Providing Data and Analysis for Advertising on Networked Devices

• US 20160086215A1
• Filed: 09/23/2015
• Published: 03/24/2016
• Est. Priority Date: 09/23/2014
• Status: Active Grant

• Current Assignee: Adelphic LLC
• Original Assignee: Adelphic, Incorporated (Capital V LLC)
• Inventors: Wang, Changfeng Charles, Rydzewski, David
• Granted Patent: US 11,023,921 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0246 Traffic
• CPC Class Codes: G06Q 30/0275 Auctions
• CPC Class Codes: H04L 67/306 User profiles

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing data and analysis for advertising on networked devices. One of the methods includes creating a vector of identifiers representing an ad opportunity. The method includes linking data attributes that describe the ad opportunity to the identifiers. The method includes expressing the data attributes following predefined scheme of hierarchy. The method includes linking a taxonomy describing data attributes. The method includes obtaining outcome measurements of ad events associated with the ad opportunity. The method also includes associating user interaction events with the ad with at least one of the identifiers or data attributes associated with the identifier.

Claims

1. A computer-implemented method for modeling ad opportunities, comprising:

creating a vector of identifiers representing an ad opportunity, comprising;

linking data attributes that describe the ad opportunity to the identifiers;

expressing the data attributes following predefined scheme of hierarchy;

linking a taxonomy describing data attributes;

obtaining outcome measurements of ad events associated with the ad opportunity; and

associating user interaction events with the ad with at least one of the identifiers or data attributes associated with the identifier.

2. The computer-implemented method of claim 1, wherein the vector comprises a user identifier, a media identifier, location identifier, and an ad spot identifier.

3. The computer-implemented method of claim 2, wherein the media identifier is associated with at least one of a publisher, an application, a website, video source, and a media segment.

4. The computer-implemented method of claim 2, wherein the user identifier is associated with at least one of a set of device identifiers or media channel identifiers or signatures.

5. The computer-implemented method of claim 1, wherein the vector further comprises data attributes identifying one or more spot positions, each spot position identifying at least one of a screen position and a frame position;

wherein the screen position is at least one of a top location for display, a bottom location for display, sides for display, and

wherein the frame position is at least one of pre-roll, mid-roll, post-roll for video.

6. The computer-implemented method of claim 1, wherein the data attribute furthers contain an identifier, a name, source, usage preference, pricing information;

and

wherein the media identifier comprises at least one of publishers, application or site, and segments of the media;

wherein the user identifier is further be linked to a set of device and media specific identifiers.

7. A computer-implemented method for providing data for advertising opportunities comprising:

receiving data from plurality of supply sources, the data including at least one of historical data, static data, build data, and access data;

building profiles of ad opportunities;

receiving a request, the request including a plurality of attributes;

encoding the request according to the model;

processing the encoded request to retrieve profiles matching the plurality of attributes; and

returning the profiles.

8. The computer-implemented method of claim 7, further comprising qualifying the request by applying a plurality of rules to the request.

9. The computer-implemented method of claim 7, further comprising updating at least one profile according to the model.

10. The computer-implemented method of claim 7, wherein the supply sources comprises at least one of include networked end user devices and applications executing on such devices, such as a Web tag that is executed in a browser, a code segment in a mobile app, tracking services, output of Web crawler, a mobile application catalog, an ad server, a media publisher content server, a video content catalog, an offline data file, or a third party data provider service.

11. The computer-implemented method of claim 7, wherein the profiles comprise:

user data associated with a plurality of user identifier keys, at least one user identifier key linked to at least one of unique user identifiers and segment identifiers;

contextual data comprising media identifiers;

device data profiles comprising at least one of a model identifier;

location data profiles comprising at least one of a physical location description, a country, a state, a province, a city, a district, a street, a zip code, a building, a natural geographic characteristic, a lake, a river, a school, a business, a type of business, a demographic composition of the area; and

a network data profile.

12. The computer-implemented method of claim 7, wherein the request involves a grouping of data attributes and bid price;

and

the grouping rule is an index involving at least one of one or more of the data attributes and one or more metrics using the data attributes as input.

13. A computer-implemented method for providing analysis for advertising transactions comprising:

creating profiles for a plurality of ad requests by aggregating inventory metrics using one or more identifiers according to a model;

identifying a plurality of inventory vectors based on the profiles;

creating a profile database of ad performance comprising performance metrics for each inventory vector, associated data attributes, and ad identifiers;

providing predictions of ad performance metrics as function of time, index of inventory attributes, and the data attributes; and

providing a user interface to enable a user to query one or more metrics associated with the one or more identifiers.

14. The computer-implemented method of claim 13, wherein the inventory metrics comprise at least one of number of request, click through rate, conversions rate, prices, bid floors and the time horizon.

15. The computer-implemented method of claim 13, wherein the predictions comprise at least one of bid prices as function of index of inventory;

winning rate as a function of price and index of inventory, inventory identifiers, index of identifiers, time;

click through rate, conversion rate, and life time value.

16. The computer-implemented method of claim 15, wherein the predictions as associated a measure of accuracy and confidence level.

17. The computer-implemented method of claim 15, wherein one or more data attributes are assigned a value, and incremental value in using the data.

18. A non-transitory computer readable medium, encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations for modeling ad opportunities comprising:

creating a vector of identifiers representing an ad opportunity, comprising;

linking data attributes that describe the ad opportunity to the identifiers;

expressing the data attributes following predefined scheme of hierarchy;

linking a taxonomy describing data attributes;

obtaining outcome measurements of ad events associated with the ad opportunity; and

associating user interaction events with the ad with at least one of the identifiers or data attributes associated with the identifier.

19. The non-transitory computer readable medium of claim 18, wherein the vector comprises a user identifier, a media identifier, location identifier, and an ad spot identifier.

20. The non-transitory computer readable medium of claim 19, wherein the media identifier is associated with at least one of a publisher, an application, a website, video source, and a media segment.

21. The non-transitory computer readable medium of claim 19, wherein the user identifier is associated with at least one of a set of device identifiers or media channel identifiers or signatures.

22. The non-transitory computer readable medium of claim 18, wherein the vector further comprises data attributes identifying one or more spot positions, each spot position identifying at least one of a screen position and a frame position;

wherein the screen position is at least one of a top location for display, a bottom location for display, sides for display, and

wherein the frame position is at least one of pre-roll, mid-roll, post-roll for video.

23. The non-transitory computer readable medium of claim 18, wherein the data attribute furthers contain an identifier, a name, source, usage preference, pricing information;

and

wherein the media identifier comprises at least one of publishers, application or site, and segments of the media;

wherein the user identifier is further be linked to a set of device and media specific identifiers.

24. A non-transitory computer readable medium, encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations for providing data for advertising opportunities comprising:

receiving data from plurality of supply sources, the data comprising at least one of historical data, static data, build data, and access data;

building profiles of ad opportunities;

receiving a request, the request comprising a plurality of attributes;

encoding the request according to the model;

processing the encoded request to retrieve profiles matching the plurality of attributes; and

returning the profiles.

25. The non-transitory computer readable medium of claim 24, further comprising qualifying the request by applying a plurality of rules to the request.

26. The non-transitory computer readable medium of claim 24, further comprising updating at least one profile according to the model.

27. The non-transitory computer readable medium of claim 24, wherein the supply sources comprises at least one of include networked end user devices and applications executing on such devices, such as a Web tag that is executed in a browser, a code segment in a mobile app, tracking services, output of Web crawler, a mobile application catalog, an ad server, a media publisher content server, a video content catalog, an offline data file, or a third party data provider service.

28. The non-transitory computer readable medium of claim 24, wherein the profiles comprise:

user data associated with a plurality of user identifier keys, at least one user identifier key linked to at least one of unique user identifiers and segment identifiers;

contextual data comprising media identifiers;

device data profiles comprising at least one of a model identifier;

location data profiles comprising at least one of a physical location description, a country, a state, a province, a city, a district, a street, a zip code, a building, a natural geographic characteristic, a lake, a river, a school, a business, a type of business, a demographic composition of the area; and

a network data profile.

29. The non-transitory computer readable medium of claim 24, wherein the request involves a grouping of data attributes and bid price;

and

the grouping rule is an index involving at least one of one or more of the data attributes and one or more metrics using the data attributes as input.

30. A non-transitory computer readable medium, encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations for providing data for providing analysis for advertising transactions comprising:

creating profiles for a plurality of ad requests by;

aggregating inventory metrics using one or more identifiers according to a model;

identifying a plurality of inventory vectors based on the profiles;

creating a profile database of ad performance comprising performance metrics for each inventory vector, associated data attributes, and ad identifiers;

providing predictions of ad performance metrics as function of time, index of inventory attributes, and the data attributes; and

providing a user interface to enable a user to query one or more metrics associated with the one or more identifiers.

31. The non-transitory computer readable medium of claim 30, wherein the inventory metrics comprise at least one of number of request, click through rate, conversions rate, prices, bid floors and the time horizon.

32. The non-transitory computer readable medium of claim 30, wherein the predictions comprise at least one of bid prices as function of index of inventory;

winning rate as a function of price and index of inventory, inventory identifiers, index of identifiers, time;

click through rate, conversion rate, and life time value.

33. The non-transitory computer readable medium of claim 32, wherein the predictions as associated a measure of accuracy and confidence level.

34. The non-transitory computer readable medium of claim 32, wherein one or more data attributes are assigned a value, and incremental value in using the data.

35. A system for modeling ad opportunities, comprising:

one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising;

creating a vector of identifiers representing an ad opportunity, comprising;

linking data attributes that describe the ad opportunity to the identifiers;

expressing the data attributes following predefined scheme of hierarchy;

linking a taxonomy describing data attributes;

obtaining outcome measurements of ad events associated with the ad opportunity; and

associating user interaction events with the ad with at least one of the identifiers or data attributes associated with the identifier.

36. The system of claim 35, wherein the vector comprises a user identifier, a media identifier, location identifier, and an ad spot identifier.

37. The system of claim 36 wherein the media identifier is associated with at least one of a publisher, an application, a website, video source, and a media segment.

38. The system of claim 36, wherein the user identifier is associated with at least one of a set of device identifiers or media channel identifiers or signatures.

39. The system of claim 35, wherein the vector further comprises data attributes identifying one or more spot positions, each spot position identifying at least one of a screen position and a frame position;

wherein the screen position is at least one of a top location for display, a bottom location for display, sides for display, and

wherein the frame position is at least one of pre-roll, mid-roll, post-roll for video.

40. The system of claim 35, wherein the data attribute furthers contain an identifier, a name, source, usage preference, pricing information;

and

wherein the media identifier comprises at least one of publishers, application or site, and segments of the media;

wherein the user identifier is further be linked to a set of device and media specific identifiers.

41. A system for providing data for advertising opportunities comprising:

one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising;

receiving data from plurality of supply sources, the data comprising at least one of historical data, static data, build data, and access data;

building profiles of ad opportunities;

receiving a request, the request comprising a plurality of attributes;

encoding the request according to the model;

processing the encoded request to retrieve profiles matching the plurality of attributes; and

returning the profiles.

42. The system of claim 41, further comprising qualifying the request by applying a plurality of rules to the request.

43. The system of claim 41, further comprising updating at least one profile according to the model.

44. The system of claim 41, wherein the supply sources comprises at least one of include networked end user devices and applications executing on such devices, such as a Web tag that is executed in a browser, a code segment in a mobile app, tracking services, output of Web crawler, a mobile application catalog, an ad server, a media publisher content server, a video content catalog, an offline data file, or a third party data provider service.

45. The system of claim 41, wherein the profiles comprise:

user data associated with a plurality of user identifier keys, at least one user identifier key linked to at least one of unique user identifiers and segment identifiers;

contextual data comprising media identifiers;

device data profiles comprising at least one of a model identifier;

location data profiles comprising at least one of a physical location description, a country, a state, a province, a city, a district, a street, a zip code, a building, a natural geographic characteristic, a lake, a river, a school, a business, a type of business, a demographic composition of the area; and

a network data profile.

46. The system of claim 41, wherein the request involves a grouping of data attributes and bid price;

and

the grouping rule is an index involving at least one of one or more of the data attributes and one or more metrics using the data attributes as input.

47. A system for providing analysis for advertising transactions comprising

one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:

creating profiles for a plurality of ad requests by;

aggregating inventory metrics using one or more identifiers according to a model;

identifying a plurality of inventory vectors based on the profiles;

creating a profile database of ad performance comprising performance metrics for each inventory vector, associated data attributes, and ad identifiers;

providing predictions of ad performance metrics as function of time, index of inventory attributes, and the data attributes; and

providing a user interface to enable a user to query one or more metrics associated with the one or more identifiers.

48. The system of claim 47, wherein the inventory metrics comprise at least one of number of request, click through rate, conversions rate, prices, bid floors and the time horizon.

49. The system of claim 47, wherein the predictions comprise at least one of bid prices as function of index of inventory;

winning rate as a function of price and index of inventory, inventory identifiers, index of identifiers, time;

click through rate, conversion rate, and life time value.

50. The system of claim 49, wherein the predictions as associated a measure of accuracy and confidence level.

51. The system of claim 49, wherein one or more data attributes are assigned a value, and incremental value in using the data.