TECHNIQUES FOR AUTOMATICALLY DISTINGUSIHING BETWEEN USERS OF A HANDHELD DEVICE

• US 20100042564A1
• Filed: 08/15/2008
• Published: 02/18/2010
• Est. Priority Date: 08/15/2008
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: LaMarca, Anthony G., HARRISON, BEVERLY, Hightower, Jeffrey R., Rea, Adam D.
• Application Number:
  US12/192,789
• Publication Number:
  US 20100042564A1
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G06F 3/01 Input arrangements or combined input and output arrangements for interaction between user and computer G06F3/16 takes precedence
• CPC Class Codes: G06F 3/0346 with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors
• CPC Class Codes: G06F 3/04817 using icons graphical or visual programming using iconic symbols G06F8/34
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/04883 for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06F 3/167 Audio in a user interface, e.g. using voice commands for navigating, audio feedback
• CPC Class Codes: G10L 15/08 Speech classification or search
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/414 Specialised client platforms, e.g. receiver in car or embedded in a mobile appliance
• 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 21/4222 Remote control device emulator integrated into a non-television apparatus, e.g. a PDA, media center or smart toy remote control device for a television receiver integrated into a mobile phone H04M1/72415
• CPC Class Codes: H04N 21/42224 Touch pad or touch panel provided on the remote control
• CPC Class Codes: H04N 21/4415 using biometric characteristics of the user, e.g. by voice recognition or fingerprint scanning cryptography using biological data H04L9/3231; authentication in networks using biometric H04L63/0861
• CPC Class Codes: H04N 21/44218 Detecting physical presence or behaviour of the user, e.g. using sensors to detect if the user is leaving the room or changes his face expression during a TV program methods or arrangements for recognising human body or animal bodies or body parts G06V40/10; methods or arrangements for acquiring or recognising human faces, facial parts, facial sketches, facial expressions G06V40/16; methods or arrangements for recognising movements or behaviour G06V40/20; arrangements for identifying users in broadcast systems H04H60/45
• 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 21/4751 for defining user accounts, e.g. accounts for children
• CPC Class Codes: H04N 7/165 Centralised control of user terminal subsequent to an upstream request signal H04N7/17345; Registering at central by two-way working H04N7/17309
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 8/183 : Processing at user equipment or user record carrier

Abstract

Various embodiments for automatically distinguishing between users of a handheld device are described. An embodiment includes collecting sensor data from a user interacting with a handheld device, where the sensor data is collected via embedded sensors in the handheld device. The embodiment further includes distinguishing the user from other users of the handheld device via the collected sensor data, at least one embedded machine learning algorithm and a profile for the user. Other embodiments are described and claimed.

Claims

1. A method comprising:

collecting sensor data from a user interacting with a handheld device, wherein the sensor data is collected via embedded sensors in the handheld device; and

distinguishing the user from other users via the collected sensor data, at least one embedded machine learning algorithm and a profile for the user.

2. The method of claim 1, further comprising:

determining a feature, wherein the feature is customized for the user based on past interaction between the user and the handheld device.

3. The method of claim 2, further comprising:

administering the feature for the user;

receiving feedback from the user regarding the desirability of the administered feature; and

updating the user profile based on the received feedback.

4. The method of claim 3, wherein the feedback is at least one of explicit feedback and implicit feedback.

5. An apparatus, comprising:

a processor;

at least one sensor; and

at least one machine learning algorithm,

wherein the apparatus is capable of collecting sensor data from a user interacting with the apparatus in a way that can be monitored by at least one sensor, and wherein the apparatus is capable of distinguishing the user from other users via the collected sensor data, at least one machine learning algorithm and a profile for the user.

6. The apparatus of claim 5, further comprising:

wherein the apparatus is capable of determining a feature, wherein the feature is customized for the user based on past interaction between the user and the apparatus.

7. The apparatus of claim 6, further comprising:

wherein the apparatus is capable of administering the feature for the user, receiving feedback from the user regarding the desirability of the administered feature, and updating the user profile based on the received feedback.

8. The apparatus of claim 7, wherein the feedback is at least one of explicit feedback and implicit feedback.

9. A machine-readable medium containing instructions which, when executed by a processing system, cause the processing system to perform instructions for:

collecting sensor data from a user interacting with a handheld device, wherein the sensor data is collected via embedded sensors in the handheld device; and

distinguishing the user from other users via the collected sensor data, at least one embedded machine learning algorithm and a profile for the user.

10. The machine-readable medium of claim 9, further comprising:

determining a feature, wherein the feature is customized for the user based on past interaction between the user and the handheld device.

11. The machine-readable medium of claim 10, further comprising:

administering the feature for the user;

receiving feedback from the user regarding the desirability of the administered feature; and

updating the user profile based on the received feedback.

12. The machine-readable medium of claim 11, wherein the feedback is at least one of explicit feedback and implicit feedback.

13. A remote control device, comprising:

at least one sensor; and

an infra-red interface,

wherein the remote control device is capable of collecting sensor data from a user interacting with the remote control device via the at least one sensor, wherein the remote control device is capable of encoding the collected sensor data into an infra-red signal, and wherein the remote control device is capable of forwarding the encoded infra-red signal via the infra-red interface to a remote processor.

14. The remote control device of claim 13, further comprising:

wherein the remote control device is capable of receiving an indication of the user distinguished from other users of the remote control device from the remote processor, wherein the user was determined based on the forwarded encoded infra-red signal, at least one machine learning algorithm and a profile for the user.

15. The remote control device of claim 14, further comprising:

wherein the remote control device is capable of causing the determination of a feature, wherein the feature is customized for the user based on past interaction between the user and the remote control device.

16. The remote control device of claim 15, further comprising:

wherein the remote control device is capable of causing the administration of the feature for the user, receiving feedback from the user regarding the desirability of the administered feature, and causing the profile for the user to be updated based on the received feedback.

17. A method, comprising:

collecting sensor data for a user interacting with a remote control device;

encoding the collected sensor data into an infra-red signal; and

forwarding the encoded infra-red signal to a remote processor.

18. The method of claim 17, further comprising:

receiving an indication of the user distinguished from other users of the remote control device from the remote processor, wherein the user was determined based on the forwarded encoded infra-red signal, at least one machine learning algorithm and a profile for the user.

19. The method of claim 18, further comprising:

causing the determination of a feature, wherein the feature is customized for the user based on past interaction between the user and the remote control device.

20. The method of claim 19, further comprising:

causing the administration of the feature for the user;

receiving feedback from the user regarding the desirability of the administered feature; and

causing the profile for the user to be updated based on the received feedback.

21. A method, comprising:

collecting sensor data from a user interacting with a handheld device, wherein the sensor data is collected via embedded sensors in the handheld device;

if the user cannot be distinguished from other users, create a subset of possible users;

provide the subset of possible users;

receive feedback from the user to distinguish the user; and

use the feedback to define a user profile.

22. The method of claim 21, wherein the subset of possible users is based on a confidence level for each user.

23. A method, comprising:

collecting sensor data from a user interacting with a handheld device, wherein the sensor data is collected via embedded sensors in the handheld device;

if the user cannot be distinguished from other users, determine a category for the user;

use a profile for the determined category to determine a feature for the user, wherein the feature is customized for the determined category.

24. The method of claim 23, further comprising:

Wherein the category represents a demographic class for the user.