Proactive user interface including evolving agent

• US 8,990,688 B2
• Filed: 09/03/2004
• Issued: 03/24/2015
• Est. Priority Date: 09/05/2003
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Lee, Jong-Goo, Linder, Natan, Ben-Yair, Ran, Eisenberg, Yariv, Toledano, Eval
• Primary Examiner(s): Belousov, Andrey
• Application Number:
  US10/933,583
• Publication Number:
  US 20050118996A1
• Time in Patent Office: 3,854 Days
• Field of Search: 715/745, 715/765
• US Class Current: 715/706
• CPC Class Codes: G06F 3/011 Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 9/451 Execution arrangements for user interfaces
• CPC Class Codes: H04M 1/72427 for supporting games or graphical animations
• CPC Class Codes: H04M 1/72436 for text messaging, e.g. short messaging services [SMS] or e-mails
• CPC Class Codes: H04M 1/72448 with means for adapting the functionality of the device according to specific conditions
• CPC Class Codes: H04M 1/72469 for operating the device by selecting functions from two or more displayed items, e.g. menus or icons
• CPC Class Codes: H04M 1/72472 wherein the items are sorted according to specific criteria, e.g. frequency of use
• CPC Class Codes: H04M 2250/12 including a sensor for measuring a physical value, e.g. temperature or motion telephones including GPS signal receivers H04M2250/10

Abstract

A proactive user interface, installed in (or otherwise control and/or be associated with) any type of computational device. The proactive user interface actively makes suggestions to the user, based upon prior experience with a particular user and/or various preprogrammed patterns from which the computational device could select, depending upon user behavior. These suggestions can be made by altering the appearance of at least a portion of the display, for example by changing a menu or a portion thereof; providing different menus for display; and/or altering touch screen functionality. The suggestions can also be made audibly.

Claims

1. A device having an operating system executing a proactive user interface, the device comprising:

a memory; and

a processor coupled to the memory and comprising;

(a) an interface unit for communication between a user of the proactive user interface and said operating system, said interface unit including an evolving avatar enabling communication with the user; and

(b) a learning module for detecting at least one pattern of interaction of the user with said interface unit, and actively suggesting, to the user, options for evolving at least one function of the user interface according to said at least one pattern by at least one of altering an appearance of at least a portion of a display, changing a menu or a portion thereof, and altering touch screen functionality,

wherein, if a suggestion or an action provided by the interface unit is approved by the user, the interface unit provides positive incentive to the proactive user interface, if the suggestion or the action provided by the interface unit is disapproved by the user, the interface provides a disincentive to the proactive user interface, and wherein at least one of the positive incentive and the disincentive are used in determining a state of the evolving avatar,

wherein at least one characteristic of an appearance of said avatar is optionally alterable according to a user command, and

wherein one or more characteristics of the appearance of said avatar are alterable according to an automated evolutionary algorithm.

2. The device of claim 1, wherein said at least one pattern is selected from the group consisting of a pattern determined according to at least one previous interaction of the user with said interface unit, a predetermined pattern, and a combination thereof.

3. The device of claim 1, wherein said computational device is selected from a group consisting of a regular computer, an ATM, mobile information devices including a cellular telephone, a PDA, and a consumer appliance having an operating system.

4. The device of claim 2, wherein said computational device is selected from a group consisting of a regular computer, an ATM, mobile information devices including a cellular telephone, a PDA, and a consumer appliance having an operating system.

5. The device of claim 1, wherein said learning module comprises a knowledge base for storing information gathered as a result of interactions with the user and/or the operating system.

6. The device of claim 5, wherein said knowledge base comprises a plurality of integrated knowledge items determined from the behavior of the user and from preprogrammed information.

7. The device of claim 5, wherein said learning module further comprises a plurality of sensors for perceiving a state of the operating system.

8. The device of claim 7, wherein said learning module further comprises a perception unit for processing output from said sensors to determine a state of the operating system and a state of said interface unit.

9. The device of claim 5, wherein said learning module further comprises a reasoning system for updating said knowledge base and for learning an association between an alteration of said interface unit and a state of the operating system.

10. The device of claim 5, wherein said learning module maximizes a percentage of proactive alterations leading to a direct user selection from said active suggestion.

11. The device of claim 7, wherein said learning module maximizes a percentage of proactive alterations leading to a direct user selection from said active suggestion.

12. The device of claim 7, wherein said learning module maximizes a percentage of proactive alterations leading to a direct user selection from said active suggestion.

13. The device of claim 9, wherein said learning module maximizes a percentage of proactive alterations leading to a direct user selection from said active suggestion.

14. The device of claim 10, wherein said maximization is performed through learning reinforcement.

15. The device of claim 11, wherein said maximization is performed through learning reinforcement.

16. The device of claim 12, wherein said maximization is performed through learning reinforcement.

17. The device of claim 13, wherein said maximization is performed through learning reinforcement.

18. The device of claim 14, wherein said learning reinforcement is performed through an iterative learning process.

19. The device of claim 15, wherein said learning reinforcement is performed through an iterative learning process.

20. The device of claim 16, wherein said learning reinforcement is performed through an iterative learning process.

21. The device of claim 17, wherein said learning reinforcement is performed through an iterative learning process.

22. The device of claim 18, wherein each iteration of said learning process is performed after said evolution has been performed.

23. The device of claim 19, wherein each iteration of said learning process is performed after said evolution has been performed.

24. The device of claim 20, wherein each iteration of said learning process is performed after said evolution has been performed.

25. The device of claim 21, wherein each iteration of said learning process is performed after said evolution has been performed.

26. The device of claim 8, wherein said learning module maximizes a percentage of proactive alterations leading to a direct user selection from said active suggestion.