Calibration of grab detection

• US 9,372,103 B2
• Filed: 07/12/2013
• Issued: 06/21/2016
• Est. Priority Date: 07/12/2013
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Hughes, Charles J., Maguire, Yael G., Shirinfar, Shafigh, Toksvig, Michael John McKenzie
• Primary Examiner(s): COSIMANO, EDWARD R
• Application Number:
  US13/941,289
• Publication Number:
  US 20150019153A1
• Time in Patent Office: 1,075 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G01D 18/00 Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00
• CPC Class Codes: G01D 21/00 Measuring or testing not otherwise provided for
• CPC Class Codes: G06F 1/169 the I/O peripheral being an integrated pointing device, e.g. trackball in the palm rest area, mini-joystick integrated between keyboard keys, touch pads or touch stripes G06F1/1643 takes precedence; constructional details of pointing devices G06F3/033
• CPC Class Codes: G06F 1/1694 the I/O peripheral being a single or a set of motion sensors for pointer control or gesture input obtained by sensing movements of the portable computer
• CPC Class Codes: G06F 1/3215 Monitoring of peripheral devices
• CPC Class Codes: G06F 1/3231 Monitoring the presence, absence or movement of users
• CPC Class Codes: G06F 11/3013 where the computing system is an embedded system, i.e. a combination of hardware and software dedicated to perform a certain function in mobile devices, printers, automotive or aircraft systems testing or monitoring of control systems or parts thereof G05B23/02
• CPC Class Codes: G06F 11/3058 Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations thermal management in cooling arrangements of a computing system G06F1/206
• CPC Class Codes: G06F 11/3438 monitoring of user actions tracking the activity of the user H04L67/535
• CPC Class Codes: G06F 17/40 Data acquisition and logging for input to computer G06F3/00
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 2200/1637 Sensing arrangement for detection of housing movement or orientation, e.g. for controlling scrolling or cursor movement on the display of an handheld computer
• CPC Class Codes: G06F 2203/0339 Touch strips, e.g. orthogonal touch strips to control cursor movement or scrolling; single touch strip to adjust parameter or to implement a row of soft keys
• CPC Class Codes: G06F 2203/0381 Multimodal input, i.e. interface arrangements enabling the user to issue commands by simultaneous use of input devices of different nature, e.g. voice plus gesture on digitizer
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06F 3/03 Arrangements for converting the position or the displacement of a member into a coded form
• 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 3/03547 Touch pads, in which fingers can move on a surface
• CPC Class Codes: G06F 3/038 Control and interface arrangements therefor, e.g. drivers or device-embedded control circuitry
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 : using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: H04M 1/724 : User interfaces specially adapted for cordless or mobile telephones
• CPC Class Codes: H04M 1/72454 : according to context-related or environment-related conditions
• 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
• CPC Class Codes: Y02D 10/00 : Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

In one embodiment, a method includes receiving real-time sensor data from N sensors on the computing device. The real-time sensor data corresponds to a transition in a physical state of the computing device caused by a user of the computing device. The method also includes applying a linear function to the real-time sensor data from each of the N sensors; determining a vector based on an N-tuple comprising the derivatives; comparing the vector with a pre-determined hyperplane with N−1 dimensions; and determining based on the comparison whether the transition is an event corresponding to any of one or more pre-determined imminent uses of the computing device by the user or a non-event not corresponding to any of the pre-determined imminent uses of the computing device by the user.

Claims

1. A method comprising:

by a computing device, receiving real-time sensor data from N sensors on the computing device, the real-time sensor data corresponding to a transition in a physical state of the computing device caused by a user of the computing device;

by the computing device, applying a linear function to the real-time sensor data from each of the N sensors;

by the computing device, determining a vector based on an N-tuple comprising the derivatives;

by the computing device, comparing the vector with a pre-determined hyperplane with N−

1 dimensions; and

by the computing device, determining based on the comparison whether the transition is;

an event corresponding to any of one or more pre-determined imminent uses of the computing device by the user;

or

a non-event not corresponding to any of the pre-determined imminent uses of the computing device by the user.

2. The method of claim 1, further comprising, by the computing device, receiving data defining the pre-determined hyperplane from a computing device of a social-networking system.

3. The method of claim 1, further comprising:

by the computing device, sending the real-time sensor data to a computing device of a social-networking system; and

by the computing device, receiving updated data re-defining the pre-determined hyperplane based at least in part on the real-time sensor data from the computing device of the social-networking system.

4. The method of claim 1, wherein the linear function comprises a filtering function, derivative function, convolution of a Heaviside or sigmoid function, or any combination thereof.

5. The method of claim 1, wherein:

the computing device is a mobile computing device;

the imminent intended use corresponds to physical contact between the user and the mobile computing device; and

the pre-determined function comprises powering on the mobile computing device.

6. The method of claim 1, wherein one or more of the sensors comprises a touch sensor, gyroscope, accelerometer, optical proximity sensor, ambient light sensor, or any combination thereof.

7. The method of claim 1, wherein the comparison comprises:

by the computing device, calculating a dot product of the vector and the pre-determined hyperplane; and

by the computing device, determining a position of the vector relative to the pre-determined hyperplane based at least in part on the calculation of the dot product.

8. The method of claim 7, wherein determining whether the transition is an event comprises, by the computing device, determining the position of the vector is on same side of the pre-determined hyperplane as training data associated with the pre-determined imminent use.

9. One or more computer-readable non-transitory storage media embodying software configured when executed to:

receive real-time sensor data from N sensors on a computing device, the real-time sensor data corresponding to a transition in a physical state of the computing device caused by a user of the computing device;

apply a linear function to the real-time sensor data from each of the N sensors;

determine a vector based on an N-tuple comprising the derivatives;

compare the vector with a pre-determined hyperplane with N−

1 dimensions; and

determine based on the comparison whether the transition is;

an event corresponding to any of one or more pre-determined imminent uses of the computing device by the user;

or

a non-event not corresponding to any of the pre-determined imminent uses of the computing device by the user.

10. The media of claim 9, wherein the software is further configured to receive data defining the pre-determined hyperplane from a computing device of a social-networking system.

11. The media of claim 9, wherein the software is further configured to:

send the real-time sensor data to a computing device of a social-networking system; and

receive updated data re-defining the pre-determined hyperplane based at least in part on the real-time sensor data from the computing device of the social-networking system.

12. The media of claim 9, wherein the linear function comprises a filtering function, derivative function, convolution of a Heaviside or sigmoid function, or any combination thereof.

13. The media of claim 9, wherein:

the computing device is a mobile computing device;

the imminent intended use corresponds to physical contact between the user and the mobile computing device; and

the pre-determined function comprises powering on the mobile computing device.

14. The media of claim 9, wherein one or more of the sensors comprises a touch sensor, gyroscope, accelerometer, optical proximity sensor, ambient light sensor, or any combination thereof.

15. The media of claim 9, wherein the software is further configured to:

calculate a dot product of the vector and the pre-determined hyperplane; and

determine a position of the vector relative to the pre-determined hyperplane based at least in part on the calculation of the dot product.

16. The media of claim 15, wherein the software is further configured to determine the position of the vector is on same side of the pre-determined hyperplane as training data associated with the pre-determined imminent use.

17. The device of claim 9, wherein the software is further configured to:

calculate a dot product of the vector and the pre-determined hyperplane; and

determine a position of the vector relative to the pre-determined hyperplane based at least in part on the calculation of the dot product.

18. The device of claim 17, wherein the software is further configured to determine the position of the vector is on same side of the pre-determined hyperplane as training data associated with the pre-determined imminent use.

19. A device comprising:

a processor; and

one or more computer-readable non-transitory storage media coupled to the processor and embodying software that is configured when executed to;

receive real-time sensor data from N sensors on the device, the real-time sensor data corresponding to a transition in a physical state of the device caused by a user of the device;

apply a linear function to the real-time sensor data from each of the N sensors;

determine a vector based on an N-tuple comprising the derivatives;

compare the vector with a pre-determined hyperplane with N−

1 dimensions; and

determine based on the comparison whether the transition is;

an event corresponding to any of one or more pre-determined imminent uses of the device by the user;

or

a non-event not corresponding to any of the pre-determined imminent uses of the device by the user.

20. The device of claim 19, wherein the software is further configured to receive data defining the pre-determined hyperplane from a computing device of a social-networking system.

21. The device of claim 19, wherein the software is further configured to:

send the real-time sensor data to a computing device of a social-networking system; and

receive updated data re-defining the pre-determined hyperplane based at least in part on the real-time sensor data from the computing device of the social-networking system.

22. The device of claim 19, wherein the linear function comprises a filtering function, derivative function, convolution of a Heaviside or sigmoid function, or any combination thereof.

23. The device of claim 19, wherein:

the device is a mobile computing device;

the imminent intended use corresponds to physical contact between the user and the device; and

the pre-determined function comprises powering on the device.

24. The device of claim 19, wherein one or more of the sensors comprises a touch sensor, gyroscope, accelerometer, optical proximity sensor, ambient light sensor, or any combination thereof.