Subscription-based Temporally Sequenced Recommendations

• US 20120096155A1
• Filed: 12/27/2011
• Published: 04/19/2012
• Est. Priority Date: 05/20/2004
• Status: Abandoned Application

• Current Assignee: Manyworlds Incorporated
• Original Assignee: Manyworlds Incorporated
• Inventors: Flinn, Steven Dennis, Moneypenny, Naomi Felina
• Application Number:
  US13/337,489
• Publication Number:
  US 20120096155A1
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06F 16/60 of audio data
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/0633 Workflow analysis
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

A method and system for temporally-sequenced recommendations in subscription-based systems delivers to users of a subscription-based system multiple recommended objects that are arranged in a temporal sequence. The delivered recommended objects are in accordance with user subscriptions and/or inferences of preferences that are based, at least in part, on usage behaviors. Variations of the system and method include delivering recommended objects in accordance with the contents of the objects and user direct feedback with regard to the objects. Information as to why objects were delivered to users may be provided to the users.

Claims

1. A method, comprising:

contributing a plurality of usage behaviors to a computer-implemented usage function through use of a portable processor-based device, wherein one behavior of the plurality of behaviors is a subscription to an object representing a user; and

interacting with a plurality of recommended objects through use of the portable processor-based device, wherein the plurality of recommended objects are arranged in a temporal sequence, and wherein the plurality of recommended objects are selected for delivery in accordance with an automatic inference of a preference based, at least in part, on a plurality of usage behaviors.

2. The method of claim 1, further comprising:

contributing the plurality of usage behaviors, wherein one behavior of the plurality of behaviors is a physiological response.

3. The method of claim 1, further comprising:

interacting with the plurality of recommended objects, wherein the plurality of recommended objects are selected for delivery in accordance with at least one direct feedback behavior that is associated with at least one object of the plurality of objects.

4. The method of claim 3, further comprising:

interacting with the plurality of recommended objects, wherein the plurality of recommended objects are selected for delivery in accordance with the at least one direct feedback behavior that is associated with the at least one object of the plurality of objects, wherein the at least one direct feedback behavior is a user comment.

5. The method of claim 1, further comprising:

interacting with the plurality of recommended objects, wherein the plurality of recommended objects are selected for delivery in accordance with the inference of the preference, wherein the automatic inference of the preference is based, at least in part, on usage behaviors associated with a plurality of users.

6. The method of claim 1, further comprising:

interacting with the plurality of recommended objects, wherein the plurality of objects are selected for delivery in accordance with the automatic inference of the preference, wherein the inference of the preference is based, at least in part, on the contents of at least one object of the plurality of objects.

7. The method of claim 1, further comprising:

tuning the plurality of recommended objects through use of a recommendation tuning function.

8. The method of claim 1, further comprising:

receiving an explanation as to why at least one object of the plurality of recommended objects was selected for delivery.

9. A computer-implemented system, comprising:

a computer-implemented function executed on a processor-based device that enables a user to contribute a plurality of usage behaviors to a computer-implemented usage function, wherein one behavior of the plurality of behaviors is a subscription to an object representing a user; and

a computer-implemented function executed on a processor-based device that enables the user to interact with a plurality of recommended objects, wherein the plurality of recommended objects are arranged in a temporal sequence, and wherein the plurality of recommended objects are selected for delivery in accordance with an automatic inference of a preference based, at least in part, on a plurality of usage behaviors.

10. The system of claim 9, further comprising:

the computer-implemented function that enables the user to contribute the plurality of behaviors, wherein one behavior of the plurality of behaviors is a physiological response.

11. The system of claim 9, further comprising:

the computer-implemented function that enables the user to interact with the plurality of recommended objects, wherein the plurality of recommended objects are selected for delivery in accordance with at least one direct feedback behavior that is associated with at least one object of the plurality of objects.

12. The system of claim 11, further comprising:

the at least one direct feedback behavior, wherein the at least one direct feedback behavior is a user comment.

13. The system of claim 9, further comprising:

the computer-implemented function that enables the user to interact with the plurality of recommended objects, wherein the plurality of recommended objects are selected for delivery in accordance with the automatic inference of the preference, wherein the inference of the preference is based, at least in part, on usage behaviors associated with a plurality of users.

14. The system of claim 9, further comprising:

the computer-implemented function that enables the user to interact with the plurality of recommended objects, wherein the plurality of objects are selected for delivery in accordance with the automatic inference of the preference, wherein the inference of the preference is based, at least in part, on the contents of at least one object of the plurality of objects.

15. The system of claim 9, further comprising:

the computer-implemented function that enables the user to interact with the plurality of recommended objects, wherein at least one object of the plurality of recommended objects comprises a user activity.

16. The system of claim 9, further comprising:

a recommendation tuning function.

17. The system of claim 9, further comprising:

a computer implemented function that delivers an explanation to the user as to why at least one object of the plurality of recommended objects was selected for delivery.

18. An article comprising a non-transitory computer-readable medium storing instructions for enabling a processor-based system to:

enable a user to contribute a plurality of usage behaviors to a computer-implemented usage function, wherein one behavior of the plurality of behaviors is a subscription to an object representing a user; and

enable the user to interact with a plurality of recommended objects, wherein the plurality of recommended objects are arranged in a temporal sequence, and wherein the plurality of recommended objects are selected for delivery in accordance with an automatic inference of a preference based, at least in part, on a plurality of usage behaviors.

19. The article of claim 18, further storing instructions for enabling a processor-based system to:

generate the plurality of recommended objects responsive to a recommendation tuning setting.

20. The article of claim 18, further storing instructions for enabling a processor-based system to:

deliver an explanation to the user as to why at least one object of the plurality of recommended objects was selected for delivery.