Patent Grant
10061453
Various technologies have been used to detect a touch input on a display area. The most popular technologies today include capacitive and resistive touch detection technology. Using resistive touch technology, often a glass panel is coated with multiple conductive layers that register touches when physical pressure is applied to the layers to force the layers to make physical contact. Using capacitive touch technology, often a glass panel is coated with material that can hold an electrical charge sensitive to a human finger. By detecting the change in the electrical charge due to a touch, a touch location can be detected. However, with resistive and capacitive touch detection technologies, the glass screen is required to be coated with a material that reduces the clarity of the glass screen. Additionally, because the entire glass screen is required to be coated with a material, manufacturing and component costs can become prohibitively expensive as larger screens are desired.
Another type of touch detection technology includes bending wave technology. One example includes the Elo Touch Systems Acoustic Pulse Recognition, commonly called APR, manufactured by Elo Touch Systems of 301 Constitution Drive, Menlo Park, Calif. 94025. The APR system includes transducers attached to the edges of a touchscreen glass that pick up the sound emitted on the glass due to a touch. However, the surface glass may pick up other external sounds and vibrations that reduce the accuracy and effectiveness of the APR system to efficiently detect a touch input. Another example includes the Surface Acoustic Wave-based technology, commonly called SAW, such as the Elo IntelliTouch Plus™ of Elo Touch Systems. The SAW technology sends ultrasonic waves in a guided pattern using reflectors on the touch screen to detect a touch. However, sending the ultrasonic waves in the guided pattern increases costs and may be difficult to achieve. Detecting additional types of inputs, such as multi-touch inputs, may not be possible or may be difficult using SAW or APR technology. Therefore there exists a need for a better way to detect an input on a surface.
Various embodiments of the invention are disclosed in the following detailed description and the accompanying drawings.
The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques. In general, the order of the steps of disclosed processes may be altered within the scope of the invention. Unless stated otherwise, a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task. As used herein, the term ‘processor’ refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions.
A detailed description of one or more embodiments of the invention is provided below along with accompanying figures that illustrate the principles of the invention. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications and equivalents. Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured.
Determining a touch input location is disclosed. For example, a user touch input on the glass surface of a display screen is detected. In some embodiments, the touch input includes multiple simultaneous touch contacts (i.e., multi-touch input). For example, a user places two fingers on a touch screen display. In some embodiments, a signal such as an acoustic or ultrasonic signal is propagated freely through a propagating medium with a surface using a transmitter coupled to the medium. When the surface is touched, the propagated signal is disturbed (e.g., the touch causes an interference with the propagated signal). In some embodiments, the disturbed signal is received at a sensor coupled to the propagating medium. By processing the received signal and comparing it against an expected signal, one or more touch contact locations on the surface associated with the touch input are at least in part determined. For example, the disturbed signal is received at a plurality of sensors and a relative time difference between when the disturbed signal was received at different sensors is used to determine time domain signals representing the time differences. In some embodiments, the time domain signals are transformed to determine spatial domain signals representing total distances traveled on the propagating medium by the respective received signals. The spatial domain signals may be compared with one or more expected signals associated with different potential touch contact locations of the touch input to select a potential location that is associated with the closest matching expected signal as the touch contact location.
In some embodiments, the touch input location is used to determine one or more of the following associated with a touch input: a gesture, a coordinate position, a time, a time frame, a direction, a velocity, a force magnitude, a proximity magnitude, a pressure, a size, and other measurable or derived parameters. In some embodiments, by detecting disturbances of a freely propagated signal, touch input detection technology can be applied to larger surface regions with less or no additional costs due to a larger surface region as compared to certain previous touch detection technologies. Additionally, the optical transparency of a touch screen may not have to be affected as compared to resistive and capacitive touch technologies. Merely by way of example, the touch detection described herein can be applied to a variety of objects such as a kiosk, an ATM, a computing device, an entertainment device, a digital signage apparatus, a cell phone, a tablet computer, a point of sale terminal, a food and restaurant apparatus, a gaming device, a casino game and application, a piece of furniture, a vehicle, an industrial application, a financial application, a medical device, an appliance, and any other objects or devices having surfaces.
Examples of transmitters 104, 106, 108, and 110 include piezoelectric transducers, electromagnetic transducers, transmitters, sensors, and/or any other transmitters and transducers capable of propagating a signal through medium 102. Examples of sensors 112, 114, 116, and 118 include piezoelectric transducers, electromagnetic transducers, laser vibrometers, transmitters, and/or any other sensors and transducers capable of detecting a signal on medium 102. In some embodiments, the transmitters and sensors shown in
Touch detector 120 is connected to the transmitters and sensors shown in
A signal detected from a sensor such as sensor 112 of
A result of DSP engine 220 may be used by microprocessor 206 to determine a location associated with a user touch input. For example, microprocessor 206 determines a hypothesis location where a touch input may been received and calculates an expected signal that is expected to be generated if a touch input was received at the hypothesis location and the expected signal is compared with a result of DSP engine 220 to determine whether a touch input was provided at the hypothesis location.
Interface 208 provides an interface for microprocessor 206 and controller 210 that allows an external component to access and/or control detector 202. For example, interface 208 allows detector 202 to communicate with application system 122 of
At 304, signal transmitters and sensors are calibrated. In some embodiments, calibrating the transmitter includes calibrating a characteristic of a signal driver and/or transmitter (e.g., strength). In some embodiments, calibrating the sensor includes calibrating a characteristic of a sensor (e.g., sensitivity). In some embodiments, the calibration of 304 is performed to optimize the coverage and improve signal-to-noise transmission/detection of a signal (e.g., acoustic or ultrasonic) to be propagated through a medium and/or a disturbance to be detected. For example, one or more components of the system of
At 306, surface disturbance detection is calibrated. In some embodiments, a test signal is propagated through a medium such as medium 102 of
In some embodiments, data determined using one or more steps of
At 308, a validation of a touch detection system is performed. For example, the system of
At 402, a signal that can be used to propagate an active signal through a surface region is sent. In some embodiments, sending the signal includes driving (e.g., using driver 214 of
At 404, the active signal that has been disturbed by a disturbance of the surface region is received. The disturbance may be associated with a user touch indication. In some embodiments, the disturbance causes the active signal that is propagating through a medium to be attenuated and/or delayed. In some embodiments, the disturbance in a selected portion of the active signal corresponds to a location on the surface that has been indicated (e.g., touched) by a user.
At 406, the received signal is processed to at least in part determine a location associated with the disturbance. In some embodiments, receiving the received signal and processing the received signal are performed on a periodic interval. For example, the received signal is captured in 5 ms intervals and processed. In some embodiments, determining the location includes extracting a desired signal from the received signal at least in part by removing or reducing undesired components of the received signal such as disturbances caused by extraneous noise and vibrations not useful in detecting a touch input. In some embodiments, determining the location includes processing the received signal and comparing the processed received signal with a calculated expected signal associated with a hypothesis touch contact location to determine whether a touch contact was received at the hypothesis location of the calculated expected signal. Multiple comparisons may be performed with various expected signals associated with different hypothesis locations until the expected signal that best matches the processed received signal is found and the hypothesis location of the matched expected signal is identified as the touch contact location(s) of a touch input. For example, signals received by sensors (e.g., sensors 112, 114, 116, and 118 of
The location, in some embodiments, is one or more locations (e.g., location coordinate(s)) on the surface region where a user has provided a touch contact. In addition to determining the location, one or more of the following information associated with the disturbance may be determined at 406: a gesture, simultaneous user indications (e.g., multi-touch input), a time, a status, a direction, a velocity, a force magnitude, a proximity magnitude, a pressure, a size, and other measurable or derived information. In some embodiments, the location is not determined at 406 if a location cannot be determined using the received signal and/or the disturbance is determined to be not associated with a user input. Information determined at 406 may be provided and/or outputted.
Although
At 502, a received signal is conditioned. In some embodiments, the received signal is a signal including a pseudorandom binary sequence that has been freely propagated through a medium with a surface that can be used to receive a user input. For example, the received signal is the signal that has been received at 404 of
At 504, an analog to digital signal conversion is performed on the signal that has been conditioned at 502. In various embodiments, any number of standard analog to digital signal converters may be used.
At 506, a time domain signal capturing a received signal time delay caused by a touch input disturbance is determined. In some embodiments, determining the time domain signal includes correlating the received signal (e.g., signal resulting from 504) to locate a time offset in the converted signal (e.g., perform pseudorandom binary sequence deconvolution) where a signal portion that likely corresponds to a reference signal (e.g., reference pseudorandom binary sequence that has been transmitted through the medium) is located. For example, a result of the correlation can be plotted as a graph of time within the received and converted signal (e.g., time-lag between the signals) vs. a measure of similarity. In some embodiments, performing the correlation includes performing a plurality of correlations. For example, a coarse correlation is first performed then a second level of fine correlation is performed. In some embodiments, a baseline signal that has not been disturbed by a touch input disturbance is removed in the resulting time domain signal. For example, a baseline signal (e.g., determined at 306 of
At 508, the time domain signal is converted to a spatial domain signal. In some embodiments, converting the time domain signal includes converting the time domain signal determined at 506 into a spatial domain signal that translates the time delay represented in the time domain signal to a distance traveled by the received signal in the propagating medium due to the touch input disturbance. For example, a time domain signal that can be graphed as time within the received and converted signal vs. a measure of similarity is converted to a spatial domain signal that can be graphed as distance traveled in the medium vs. the measure of similarity.
In some embodiments, performing the conversion includes performing dispersion compensation. For example, using a dispersion curve characterizing the propagating medium, time values of the time domain signal are translated to distance values in the spatial domain signal. In some embodiments, a resulting curve of the time domain signal representing a distance likely traveled by the received signal due to a touch input disturbance is narrower than the curve contained in the time domain signal representing the time delay likely caused by the touch input disturbance. In some embodiments, the time domain signal is filtered using a match filter to reduce undesired noise in the signal. For example, using a template signal that represents an ideal shape of a spatial domain signal, the converted spatial domain signal is match filtered (e.g., spatial domain signal correlated with the template signal) to reduce noise not contained in the bandwidth of the template signal. The template signal may be predetermined (e.g., determined at 306 of
At 510, the spatial domain signal is compared with one or more expected signals to determine a touch input captured by the received signal. In some embodiments, comparing the spatial domain signal with the expected signal includes generating expected signals that would result if a touch contact was received at hypothesis locations. For example, a hypothesis set of one or more locations (e.g., single touch or multi-touch locations) where a touch input might have been received on a touch input surface is determined, and an expected spatial domain signal that would result at 508 if touch contacts were received at the hypothesis set of location(s) is determined (e.g., determined for a specific transmitter and sensor pair using data measured at 306 of
The proximity of location(s) of a hypothesis set to the actual touch contact location(s) captured by the received signal may be proportional to the degree of similarity between the expected signal of the hypothesis set and the spatial signal determined at 508. In some embodiments, signals received by sensors (e.g., sensors 112, 114, 116, and 118 of
At 602, a first correlation is performed. In some embodiments, performing the first correlation includes correlating a received signal (e.g., resulting converted signal determined at 504 of
At 604, a second correlation is performed based on a result of the first correlation. Performing the second correlation includes correlating (e.g., cross-correlation or convolution similar to step 602) a received signal (e.g., resulting converted signal determined at 504 of
At 702, a hypothesis of a number of simultaneous touch contacts included in a touch input is determined. In some embodiments, when detecting a location of a touch contact, the number of simultaneous contacts being made to a touch input surface (e.g., surface of medium 102 of
At 704, one or more hypothesis sets of one or more touch contact locations associated with the hypothesis number of simultaneous touch contacts are determined. In some embodiments, it is desired to determine the coordinate locations of fingers touching a touch input surface. In some embodiments, in order to determine the touch contact locations, one or more hypothesis sets are determined on potential location(s) of touch contact(s) and each hypothesis set is tested to determine which hypothesis set is most consistent with a detected data.
In some embodiments, determining the hypothesis set of potential touch contact locations includes dividing a touch input surface into a constrained number of points (e.g., divide into a coordinate grid) where a touch contact may be detected. For example, in order to initially constrain the number of hypothesis sets to be tested, the touch input surface is divided into a coordinate grid with relatively large spacing between the possible coordinates. Each hypothesis set includes a number of location identifiers (e.g., location coordinates) that match the hypothesis number determined in 702. For example, if two was determined to be the hypothesis number in 702, each hypothesis set includes two location coordinates on the determined coordinate grid that correspond to potential locations of touch contacts of a received touch input. In some embodiments, determining the one or more hypothesis sets includes determining exhaustive hypothesis sets that exhaustively cover all possible touch contact location combinations on the determined coordinate grid for the determined hypothesis number of simultaneous touch contacts. In some embodiments, a previously determined touch contact location(s) of a previous determined touch input is initialized as the touch contact location(s) of a hypothesis set.
At 706, a selected hypothesis set is selected among the one or more hypothesis sets of touch contact location(s) as best corresponding to touch contact locations captured by detected signal(s). In some embodiments, one or more propagated active signals (e.g., signal transmitted at 402 of
At 708, it is determined whether additional optimization is to be performed. In some embodiments, determining whether additional optimization is to be performed includes determining whether any new hypothesis set(s) of touch contact location(s) should be analyzed in order to attempt to determine a better selected hypothesis set. For example, a first execution of step 706 utilizes hypothesis sets determined using locations on a larger distance increment coordinate grid overlaid on a touch input surface and additional optimization is to be performed using new hypothesis sets that include locations from a coordinate grid with smaller distance increments. Additional optimizations may be performed any number of times. In some embodiments, the number of times additional optimizations are performed is predetermined. In some embodiments, the number of times additional optimizations are performed is dynamically determined. For example, additional optimizations are performed until a comparison threshold indicator value for the selected hypothesis set is reached and/or a comparison indicator for the selected hypothesis does not improve by a threshold amount. In some embodiments, for each optimization iteration, optimization may be performed for only a single touch contact location of the selected hypothesis set and other touch contact locations of the selected hypothesis may be optimized in a subsequent iteration of optimization.
If at 708 it is determined that additional optimization should be performed, at 710, one or more new hypothesis sets of one or more touch contact locations associated with the hypothesis number of the touch contacts are determined based on the selected hypothesis set. In some embodiments, determining the new hypothesis sets includes determining location points (e.g., more detailed resolution locations on a coordinate grid with smaller distance increments) near one of the touch contact locations of the selected hypothesis set in an attempt to refine the one of the touch contact locations of the selected hypothesis set. The new hypothesis sets may each include one of the newly determined location points, and the other touch contact location(s), if any, of a new hypothesis set may be the same locations as the previously selected hypothesis set. In some embodiments, the new hypothesis sets may attempt to refine all touch contact locations of the selected hypothesis set. The process proceeds back to 706, whether or not a newly selected hypothesis set (e.g., if previously selected hypothesis set still best corresponds to detected signal(s), the previously selected hypothesis set is retained as the new selected hypothesis set) is selected among the newly determined hypothesis sets of touch contact location(s).
If at 708 it is determined that additional optimization should not be performed, at 712, it is determined whether a threshold has been reached. In some embodiments, determining whether a threshold has been reached includes determining whether the determined hypothesis number of contact points should be modified to test whether a different number of contact points has been received for the touch input. In some embodiments, determining whether the threshold has been reached includes determining whether a comparison threshold indicator value for the selected hypothesis set has been reached and/or a comparison indicator for the selected hypothesis did not improve by a threshold amount since a previous determination of a comparison indicator for a previously selected hypothesis set. In some embodiments, determining whether the threshold has been reached includes determining whether a threshold amount of energy still remains in a detected signal after accounting for the expected signal of the selected hypothesis set. For example, a threshold amount of energy still remains if an additional touch contact needs be included in the selected hypothesis set.
If at 712, it is determined that the threshold has not been reached, the process continues to 702 where a new hypothesis number of touch inputs is determined. The new hypothesis number may be based on the previous hypothesis number. For example, the previous hypothesis number is incremented by one as the new hypothesis number.
If at 712, it is determined that the threshold has been reached, at 714, the selected hypothesis set is indicated as the detected location(s) of touch contact(s) of the touch input. For example, a location coordinate(s) of a touch contact(s) is provided.
At 802, for each hypothesis set (e.g., determined at 704 of
In some embodiments, in the event the hypothesis set includes more than one touch contact location (e.g., multi-touch input), expected signal for each individual touch contact location is determined separately and combined together. For example, an expected signal that would result if a touch contact was provided at a single touch contact location is added with other single touch contact expected signals (e.g., effects from multiple simultaneous touch contacts add linearly) to generate a single expected signal that would result if the touch contacts of the added signals were provided simultaneously.
In some embodiments, the expected signal for a single touch contact is modeled as the function:
C*P (x−d)
where C is a function coefficient (e.g., complex coefficient) and P(x) is a function and d is the total path distance between a transmitter (e.g., transmitter of a signal desired to be simulated) to a touch input location and between the touch input location and a sensor (e.g., receiver of the signal desired to be simulated).
In some embodiments, the expected signal for one or more touch contacts is modeled as the function:
where j indicates which touch contact and N is the number of total simultaneous touch contacts being modeled (e.g., hypothesis number determined at 702 of
At 804, corresponding detected signals are compared with corresponding expected signals. In some embodiments, the detected signals include spatial domain signals determined at 508 of
where ε(rx, tx) is the cost function, q(x) is the detected signal, and Σj=1NCj P(x−dj) is the expected signal. In some embodiments, the global cost function for a hypothesis set analyzed for more than one (e.g., all) transmitter/sensor pairs is modeled as:
where ε is the global cost function, Z is the number of total transmitter/sensor pairs, i indicates the particular transmitter/sensor pair, and ε(rx, tx)i is the cost function of the particular transmitter/sensor pair.
At 806, a selected hypothesis set of touch contact location(s) is selected among the one or more hypothesis sets of touch contact location(s) as best corresponding to detected signal(s). In some embodiments, the selected hypothesis set is selected among hypothesis sets determined at 704 or 710 of
Although the foregoing embodiments have been described in some detail for purposes of clarity of understanding, the invention is not limited to the details provided. There are many alternative ways of implementing the invention. The disclosed embodiments are illustrative and not restrictive.
This application is a continuation of co-pending U.S. patent application Ser. No. 13/913,092, entitled DETECTING MULTI-TOUCH INPUTS filed Jun. 7, 2013 which is incorporated herein by reference for all purposes.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4488000 | Glenn | Dec 1984 | A |
| 4594695 | Garconnat | Jun 1986 | A |
| 4966150 | Etienne | Oct 1990 | A |
| 5334805 | Knowles | Aug 1994 | A |
| 5451723 | Huang | Sep 1995 | A |
| 5563849 | Hall | Oct 1996 | A |
| 5573077 | Knowles | Nov 1996 | A |
| 5635643 | Maji | Jun 1997 | A |
| 5637839 | Yamaguchi | Jun 1997 | A |
| 5708460 | Young | Jan 1998 | A |
| 5854450 | Kent | Dec 1998 | A |
| 5912659 | Rutledge | Jun 1999 | A |
| 6232960 | Goldman | May 2001 | B1 |
| 6236391 | Kent | May 2001 | B1 |
| 6307942 | Azima | Oct 2001 | B1 |
| 6492979 | Kent | Dec 2002 | B1 |
| 6498603 | Wallace | Dec 2002 | B1 |
| 6633280 | Matsumoto et al. | Oct 2003 | B1 |
| 6788296 | Ikeda | Sep 2004 | B2 |
| 6798403 | Kitada | Sep 2004 | B2 |
| 7218248 | Kong | May 2007 | B2 |
| RE39881 | Flowers | Oct 2007 | E |
| 7554246 | Maruyama | Jun 2009 | B2 |
| 7880721 | Suzuki | Feb 2011 | B2 |
| 8358277 | Mosby | Jan 2013 | B2 |
| 8436806 | Almalki | May 2013 | B2 |
| 8787599 | Grattan | Jul 2014 | B2 |
| 8917249 | Buuck | Dec 2014 | B1 |
| 9030436 | Ikeda | May 2015 | B2 |
| 9099971 | Lynn | Aug 2015 | B2 |
| 9189109 | Sheng | Nov 2015 | B2 |
| 9348468 | Altekar | May 2016 | B2 |
| 9594450 | Lynn | Mar 2017 | B2 |
| 9983718 | Sheng | May 2018 | B2 |
| 20010050677 | Tosaya | Dec 2001 | A1 |
| 20020047833 | Kitada | Apr 2002 | A1 |
| 20020185981 | Dietz | Dec 2002 | A1 |
| 20030161484 | Kanamori | Aug 2003 | A1 |
| 20030197691 | Fujiwara | Oct 2003 | A1 |
| 20030206162 | Roberts | Nov 2003 | A1 |
| 20040133366 | Sullivan | Jul 2004 | A1 |
| 20040160421 | Sullivan | Aug 2004 | A1 |
| 20040183788 | Kurashima | Sep 2004 | A1 |
| 20040203594 | Kotzin | Oct 2004 | A1 |
| 20040239649 | Ludtke | Dec 2004 | A1 |
| 20040246239 | Knowles | Dec 2004 | A1 |
| 20050063553 | Ozawa | Mar 2005 | A1 |
| 20050146511 | Hill et al. | Jul 2005 | A1 |
| 20050146512 | Hill | Jul 2005 | A1 |
| 20050174338 | Ing | Aug 2005 | A1 |
| 20050226455 | Aubauer | Oct 2005 | A1 |
| 20050248540 | Newton | Nov 2005 | A1 |
| 20060071912 | Hill | Apr 2006 | A1 |
| 20060109261 | Chou | May 2006 | A1 |
| 20060114233 | Radivojevic | Jun 2006 | A1 |
| 20060139340 | Geaghan | Jun 2006 | A1 |
| 20060152499 | Roberts | Jul 2006 | A1 |
| 20060166681 | Lohbihler | Jul 2006 | A1 |
| 20060197753 | Hotelling | Sep 2006 | A1 |
| 20060262104 | Sullivan | Nov 2006 | A1 |
| 20060279548 | Geaghan | Dec 2006 | A1 |
| 20070019825 | Marumoto | Jan 2007 | A1 |
| 20070109274 | Reynolds | May 2007 | A1 |
| 20070165009 | Sakurai | Jul 2007 | A1 |
| 20070171212 | Sakurai | Jul 2007 | A1 |
| 20070211022 | Boillot | Sep 2007 | A1 |
| 20070214462 | Boillot | Sep 2007 | A1 |
| 20070229479 | Choo | Oct 2007 | A1 |
| 20070279398 | Tsumura | Dec 2007 | A1 |
| 20080018618 | Hill | Jan 2008 | A1 |
| 20080030479 | Lowles | Feb 2008 | A1 |
| 20080062151 | Kent | Mar 2008 | A1 |
| 20080081671 | Wang | Apr 2008 | A1 |
| 20080105470 | Van De Ven | May 2008 | A1 |
| 20080111788 | Rosenberg | May 2008 | A1 |
| 20080169132 | Ding et al. | Jul 2008 | A1 |
| 20080174565 | Chang | Jul 2008 | A1 |
| 20080198145 | Knowles | Aug 2008 | A1 |
| 20080231612 | Hill | Sep 2008 | A1 |
| 20080259030 | Holtzman et al. | Oct 2008 | A1 |
| 20080266266 | Kent et al. | Oct 2008 | A1 |
| 20080284755 | Hardie-Bick | Nov 2008 | A1 |
| 20090009488 | D' Souza | Jan 2009 | A1 |
| 20090103853 | Daniel | Apr 2009 | A1 |
| 20090116661 | Hetherington | May 2009 | A1 |
| 20090146533 | Leskinen | Jun 2009 | A1 |
| 20090160728 | Emrick | Jun 2009 | A1 |
| 20090167704 | Terlizzi | Jul 2009 | A1 |
| 20090237372 | Kim | Sep 2009 | A1 |
| 20090271004 | Zecchin | Oct 2009 | A1 |
| 20090273583 | Norhammar | Nov 2009 | A1 |
| 20090315848 | Ku | Dec 2009 | A1 |
| 20100026667 | Bernstein | Feb 2010 | A1 |
| 20100027810 | Marton | Feb 2010 | A1 |
| 20100044121 | Simon | Feb 2010 | A1 |
| 20100045635 | Soo | Feb 2010 | A1 |
| 20100079264 | Hoellwarth | Apr 2010 | A1 |
| 20100117933 | Gothard | May 2010 | A1 |
| 20100141408 | Doy | Jun 2010 | A1 |
| 20100156818 | Burrough | Jun 2010 | A1 |
| 20100165215 | Shim | Jul 2010 | A1 |
| 20100185989 | Shiplacoff | Jul 2010 | A1 |
| 20100188356 | Vu | Jul 2010 | A1 |
| 20100245265 | Sato | Sep 2010 | A1 |
| 20100269040 | Lee | Oct 2010 | A1 |
| 20100277431 | Klinghult | Nov 2010 | A1 |
| 20100315373 | Steinhauser | Dec 2010 | A1 |
| 20100321312 | Han | Dec 2010 | A1 |
| 20100321325 | Springer | Dec 2010 | A1 |
| 20100321337 | Liao | Dec 2010 | A1 |
| 20110001707 | Faubert | Jan 2011 | A1 |
| 20110001708 | Sleeman | Jan 2011 | A1 |
| 20110012717 | Pance | Jan 2011 | A1 |
| 20110018695 | Bells | Jan 2011 | A1 |
| 20110025649 | Sheikhzadeh | Feb 2011 | A1 |
| 20110042152 | Wu | Feb 2011 | A1 |
| 20110057903 | Yamano | Mar 2011 | A1 |
| 20110063228 | St. Pierre et al. | Mar 2011 | A1 |
| 20110080350 | Almalki | Apr 2011 | A1 |
| 20110084912 | Almalki | Apr 2011 | A1 |
| 20110084937 | Chang | Apr 2011 | A1 |
| 20110155479 | Oda | Jun 2011 | A1 |
| 20110156967 | Oh | Jun 2011 | A1 |
| 20110167391 | Momeyer | Jul 2011 | A1 |
| 20110175813 | Sarwar | Jul 2011 | A1 |
| 20110182443 | Gant | Jul 2011 | A1 |
| 20110191680 | Chae | Aug 2011 | A1 |
| 20110199342 | Vartanian | Aug 2011 | A1 |
| 20110213223 | Kruglick | Sep 2011 | A1 |
| 20110222372 | O'Donovan | Sep 2011 | A1 |
| 20110234545 | Tanaka et al. | Sep 2011 | A1 |
| 20110260990 | Ali | Oct 2011 | A1 |
| 20110279382 | Pertuit | Nov 2011 | A1 |
| 20110298670 | Jung | Dec 2011 | A1 |
| 20110300845 | Lee | Dec 2011 | A1 |
| 20110304577 | Brown | Dec 2011 | A1 |
| 20110316790 | Ollila | Dec 2011 | A1 |
| 20120002820 | Leichter | Jan 2012 | A1 |
| 20120007837 | Kent | Jan 2012 | A1 |
| 20120026114 | Lee | Feb 2012 | A1 |
| 20120050230 | Harris | Mar 2012 | A1 |
| 20120062564 | Miyashita | Mar 2012 | A1 |
| 20120068939 | Pemberton-Pigott | Mar 2012 | A1 |
| 20120068970 | Pemberton-Pigott | Mar 2012 | A1 |
| 20120081337 | Camp, Jr. | Apr 2012 | A1 |
| 20120088548 | Yun | Apr 2012 | A1 |
| 20120092964 | Badiey | Apr 2012 | A1 |
| 20120120031 | Thuillier | May 2012 | A1 |
| 20120126962 | Ujii | May 2012 | A1 |
| 20120127088 | Pance | May 2012 | A1 |
| 20120140954 | Ranta | Jun 2012 | A1 |
| 20120149437 | Zurek | Jun 2012 | A1 |
| 20120188194 | Sulem | Jul 2012 | A1 |
| 20120188889 | Sambhwani | Jul 2012 | A1 |
| 20120194466 | Posamentier | Aug 2012 | A1 |
| 20120200517 | Nikolovski | Aug 2012 | A1 |
| 20120229407 | Harris | Sep 2012 | A1 |
| 20120232834 | Roche | Sep 2012 | A1 |
| 20120242603 | Engelhardt | Sep 2012 | A1 |
| 20120270605 | Garrone | Oct 2012 | A1 |
| 20120272089 | Hatfield | Oct 2012 | A1 |
| 20120278490 | Sennett | Nov 2012 | A1 |
| 20120282944 | Zhao | Nov 2012 | A1 |
| 20120300956 | Horii | Nov 2012 | A1 |
| 20120306823 | Pance | Dec 2012 | A1 |
| 20130011144 | Amiri Farahani | Jan 2013 | A1 |
| 20130050133 | Brakensiek | Feb 2013 | A1 |
| 20130050154 | Guy | Feb 2013 | A1 |
| 20130057491 | Chu | Mar 2013 | A1 |
| 20130059532 | Mahanfar | Mar 2013 | A1 |
| 20130082970 | Frey | Apr 2013 | A1 |
| 20130141365 | Lynn | Jun 2013 | A1 |
| 20130194208 | Miyanaka | Aug 2013 | A1 |
| 20130249831 | Harris | Sep 2013 | A1 |
| 20140028576 | Shahparnia | Jan 2014 | A1 |
| 20140078070 | Armstrong-Muntner | Mar 2014 | A1 |
| 20140078086 | Bledsoe | Mar 2014 | A1 |
| 20140078109 | Armstrong-Muntner | Mar 2014 | A1 |
| 20140078112 | Sheng | Mar 2014 | A1 |
| 20140185834 | Frömel | Jul 2014 | A1 |
| 20140247230 | Sheng | Sep 2014 | A1 |
| 20140247250 | Sheng | Sep 2014 | A1 |
| 20140317722 | Tartz | Oct 2014 | A1 |
| 20140368464 | Singnurkar | Dec 2014 | A1 |
| 20150002415 | Lee | Jan 2015 | A1 |
| 20150009185 | Shi | Jan 2015 | A1 |
| 20150109239 | Mao | Apr 2015 | A1 |
| 20150199035 | Chang | Jul 2015 | A1 |
| 20150253895 | Kim | Sep 2015 | A1 |
| 20150346850 | Vandermeijden | Dec 2015 | A1 |
| 20160070404 | Kerr | Mar 2016 | A1 |
| 20160091308 | Oliaei | Mar 2016 | A1 |
| 20160162044 | Ciou | Jun 2016 | A1 |
| 20160179249 | Ballan | Jun 2016 | A1 |
| 20160209944 | Shim | Jul 2016 | A1 |
| 20160282965 | Jensen | Sep 2016 | A1 |
| 20160349922 | Choi | Dec 2016 | A1 |
| 20170010697 | Jiang | Jan 2017 | A1 |
| 20170083164 | Sheng | Mar 2017 | A1 |
| 20180032211 | King | Feb 2018 | A1 |
| Number | Date | Country |
|---|---|---|
| 101373415 | Feb 2009 | CN |
| 101669088 | Mar 2010 | CN |
| 2315101 | Apr 2011 | EP |
| 2315101 | Jan 2014 | EP |
| 2948787 | Feb 2011 | FR |
| 20040017272 | Feb 2004 | KR |
| 20070005580 | Jan 2007 | KR |
| 20080005990 | Jan 2008 | KR |
| 20110001839 | Jan 2011 | KR |
| WO-03005292 | Jan 2003 | WO |
| WO-2006115947 | Jun 2007 | WO |
| WO-2011010037 | Jan 2011 | WO |
| WO-2011024434 | Mar 2011 | WO |
| WO-2011048433 | Apr 2011 | WO |
| WO-2011051722 | May 2011 | WO |
| WO-2012010912 | Jan 2012 | WO |
| WO-2014209757 | Dec 2014 | WO |
| WO-2015027017 | Feb 2015 | WO |
| Entry |
|---|
| Liu et al., ‘Acoustic Wave Approach for Multi-Touch Tactile Sensing’, Micro-NanoMechatronics and Human Science, 2009. MHS 2009. International Symposium, Nov. 9-11, 2009. |
| T Benedict et al. ‘The joint estimation of signal and noise from the sum envelope.’ IEEE Transactions on Information Theory 13.3, pp. 447-454. Jul. 1, 1967. |
| Number | Date | Country | |
|---|---|---|---|
| 20160224162 A1 | Aug 2016 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13913092 | Jun 2013 | US |
| Child | 15096587 | US |
