Enhanced online user-interaction tracking

• US 10,650,059 B2
• Filed: 08/21/2017
• Issued: 05/12/2020
• Est. Priority Date: 05/19/2015
• Status: Active Grant

• Current Assignee: ResearchGATE GmbH
• Original Assignee: ResearchGATE GmbH
• Inventors: Haeusler, Michael, Zholudev, Vyacheslav, Alvares, Darren, Fickenscher, Horst, Kelly, Niall
• Primary Examiner(s): Keehn, Richard G
• Application Number:
  US15/682,236
• Publication Number:
  US 20170344541A1
• Time in Patent Office: 995 Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 67/01 Protocols
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/1044 Group management mechanisms management of multicast group membership H04L12/185; reconfiguring of node membership in a computing system to eliminate errors G06F11/1425
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Disclosed are systems and methods for enhanced tracking of user interactions with online documents, such as, in accordance with various embodiments, interaction tracking on a sub-document level of granularity and/or interaction tracking that involves storing one or more interaction parameters (e.g., an identifier of the document or of the interacting user) for each interaction.

Claims

1. A method comprising:

for a plurality of documents each comprising multiple individual document elements, storing database entries for the individual document elements in one or more computer-readable media, each database entry comprising a unique document-element identifier;

storing, for at least some of the document elements, respective topics of the document elements in the one or more computer-readable media;

displaying the documents to users and tracking, at sub-document level, interactions of the users with the document elements;

storing, for each of the interactions of one of the users with one of the document elements, at least the document-element identifier of the respective document element and an identifier of the respective user in the one or more computer-readable media; and

using one or more processors to determine interests or fields of expertise for at least some of the users based at least in part on the tracked user interactions with the document elements and the topics associated with the respective document elements, and to match the users with content or with other users based on the determined interests or fields of expertise.

2. The method of claim 1, wherein at least one of the users is matched to content as a potential reviewer, based on the field of expertise of the user and a topic associated with the content.

3. The method of claim 1, wherein at least one of the users is matched with content, the method further comprising recommending the matched content to the user.

4. The method of claim 1, wherein at least one of the users is matched with another user, the method further comprising automatically suggesting that the user and the other user connect with each other.

5. The method of claim 1, wherein interests of the users are determined based at least in part on consumer interactions of the respective users with the document elements.

6. The method of claim 1, wherein fields of expertise of users are determined based at least in part on contributor interactions of the respective users with the document elements.

7. The method of claim 1, further comprising, using the one or more processors to group users based on user attributes to define one or more user groups, and to determine interests of the one or more user groups based at least in part on the tracked interactions of users belonging to the respective user group and the topics associated with the interacted-with document elements.

8. The method of claim 7, wherein the user attributes comprise at least one of a research discipline, a geographic region, an institutional affiliation, an occupation, a seniority, or a reputation score associated with the respective user.

9. The method of claim 8, wherein at least some users are matched with content or with other users based on matches between topics of the content and the interests determined for the respective user groups to which the users belong.

10. The method of claim 1, further comprising further storing, for each of the interactions, at least one of a duration of interaction or a type of interaction, and weighting different types of interaction or different durations of interaction differently in determining the interests or fields of expertise.

11. A system comprising:

one or more non-transistor computer-readable media storing, for a plurality of documents each comprising multiple document elements, a unique document-element identifier and a topic for each of the document elements;

one or more computer processors executing instructions stored memory to perform operations comprising;

displaying the documents to users and tracking, at sub-document level, interactions of the users with the document elements;

causing, for each of the interactions of one of the users with one of the document elements, at least the document-element identifier of the respective document element and an identifier of the respective user to be stored in the one or more computer-readable media;

determining interests or fields of expertise for at least some of the users based at least in part on the tracked user interactions with the document elements and the topics associated with the respective document elements; and

matching the users with content or with other users based on the determined interests or fields of expertise.

12. The system of claim 11, wherein the operations comprise matching at least one of the users to content as a potential reviewer, based on the field of expertise of the user and a topic associated with the content.

13. The system of claim 11, wherein the operations comprise matching at least one of the users with content, the method further comprising recommending the matched content to the user.

14. The system of claim 11, wherein the operations comprise matching at least one of the users with another user and automatically suggesting that the user and the other user connect with each other.

15. One or more non-transitory machine-readable media storing instructions which, when executed by one or more processors of a machine, cause the one or more processors to perform operations comprising:

causing a plurality of documents to be displayed to users, each document comprising multiple individual document elements for which respective database entries comprising unique document-element identifiers are stored in a database;

tracking, at sub-document level, interactions of the users with the document elements and causing, for each of the interactions of one of the users with one of the document elements, at least the document-element identifier of the respective document element and an identifier of the respective user to be stored;

determining interests or fields of expertise for at least some of the users based at least in part on the tracked user interactions with the document elements and the topics associated with the respective document elements; and

matching the users with content or with other users based on the determined interests or fields of expertise.

16. The one or more machine-readable media of claim 15, wherein the operations comprise matching at least one of the users to content as a potential reviewer, based on the field of expertise of the user and a topic associated with the content.

17. The one or more machine-readable media of claim 15, wherein the operations comprise matching at least one of the users with content, the method further comprising recommending the matched content to the user.

18. The one or more machine-readable media of claim 15, wherein the operations comprise matching at least one of the users with another user and automatically suggesting that the user and the other user connect with each other.

19. The one or more machine-readable media of claim 15, wherein the operations further comprise grouping users based on user attributes to define one or more user groups, and determining interests of the one or more user groups based at least in part on the tracked interactions of users belonging to the respective user group and the topics associated with the interacted-with document elements.

20. The one or more machine-readable media of claim 15, wherein the operations further comprise causing, for each of the interactions, at least one of a duration of interaction or a type of interaction to be stored, and weighting different types of interaction or different durations of interaction differently in determining the interests or fields of expertise.