation module, or the recommender is implemented by one or more hardware processors.

31. The system of claim 30 , wherein:

the metadata model indicates that the tier within which the first descriptor and the second descriptor are represented has a weight that exceeds a further weight of a further tier among the multiple tiers of the metadata model; and

the cluster module configures the processor to group the first descriptor and the second descriptor into the group of descriptors based on the weight of the tier exceeding the further weight of the further tier.

32. A method comprising:

accessing, by executing an instruction with a processor, descriptors in metadata that is descriptive of a first item and of a second item, the descriptors and metadata corresponding to a metadata model that organizes the descriptors into multiple tiers of the metadata model, the descriptors including a first descriptor of the first item and a second descriptor of the second item;

accessing, from a database communicatively coupled to the processor, the metadata model that organizes the descriptors into the multiple tiers;

creating, by executing an instruction with the processor, a group of descriptors by grouping the accessed first and second descriptors into the group of descriptors based on the accessed first and second descriptors being both represented in a same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors, the grouping being performed by a processor of a machine;

accessing, via a device of a user communicatively coupled to the processor via a network, biometric data including a heart rate of the user;

determining, by executing an instruction with the processor, an anomalous phase of the user based on contextual data that correlates the first item and the second item with a time period that has a duration shorter than a threshold duration and the biometric data of the user received from the device of the user via the network;

generating, by executing an instruction with the processor, a user profile based on the anomalous phase of the user and the created group of descriptors into which the first and second descriptors were grouped based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors, the user profile omitting a name of the group; and

recommending, by executing an instruction with the processor, a third item based on the user profile, the user profile generated based on the created group of descriptors into which the first and second descriptors were grouped, the grouping performed based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model.

33. The method of claim 32 , wherein the first item and the second item are specimens of a collection of items that belong to the user.

34. The method of claim 33 , wherein:

the collection of items is a media library of the user;

the first item is a first media file in the media library of the user; and

the second item is a second media file in the media library of the user.

35. The method of claim 32 , wherein the metadata model organizes the descriptors into a hierarchy of descriptors that includes the multiple tiers of the metadata model.

36. The method of claim 32 , wherein the grouping of the first descriptor and the second descriptor into the group of descriptors includes determining that the first descriptor and the second descriptor match each other.

37. The method of claim 36 further including:

determining a name of the group based on the first descriptor being determined to match the second descriptor, and wherein

the generating of the user profile includes storing the name of the group within the user profile as a taste descriptor that describes a taste of the user that corresponds to the user profile.

38. The method of claim 32 , wherein the grouping of the first descriptor and the second descriptor into the group of descriptors includes determining that the first descriptor and the second descriptor are similar to each other.

39. The method of claim 38 , further including:

determining a name of the group based on a third descriptor of the first item being determined to match a fourth descriptor of the second item; and wherein

the generating of the user profile includes storing the name of the group within the user profile as a taste descriptor that describes a taste of the user that corresponds to the user profile.

40. The method of claim 32 , wherein:

the metadata model indicates that the tier within which the first descriptor and the second descriptor are represented has a weight that exceeds a further weight of a further tier among the multiple tiers of the metadata model; and

the grouping of the first descriptor and the second descriptor into the group of descriptors is based on the weight of the tier exceeding the further weight of the further tier.

41. The method of claim 40 , wherein the metadata model includes a hierarchy of the multiple tiers in which a top tier is weighted lower than the weight of the tier in which the first descriptor and the second descriptor are represented.

42. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to at least:

access descriptors in metadata that is descriptive of a first item and of a second item, the descriptors and metadata corresponding to a metadata model that organizes the descriptors into multiple tiers of the metadata model, the descriptors including a first descriptor of the first item and a second descriptor of the second item;

access, from a database communicatively coupled to the machine, the metadata model that organizes the descriptors into the multiple tiers;

create a group of descriptors by grouping the accessed first and second descriptors into the group of descriptors based on the accessed first and second descriptors being both represented in a same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors, the grouping being performed by the one or more processors of the machine;

access, via a device of a user communicatively coupled to the machine via a network, biometric data including a heart rate of the user;

determine an anomalous phase of the user based on contextual data that correlates the first item and the second item with a time period that has a duration shorter than a threshold duration and the biometric data of the user received from the device of the user via the network;

generate a user profile based on the anomalous phase of the user and the created group of descriptors into which the first and second descriptors were grouped based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors, the user profile omitting a name of the group; and

recommend a third item based on the user profile, the user profile generated based on the created group of descriptors into which the first and second descriptors were grouped, the grouping performed based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model.

43. The non-transitory machine-readable storage medium of claim 42 , wherein:

the metadata model indicates that the tier within which the first descriptor and the second descriptor are represented has a weight that exceeds a further weight of a further tier among the multiple tiers of the metadata model; and

the grouping of the first descriptor and the second descriptor into the group of descriptors.

44. A system comprising:

an access module to:

access descriptors in metadata that is descriptive of a first item and of a second item, the descriptors and metadata corresponding to a metadata model that organizes the descriptors into multiple tiers of the metadata model, the descriptors including a first descriptor of the first item and a second descriptor of the second item; and

access, from a database communicatively coupled to the access module, the metadata model that organizes the descriptors into the multiple tiers;

a cluster module to create a group of descriptors by grouping the accessed first and second descriptors into the group of descriptors based on the first descriptor and the second descriptor being both represented in a same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors;

a context module to access, via a device of a user communicatively coupled to the context module via a network, biometric data including a heart rate of the user;

a correlation module to determine an anomalous phase of the user based on contextual data that correlates the first item and the second item with a time period that has a duration shorter than a threshold duration and the biometric data of the user received from the device of the user via the network;

a profile module to generate a user profile based on the anomalous phase of the user and the created group of descriptors into which the first and second descriptors were grouped based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model that corresponds to the first and second descriptors, the user profile omitting a name of the group; and

a recommender to recommend a third item based on the user profile, the user profile generated based on the created group of descriptors into which the first and second descriptors were grouped, the grouping performed based on the first and second descriptors being both represented in the same tier among the multiple tiers of the accessed metadata model, at least one of the access module, the cluster module, the context module, the correlation module, or the recommender is implemented by one or more hardware processors.

45. The system of claim 44 , wherein:

the metadata model indicates that the tier within which the first descriptor and the second descriptor are represented has a weight that exceeds a further weight of a further tier among the multiple tiers of the metadata model; and

the cluster module configures the processor to group the first descriptor and the second descriptor into the group of descriptors based on the weight of the tier exceeding the further weight of the further tier.

46. The method of claim 1 , wherein the biometric data includes at least one of a blood pressure of the user, a temperature of the user, or a galvanic skin response of the user.

47. The method of claim 1 , wherein the multiple locations of the user are determined by processing geo-location data, the geo-location data received from the device of the user.

48. The method of claim 1 , wherein the first and second activity of the user includes at least one of jogging, commuting, resting, waking up, or exercising.

Assignments (12)
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 →
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 →
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 →
RELEASE (REEL 042262 / FRAME 0601) Recorded Oct 13, 2022
From: CITIBANK, N.A.
To: GRACENOTE, INC.; GRACENOTE DIGITAL VENTURES, LLC
Reel/Frame 061748/0001 →
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 →
SUPPLEMENTAL SECURITY AGREEMENT Recorded Apr 13, 2017
From: GRACENOTE, INC.; GRACENOTE MEDIA SERVICES, LLC; GRACENOTE DIGITAL VENTURES, LLC
To: CITIBANK, N.A., AS COLLATERAL AGENT
Reel/Frame 042262/0601 →
RELEASE OF SECURITY INTEREST IN PATENT RIGHTS Recorded Feb 8, 2017
From: JPMORGAN CHASE BANK, N.A.
To: GRACENOTE, INC.; CASTTV INC.; TRIBUNE MEDIA SERVICES, LLC; TRIBUNE DIGITAL VENTURES, LLC
Reel/Frame 041656/0804 →
SECURITY INTEREST Recorded Mar 19, 2014
From: GRACENOTE, INC.
To: JPMORGAN CHASE BANK, N.A., AS COLLATERAL AGENT
Reel/Frame 032480/0272 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 12, 2012
From: POPP, PHILLIP; CHEN, CHING-WEI; DIMARIA, PETER C.; CREMER, MARKUS K.
To: GRACENOTE, INC.
Reel/Frame 028946/0490 →