Contextual responses based on automated learning techniques

• US 20060004680A1
• Filed: 01/11/2005
• Published: 01/05/2006
• Est. Priority Date: 12/18/1998
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Robarts, James O., Matteson, Eric L.
• Granted Patent: US 8,020,104 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 1/163 Wearable computers, e.g. on a belt
• CPC Class Codes: G06F 16/40 of multimedia data, e.g. slideshows comprising image and additional audio data retrieval of still image data G06F16/50; retrieval of audio data G06F16/60; retrieval of video data G06F16/70
• CPC Class Codes: G06F 21/00 Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• CPC Class Codes: G06F 21/606 by securing the transmission between two devices or processes
• CPC Class Codes: G06F 21/6245 Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G06F 2221/2149 Restricted operating environment
• 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 3/016 Input arrangements with force or tactile feedback as computer generated output to the user
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/004 Artificial life, i.e. computing arrangements simulating life

Abstract

Techniques are disclosed for using a combination of explicit and implicit user context modeling techniques to identify and provide appropriate computer actions based on a current context, and to continuously improve the providing of such computer actions. The appropriate computer actions include presentation of appropriate content and functionality. Feedback paths can be used to assist automated machine learning in detecting patterns and generating inferred rules, and improvements from the generated rules can be implemented with or without direct user control. The techniques can be used to enhance software and device functionality, including self-customizing of a model of the user'"'"'s current context or situation, customizing received themes, predicting appropriate content for presentation or retrieval, self-customizing of software user interfaces, simplifying repetitive tasks or situations, and mentoring of the user to promote desired change.

Claims

1-25. -25. (canceled)

26. A method in a computing device for improving responses to context information based on automated learning techniques, multiple responses each being associated with a group of context information, the method comprising:

receiving indications of multiples responses previously indicated by at least one user and of context information related to each of the multiple responses;

automatically detecting a relationship between a group of context information and one of the responses based on the received indications; and

creating an association between the group of context information and the one response, so that the one response can be provided in the future in response to a context that matches the group of context information.

27. The method of claim 26 wherein the computing device performs the method on behalf of a user of a thin client computing device.

28. A computing device for improving responses to context information based on automated learning techniques, multiple responses each being associated with a group of context information, comprising:

a learning component capable of receiving indications of multiples responses previously indicated by at least one user and of context information related to each of the multiple responses, and capable of automatically detecting a relationship between a group of context information and one of the responses based on the received indications; and

a response modifier component capable of creating an association between the group of context information and the one response.

29. The computing device of claim 28 wherein the computing device is a wearable computer.

30. A computer system for improving responses to context information based on automated learning techniques, multiple responses each being associated with a group of context information, comprising:

means for receiving indications of multiples responses previously indicated by at least one user and of context information related to each of the multiple responses;

means for automatically detecting a relationship between a group of context information and one of the responses based on the received indications; and

means for creating an association between the group of context information and the one response.

31. A computer-readable medium whose contents cause a computing device to improve responses to context information based on automated learning techniques, multiple responses each being associated with a group of context information, by:

receiving indications of multiples responses previously indicated by at least one user and of context information related to each of the multiple responses;

automatically detecting a relationship between a group of context information and one of the responses based on the received indications; and

creating an association between the group of context information and the one response.

32. The computer-readable medium of claim 31 wherein the computer-readable medium is a data transmission medium transmitting a generated data signal containing the contents.

33. The computer-readable medium of claim 31 wherein the computer-readable medium is a memory of a computer system.