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Claims 1-20 are pending of which 14-20 are withdrawn. Therefore, claims 1-13 are pending and examined below.
Response to Arguments
The remarks of 01/08/2026 have been fully considered but they are not persuasive.
Applicant argues that the prior art of record doesn't explicitly teach or disclose all of the elements of claim 1. In particular applicant argues the following claim language:
“wherein the myoelectric prosthesis controller is configured to be calibrated to eliminate errors in the EMG signal data of the user captured during the coaching session”
Regarding the first bullet point – The claim limitation “captured during the coaching session” is understood, in the broadest reasonable interpretation, to clarify the time during which the EMG signal data is captured – not when the errors are eliminated. Lock discloses a controller configured to replace the EMG signal data via a recalibration as described in Paragraph 0041. Because this eliminates errors in the EMG signal data captured during the coaching session this satisfies the claim limitation.
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) 1-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 02/49534 (Levin) in view of US 2019/0209345 A1 (LaChappelle) in view of US 2014/0032462 (Lock)
Regarding claim 1, Levin discloses an electromyographic control system (20, Fig. 1) configured to coach prosthetic users to calibrate prosthetic devices (Page 16, Lines 1-2, wherein 20 trains subject to control virtual prosthesis 50 and prosthesis 110) the electromyographic control system comprising:
a myoelectric prosthetic controller (Fig. 2, 120) configured to control a prosthetic device (Page 15, Lines 16-20, wherein 120 controls electro-mechanical prosthesis 110)
an electromyographic software component (Fig. 1, 22) communicatively coupled to a plurality of electrodes (Fig. 1, 100) in myoelectric contact with a user (see Fig. 1, wherein electrodes 100 are placed on user), wherein the electromyograph software component is configured to perform an analysis of electromyographic (EMG) signal data of the user (Page 16, Lines 27-30, wherein 22 processes EMG signal from user), the EMG signal data received from the plurality of electrodes (Page 16, Lines 21-24, wherein processing unit 22 receives myoelectric signals from electrodes 100); and
a user interface (Fig. 1, 40) configured to provide a feedback indication to the user as to a calibration quality of the EMG signal data (Page 19, Lines 13-19, wherein EMG signals are displayed on screen and coded to 0, 1 or 2), wherein the feedback indication is based on analysis of the EMG signal data by the electromyographic software component (Page 19, Lines 13-15, wherein computer 40 analyzes EMG signal to display feedback),
wherein the user interface is configured to initiate a calibration procedure (Page 16, Lines 18-21, wherein the computer 40 displaying instructions corresponds to initiating a calibration procedure) to calibrate the myoelectric prosthetic controller during a myoelectric coaching session with the user (Page 16, Fig. 1, wherein computer 40 is capable of this intended use), and
wherein the user interface (computer 40, Fig. 1) is configured to be implemented during the myoelectric coaching session (Page 16, Lines 18-21 wherein the myoelectric coaching session starts with training on the computer and ends with transferring the calibration to the prosthesis, as described on Page 15, Lines 26-32) for improving the calibration quality between the user and the prosthetic device by calibrating the myoelectric controller of the prosthetic device (Page 15, Lines 24-29, wherein the calibration is transferred to control unit 120 – which corresponds to the myoelectric controller of the prosthetic device) for providing more accurate control of the prosthetic device by the user based on the EMG signal data of the user (Page 21, Lines 3-7, wherein calibration is based on the EMG signal data of the user).
Levin discloses a user interface (Fig. 1, 40) but does not explicitly teach or disclose a button user interface.
LaChapelle discloses a button user interface including a calibration button (“push button”, ¶0106) configured to provide as output the feedback indication corresponding the calibration quality of the EMG signal data (¶0106, wherein the button is coupled with LEDs to provide visual feedback to communicate a current state of calibration) during the myoelectric coaching session,
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify the user interface of Levin with an additional calibration button mounted on the prosthesis, as taught by LaChappelle, in order to communicate a current state of the calibration to the user (¶0106) as muscles can fatigue throughout the day and surface electrodes are prone to drift. Additionally, users are not always in close proximity to a computer when re-calibration is required.
LaChapelle discloses a myoelectric prosthetic controller (Fig. 2, 120) but doesn’t explicitly teach or disclose that it is calibrated to eliminate errors in the EMG signal data of the user captured during the coaching session. LaChappelle doesn’t explicitly teach or disclose a myoelectric prosthetic controller calibrated to eliminate errors in the EMG signal data of the user captured during the coaching session.
Lock discloses a myoelectric prosthetic control system (Fig. 2) comprising a myoelectric prosthetic controller (Fig. 2, 106) which is configured to be calibrated to eliminate errors in the EMG signal data of the user captured during the myoelectric coaching session (¶0041, wherein 300 recalibrates the EMG signal data of 106 by replacing the EMG signal data in memory 110 – which corresponds to eliminating errors in the EMG signal data captured during the myoelectric coaching session)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to program the myoelectric controller of Levin in view of LaChapelle to eliminate errors, as taught by Lock, in order to correct problems with the control algorithm that may be caused by a change in device fit, user fatigue, or skin conditions (Lock, ¶0041)
Regarding claim 2, Levin discloses a message (Page 26, “a large range of diagnostic or other messages” comprises at least one of (a) an indication of a cause for a non-optimal signal data input of the EMG signal data (Page 26, “contractions are too long”), or (b) a recommended procedure for optimizing signal data input (Page 26, wherein “try producing short bursts” corresponds to a recommended procedure for optimizing signal data input)
Regarding claim 3, Levin discloses wherein a further calibration procedure is initiated from the user interface (Page 26, wherein the control unit prompts user to enter 1-2-1 to recalibrate) configured during a further calibration session to recalibrate the myoelectric prosthetic controller based on the recommended procedure (Page 26, wherein control unit is capable of this intended use).
Regarding claim 4, Levin discloses wherein the further calibration session is configured to facilitate at least one of:
(a) deleting EMG signal data corresponding to one or more data sets or movements,
(b) adding EMG signal data corresponding to one or more data sets or movements,
(c) replacing EMG signal data corresponding to one or more data sets or movements with new EMG signal data (Page 26, Lines 25-27, wherein promoting the subject to enter 1-2-1 corresponds to replacing EMG signal data sets).
Regarding claim 5, Levin discloses a myoelectric prosthetic controller (120, Fig. 2) which is configured to be calibrated to control the prosthetic device (110, Fig. 3) based on the EMG signal data (Lines 27-30, Page 24, wherein 120 receives EMG signals from electrodes 100 to operate electro-mechanical prosthesis 110).
Regarding claim 6, Levin discloses a feedback indication (see rejection of Claim 1 above) but doesn’t explicitly teach or disclose a calibration button.
LaChappelle discloses wherein the calibration button (¶0070, wherein the button in recess 274 corresponds to a calibration button) is configured to provide the feedback indication by at least one of an auditory stimulus, a tactile stimulus, or a visual stimulus (¶0070, “visual feedback on calibration”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify Levin with an additional calibration button to provide the feedback indication, as taught by LaChappelle, in order communicate a current state of the calibration to the user (¶0106) as muscles can fatigue throughout the day and surface electrodes are prone to drift. Additionally, users are not always in close proximity to a computer when re-calibration is required.
Regarding claim 7, Levin discloses a virtual user interface (Fig. 1, 42) which displays a visualization of the EMG signal data in real time (Fig. 1, wherein 60 corresponds to a visualization of EMG signal data in real time)
Regarding claim 8, Levin discloses wherein the user interface (Fig. 1, 40) is configured to instruct the user to perform one or more indicated motions in relation to the prosthetic device (Fig. 1, wherein 40 instructs user to “turn key to the right” corresponds to one or more indicated motions), and wherein the one or more indicated motions produce the EMG signal data as received from the plurality of electrodes (see Page 16, Lines 24, wherein 40 receives EMG signal from electrodes 100).
Regarding claim 9, Levin discloses a virtual user interface (Fig. 1, 42) which is configured to receive one or more selections (Page 16, Lines 18-20, “instructions are displayed in a box 46 on screen 42”) indicating at least one of the one or more indicated motions for the user to perform (Fig. 1, wherein “turning a key to the right” corresponds to an indicated motion for the user to perform)
Regarding claim 10, Levin discloses an electromyographic control unit (120, Fig. 2) but doesn’t explicitly teach or disclose a pattern recognition component.
LaChappelle discloses a pattern recognition component (¶0121, wherein the neural network corresponds to the pattern recognition component) configured to analyze the EMG signal data of the user (¶0121, wherein looking for patterns corresponds to analyzing the EMG data)), the pattern recognition component further configured to identify or categorize the EMG signal data of the user based on a particular motion performed by the user (¶0121, looking for patterns and trigger a particular grip in response to a recognized pattern corresponds to categorizing the EMG data)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify the control unit of Levin with a pattern recognition component, as taught by LaChappelle, in order to improve accuracy in matching EMG signals to the users intended motion (¶0121).
Regarding claim 11, Levin doesn’t explicitly teach or disclose a pattern recognition component or an adaptive machine learning component.
LaChappelle discloses a pattern recognition component which comprises an adaptive machine learning component (¶0121, “machine learning”) configured to determine the particular motion performed by the user based on the EMG signal data of the user (¶0121, wherein machine learning is used to trigger a particular grip in response to a recognized pattern).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify the control unit of Levin with an adaptive machine learning component, as taught by LaChappelle, in order to improve accuracy in matching EMG signals to the users intended motion (¶0121).
Regarding claim 12, Levin discloses an electromyographic software component (120, Fig. 2) that is configured to configured to determine an appropriate feedback indication (Page 26, Lines 14-18, wherein the diagnostic message “contractions are too long – try producing short bursts” correspond to an appropriate feedback indication) based on the EMG signal data of the user (Page 26, Lines 14-18, wherein the diagnostic messages are generated by control unit 120 based on the EMG signals). Levin doesn’t explicitly teach or disclose a machine learning component.
LaChappelle discloses a machine learning component (¶0121, neural networking that utilizes machine learning) configured to analyze EMG signal data of the user (¶0121, wherein machine learning looks for patterns in the EMG data to tripper a particular grip).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to produce the feedback indications of Levin with machine learning, as taught by LaChappelle, in order to enable the user to calibrate the prosthesis more accurately and more easily.
Regarding claim 13, Levin discloses wherein the user interface (132, Fig. 2) is configured to reset calibration data of the user to calibrate the myoelectric prosthetic controller (Page 26, Lines 25-27, wherein display 132 prompts subject to enter a specific EMG input to recalibrate control unit 120).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/MAXIMILIAN TOBIAS SPENCER/Examiner, Art Unit 3774
/JERRAH EDWARDS/Supervisory Patent Examiner, Art Unit 3774
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