Ergonomic man-machine interface incorporating adaptive pattern recognition based control system

• US 6,418,424 B1
• Filed: 05/04/1999
• Issued: 07/09/2002
• Est. Priority Date: 12/23/1991
• Status: Expired due to Fees

• Current Assignee: Hoffberg Family Trust 2
• Original Assignee: Linda I. Hoffberg-Borghesani, Steven Mark Hoffberg
• Inventors: Hoffberg, Steven M., Hoffberg-Borghesani, Linda I.
• Primary Examiner(s): Davis, George B.
• Application Number:
  US09/304,536
• Time in Patent Office: 1,162 Days
• Field of Search: 706/21, 706/52, 345/327, 434/178
• US Class Current: 706/21
• CPC Class Codes: G06F 3/00 Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
• CPC Class Codes: G06F 9/453 Help systems
• CPC Class Codes: G06V 40/103 Static body considered as a whole, e.g. static pedestrian or occupant recognition
• CPC Class Codes: G11B 27/107 of operating tapes
• CPC Class Codes: G11B 27/11 by using information not detectable on the record carrier
• CPC Class Codes: G11B 27/328 on a tape [TTOC]
• CPC Class Codes: H04B 1/205 with control bus for exchanging commands between units
• CPC Class Codes: H04H 60/33 Arrangements for monitoring the users' behaviour or opinions
• CPC Class Codes: H04H 60/65 for using the result on users' side
• CPC Class Codes: H04N 2005/91328 the copy protection signal being a copy management signal, e.g. a copy generation management signal [CGMS]
• CPC Class Codes: H04N 2005/91364 the video signal being scrambled
• CPC Class Codes: H04N 21/4147 PVR [Personal Video Recorder] H04N5/76 takes precedence ; arrangements for broadcast with accumulation-type receivers H04H20/40
• CPC Class Codes: H04N 21/42201 biosensors, e.g. heat sensor for presence detection, EEG sensors or any limb activity sensors worn by the user input arrangements for interaction with the human body based on nervous system activity detection G06F3/015
• CPC Class Codes: H04N 21/4334 Recording operations recording of a television signal H04N5/76; arrangements for recording or accumulating broadcast information or broadcast-related information H04H60/27
• CPC Class Codes: H04N 21/44008 involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59
• CPC Class Codes: H04N 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/454 Content or additional data filtering, e.g. blocking advertisements
• CPC Class Codes: H04N 21/4583 Automatically resolving scheduling conflicts, e.g. when a recording by reservation has been programmed for two programs in the same time slot
• CPC Class Codes: H04N 21/4666 using neural networks, e.g. processing the feedback provided by the user
• CPC Class Codes: H04N 21/4667 : Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 5/782 : on tape
• CPC Class Codes: H04N 5/913 : for scrambling ; for copy protection

Abstract

An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation which may be efficiently processed to determine correspondence. Applications of the interface and system include a VCR, medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Claims

1. A method for defining a set of media preferences for a user, comprising the steps

(a) monitoring a media consumption of a user;

(b) receiving feedback from the user regarding a media program;

(c) analyzing characteristics of a set of media programs, including media consumed by the user and media program for which user feedback is available;

(d) predicting, based on said analysis, a user preference for media programs; and

(e) presenting to the user a set of predicted preferred media programs.

2. The method according to claim 1, wherein said media consumed by the user comprises a video program.

3. The method according to claim 1, wherein the feedback is explicitly provided by the user.

4. The method according to claim 1, wherein the feedback is implicitly provided by the user.

5. The method according to claim 1, wherein the characteristics of the set of media programs are extracted from received metadata.

6. The method according to claim 5, wherein the characteristics of the set of media programs are extracted from a received electronic program guide.

7. The method according to claim 1, wherein the characteristics of the set of media programs are derived from the media programs.

8. The method according to claim 7, wherein a content based-analysis of a media program is performed locally to the user.

9. The method according to claim 7, wherein a content based-analysis of a media program is performed remotely from the user.

10. The method according to claim 1, wherein said predicting comprises performing a statistical analysis.

11. The method according to claim 1, wherein said predicting comprises performing a fuzzy logic analysis.

12. The method according to claim 1, wherein said predicting comprises creating a profile of a group of users.

13. The method according to claim 1, wherein said predicting comprises implementing an artificial neural network.

14. The method according to claim 1, wherein said presenting comprises displaying an ordered list.

15. The method according to claim 1, wherein said presenting comprises displaying a filtered list.

16. The method according to claim 1, further comprising the steps of:

(a1) monitoring a media consumption of a second user;

(b1) receiving feedback from the second user regarding a media program;

(c1) said analyzing step analyzing characteristics of a set of media programs, including media consumed by the user and the second user and media programs for which feedback is available;

(d1) said predicting step predicting, based on said analysis, a composite user preference for media programs; and

(e1) said presenting step presenting a set of composite predicted preferred media programs.

17. The method according to claim 16, wherein said predicting comprises selecting a least objectionable media program based on the user and the second user.

18. The method according to claim 16, further comprising the step of maintaining separate user profiles for the user and the second user.

19. The method according to claim 1, wherein said predicting is based on a system status, past history of use and context.

20. The method according to claim 1, wherein said feedback is received from the user through a remote control device.

21. The method according to claim 1, further comprising the step of recording a predicted preferred media program.

22. The method according to claim 21, wherein the recording is automatic.

23. The method according to claim 1, further comprising the step of decoding a digital video media program.

24. The method according to claim 1, further comprising the step of recording a media program in digitally-encoded format.

25. The method according to claim 1, further comprising the step of decrypting a predicted preferred media program.

26. The method according to claim 1, further comprising the step of accounting for media consumption.

27. The method according to claim 1, further comprising the step of inferring an identity of the user.

28. A method for predicting user program preferences, comprising:

(a) providing an electronic program guide to the user;

(b) providing a user interface through which the user may select programs from the program guide;

(c) monitoring a selection of programs by the user;

(d) monitoring a feedback from the user relating to media programs; and

(e) predicting a user preferred program based on said selection and said feedback.

29. A method for predicting user program preferences, comprising:

(a) providing a user interface through which the user may select programs and providing user feedback relating to selected programs;

(b) monitoring a selection of programs by the user;

(c) monitoring a feedback from the user relating to selected media programs;

(d) predicting a user preferred program based on said selection and said feedback; and

(e) presenting, as a selection choice to the user, a predicted preferred program.

30. A method for predicting user preferences, comprising the steps of:

(a) monitoring media-related activities of a user;

(b) receiving feedback from the user relating to the media-related activities;

(c) analyzing characteristics of a set of media, including media consumed by the user; and

(d) predicting, based on said analysis and feedback, a user preference.

31. The method according to claim 30, wherein a predicted user preference is applied as a ranking factor to each of a plurality of associated objects.

32. The method according to claim 31, wherein the plurality of associated objects are presented to the user in rank order.

33. The method according to claim 31, wherein an associated object is hypertext linked to a related object.

34. A method for filtering media for a user based on predicted user preferences, comprising the steps of:

(a) monitoring media consumption of a user;

(b) analyzing characteristics of a set of media, including media consumed by the user;

(c) predicting, based on said analysis, a user media preference, and

(d) selectively filtering media, from the set of media, based on the predicted user media preference.

35. An apparatus for filtering media for a user based on predicted user preferences, comprising:

(a) means for monitoring media consumption of a user;

(b) means for analyzing characteristics of a set of media, including media consumed by the user;

(c) means for predicting, based on said analysis, a user media preference, and

(d) means for selectively filtering media, from the set of media, based on the predicted user media preference.

36. The apparatus according to claim 35, comprising:

(a) a memory for storing the electronic program guide describing a set of available media programs;

(b) an input for receiving media programs;

(c) an input for receiving a user generated signal for defining a user preferred subset of available media programs from the electronic program guide, the user preferred subset having at least two members and being capable of being defined without explicit reference to members of the subset; and

(d) a media processor, for selectively processing at least one program based on the defined user preferred subset of available media programs.

37. The apparatus according to claim 35, wherein said means for monitoring media consumption of a user comprises means for storing a programming guide entry associated with respective consumed media.

38. The apparatus according to claim 35, wherein said analyzing means receives a programming guide corresponding to the set of media.

39. The apparatus according to claim 35, wherein said predicting means records characteristics of media consumed by a user and produces a composite user preference profile.

40. The apparatus according to claim 39, wherein said selectively filtering means comprises means for correlating said user preference profile with programming guide entries for respective media.