DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are presently pending and under examination.
Information Disclosure Statement
The information disclosure statements were (IDS) submitted on 03/24/2025 and 07/28/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-10 and 15-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Stern et al. (US 2023/0019413 A1), hereinafter Stern.
Regarding claim 1, Stern discloses a wearable electronic device (wearable device 110) comprising:
a plurality of electrodes (electrodes 235, [0031] “The wearable device may include a plurality of electrodes disposed on an interior of the wearable device and configured to obtain biopotential signals from the user's arm.”);
sensing circuitry (biopotential chip 250);
pose detection circuitry (IMU 250B), different from the sensing circuitry (Examiner notes that although IMU 250B is integrated into the biopotential chip, it is distinct from the signals received from electrodes while IMU receives signals from an accelerometer and gyroscope: [0058]); and
one or more processors (CPU 210), wherein the one or more processors are configured to:
in a first mode of operation ([0049] gesture detection mode, “the biopotential sensor application, via the CPU 210, may instruct the biopotential sensor 205 to change connection states, such as from gesture detection mode to ECG detection mode”, [0063] biopotential sensing mode):
configure the plurality of electrodes and the sensing circuitry in a first configuration to detect first physiological signals corresponding to a first type of physiological measurement using the plurality of electrodes and using the pose detection circuitry ([0106] “the wearable device 110 may obtain biopotential data based on signals received by both hub electrodes 508 and wristband electrodes 512 and processed by the ADCs 250E of the biopotential chip 502. In some cases, the biopotential chip 502 may obtain wrist location data based on outputs from the accelerometer and the gyroscope”, [0074] “For example, an impendence measurements may be periodically or simultaneously obtained with gesture data (e.g., EMG and wrist motion data), and the multiple data sources may analyzed by the ML classifier to determine gesture classifications and/or to modify confidence rating or others parameters relating to classifications or confidences.”); and
in a second mode of operation ([0049] ECG detection mode, “the biopotential sensor application, via the CPU 210, may instruct the biopotential sensor 205 to change connection states, such as from gesture detection mode to ECG detection mode”, [0063] an ECG detection mode):
configure the plurality of electrodes and the sensing circuitry in a second configuration to detect second physiological signals corresponding to a second type of physiological measurement, different from the first type of physiological measurement, using the plurality of electrodes ([0063] “the processor 250A may control connection states between electrodes 235 and biopotential chips, an ECG chip 270, or specific differential amplifiers within biopotential chips, as discussed herein. In these cases, the processor 250A may cause switches or a multiplexer to change signal pathways from form a currently active connection state (for a first mode) to a new active connection state (for a second mode). For instance, the connection states may correspond to various modes, such as a biopotential sensing mode, a training mode, an ECG detection mode…”, [0069] “a processor of the biopotential sensor 205 may detect that a user has contacted the ECG electrode (e.g., with one or more fingers of the hand opposite the arm on which the biopotential sensor 205 is worn), and in response to determining that the user has contacted the ECG electrode, the system may switch the signal pathway components for the hub electrodes and/or wristband electrodes such that at least some of the signals from these electrodes are directed to the ECG chip 270.”).
Regarding claim 2, Stern discloses the wearable electronic device of claim 1 (as shown above), wherein the first type of physiological measurement is an electromyogram ([0074] “an impendence measurements may be periodically or simultaneously obtained with gesture data (e.g., EMG and wrist motion data), and the multiple data sources may analyzed by the ML classifier to determine gesture classifications and/or to modify confidence rating or others parameters relating to classifications or confidences.”).
Regarding claim 3, Stern discloses the wearable electronic device of claim 1 (as shown above), wherein the second type of physiological measurement is an electrocardiogram (ECG electrode 275, [0070] “The ECG chip 270 may process the biopotential signals from all (or subsets of) the hub electrodes 235A and the wristband electrodes 235B and the biopotential signal from the ECG electrode 275, and generate ECG data… The ECG processor 280 may receive the ECG data and produce an electrocardiogram based on the ECG data.”).
Regarding claim 4, Stern discloses the wearable electronic device of claim 1 (as shown above), wherein the pose detection circuitry includes one or more inertial measurement units (Figure 2B: IMU 250B, [0058]).
Regarding claim 5, Stern discloses the wearable electronic device of claim 1 (as shown above), further comprising: switching circuitry (multiplexer 310); and a plurality of amplifiers including a first amplifier and a second amplifier (Figure 3A: amplifiers 315, [0064] “the switches or multiplexer may be configured to apply a plurality of connection states between the plurality of electrodes 235 and the differential amplifiers (of a same or a different biopotential chip) or analog inputs of an ECG chip”), wherein: in the first mode of operation, a first pair of the plurality of electrodes are differentially coupled to the first amplifier via the switching circuitry ([0064] “the switches or multiplexer may apply a first connection state in which a first pair of electrodes of the plurality of electrodes 235 is connected to a first differential amplifier. The first differential amplifier may be configured to amplify a difference in signals obtained by the first pair of electrodes in the first connection state”), and in the second mode of operation, a second pair of the plurality of electrodes, different from the first pair of the plurality of electrodes, are differentially coupled to the second amplifier via the switching circuitry ([0064] “The switches or the multiplexer may then apply a second connection state in which a second pair of electrodes of the plurality of electrodes 235 is connected to the first differential amplifier, and the first differential amplifier may be configured to amplify a difference in signals obtained by the second pair of electrodes in the second connection state. In some cases, at least one of the electrodes of the second pair of electrodes is not included in the first pair of electrodes”, [0069] “For instance, the switches or multiplexer may be controlled by the processor 250A to change the connection state between the first connection state or the second connection state to the third connection state. For example, a processor of the biopotential sensor 205 may detect that a user has contacted the ECG electrode (e.g., with one or more fingers of the hand opposite the arm on which the biopotential sensor 205 is worn), and in response to determining that the user has contacted the ECG electrode, the system may switch the signal pathway components for the hub electrodes and/or wristband electrodes such that at least some of the signals from these electrodes are directed to the ECG chip 270.”).
Regarding claim 6, Stern discloses the wearable electronic device of claim 5 (as shown above), wherein the sensing circuitry includes one or more analog-to-digital converters (Figure 2B: ADCs 250E) coupled to outputs of the first amplifier and the second amplifier ([0017] “The biopotential microchip may include a plurality of analog inputs, a plurality of analog-to-digital converters (ADCs) configured to receive signals from the plurality of analog inputs, an accelerometer, and a gyroscope.”, [0057].
Regarding claim 7, Stern discloses the wearable electronic device of claim 1 (as shown above), wherein the one or more processors are further configured to: during the first mode of operation, use one or more machine learning models to detect a movement of a portion of a body of a user wearing the wearable electronic device ([0046] “the biopotential chip 250 may have the ML classifier 230 onboard and the biopotential chip 250 may provide the gesture data to the ML classifier 230, so that the ML classifier 230 may generate a gesture output indicating a gesture performed by the user 105.”, [0050] “the gestures may include: index finger lift, index finger lift-and-hold, index finger swipe, thumbs up, wrist roll (e.g., palm open, first closed, index finger or thumb extended), wrist shake, and others.”, Examiner notes that gesture is defined as a movement usually of the body or limbs. https://www.merriam-webster.com/dictionary/gesture).
Regarding claim 8, Stern discloses the wearable electronic device of claim 7 (as shown above), wherein the portion of the body of the user includes a hand of the user wearing the wearable electronic device ([0038] “The user 105 may wear the wearable device 110 on a portion of an arm of the user 105, such as the wrist and/or the forearm of the user 105. The wearable device 110 may be gesture control device, a smartwatch, or other wrist or forearm wearable (e.g., a smart sleeve).”), and using the one or more machine learning models to detect the movement of the portion of the body of the user includes determining a predicted gesture corresponding to movement of the hand ([0046]-[0047] “the biopotential chip 250 may provide the gesture data to the ML classifier 230, so that the ML classifier 230 may generate a gesture output indicating a gesture performed by the user 105.”, [0074] “For instance, the indication may be a haptic feedback, an audio noise, a display graphic, and the like. In some embodiments, the impedance measurement, or derivative thereof, may be provided to the ML classifier 230 and used as an input for gesture determination. For example, the ML classifier may be trained to apply higher confidence or to make gesture classifications more quickly, based on less data, or based on smaller signal deviations…For example, an impendence measurements may be periodically or simultaneously obtained with gesture data (e.g., EMG and wrist motion data), and the multiple data sources may analyzed by the ML classifier to determine gesture classifications and/or to modify confidence rating or others parameters relating to classifications or confidences.”).
Regarding claim 9, Stern discloses the wearable electronic device of claim 1 (as shown above), wherein the one or more processors are further configured to: during the first mode of operation, use one or more machine learning models to detect a pose of a portion of a body of a user wearing the wearable electronic device ([0046] “the biopotential chip 250 may have the ML classifier 230 onboard and the biopotential chip 250 may provide the gesture data to the ML classifier 230, so that the ML classifier 230 may generate a gesture output indicating a gesture performed by the user 105.”, [0050] “the gestures may include: index finger lift, index finger lift-and-hold, index finger swipe, thumbs up, wrist roll (e.g., palm open, first closed, index finger or thumb extended), wrist shake, and others.”, Examiner notes that a gesture would imply a transition from one pose to another.).
Regarding claim 10, Stern discloses the wearable electronic device of claim 8 (as shown above), wherein the portion of the body of the user includes a hand of the user wearing the wearable electronic device ([0038] “The user 105 may wear the wearable device 110 on a portion of an arm of the user 105, such as the wrist and/or the forearm of the user 105. The wearable device 110 may be gesture control device, a smartwatch, or other wrist or forearm wearable (e.g., a smart sleeve).”), and using the one or more machine learning models to detect the pose of the portion of the body of the user includes determining a predicted pose of the hand ([0046]-[0047] “the biopotential chip 250 may provide the gesture data to the ML classifier 230, so that the ML classifier 230 may generate a gesture output indicating a gesture performed by the user 105.”, [0074] “For instance, the indication may be a haptic feedback, an audio noise, a display graphic, and the like. In some embodiments, the impedance measurement, or derivative thereof, may be provided to the ML classifier 230 and used as an input for gesture determination. For example, the ML classifier may be trained to apply higher confidence or to make gesture classifications more quickly, based on less data, or based on smaller signal deviations…For example, an impendence measurements may be periodically or simultaneously obtained with gesture data (e.g., EMG and wrist motion data), and the multiple data sources may analyzed by the ML classifier to determine gesture classifications and/or to modify confidence rating or others parameters relating to classifications or confidences.”).
Regarding claim 15, Stern discloses a non-transitory computer readable medium storing instructions ([0015] “systems, methods, and computer readable memory are disclosed for gesture control using biopotential sensing wearable devices”), wherein the one or instructions are configured to cause one or more processors ([0145] “As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution”) included in a wearable electronic device (wearable device 110) further including a plurality of electrodes (electrodes 235, [0031] “The wearable device may include a plurality of electrodes disposed on an interior of the wearable device and configured to obtain biopotential signals from the user's arm.”), sensing circuitry (biopotential chip 250), and pose detection circuitry (IMU 250B), different from the sensing circuitry (Examiner notes that although IMU 250B is integrated into the biopotential chip, it is distinct from the signals received from electrodes while IMU receives signals from an accelerometer and gyroscope: [0058]), to:
in a first mode of operation ([0049] gesture detection mode, “the biopotential sensor application, via the CPU 210, may instruct the biopotential sensor 205 to change connection states, such as from gesture detection mode to ECG detection mode”, [0063] biopotential sensing mode):
configure the plurality of electrodes and the sensing circuitry in a first configuration to detect first physiological signals corresponding to a first type of physiological measurement using the plurality of electrodes and using the pose detection circuitry ([0106] “the wearable device 110 may obtain biopotential data based on signals received by both hub electrodes 508 and wristband electrodes 512 and processed by the ADCs 250E of the biopotential chip 502. In some cases, the biopotential chip 502 may obtain wrist location data based on outputs from the accelerometer and the gyroscope”, [0074] “For example, an impendence measurements may be periodically or simultaneously obtained with gesture data (e.g., EMG and wrist motion data), and the multiple data sources may analyzed by the ML classifier to determine gesture classifications and/or to modify confidence rating or others parameters relating to classifications or confidences.”); and
in a second mode of operation ([0049] ECG detection mode, “the biopotential sensor application, via the CPU 210, may instruct the biopotential sensor 205 to change connection states, such as from gesture detection mode to ECG detection mode”, [0063] an ECG detection mode):
configure the plurality of electrodes and the sensing circuitry in a second configuration to detect second physiological signals corresponding to a second type of physiological measurement, different from the first type of physiological measurement, using the plurality of electrodes ([0063] “the processor 250A may control connection states between electrodes 235 and biopotential chips, an ECG chip 270, or specific differential amplifiers within biopotential chips, as discussed herein. In these cases, the processor 250A may cause switches or a multiplexer to change signal pathways from form a currently active connection state (for a first mode) to a new active connection state (for a second mode). For instance, the connection states may correspond to various modes, such as a biopotential sensing mode, a training mode, an ECG detection mode…”, [0069] “a processor of the biopotential sensor 205 may detect that a user has contacted the ECG electrode (e.g., with one or more fingers of the hand opposite the arm on which the biopotential sensor 205 is worn), and in response to determining that the user has contacted the ECG electrode, the system may switch the signal pathway components for the hub electrodes and/or wristband electrodes such that at least some of the signals from these electrodes are directed to the ECG chip 270.”).
Regarding claim 16, Stern discloses a method of operating a wearable electronic device ([0015] “systems, methods, and computer readable memory are disclosed for gesture control using biopotential sensing wearable devices”, wearable device 110), comprising a plurality of electrodes (electrodes 235, [0031] “The wearable device may include a plurality of electrodes disposed on an interior of the wearable device and configured to obtain biopotential signals from the user's arm.”), sensing circuitry (biopotential chip 250), and pose detection circuitry (IMU 250B), different from the sensing circuitry (Examiner notes that although IMU 250B is integrated into the biopotential chip, it is distinct from the signals received from electrodes while IMU receives signals from an accelerometer and gyroscope: [0058]), the method comprising:
in a first mode of operation ([0049] gesture detection mode, “the biopotential sensor application, via the CPU 210, may instruct the biopotential sensor 205 to change connection states, such as from gesture detection mode to ECG detection mode”, [0063] biopotential sensing mode):
configuring the plurality of electrodes and the sensing circuitry in a first configuration to detect first physiological signals corresponding to a first type of physiological measurement using the plurality of electrodes and using the pose detection circuitry ([0106] “the wearable device 110 may obtain biopotential data based on signals received by both hub electrodes 508 and wristband electrodes 512 and processed by the ADCs 250E of the biopotential chip 502. In some cases, the biopotential chip 502 may obtain wrist location data based on outputs from the accelerometer and the gyroscope”, [0074] “For example, an impendence measurements may be periodically or simultaneously obtained with gesture data (e.g., EMG and wrist motion data), and the multiple data sources may analyzed by the ML classifier to determine gesture classifications and/or to modify confidence rating or others parameters relating to classifications or confidences.”); and
in a second mode of operation ([0049] ECG detection mode, “the biopotential sensor application, via the CPU 210, may instruct the biopotential sensor 205 to change connection states, such as from gesture detection mode to ECG detection mode”, [0063] an ECG detection mode):
configuring the plurality of electrodes and the sensing circuitry in a second configuration to detect second physiological signals corresponding to a second type of physiological measurement, different from the first type of physiological measurement, using the plurality of electrodes ([0063] “the processor 250A may control connection states between electrodes 235 and biopotential chips, an ECG chip 270, or specific differential amplifiers within biopotential chips, as discussed herein. In these cases, the processor 250A may cause switches or a multiplexer to change signal pathways from form a currently active connection state (for a first mode) to a new active connection state (for a second mode). For instance, the connection states may correspond to various modes, such as a biopotential sensing mode, a training mode, an ECG detection mode…”, [0069] “a processor of the biopotential sensor 205 may detect that a user has contacted the ECG electrode (e.g., with one or more fingers of the hand opposite the arm on which the biopotential sensor 205 is worn), and in response to determining that the user has contacted the ECG electrode, the system may switch the signal pathway components for the hub electrodes and/or wristband electrodes such that at least some of the signals from these electrodes are directed to the ECG chip 270.”).
Regarding claim 17, Stern discloses the non-transitory computer readable medium storing instructions of claim 15 (as shown above), wherein the first type of physiological measurement is an electromyogram ([0074] “an impendence measurements may be periodically or simultaneously obtained with gesture data (e.g., EMG and wrist motion data), and the multiple data sources may analyzed by the ML classifier to determine gesture classifications and/or to modify confidence rating or others parameters relating to classifications or confidences.”).
Regarding claim 18, Stern discloses the non-transitory computer readable medium storing instructions of claim 15 (as shown above), wherein the second type of physiological measurement is an electrocardiogram (ECG electrode 275, [0070] “The ECG chip 270 may process the biopotential signals from all (or subsets of) the hub electrodes 235A and the wristband electrodes 235B and the biopotential signal from the ECG electrode 275, and generate ECG data… The ECG processor 280 may receive the ECG data and produce an electrocardiogram based on the ECG data.”).
Regarding claim 19, Stern discloses the method of claim 16 (as shown above), wherein the first type of physiological measurement is an electromyogram ([0074] “an impendence measurements may be periodically or simultaneously obtained with gesture data (e.g., EMG and wrist motion data), and the multiple data sources may analyzed by the ML classifier to determine gesture classifications and/or to modify confidence rating or others parameters relating to classifications or confidences.”).
Regarding claim 20, Stern discloses the method of claim 16 (as shown above), wherein the second type of physiological measurement is an electrocardiogram (ECG electrode 275, [0070] “The ECG chip 270 may process the biopotential signals from all (or subsets of) the hub electrodes 235A and the wristband electrodes 235B and the biopotential signal from the ECG electrode 275, and generate ECG data… The ECG processor 280 may receive the ECG data and produce an electrocardiogram based on the ECG data.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 11-12 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stern as applied to claim 1 above, in view of Shui et al. (US 2020/0077955 A1), hereinafter Shui, and further in view of Goudge (US 2024/0284179 A1), hereinafter Goudge.
Regarding claim 11, Stern discloses the wearable electronic device of claim 1 (as shown above). Stern fails to explicitly disclose the device further comprising: stimulation circuitry couplable to one of the plurality of electrodes and the sensing circuitry for injecting a test signal into the sensing circuitry, wherein the one or more processors are further configured to: determine a quality of the first physiological signals from the sensing circuitry based on the injected test signal and in accordance with a determination that the quality of the first physiological signals satisfy one or more criteria: detect a movement of a portion of a body of a user wearing the wearable electronic device using the first physiological signals and using the pose detection circuitry, and in accordance with a determination that the quality of the first physiological signals does not satisfy the one or more criteria, detect the movement of the portion of the user’s body using the pose detection circuitry, forgoing use of the first physiological signals to detect the movement.
However, Shui teaches a system and method of processing physiological signals measures from one or more electrodes ([0002]) device further comprising: stimulation circuitry ([0020] stimulation circuit) couplable to one of the plurality of electrodes ([0020] “The stimulation circuit can drive a stimulation signal on one of the measurement electrodes”) and the sensing circuitry (Abstract: “The stimulation circuit can drive one or more stimulation signals on one or more electrodes, the resulting signal(s) can be measured (e.g., by the sensing circuitry)”) for injecting a test signal into the sensing circuitry ([0036] “the test signal circuitry (e.g., stimulation circuit) can include test signal generator 530 and capacitor 538…”, [0047] “the one or more signals measured by the first sense circuit can include a signal measured in response to the stimulation signal (e.g., the resulting test signals 540 or 542).”, Figure 7: 704), wherein the one or more processors are further configured to: determine a quality of the first physiological signals from the sensing circuitry based on the injected test signal ([0068],[0049],[0020] “The mobile or wearable device can comprise one or more measurement electrodes, one or more reference electrodes, and processing circuitry coupled to the electrodes. In some examples, the device can include a stimulation circuit. The stimulation circuit can drive a stimulation signal on one of the measurement electrodes. In some examples, the processing circuitry can detect a signal resulting from the stimulation signal and, based on the detected signal, determine whether a user is in contact with the one or more measurement electrodes. In some examples, upon determining that a user is in contact with the one or more measurement electrodes, the processing circuitry can measure a physiological signal of the user.”).
Shui further teaches “At 706, in accordance with the one or more signals measured by the first and second sensing circuit meeting one or more criteria (e.g., as determined by processor 650), the system can measure a physiological signal at 714, or in accordance with the one or more signals measured by the first and second sensing circuit not meeting one of more criteria (e.g., as determined by processor 650), the system can forgo measuring a physiological signal at 712.” ([0048], emphasis added) and Stern discloses “the ML classifier may be trained to apply higher confidence or to make gesture classifications more quickly, based on less data, or based on smaller signal deviations when it is determined that impedance measurements indicate high contact quality.” ([0074]), thus Examiner notes that when an electrode produces a physiological signal that meets a criteria/threshold the physiological signal will be measured and used for detecting movement of a portion of a body of the user.
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Stern to incorporate the teachings of Shui to have the device further comprising: stimulation circuitry couplable to one of the plurality of electrodes and the sensing circuitry for injecting a test signal into the sensing circuitry, wherein the one or more processors are further configured to: determine a quality of the first physiological signals from the sensing circuitry based on the injected test signal and in accordance with a determination that the quality of the first physiological signals satisfy one or more criteria: detect a movement of a portion of a body of a user wearing the wearable electronic device using the first physiological signals and using the pose detection circuitry, as these prior art references are directed to physiological signal sensing through electrodes. One would be motivated to do this to utilize only the reliable electrodes that can generate accurate waveforms, as recognized by Shui ([0004]).
Shui and Stern, alone or in combination fail to teach in accordance with a determination that the quality of the first physiological signals does not satisfy the one or more criteria, detect the movement of the portion of the user’s body using the pose detection circuitry, forgoing use of the first physiological signals to detect the movement.
However, Goudge teaches biopotential-signal-sensing devices, systems, and methods including but not limited to techniques for providing data integrity for wearable biopotential-signal-sensing devices wherein “the machine-learning module 358 is configured to use data from less than all of the neuromuscular-signal-sensing components that the controller component 350 is receiving data from, which may be based on a signal-to-noise ratio that is determined based on values of respective data received from the neuromuscular-signal-sensing components.” ([0059]) and “EMG functionality of the device may be disabled or restricted based on any of the neuromuscular-signal-sensing components of the wearable electronic device being unvalidated… In some embodiments, the mitigation operations include disabling the invalid sensor (e.g., forgo providing power to the invalid sensors) and/or disabling communications with the invalid sensor. In some embodiments, the mitigation operations include performing gesture recognition without using biopotential information from the invalid sensor.” ([0075]-[0076]).
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Stern and Shui to incorporate the teachings of Goudge to in accordance with a determination that the quality of the first physiological signals does not satisfy the one or more criteria, detect the movement of the portion of the user’s body using the pose detection circuitry, forgoing use of the first physiological signals to detect the movement, as these prior art references are directed to obtaining quality data from electrodes. One would be motivated to do this to use the most accurate and pertinent information for detecting gestures.
Regarding claim 12, Stern in view of Shui in view of Goudge teaches the wearable electronic device of claim 11 (as shown above). Stern discloses wherein the one or more criteria include a criterion that is satisfied when a conductance between a first electrode of the plurality of electrodes and a second electrode of the plurality of electrodes is greater than a threshold level of conductance ([0074] “For instance, the signal quality may be impaired if the determined impedance between the at least one first electrode and the at least one second electrode satisfied an impairment condition (e.g., is greater than a first threshold or less than a second threshold). In some cases, based on the determined impedance between the at least one first electrode and the at least one second electrode and/or the impairment condition being satisfied, the wearable device 110 (or the user device 115) may present to the user 105 an indication that signal quality is impaired.”).
Regarding claim 14, Stern discloses the wearable electronic device of claim 1 (as shown above). Stern fails to explicitly disclose the device further comprising: stimulation circuitry couplable to one of the plurality of electrodes and the sensing circuitry for injecting a test signal into the sensing circuitry, wherein the one or more processors are further configured to: determine a quality of the first physiological signals from the sensing circuitry based on the injected test signal and in accordance with a determination that the quality of the first physiological signals satisfy one or more criteria: detect a pose of a portion of a body of a user wearing the wearable electronic device using the first physiological signals and using the pose detection circuitry, and in accordance with a determination that the quality of the first physiological signals does not satisfy the one or more criteria, detect the pose of the portion of the user’s body using the pose detection circuitry, forgoing use of the first physiological signals to detect the pose.
However, Shui teaches a system and method of processing physiological signals measures from one or more electrodes ([0002]) device further comprising: stimulation circuitry ([0020] stimulation circuit) couplable to one of the plurality of electrodes ([0020] “The stimulation circuit can drive a stimulation signal on one of the measurement electrodes”) and the sensing circuitry (Abstract: “The stimulation circuit can drive one or more stimulation signals on one or more electrodes, the resulting signal(s) can be measured (e.g., by the sensing circuitry)”) for injecting a test signal into the sensing circuitry ([0036] “the test signal circuitry (e.g., stimulation circuit) can include test signal generator 530 and capacitor 538…”, [0047] “the one or more signals measured by the first sense circuit can include a signal measured in response to the stimulation signal (e.g., the resulting test signals 540 or 542).”, Figure 7: 704), wherein the one or more processors are further configured to: determine a quality of the first physiological signals from the sensing circuitry based on the injected test signal ([0068],[0049],[0020] “The mobile or wearable device can comprise one or more measurement electrodes, one or more reference electrodes, and processing circuitry coupled to the electrodes. In some examples, the device can include a stimulation circuit. The stimulation circuit can drive a stimulation signal on one of the measurement electrodes. In some examples, the processing circuitry can detect a signal resulting from the stimulation signal and, based on the detected signal, determine whether a user is in contact with the one or more measurement electrodes. In some examples, upon determining that a user is in contact with the one or more measurement electrodes, the processing circuitry can measure a physiological signal of the user.”).
Shui further teaches “At 706, in accordance with the one or more signals measured by the first and second sensing circuit meeting one or more criteria (e.g., as determined by processor 650), the system can measure a physiological signal at 714, or in accordance with the one or more signals measured by the first and second sensing circuit not meeting one of more criteria (e.g., as determined by processor 650), the system can forgo measuring a physiological signal at 712.” ([0048], emphasis added) and Stern discloses “the ML classifier may be trained to apply higher confidence or to make gesture classifications more quickly, based on less data, or based on smaller signal deviations when it is determined that impedance measurements indicate high contact quality.” ([0074]), thus Examiner notes that when an electrode produces a physiological signal that meets a criteria/threshold the physiological signal will be measured and used for detecting pose of a portion of a body of the user.
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Stern to incorporate the teachings of Shui to have the device further comprising: stimulation circuitry couplable to one of the plurality of electrodes and the sensing circuitry for injecting a test signal into the sensing circuitry, wherein the one or more processors are further configured to: determine a quality of the first physiological signals from the sensing circuitry based on the injected test signal and in accordance with a determination that the quality of the first physiological signals satisfy one or more criteria: detect a pose of a portion of a body of a user wearing the wearable electronic device using the first physiological signals and using the pose detection circuitry, as these prior art references are directed to physiological signal sensing through electrodes. One would be motivated to do this to utilize only the reliable electrodes that can generate accurate waveforms, as recognized by Shui ([0004]).
Shui and Stern, alone or in combination fail to teach in accordance with a determination that the quality of the first physiological signals does not satisfy the one or more criteria, detect the pose of the portion of the user’s body using the pose detection circuitry, forgoing use of the first physiological signals to detect the pose.
However, Goudge teaches biopotential-signal-sensing devices, systems, and methods including but not limited to techniques for providing data integrity for wearable biopotential-signal-sensing devices wherein “the machine-learning module 358 is configured to use data from less than all of the neuromuscular-signal-sensing components that the controller component 350 is receiving data from, which may be based on a signal-to-noise ratio that is determined based on values of respective data received from the neuromuscular-signal-sensing components.” ([0059]) and “EMG functionality of the device may be disabled or restricted based on any of the neuromuscular-signal-sensing components of the wearable electronic device being unvalidated… In some embodiments, the mitigation operations include disabling the invalid sensor (e.g., forgo providing power to the invalid sensors) and/or disabling communications with the invalid sensor. In some embodiments, the mitigation operations include performing gesture recognition without using biopotential information from the invalid sensor.” ([0075]-[0076]).
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Stern and Shui to incorporate the teachings of Goudge to in accordance with a determination that the quality of the first physiological signals does not satisfy the one or more criteria, detect the pose of the portion of the user’s body using the pose detection circuitry, forgoing use of the first physiological signals to detect the pose, as these prior art references are directed to obtaining quality data from electrodes. One would be motivated to do this to use the most accurate and pertinent information for detecting gestures.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stern in view of Shui in view of Goudge as applied to claim 11 above, and further in view of Lee et al. (US 2022/0087615 A1), hereinafter Lee.
Regarding claim 13, Stern in view of Shui in view of Goudge teaches the wearable electronic device of claim 11 (as shown above). Stern, Shui, and Goudge, alone or in combination, fail to teach wherein the one or more criteria include a criterion that is satisfied when a level of noise associated with the first physiological signals is greater than a threshold level of noise.
However, Lee teaches a method and apparatus for measuring a biometric signal using an electrode ([0001]) such as an ECG or EMG ([0003]) wherein “a signal to noise ratio (SNR) of a biometric signal can be increased by reducing noise of a biometric signal, and thus, a biometric signal with a good quality can be obtained.” ([0010]) such that “The control circuit 380 may determine whether the magnitude of noise included in the biometric signal exceeds a noise threshold value. For example, if the magnitude of the noise exceeds the noise threshold value (e.g., changing of the settings is needed), the control circuit 380 proceeds with operation 1109. If the magnitude of the noise is less than or equal to the noise threshold value (e.g., changing of the settings is not needed), the control circuit 380 may proceed with operation 1107.” ([0158]).
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Stern, Shui, and Goudge to incorporate the teachings of Lee to have wherein the one or more criteria include a criterion that is satisfied when a level of noise associated with the first physiological signals is greater than a threshold level of noise, as these prior art references are directed to obtaining physiological signals from an electrode. One would be motivated to do this to obtain a good quality signal, as recognized by Lee ([0010]).
Conclusion
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/ATTIYA SAYYADA HUSSAINI/Examiner, Art Unit 3792
/NIKETA PATEL/Supervisory Patent Examiner, Art Unit 3792
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