Input to an electronic apparatus

• US 8,451,254 B2
• Filed: 07/23/2010
• Issued: 05/28/2013
• Est. Priority Date: 01/25/2008
• Status: Active Grant

• Current Assignee: InputDynamics Limited
• Original Assignee: InputDynamics Limited
• Inventors: Bisutti, Giovanni, Godsill, Simon John
• Primary Examiner(s): Lefkowitz, Sumati
• Assistant Examiner(s): HORNER, JONATHAN R
• Application Number:
  US12/842,777
• Publication Number:
  US 20110316784A1
• Time in Patent Office: 1,040 Days
• Field of Search: 345156-157, 345/168, 345/177
• US Class Current: 345/177
• CPC Class Codes: G06F 1/1626 with a single-body enclosure integrating a flat display, e.g. Personal Digital Assistants [PDAs]
• CPC Class Codes: G06F 1/1684 Constructional details or arrangements related to integrated I/O peripherals not covered by groups G06F1/1635 - G06F1/1675
• 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 2200/1636 Sensing arrangement for detection of a tap gesture on the housing
• 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 2203/04806 Zoom, i.e. interaction techniques or interactors for controlling the zooming operation
• CPC Class Codes: G06F 2203/04808 Several contacts: gestures triggering a specific function, e.g. scrolling, zooming, right-click, when the user establishes several contacts with the surface simultaneously; e.g. using several fingers or a combination of fingers and pen
• CPC Class Codes: G06F 3/0433 in which the acoustic waves are either generated by a movable member and propagated within a surface layer or propagated within a surface layer and captured by a movable member
• CPC Class Codes: G06F 3/04883 for inputting data by handwriting, e.g. gesture or text

Abstract

An electronic device, comprising a processing system; a sensor for detecting vibrations caused by a user striking the device and outputting a signal indicative of a parameter of those vibrations; wherein the processing system is configured to analyse the signal output from the sensor and determine a parameter related to the location of the strike. Other methods and systems for making inputs to electronic devices are also described.

Claims

1. An electronic device, comprising

a processing system;

a sensor for detecting vibrations caused by a user striking the device and outputting a signal indicative of a parameter of those vibrations;

wherein

the processing system is configured to analyse the signal output from the sensor and detect a location of the strike,

wherein the detected location of the strike is utilised by the device to select a function to perform;

wherein the device is a mobile phone handset comprising at least one keypad and one display screen;

wherein the sensor is a microphone provided primarily to detect sound from a user speaking;

wherein the signal indicative of a parameter of the vibrations is a signal describing the pattern of vibrations;

wherein one or more input keys or areas are located on the display screen, the edges of the handset, the keypad, the keypad buttons or other surfaces of the apparatus, operable by a user to enter a character or command; and

wherein each input area produces a unique acoustic signature when struck by the user characteristic of said input area due to an asymmetric internal layout of the handset internal components or to variation in design features of the input areas,

wherein said mobile phone is configured to use said microphone for picking up the acoustic signature of an input area struck by the user; and

wherein said processing system is configured to;

process said unique acoustic signature to detect said location of said strike using a stochastic process model defined by statistical parameter data comprising a set of statistical parameters representing signals from each of a plurality of different locations of said strike on said mobile phone handset; and

to;

input signal data from said sensor for a single said strike on the surface of said mobile phone;

express characteristics of said signal data using said stochastic process model defined by said statistical parameter data to determine for each of said different locations a probability that said single said strike is in said location; and

determine a probable closest match of data from said sensor for said single said strike to a said location to determine the location of said single said strike;

wherein said processing system is able to identify said location of a single said strike from said unique acoustic signature due to said asymmetric internal layout of said handset internal components.

2. The electronic device according to claim 1, wherein the determination of the area struck by the user utilizes a stochastic process model having different parameters for different tapping positions and different styles of tapping, where the model uses templates learned from training data from the mobile phone handset and the classification of the signal takes place according to which, if any, of the templates is a closest match.

3. The electronic device according to claim 2, wherein said classification is configured to use multi-class matched filtering.

4. The electronic device according to claim 1, wherein said process to detect said location of said strike comprises a feature extraction process to extract features of said signal and a classification process to classify said features.

5. The electronic device according to claim 1, wherein the determination of location comprises providing compensation for different striking implements.

6. The electronic device according to claim 1, wherein color coded input areas are provided on the exterior of the device which enable the quick selection of items.

7. The electronic device according to claim 1, wherein the user is able to select areas or implement commands on a display screen by striking two or more input areas at the same time.

8. The electronic device according to claim 7, wherein the command is a zooming in command triggered by striking at the same time with two fingers or implements the opposite corner of a particular area of the display screen.

9. The electronic device according to claim 1, wherein the user is able to manually calibrate or to initiate a semi-automatic calibration procedure of the input areas in case of reduction of the reliability of the recognition process.

10. The electronic device according to claim 1, further comprising at least one predefined input area operable by a user to input information to the device;

and in which the input area is operable to enter one of two or more different characters by striking the area in one of two or more different ways.

11. The electronic device according to claim 1, wherein said processing system is further configured to identify a sliding or rotating action by said user on said mobile phone handset.

12. A method of detecting user input to an electronic device, comprising detecting vibrations caused by a user striking an area of the device, determining the area struck by the user from a parameter of the vibrations, and executing a command in response to the detection, wherein the command is selected dependent on the detected location, wherein each input area of a plurality of areas each input area produces a unique acoustic signature when struck by the user characteristic of said input area (80-83, 89) due to an asymmetric internal layout of the electronic device internal components or to variation in design features of the input areas, and

wherein the method comprises processing said unique acoustic signature to detect said location of said strike using a stochastic process model defined by statistical parameter data comprising a set of statistical parameters representing signals from each of a plurality of different locations of said strike on said mobile phone handset;

wherein said processing comprises;

inputting signal data from said sensor for a single said strike on the surface of said mobile phone;

expressing characteristics of said signal data using said stochastic process model defined by said statistical parameter data to determine for each of said different locations a probability that said single said strike is in said location; and

determining a probable closest match of data from said sensor for said single said strike to a said location to determine the location of said single said strike;

wherein said processing is able to identify said location of a single said strike from said unique acoustic signature due to said asymmetric internal layout of said handset internal components.

13. The method as claimed in claim 12, wherein said processing comprises either

i) using a stochastic process model having different parameters for different tapping positions and different styles of tapping, where the model is based on templates learned from training data from the electronic device and the classification of the signal takes place according to which, if any, of the templates is a closest match;

or

ii) performing a feature extraction process followed by a classification process.

14. The method as claimed in claim 12 used for upgrading a mobile phone handset to add touch-screen functionality using an existing microphone of the mobile phone handset, the method comprising adding processing capability to implement the method of claim 12.

15. A non-transitory machine readable storage medium storing instructions, which when executed by a computing device implement the method of claim 12.

16. An electronic device, comprising

a processing system;

a sensor for detecting vibrations caused by a user striking the device and outputting a signal indicative of a parameter of those vibrations;

wherein

the processing system is configured to analyse the signal output from the sensor and detect a location of the strike,

wherein the detected location of the strike is utilised by the device to select a function to perform;

wherein the device is a mobile phone handset comprising at least one keypad and one display screen;

wherein the sensor is a microphone provided primarily to detect sound from a user speaking;

wherein the signal indicative of a parameter of the vibrations is a signal describing the pattern of vibrations;

wherein one or more input keys or areas are located on the display screen, the edges of the handset, the keypad, the keypad buttons or other surfaces of the apparatus, operable by a user to enter a character or command; and

wherein each input area produces a unique acoustic signature when struck by the user characteristic of said input area due to an asymmetric internal layout of the handset internal components or to variation in design features of the input areas,

wherein said mobile phone is configured to use said microphone for picking up the acoustic signature of an input area struck by the user;

wherein said mobile phone handset is configured to process said unique acoustic signature to detect said location of said strike; and

wherein said processing system is configured to;

capture training data representing signals from said sensor for a plurality of different said training locations of said strike on said mobile phone handset;

process said training data to determine statistical parameter data for said different locations of said strike, said statistical parameter data comprising a set of statistical parameters derived from signals from each of said plurality of said different said locations, wherein said set of statistical parameters defines a stochastic process model;

input signal data from said sensor for a single said strike on the surface of said mobile phone;

express characteristics of said signal data using said stochastic process model defined by said statistical parameter data to determine for said training locations a probability that said single said strike is in said training location; and

determine a probable closest match of data from said sensor for said single said strike to a said training location to determine said location of said single said strike;

wherein said processing system is able to identify said location of a single said strike from said unique acoustic signature due to said asymmetric internal layout of said handset internal components.