Adaptive social computing methods

• US 7,606,772 B2
• Filed: 05/22/2006
• Issued: 10/20/2009
• Est. Priority Date: 11/28/2003
• Status: Expired

• Current Assignee: World Assets Consulting AG LLC (Intellectual Ventures LLC)
• Original Assignee: Manyworlds Incorporated
• Inventors: Moneypenny, Naomi Felina, Flinn, Steven Dennis
• Primary Examiner(s): Holmes, Michael B
• Application Number:
  US11/419,604
• Publication Number:
  US 20060200435A1
• Time in Patent Office: 1,247 Days
• Field of Search: 706/12
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/048 Fuzzy inferencing

Abstract

Methods of applying adaptive social computing systems are disclosed. The social computing systems include capabilities to generate adaptive recommendations and representations of social networks derived, at least in part, from inferences of the preferences and interests of system users based on a plurality of usage behaviors, spanning a plurality of usage behavior categories. The behavioral categories include system navigation behaviors, content referencing behaviors, collaborative behaviors, and the monitoring of physical location and changes in location. Privacy control functions and compensatory functions related to insincere usage behaviors can be applied. Adaptive recommendation delivery can take the form of visual-based or audio-based formats.

Claims

1. An adaptive recommendation method comprising:

interacting with a content aspect comprising information;

interacting with a computer-implemented structural aspect comprising the content aspect and associated relationships;

contributing behaviors to a usage aspect, the usage aspect comprising captured usage behaviors, wherein the usage behaviors are associated with one or more users corresponding to a plurality of usage behavior categories; and

applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories.

2. The method of claim 1, wherein the information is selected from a group consisting of text, graphics, audio, video, interactive forms of content, applets, tutorials, advertising content, courseware, demonstrations, representations of people, modules, executable code, and computer programs.

3. The method of claim 1, wherein the structural aspect further comprises:

one or more objects, each object comprising the information; and

one or more relationships, wherein each relationship is associated with each pair of the one or more objects.

4. The method of claim 1, the usage aspect further comprising one or more usage behaviors, wherein each usage behavior is associated with either a user, one or more user communities, or a user and one or more user communities simultaneously, wherein the user comprises a single-member subset of the one or more users and a community of the one or more user communities comprises a multiple-member subset of the one or more users.

5. The method of claim 1, further comprising a privacy control, the privacy control enabling a user of the one or more users to restrict usage behaviors associated with the user from being deemed non-private behaviors.

6. The method of claim 1, wherein applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:

applying usage behavior categories, wherein the usage behavior categories are selected from a group consisting of navigation and access patterns, collaborative patterns, direct feedback patterns, subscription patterns, self-profiling patterns, reference patterns, and physical location patterns.

7. The method of claim 1, applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:

inferring a user interest derived from, at least in part, usage behaviors.

8. The method of claim 1, wherein applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories:

applying a compensatory algorithm associated with the detection of apparent insincere system usage behaviors or other inferred “

gaming”

behaviors by the one or more users.

9. The method of claim 1, wherein applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:

applying an algorithm that performs pattern matching of information embodied in the structural aspect and content aspect to produce content interpretation patterns, and associates the content interpretation patterns with usage patterns.

10. The method of claim 1, wherein applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:

receiving a recommendation in a delivery mode, wherein the recommendation delivery mode is selected from a group consisting of visual, audio, and a combination of visual and audio.

11. The method of claim 1, wherein applying a user tunable adaptive recommendation to navigate the computer-implemented structural aspect, the adaptive recommendation being based, at least in part, on an automatic inference of a preference of a user from a plurality of the captured usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:

modifying a structural element, wherein the structural element is selected from the group consisting of a function to generate a new relationship, a function to modify an existing relationship, a function to delete an existing relationship, a function to add an object, a function to modify an object, and a function to delete an object.

12. A mobile adaptive recommendation method comprising:

contributing behaviors to a computer-implemented usage aspect, the usage aspect comprising captured usage behaviors, wherein the usage behaviors are associated with one or more users; and

receiving on a mobile device an automatically generated user tunable adaptive recommendation based, at least in part, on automatically determining the location of a first user of the one or more users and on at least one other usage behavior of the captured usage behaviors associated with the one or more users corresponding to at least one other usage behavior category.

13. The method of claim 12, further comprising:

determining the change in location of a user as a function of time.

14. The method of claim 12, further comprising:

receiving the automatically generated adaptive recommendation, wherein the recommendation is based, at least in part, on the first user'"'"'s proximity to a second user.