Office Action 3: Non-Final Rejection (2024-03-20)
Rejection types: 102 - Lack of Novelty, 103 - Obviousness

CLAIMS:
Amendments to the Claims

1\. (Currently amended) A method performed by an anesthetizing monitoring unit configured to generate an improved evoked electromyography response signal (EResponse), the method comprising:tas~tig-exrtn a single stimuli signal SVG {EMBED HERE} 0.1572.34SVG {EMBED HERE} 0.1576.193SVG {EMBED HERE} 0.1431.79SVG {EMBED HERE} 0.1575.90&lltin an output port of the anesthetizing monitoring unit,receiving a single evoked electromyography, EMG, response signal (SRespoSVG {EMBED HERE} 0.1530.333SVG {EMBED HERE} 0.1536.387unt having a duration (TResponse), in response to the transmitted stimuli signal SVG {EMBED HERE} 0.1570.593SVG {EMBED HERE} 0.1534.033estimating a periodic noise waveform (SpeSVG {EMBED HERE} 0.1530.403 having the duration (TRespoSVG {EMBED HERE} 0.1530.323 by using temporal segments of a noise signal (SNoise),generating the improved response signal (EResponse) by subtracting the noise waveform from the response signal (SRespoSVG {EMBED HERE} 0.1530.317 storing the improved response signal (EResponse) in a memory in the anesthetizing monitoring unit, and
presenting the improved response signal (EResponse) on a display in the anesthetizing monitoring unit.

2\. (Previously Presented) The method according to claim 1, wherein the step of estimating the periodic noise waveform further comprises:
splitting the noise signal (SNoise) into a number N of temporal segments (Si, S2, ..., SN),
generating a periodic noise template (STemplate) by aggregating the N temporal segments (Si, S2, ..., SN),wherein the periodic noise waveform (SpeSVG {EMBED HERE} 0.1530.403 is estimated by subsequSVG {EMBED HERE} 0.1530.43 repeating the periodic noise template (STemplate) N times.

3\. (Original) The method according to claim 2, wherein the periodic noise template (STemplate) is generated by averaging discrete samplesover the N temporal segments (S1, S2, ..., SN).
4\. (Previously Presented) The method according to claim 1, wherein the method further comprises determining an anesthetized patient state by determining a neuromuscular function value using properties of the improved response signal .SVG {EMBED HERE} 0.1530.693

5\. (Previously Presented) The method according to claim 1, wherein the anesthetized patient state is a selection of any of "insufficient anesthetic level", "low anesthetic level", "sufficient anesthetic level", "normal breathing function", "capable of sustaining breathing" or "in need of ventilator".
6\. (Previously Presented) The method according to claim 4, wherein the properties of the improved response signal (EResponse) include amplitude value/s (AResponse') and wherein the neuromuscular function value is determined by comparing a quota of amplitude value/s of the improved response signal (EResponse) and additional amplitude value/s to a set of predetermined amplitude value/s thresholds, wherein the additional amplitude values are selected from amplitude value/s of the stimuli signal (Sstimuni) and historical amplitude value/s of the improved response signal (EResponse).SVG {EMBED HERE} 0.0870.073

7\. (Cancelled).
8\. (Previously Presented) The method according to claim 2, wherein the temporal segments (Si, S2, ..., SN) have a duration (Tpe proportional to the noise signal (SNoise).SVG {EMBED HERE} 0.1530.37
9\. (Original) The method according to claim 8, wherein the duration (TpeSVG {EMBED HERE} 0.1530.373 is selected using a strongest periodic noise component of the noise signal (SNoSVG {EMBED HERE} 0.1530.247 .

10\. (Previously Presented) An anesthetizing monitoring unit configured to generating an improved evoked electromyography response signal (EResponse), the anesthetizing monitoring unit comprising:
an input port,
an output port, and
processing circuitry being configured to perform the method according to claim 1.

11\. (Previously Presented) An anesthetizing monitoring system configured to determine an anesthetized patient state, the anesthetizing monitoring unit comprising:
an anesthetizing monitoring unit comprising an input port and an output port,
stimulating electrodes electrically coupled to the output port and being configured to receive a stimuli signal (Sstimuii) from the output port and deliver the stimuli signal (Sstimuii) to the anesthetized patient,
receiving electrodes electrically coupled to the input port and being configured to obtain an evoked electromyography, EMG, response signal (SResponse), in response to the stimuli signal (Sstimuii) and a noise signal (SNoise), from the anesthetized patient, the anesthetizing monitoring unit being configured to perform the method according to claim 1.

12\. (Previously Presented) A computer program is provided comprising computer-executable instructions for causing an anesthetizing monitoring unit, when the computer-executable instructions are executed on a processing unit comprised in the anesthetizing monitoring unit, to perform the method according claim 1.

13\. (Original) A computer program product comprising a computer-readable storage medium, the computer-readable storage medium having the computer program according to claim 12 embodied therein.

14\. (Original) A carrier comprising the computer program according to claim 12, wherein the carrier is one of an electronic signal, optical signal, radio signal, or computer readable storage medium.

15\. (Previously Presented) The method according to claim 6, wherein the threshold values are predetermined properties of historical improved response signals received at the anesthetizing monitoring unit prior to the response signal (SResponse) being received by the anesthetizing monitoring unit, wherein the threshold values are stored in the memory of the anesthetizing monitoring unit.

16\. (Previously Presented) The method according to claim 1, wherein the temporal segments of the noise signal (SNoise) are gathered by the anesthetizing monitoring unit within an adjacent time interval (TNoise) notoverlapping with astimuli signal interval (Tstimuius) and the duration of the response signal (TResponse).

17\. (New) The method according to claim 1, wherein the temporal segments of the noise signal (SNoise) used during the estimating of the periodic noise waveform(Spenodic) are detected during a time interval (TNoise) notoverlapping with astimuli signal interval (Tstimuius) of the single stimuli signal (Sstimuii) and not overlapping with the duration of the response signal (TResponse).

18\. (New) The method according to claim 1, wherein the single stimuli signal (Sstimuii) comprises a single pulse.

OFFICE ACTION:
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection.  Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.  Applicant's submission filed on December 28, 2023 has been entered.
Response to Amendment
The amendment filed December 28, 2023 has been filed. Claims 1-6 and 8-18 remain pending in the application, claims 17-18 were recently added, and claim 7 was previously cancelled. Applicant’s amendments to the claims have overcome each and every 101 rejection previously set forth in the Final Office Action mailed October 2, 2023. Applicant’s amendments to the claims necessitate new grounds of rejection, described in the Response to Arguments and 102/103 Rejections below.
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.
Claims 1-6 and 8-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20170245776 A1 (Kurtz et al.).
Regarding claim 1, Kurtz teaches a method performed by an anesthetizing monitoring unit ([0022] “apparatus”) configured to generate an improved evoked electromyography response signal (ΣResponse) ([0002] “The present application relates to an apparatus, system and method for detecting evoked responses to a stimulus (a stimulus-response signal).”) the method comprising:
generating a single stimuli signal (SStimuli) ([0002] “a stimulus”; [0006] “a single instance of the stimulus”; [0063] “may include multiple stimulus sources, such as visual and auditory, or may be a single source”) using the anesthetizing monitoring unit, wherein the single stimuli signal (SStimuli) is configured to stimulate a motor nerve of a patient ([0003] “Electrophysiological evoked responses to a variety of stimuli are known to contain valuable clinical and scientific information in the assessment of the sensorineural systems of humans and animals. Evoked responses (ER), such as, for example, auditory evoked potentials, somatosensory evoked potentials, visual evoked potentials, otoacoustic emissions, or the like”),
outputting the generated single stimuli signal (SStimuli) to stimulating electrodes on the patient using an output port ([0030] “output device”) of the anesthetizing monitoring unit,
receiving a single evoked electromyography, EMG, response signal (SResponse) from receiving electrodes on the patient at an input port ([0022] “input device”) of the anesthetizing monitoring unit ([0002] “detecting evoked responses to a stimulus (a stimulus-response signal)”; [0022] “receive the noisy signal to the plurality of stimuli”), having a duration (TResponse), in response to the transmitted stimuli signal (SStimuli) ([0006] “Signal averaging involves stimulating the patient with multiple stimuli, obtaining multiple time-based data series”),
estimating a periodic noise waveform (SPeriodic) ([0008] “the noise is quasi-stationary”), having the duration (TResponse), by using temporal segments of a noise signal (SNoise) ([0006] “stimulating the patient with multiple stimuli, obtaining multiple time-based data series”),
generating the improved response signal (ΣResponse) by subtracting the noise waveform from the response signal (SResponse) ([0059] “The final waveform or averaged response (sometimes called the “final response”), is the estimate of the synchronized evoked response, with reduced noise. It will be understood that the type of evoked response will generally be dependent on the stimulus and on the subject. For example, if a subject cannot hear an auditory stimulus, then there would not be an evoked response to the auditory stimulus.”; [0056] “reduce the negatively correlated noise from the resultant average”),
storing the improved response signal (ΣResponse) in a memory in the anesthetizing monitoring unit ([0064] “generated stimuli or the noisy signal relating thereto may also be stored in memory 140”), and
presenting the improved response signal (ΣResponse) on a display in the anesthetizing monitoring unit ([0066] “the final response may be output via the output module 145 to, for example, the display 150”).
Regarding claim 2, Kurtz teaches the method according to claim 1, wherein the step of estimating the periodic noise waveform further comprises: splitting the noise signal (SNoise) into a number N of temporal segments (S1, S2, ... , SN) ([0014] “divide the noisy signal into a plurality of responses to the plurality of stimuli”),
generating a periodic noise template (STemplate) by aggregating the N temporal segments (S1, S2, ... , SN) ([0016] “estimate a statistic matrix for the plurality of responses”),
wherein the periodic noise waveform (SPeriodic), is estimated by subsequently repeating the periodic noise template (STemplate) N times ([0017] “performing a multilevel discrete wavelet transform on individual responses in a loop for each scale of the multilevel discrete wavelet transform”).
Regarding claim 3, Kurtz teaches the method according to claim 2, wherein the periodic noise template (STemplate) is generated by averaging discrete samples over the N temporal segments (S1, S2, … SN) ([0059] “The final waveform or averaged response (sometimes called the “final response”), is the estimate of the synchronized evoked response, with reduced noise. It will be understood that the type of evoked response will generally be dependent on the stimulus and on the subject. For example, if a subject cannot hear an auditory stimulus, then there would not be an evoked response to the auditory stimulus.”).
Regarding claim 4, Kurtz teaches the method according to claim 1, wherein the method further comprises determining an anesthetized patient state by determining a neuromuscular function value using properties of the improved response signal (ΣResponse) ([0134-0135] “an MMN response depends on the consciousness state of the subject, in the awake pre-op state, we expect there to be a significant MMN, while in the anesthesia state, we do not expect there to be an MMN present”).
Regarding claim 5, Kurtz teaches the method according to claim 1, wherein the anesthetized patient state is a selection of any of insufficient anesthetic level, low anesthetic level, sufficient anesthetic level, normal breathing function, capable of sustaining breathing or in need of ventilator ([0135] “an MMN response depends on the consciousness state of the subject, in the awake pre-op state, we expect there to be a significant MMN, while in the anesthesia state, we do not expect there to be an MMN present.”).
Regarding claim 6, Kurtz teaches the method according to claim 4, wherein the properties of the improved response signal (ΣResponse) include amplitude value/s (AResponse’) ([0135] “peak amplitude”) and wherein the neuromuscular function value is determined by comparing a quota of amplitude value/s of the improved response signal (ΣResponse) and additional amplitude value/s to a set of predetermined amplitude value/s thresholds, wherein the additional amplitude values are selected from amplitude value/s of the stimuli signal (SStimuli) and historical amplitude value/s of the improved response signal (ΣResponse) ([0135] “An MMN response is determined to be present when the peak amplitude of the processed deviant response is statistically different than that of a standard response in the region of expected MMN response”).
Regarding claim 8, Kurtz teaches the method according to claim 2, wherein the temporal segments (S1, S2, ... , SN) have a duration (TPeriodic) proportional to the noise signal (SNoise) ([0068] “by weighting data series or groups of data series in inverse proportion to their noise content”).
Regarding claim 9, Kurtz teaches the method according to claim 8, wherein the duration (TPeriodic) is selected using a strongest periodic noise component of the noise signal (SNoise) ([0009] “In this case, the group is greater than 8 seconds in duration. The drawback of this technique is that noise in evoked potential measurements is, in general, not stationary over an 8 second duration, especially when the time series is contaminated with interference from the patient's EMG caused by muscle activity.”; The time period with the most noise is selected, indicative of muscle activity.).
Regarding claim 10, Kurtz teaches an anesthetizing monitoring unit configured to generating an improved evoked electromyography response signal (ΣResponse), the anesthetizing monitoring unit comprising:
an input port ([0002] “input device”),
an output port ([0030] “output device”), and
processing circuitry ([0140] “The machine-readable medium can contain various sets of instructions, code sequences, configuration information, or other data, which, when executed, cause a processor to perform steps in a method according to an embodiment of the disclosure”) being configured to perform the method according to claim 1 (see rejection above).
Regarding claim 11, Kurtz teaches an anesthetizing monitoring system configured to determine an anesthetized patient state, the anesthetizing monitoring unit ([0022] “an apparatus for detection of an evoked response signal in noise”) comprising:
an anesthetizing monitoring unit comprising an input port (111) ([0022] “input device”) and an output port ([0030] “output device”),
stimulating electrodes electrically coupled to the output port and being configured to receive a stimuli signal (SStimuli) from the output port and deliver the stimuli signal (SStimuli) to the anesthetized patient ([0058] “A sensor or sensors 115, for example an electrode or electrodes, are provided to the subject to detect a noisy signal including a response to the stimulus, which is sent to an input module 120”),
receiving electrodes electrically coupled to the input port and being configured to obtain an evoked electromyography, EMG, response signal (SResponse) ([0059] “electrodes receive continuous data, sometimes referred to as “noisy data” or “noisy signal”. For each stimulus, a synchronized evoked response within the subject's brain will be generated.”), in response to the stimuli signal (SStimuli) ([0058] “response to the stimulus”) and a noise signal (SNoise) ([0059] “noisy signal”), from the anesthetized patient, the anesthetizing monitoring unit being configured to perform the method according to claim 1 ([0059] “this evoked response will also be detected by the electrodes but is generally hidden in the noisy signal. Embodiments of the system, apparatus and method herein are intended to separate the evoked response, or stimulus-synchronized component, (sometimes referred to as a “response”) from the non-synchronized components (i.e. noise).”).
Regarding claim 12, Kurtz teaches a computer program is provided comprising computer-executable instructions for causing an anesthetizing monitoring unit, when the computer-executable instructions are executed on a processing unit comprised in the anesthetizing monitoring unit, to perform the method according to claim 1 ([0140] “The machine-readable medium can contain various sets of instructions, code sequences, configuration information, or other data, which, when executed, cause a processor to perform steps in a method according to an embodiment of the disclosure.”).
Regarding claim 13, Kurtz teaches a computer program product comprising a computer-readable storage medium, the computer-readable storage medium having the computer program according to claim 12 embodied therein ([0140] “Embodiments of the disclosure or portions thereof can be represented as a computer program product stored in a machine-readable medium (also referred to as a computer-readable medium, a processor-readable medium, or a computer usable medium having a computer-readable program code embodied therein).”).
Regarding claim 14, Kurtz teaches a carrier comprising the computer program according to claim 12, wherein the carrier is one of an electronic signal, optical signal, radio signal, or computer readable storage medium ([0140] “computer-readable medium”).
Regarding claim 15, Kurtz teaches the method according to claim 6, wherein the threshold values are predetermined properties of historical improved response signals received at the anesthetizing monitoring unit prior to the response signal (SResponse) being received by the anesthetizing monitoring unit, wherein the threshold values are stored in the memory of the anesthetizing monitoring unit ([0134] “responses to the standard stimulus and 107 responses to the randomly inserted deviant stimuli were collected from electrodes on the patient's scalp prior to the administration of anesthesia”; [0135] “An MMN response is determined to be present when the peak amplitude of the processed deviant response is statistically different than that of a standard response in the region of expected MMN response”; [0140]).
Regarding claim 16, Kurtz teaches the method according to claim 1, wherein the temporal segments of the noise signal (SNoise) are gathered by the anesthetizing monitoring unit within an adjacent time interval (TNoise) not overlapping with a stimuli signal interval (TStimulus) and the duration of the response signal (TResponse) ([0065] “data outside that region of interest would be noise and the removal of that noise prior to subsequent signal processing may improve the analysis to determine the evoked response”; [0068] “the noise from data series to data series is independent”).
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.
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170245776 A1 (Kurtz et al.) in view of US 20130204155 A1 (Brull et al.).
Regarding claim 17, Kurtz teaches the method according to claim 1.
Kurtz does not explicitly teach wherein the temporal segments of the noise signal (SNoise) used during the estimating of the periodic noise waveform (SPeriodic) are detected during a time interval (TNoise) not overlapping with a stimuli signal interval (TStimulus) of the single stimuli (SStimuli) and not overlapping with the duration of the response signal (TResponse).
However,
Brull teaches wherein the temporal segments of the noise signal (SNoise) used during the estimating of the periodic noise waveform (SPeriodic) are detected during a time interval (TNoise) not overlapping with a stimuli signal interval (TStimulus) of the single stimuli (SStimuli) and not overlapping with the duration of the response signal (TResponse) ([0022] “the start of the evoked muscle response is optionally identified by locating the time at which the level of muscle electrical activity exceeds the baseline level of electrical noise in the subject prior to stimulation of the motor nerve or immediately following completion of the evoked response”; [0097] “In one implementation, the noise, nerve response and muscle response may be separated from one another before each is measured.”; [0098] “the fiducial marks 602 as shown in FIG. 6C are noise-only segments”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the method taught by Kurtz to include the segments of the noise signal not overlapping with the stimuli signal interval and the response signal. One would have been motivated to make this modification because the background noise is able to be determined pre-stimulus or immediately post-response, not overlapping with these periods, which allows the neuromuscular activity to be detected by determining how the signal deviates from the baseline, as suggested by Brull [0098].
Regarding claim 18, Kurtz teaches the method according to claim 1.
Kurtz does not explicitly teach wherein the single stimuli signal (SStimuli) comprises a single pulse.
However,
Brull teaches wherein the single stimuli signal (SStimuli) comprises a single pulse ([0076] “In one implementation, the nerve stimulator 108 may deliver a 200 .mu.s or 300 .mu.s square-wave, monophasic, constant electrical pulse. The electrical pulse delivered by the nerve stimulator 108 should be sufficient in strength to elicit nerve responses”; [0091] “a single electrical pulse”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the method taught by Kurtz to include a single pulse. One would have been motivated to make this modification because the evoked response may be recorded from a stimulus consisting of multiple electrical pulses or a single pulse, as suggested by Brull [0091, 0109].
Response to Arguments
Applicant's arguments filed December 28, 2023 have been fully considered but they are not persuasive. With respect to the 102 Rejections in the Non-Final Office Action (See Pages 8-14 of Applicant’s Response “CLAIM REJECTIONS – 35 U.S.C. §102”), Applicant argues that Kurtz does not disclose “estimating a periodic noise waveform”. Applicant states that instead of quantifying the noise in a signal and using the quantified noise to remove the noise from a signal, Kurtz averages multiple signals to average out the noise from the signal. Newly added claim 17 clarifies this estimation process, however independent claim 1 recites “estimating a periodic noise waveform”, which encompasses the noise estimation taught by Kurtz [0006] under broadest reasonable interpretation (BRI).
MPEP § 2111 discusses proper claim interpretation, including giving claims their broadest reasonable interpretation in light of the specification during examination. Under BRI, the words of a claim must be given their plain meaning unless such meaning is inconsistent with the specification, and it is improper to import claim limitations from the specification into the claim.
Claims 2-6 and 8-18 are rejected based on their dependency on claim 1 for the reasons above, and for the reasons described in the 102 and 103 Rejections above.
Conclusion

APPLICANT REBUTTAL: Remarks
Claims 1-6 and 8-18 are pending in this application. The various parts of the Office Action (and other matters, if any) are discussed below under appropriate headings.
Claim Rejections - 35 U.S.C. § 103
Claims 1-6 and 8-16 stand rejected as anticipated by U.S. 2017/0245776 ("Kurtz"). Claims 17 and 18 stand rejected as unpatentable over Kurtz in view of U.S. 2013/0204155 ("Brull"). Applicant asks the Examiner to reconsider and withdraw the rejection(s) for at least the following reasons.
Claim 1 has been amended to include the subject matter of claims 17 and 18. Consequently, the rejection of amended claim 1 is discussed below in view of the rejection of claims 17 and 18.
Independent Claim 1
The method "generat[es] a single stimuli signal (SstimuIi) using theanesthetizing monitoring unit." The "single stimuli signal (SstimuIi) comprises a single pulse and is configured to stimulate a motor nerve of a patient."
The method "output[s] the generated single stimuli signal (Sstimuii) to stimulating electrodes on the patient using an output port of the anesthetizing monitoring unit." The method also "receiv[es] a single evoked electromyography, EMG, response signal (SResponse) from receiving electrodes on the patient at an input port of the anesthetizing monitoring unit, having a duration (TResponse), in response to the transmitted stimuli signal (SstimuIi)." The method "estimat[es] a periodic noise waveform (SPeriodic), having the duration (TResponse), by using temporal segments of a noise signal (SNoise)."
The "temporal segments of the noise signal (SNoise) used during the estimating of the periodic noise waveform (SPeriodic) are detected during a time interval (TNoise) not
Docket No. BRNN16PUS
O1
overlapping with a stimuli signal interval (Tstimuius) of the single stimuli signal (Sstimuii) and not overlapping with the duration of the response signal (TResponse)."
is presented on a display in theanesthetizing monitoring unit.
Analysis: Amended Claim 1 Rejection
The cited reference combination does not disclose -as set forth in amended claim 1- "estimating a periodic noise waveform (SPeriodic), having the duration (TResponse), by using temporal segments of a noise signal (SNoise)."
In the Office Action, the Examiner points to Brull as disclosing amended claim 1's estimating a periodic noise waveform. In particular, the Examiner points to Brull, paragraph 0022, which reads as follows:
"For
purp
oses of determinin
g
the am
p
litude
of the evoked muscle
response, the
start of the evoked muscle response
by locating the time at which the level of muscle electrical a
the
f electrical noise in the subject prior to s
the
immediately following completion of the evoked
response. The baseline level of electrical noise is the background noise
and may include all ambient, intrinsic and extrinsic noise. The amplitude is
determined as the difference in measured voltage from the baseline level
to the positive or negative peak of the evoked muscle response caused by
stimulating the motor nerve." (Emphasis added.)
Brull does not disclose estimating a periodic noise waveform using temporal segments of a noise signal. While Brull mentions a baseline noise level in paragraph 0022, Brull is silent regarding how this baseline noise level is determined. At most, Brull describes determining an amplitude value, but is silent regarding estimating a periodic noise waveform.
Brull's baseline level indicates a fixed level or value, not a waveform. In this way, Brull teaches against the claimed invention that is based on the realization that a dominant part of the signal is periodic noise varying significantly over time, but having a periodic character exploited by the invention. (See, e.g., current application, page 7.)
As argued in the previous rejection, Kurtz teaches averaging multiple responses SResponse received during the time period TResponse. That is, Kurtz describes receiving multiple responses including noise and signal, and averaging these responses. Kurtz is silent regarding a waveform.
RESPONSE5
TIME
Kurtz teaches averaging the sum of amplitude
a
nd
noise of "multiple time-based data series". This implies that a period of time over, e.g., five responses must elapse before an amplitude value is obtained. Kurtz therefore also teaches calculating a fixed level or value not reflecting the dynamic variation of noise.
Additionally, it is not clear how Brull can be applied to Kurtz. If Brull and Kurtz were combined, the resulting method would not calculate an aggregated amplitude value from RESPONSE 1-5 and then subtract a fixed level or value representing noise from that aggregated amplitude value.
In the claimed subject matter, the noise is determined as waveforms in the period "between" a stimuli pulse and a response signal RESPONSE 1-5. As can be seen from
the example below, the periodic noise waveform varies over time, and each response signal in the current application is improved by a dedicated noise waveform estimation for each response signal RESPONSE 1-5 based on a group of segments of the periodic noise signal SEGM 1-5.
SES
This improves each individual response signal compared to Kurtz's aggregating the compounded measurements of noise and signal. That is, an improved amplitude value of the response signal is generated for each response signal.
This further reduces the delay in amplitude values compared to Kurtz, where all of the multiple time-based data series needs to be obtained before processing by weighted average can be performed. Further, the present disclosure has the advantage of better performance when the assumption of quasi-stationary noise over the period of the weighted averaging is not true. The present disclosure then adapts to the noise at each received response SResponse.
For at least the above reason, the cited reference combination does not disclose the subject matter of amended claim 1. Withdrawal of the rejection is respectfully requested.
Conclusion

NEXT OFFICE ACTION: 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 .
Response to Amendment
The amendment filed April 29, 2024 has been filed. Claims 1-6 and 8-16 remain pending in the application, and claims 17-18 were cancelled. Applicant’s amendments to the claims have overcome each and every 102 rejection previously set forth in the Non-Final Office Action mailed March 20, 2024. Applicant’s amendments to the claims necessitate new grounds of rejection, described in the Response to Arguments and 103 Rejections below.
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.
Claims 1-6 and 8-16 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170245776 A1 (Kurtz et al.) in view of US 20180256097 A1 (Bray, Robert S.).
Regarding claim 1, Kurtz teaches a method performed by an anesthetizing monitoring unit ([0022] “apparatus”) configured to generate an improved evoked electromyography response signal (ΣResponse) ([0002] “The present application relates to an apparatus, system and method for detecting evoked responses to a stimulus (a stimulus-response signal).”) the method comprising:
generating a single stimuli signal (SStimuli) ([0002] “a stimulus”; [0006] “a single instance of the stimulus”; [0063] “may include multiple stimulus sources, such as visual and auditory, or may be a single source”) using the anesthetizing monitoring unit, wherein the single stimuli signal (SStimuli) is configured to stimulate a motor nerve of a patient ([0003] “Electrophysiological evoked responses to a variety of stimuli are known to contain valuable clinical and scientific information in the assessment of the sensorineural systems of humans and animals. Evoked responses (ER), such as, for example, auditory evoked potentials, somatosensory evoked potentials, visual evoked potentials, otoacoustic emissions, or the like”),
outputting the generated single stimuli signal (SStimuli) to stimulating electrodes on the patient using an output port ([0030] “output device”) of the anesthetizing monitoring unit,
receiving a single evoked electromyography, EMG, response signal (SResponse) from receiving electrodes on the patient at an input port ([0022] “input device”) of the anesthetizing monitoring unit ([0002] “detecting evoked responses to a stimulus (a stimulus-response signal)”; [0022] “receive the noisy signal to the plurality of stimuli”), having a duration (TResponse), in response to the transmitted stimuli signal (SStimuli) ([0006] “Signal averaging involves stimulating the patient with multiple stimuli, obtaining multiple time-based data series”),
estimating a periodic noise waveform (SPeriodic) ([0008] “the noise is quasi-stationary”), having the duration (TResponse), by using temporal segments of a noise signal (SNoise) ([0006] “stimulating the patient with multiple stimuli, obtaining multiple time-based data series”),
generating the improved response signal (ΣResponse) by subtracting the noise waveform from the response signal (SResponse) ([0059] “The final waveform or averaged response (sometimes called the “final response”), is the estimate of the synchronized evoked response, with reduced noise. It will be understood that the type of evoked response will generally be dependent on the stimulus and on the subject. For example, if a subject cannot hear an auditory stimulus, then there would not be an evoked response to the auditory stimulus.”; [0056] “reduce the negatively correlated noise from the resultant average”),
storing the improved response signal (ΣResponse) in a memory in the anesthetizing monitoring unit ([0064] “generated stimuli or the noisy signal relating thereto may also be stored in memory 140”), and
presenting the improved response signal (ΣResponse) on a display in the anesthetizing monitoring unit ([0066] “the final response may be output via the output module 145 to, for example, the display 150”).
Kurtz does not explicitly teach wherein the single stimuli signal (SStimuli) comprises a single pulse, and wherein the temporal segments of the noise signal (SNoise) used during the estimating of the periodic noise waveform (SPeriodic) are detected during a time interval (TNoise) not overlapping with a stimuli signal interval (TStimulus) of the single stimuli (SStimuli) and not overlapping with the duration of the response signal (TResponse).
However,
Bray teaches wherein the single stimuli signal (SStimuli) comprises a single pulse ([0032] “monitoring system 100 is a constant monitoring device that provides a single impulse to the nerves of a patient”; [0046] “a response to the single pulse stimuli”), and
wherein the temporal segments of the noise signal (SNoise) used during the estimating of the periodic noise waveform (SPeriodic) are detected during a time interval (TNoise) not overlapping with a stimuli signal interval (TStimulus) of the single stimuli (SStimuli) and not overlapping with the duration of the response signal (TResponse) ([0057] “such background noise could be filtered out with a timing mechanism that would time the stimulation pulse to the signal and filter out background noise fasciculations. Thus, a baseline recording by the monitoring system provides an indication of the of the individual's muscle activity that can be used to calibrate the signal. In other words, the baseline recording can serve as a basis of comparison for subsequent data collection.”; Fig. 11, [0081] “first electrical signal 960 and second electrical signal 962 may have different waveforms or waveform characteristics. For example, first electrical signal 960 includes regions of baseline noise with a large peak 970 corresponding to a muscle response. Second electrical signal 962 includes a moderately sized peak 974 followed immediately by a large dip 976. It may be appreciated that first electrical signal 960 and second electrical signal 962 have distinct waveforms that may not only be distinguished by quantitative analysis but may also be qualitatively distinct.”; [0083] “the features (e.g., peak 974 and dip 976) recorded by secondary recording device 914 are sufficiently distinct from any background or baseline noise so as to be immediately identifiable as a response to a pulse from stimulation device 910”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the method taught by Kurtz to include a single pulse and to include the segments of the noise signal not overlapping with the stimuli signal interval and the response signal. The single pulse allows multiple data points to be collected to determine the effect of paralyzing, or anesthetizing, agents [0053]. One would have been motivated to make this modification because using timing to filter out background noise is able to calibrate the signal so that subsequent stimuli and response data may be collected, as suggested by Bray ([0057]).
Regarding claim 2, Kurtz teaches the method according to claim 1, wherein the step of estimating the periodic noise waveform further comprises: splitting the noise signal (SNoise) into a number N of temporal segments (S1, S2, ... , SN) ([0014] “divide the noisy signal into a plurality of responses to the plurality of stimuli”),
generating a periodic noise template (STemplate) by aggregating the N temporal segments (S1, S2, ... , SN) ([0016] “estimate a statistic matrix for the plurality of responses”),
wherein the periodic noise waveform (SPeriodic), is estimated by subsequently repeating the periodic noise template (STemplate) N times ([0017] “performing a multilevel discrete wavelet transform on individual responses in a loop for each scale of the multilevel discrete wavelet transform”).
Regarding claim 3, Kurtz teaches the method according to claim 2, wherein the periodic noise template (STemplate) is generated by averaging discrete samples over the N temporal segments (S1, S2, … SN) ([0059] “The final waveform or averaged response (sometimes called the “final response”), is the estimate of the synchronized evoked response, with reduced noise. It will be understood that the type of evoked response will generally be dependent on the stimulus and on the subject. For example, if a subject cannot hear an auditory stimulus, then there would not be an evoked response to the auditory stimulus.”).
Regarding claim 4, Kurtz teaches the method according to claim 1, wherein the method further comprises determining an anesthetized patient state by determining a neuromuscular function value using properties of the improved response signal (ΣResponse) ([0134-0135] “an MMN response depends on the consciousness state of the subject, in the awake pre-op state, we expect there to be a significant MMN, while in the anesthesia state, we do not expect there to be an MMN present”).
Regarding claim 5, Kurtz teaches the method according to claim 1, wherein the anesthetized patient state is a selection of any of insufficient anesthetic level, low anesthetic level, sufficient anesthetic level, normal breathing function, capable of sustaining breathing or in need of ventilator ([0135] “an MMN response depends on the consciousness state of the subject, in the awake pre-op state, we expect there to be a significant MMN, while in the anesthesia state, we do not expect there to be an MMN present.”).
Regarding claim 6, Kurtz teaches the method according to claim 4, wherein the properties of the improved response signal (ΣResponse) include amplitude value/s (AResponse’) ([0135] “peak amplitude”) and wherein the neuromuscular function value is determined by comparing a quota of amplitude value/s of the improved response signal (ΣResponse) and additional amplitude value/s to a set of predetermined amplitude value/s thresholds, wherein the additional amplitude values are selected from amplitude value/s of the stimuli signal (SStimuli) and historical amplitude value/s of the improved response signal (ΣResponse) ([0135] “An MMN response is determined to be present when the peak amplitude of the processed deviant response is statistically different than that of a standard response in the region of expected MMN response”).
Regarding claim 8, Kurtz teaches the method according to claim 2, wherein the temporal segments (S1, S2, ... , SN) have a duration (TPeriodic) proportional to the noise signal (SNoise) ([0068] “by weighting data series or groups of data series in inverse proportion to their noise content”).
Regarding claim 9, Kurtz teaches the method according to claim 8, wherein the duration (TPeriodic) is selected using a strongest periodic noise component of the noise signal (SNoise) ([0009] “In this case, the group is greater than 8 seconds in duration. The drawback of this technique is that noise in evoked potential measurements is, in general, not stationary over an 8 second duration, especially when the time series is contaminated with interference from the patient's EMG caused by muscle activity.”; The time period with the most noise is selected, indicative of muscle activity.).
Regarding claim 10, Kurtz teaches an anesthetizing monitoring unit configured to generating an improved evoked electromyography response signal (ΣResponse), the anesthetizing monitoring unit comprising:
an input port ([0002] “input device”),
an output port ([0030] “output device”), and
processing circuitry ([0140] “The machine-readable medium can contain various sets of instructions, code sequences, configuration information, or other data, which, when executed, cause a processor to perform steps in a method according to an embodiment of the disclosure”) being configured to perform the method according to claim 1 (see rejection above).
Regarding claim 11, Kurtz teaches an anesthetizing monitoring system configured to determine an anesthetized patient state, the anesthetizing monitoring unit ([0022] “an apparatus for detection of an evoked response signal in noise”) comprising:
an anesthetizing monitoring unit comprising an input port (111) ([0022] “input device”) and an output port ([0030] “output device”),
stimulating electrodes electrically coupled to the output port and being configured to receive a stimuli signal (SStimuli) from the output port and deliver the stimuli signal (SStimuli) to the anesthetized patient ([0058] “A sensor or sensors 115, for example an electrode or electrodes, are provided to the subject to detect a noisy signal including a response to the stimulus, which is sent to an input module 120”),
receiving electrodes electrically coupled to the input port and being configured to obtain an evoked electromyography, EMG, response signal (SResponse) ([0059] “electrodes receive continuous data, sometimes referred to as “noisy data” or “noisy signal”. For each stimulus, a synchronized evoked response within the subject's brain will be generated.”), in response to the stimuli signal (SStimuli) ([0058] “response to the stimulus”) and a noise signal (SNoise) ([0059] “noisy signal”), from the anesthetized patient, the anesthetizing monitoring unit being configured to perform the method according to claim 1 ([0059] “this evoked response will also be detected by the electrodes but is generally hidden in the noisy signal. Embodiments of the system, apparatus and method herein are intended to separate the evoked response, or stimulus-synchronized component, (sometimes referred to as a “response”) from the non-synchronized components (i.e. noise).”).
Regarding claim 12, Kurtz teaches a computer program is provided comprising computer-executable instructions for causing an anesthetizing monitoring unit, when the computer-executable instructions are executed on a processing unit comprised in the anesthetizing monitoring unit, to perform the method according to claim 1 ([0140] “The machine-readable medium can contain various sets of instructions, code sequences, configuration information, or other data, which, when executed, cause a processor to perform steps in a method according to an embodiment of the disclosure.”).
Regarding claim 13, Kurtz teaches a computer program product comprising a computer-readable storage medium, the computer-readable storage medium having the computer program according to claim 12 embodied therein ([0140] “Embodiments of the disclosure or portions thereof can be represented as a computer program product stored in a machine-readable medium (also referred to as a computer-readable medium, a processor-readable medium, or a computer usable medium having a computer-readable program code embodied therein).”).
Regarding claim 14, Kurtz teaches a carrier comprising the computer program according to claim 12, wherein the carrier is one of an electronic signal, optical signal, radio signal, or computer readable storage medium ([0140] “computer-readable medium”).
Regarding claim 15, Kurtz teaches the method according to claim 6, wherein the threshold values are predetermined properties of historical improved response signals received at the anesthetizing monitoring unit prior to the response signal (SResponse) being received by the anesthetizing monitoring unit, wherein the threshold values are stored in the memory of the anesthetizing monitoring unit ([0134] “responses to the standard stimulus and 107 responses to the randomly inserted deviant stimuli were collected from electrodes on the patient's scalp prior to the administration of anesthesia”; [0135] “An MMN response is determined to be present when the peak amplitude of the processed deviant response is statistically different than that of a standard response in the region of expected MMN response”; [0140]).
Regarding claim 16, Kurtz teaches the method according to claim 1, wherein the temporal segments of the noise signal (SNoise) are gathered by the anesthetizing monitoring unit within an adjacent time interval (TNoise) not overlapping with a stimuli signal interval (TStimulus) and the duration of the response signal (TResponse) ([0065] “data outside that region of interest would be noise and the removal of that noise prior to subsequent signal processing may improve the analysis to determine the evoked response”; [0068] “the noise from data series to data series is independent”).
Response to Arguments
Applicant's arguments filed April 29, 2024 have been fully considered. With respect to the 103 Rejections in the Non-Final Office Action (See Pages 7-9 of Applicant’s Response “Analysis: Amended Claim 1 Rejection”), Applicant argues that the cited reference combination does not disclose the limitations of claim 1 regarding estimating a periodic noise waveform, having the duration, by using temporal segments of a noise signal. Applicant states that Brull does not disclose estimating a periodic noise waveform using temporal segments of a noise signal. The amendments necessitate new grounds of rejection under Kurtz in view of Bray.
MPEP § 2111 discusses proper claim interpretation, including giving claims their broadest reasonable interpretation in light of the specification during examination. Under broadest reasonable interpretation (BRI), the words of a claim must be given their plain meaning unless such meaning is inconsistent with the specification, and it is improper to import claim limitations from the specification into the claim. Under BRI, the claim limitation reciting “wherein the temporal segments of the noise signal (SNoise) used during the estimating of the periodic noise waveform (SPeriodic) are detected during a time interval (TNoise) not overlapping with a stimuli signal interval (TStimulus) of the single stimuli (SStimuli) and not overlapping with the duration of the response signal (TResponse)” may be interpreted to mean that the time-based segments of noise data in the periodic waveform do not overlap with the time during which the stimuli signal is being measured or the response signal is being measured. This is taught by Bray as shown in Figure 11 and described in [0080-0081]. Figure 11 of Bray shows element 960, which is the measured noise waveform, which is independent of the large peak 970 corresponding to a muscle response. Additionally, [0057] teaches baseline recording for background noise being taken for a basis of comparison for subsequent data collection. This subsequent data collection includes the stimuli interval and the response signal duration, so this background noise signal is collected independently from the other data.
Claims 2-6 and 8-16 are rejected based on their dependency on claim 1 for the reasons above, and for the reasons described in the 103 Rejections above.
Conclusion
THIS ACTION IS MADE FINAL.  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 mailing date of this final action.

CITED REFERENCE SUMMARIES: Ref US20170245776A1: Claims:
We claim: 1. A method for detection of an evoked response signal in noise, the method comprising: generating a plurality of stimuli; receiving a noisy signal related to an evoked response to the plurality of stimuli; divide the noisy signal into a plurality of responses to the plurality of stimuli; estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; apply weights to the plurality of responses to construct a final response; and output the final response. 2. A method for detection of claim 1, wherein the shrinking the statistic matrix comprises: calculate a correlation between combinations of responses; create a list of negatively correlated pairs; for each negatively correlated pair in the list: determine if one of the responses in the pair is in a shrinkage list, if so, remove the pair from the list of negatively correlated pairs, otherwise, add both responses of the pair to the shrinkage list; when the list of negatively correlated pairs is empty, set all non-diagonal elements of the statistic matrix corresponding to responses in the shrinkage list to 0 to provide a shrunk statistic matrix; and return the shrunk statistic matrix. 3. A method for detection of claim 1, further comprising decomposing each response into a plurality of sub-responses to create a plurality of sets of sub-responses and performing the: estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; apply weights to the plurality of responses to construct a final response; of claim 1 for each of the plurality of sets of sub-responses. 4. A method for detection of claim 3, wherein the decomposing comprises: performing a multilevel discrete wavelet transform on individual responses in a loop for each scale of the multilevel discrete wavelet transform, selecting a scale of the multilevel discrete wavelet transform and, for the selected scale: set wavelet coefficients for non-selected scales to 0; and perform a multilevel inverse discrete wavelet transform to obtain a time domain sub-response for the selected scale; and return a plurality of sets of sub-responses, each set comprising sub-responses having the same scale. 5. A method for detection of claim 1, wherein the statistic is covariance and the statistic matrix is a covariance matrix. 6. A method for detection of claim 1, wherein the statistic matrix is an array of greater than two dimensions. 7. A method for detection of claim 1, wherein the statistic is root mean square and the statistic matrix is a root mean square array of greater than two dimensions. 8. A method for detection of claim 1, wherein the divide the noisy signal into a plurality of responses to the plurality of stimuli is based on the plurality of responses being synchronized with the plurality of stimuli. 9. An apparatus for detection of an evoked response signal in noise, the apparatus comprising: an input device configured to receive data related to a plurality of stimuli and a noisy signal related to the evoked response signal to the plurality of stimuli; and a processor configured to: receive the noisy signal from the input device and divide the noisy signal into a plurality of responses to the plurality of stimuli; estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; and apply weights to the plurality of responses to construct a final response representing the evoked response signal. 10. An apparatus for detection of claim 9, wherein when shrinking the statistic matrix the processor is further configured to: calculate a correlation between combinations of responses; create a list of negatively correlated pairs; for each negatively correlated pair in the list: determine if one of the responses in the pair is in a shrinkage list, if so, remove the pair from the list of negatively correlated pairs, otherwise, add both responses of the pair to the shrinkage list; when the list of negatively correlated pairs is empty, set all non-diagonal elements of the statistic matrix corresponding to responses in the shrinkage list to 0 to provide a shrunk statistic matrix; and return the shrunk statistic matrix. 11. An apparatus for detection of claim 9, wherein the processor is further configured to decompose each response into a plurality of sub-responses to create a plurality of sets of sub-responses and performing the: estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; apply weights to the plurality of responses to construct a final response; of claim 9 for each of the plurality of sets of sub-responses. 12. An apparatus for detection of claim 11, wherein the processor, when decomposing each response, is further configure to: perform a multilevel discrete wavelet transform on individual responses in a loop for each scale of the multilevel discrete wavelet transform, selecting a scale of the multilevel discrete wavelet transform and, for the selected scale: set wavelet coefficient for non-selected scales to 0; and perform a multilevel inverse discrete wavelet transform to obtain a time domain sub-response for the selected scale; and return a plurality of sets of sub-responses, each set comprising sub-responses having the same scale. 13. An apparatus for detection of claim 9, wherein the statistic is covariance and the statistic matrix is a covariance matrix. 14. An apparatus for detection of claim 9, wherein the statistic matrix is an array of greater than two dimensions. 15. An apparatus for detection of claim 9, wherein the statistic is root mean square and the statistic matrix is a root mean square array of greater than two dimensions. 16. An apparatus for detection of claim 9, wherein the divide the noisy signal into a plurality of responses to the plurality of stimuli is based on the plurality of responses being synchronized with the plurality of stimuli. 17. A system for detection of an evoked response signal in noise, the system comprising: a stimulus generator configured to generate a plurality of stimuli; a plurality of sensors configured to receive a noisy signal including an evoked response signal to the plurality of stimuli; an input device configured to receive data related to the plurality of stimuli and the noisy signal; a processor configured to: receive the noisy signal from the input device and divide the noisy signal into a plurality of responses to the plurality of stimuli; estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; and apply weights to the plurality of responses to construct a final response representing the evoked response signal; and an output device to output the final response received from the processor. 18. A system for detection of claim 17, wherein when shrinking the statistic matrix the processor is further configured to: calculate a correlation between combinations of responses; create a list of negatively correlated pairs; for each negatively correlated pair in the list: determine if one of the responses in the pair is in a shrinkage list, if so, remove the pair from the list of negatively correlated pairs, otherwise, add both responses of the pair to the shrinkage list; when the list of negatively correlated pairs is empty, set all non-diagonal elements of the statistic matrix corresponding to responses in the shrinkage list to 0 to provide a shrunk statistic matrix; and return the shrunk statistic matrix. 19. A system for detection of claim 17, wherein the processor is further configured to decompose each response into a plurality of sub-responses to create a plurality of sets of sub-responses and performing the: estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; apply weights to the plurality of responses to construct a final response; of claim 17 for each of the plurality of sets of sub-responses. 20. A system for detection of claim 19, wherein the processor, when decomposing each response, is further configure to: perform a multilevel discrete wavelet transform on individual responses in a loop for each scale of the multilevel discrete wavelet transform, selecting a scale of the multilevel discrete wavelet transform and, for the selected scale: set wavelet coefficients for non-selected scales to 0; and perform a multilevel inverse discrete wavelet transform to obtain a time domain sub-response for the selected scale; and return a plurality of sets of sub-responses, each set comprising sub-responses having the same scale. 21. A system for detection of claim 17, wherein the statistic is covariance and the statistic matrix is a covariance matrix. 22. A system for detection of claim 17, wherein the statistic matrix is an array of greater than two dimensions 23. A system for detection of claim 17, wherein the statistic is root mean square and the statistic matrix is a root mean square array of greater than two dimensions. 24. A system for detection of claim 17, wherein the divide the noisy signal into a plurality of responses to the plurality of stimuli is based on the plurality of responses being synchronized with the plurality of stimuli.
A method for detection of an evoked response signal in noise, the method comprising: generating a plurality of stimuli; receiving a noisy signal related to an evoked response to the plurality of stimuli; divide the noisy signal into a plurality of responses to the plurality of stimuli; estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; apply weights to the plurality of responses to construct a final response; and output the final response.
A method for detection of claim 1, wherein the shrinking the statistic matrix comprises: calculate a correlation between combinations of responses; create a list of negatively correlated pairs; for each negatively correlated pair in the list: determine if one of the responses in the pair is in a shrinkage list, if so, remove the pair from the list of negatively correlated pairs, otherwise, add both responses of the pair to the shrinkage list; when the list of negatively correlated pairs is empty, set all non-diagonal elements of the statistic matrix corresponding to responses in the shrinkage list to 0 to provide a shrunk statistic matrix; and return the shrunk statistic matrix.
A method for detection of claim 1, further comprising decomposing each response into a plurality of sub-responses to create a plurality of sets of sub-responses and performing the: estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; apply weights to the plurality of responses to construct a final response; of claim 1 for each of the plurality of sets of sub-responses.
A method for detection of claim 1, further comprising decomposing each response into a plurality of sub-responses to create a plurality of sets of sub-responses and performing the: estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; apply weights to the plurality of responses to construct a final response;
of claim 1 for each of the plurality of sets of sub-responses.
A method for detection of claim 3, wherein the decomposing comprises: performing a multilevel discrete wavelet transform on individual responses in a loop for each scale of the multilevel discrete wavelet transform, selecting a scale of the multilevel discrete wavelet transform and, for the selected scale: set wavelet coefficients for non-selected scales to 0; and perform a multilevel inverse discrete wavelet transform to obtain a time domain sub-response for the selected scale; and return a plurality of sets of sub-responses, each set comprising sub-responses having the same scale.
A method for detection of claim 1, wherein the statistic is covariance and the statistic matrix is a covariance matrix.
A method for detection of claim 1, wherein the statistic matrix is an array of greater than two dimensions.
A method for detection of claim 1, wherein the statistic is root mean square and the statistic matrix is a root mean square array of greater than two dimensions.
A method for detection of claim 1, wherein the divide the noisy signal into a plurality of responses to the plurality of stimuli is based on the plurality of responses being synchronized with the plurality of stimuli.
An apparatus for detection of an evoked response signal in noise, the apparatus comprising: an input device configured to receive data related to a plurality of stimuli and a noisy signal related to the evoked response signal to the plurality of stimuli; and a processor configured to: receive the noisy signal from the input device and divide the noisy signal into a plurality of responses to the plurality of stimuli; estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; and apply weights to the plurality of responses to construct a final response representing the evoked response signal.
An apparatus for detection of claim 9, wherein when shrinking the statistic matrix the processor is further configured to: calculate a correlation between combinations of responses; create a list of negatively correlated pairs; for each negatively correlated pair in the list: determine if one of the responses in the pair is in a shrinkage list, if so, remove the pair from the list of negatively correlated pairs, otherwise, add both responses of the pair to the shrinkage list; when the list of negatively correlated pairs is empty, set all non-diagonal elements of the statistic matrix corresponding to responses in the shrinkage list to 0 to provide a shrunk statistic matrix; and return the shrunk statistic matrix.
An apparatus for detection of claim 9, wherein the processor is further configured to decompose each response into a plurality of sub-responses to create a plurality of sets of sub-responses and performing the: estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; apply weights to the plurality of responses to construct a final response; of claim 9 for each of the plurality of sets of sub-responses.
An apparatus for detection of claim 9, wherein the processor is further configured to decompose each response into a plurality of sub-responses to create a plurality of sets of sub-responses and performing the: estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; apply weights to the plurality of responses to construct a final response;
of claim 9 for each of the plurality of sets of sub-responses.
An apparatus for detection of claim 11, wherein the processor, when decomposing each response, is further configure to: perform a multilevel discrete wavelet transform on individual responses in a loop for each scale of the multilevel discrete wavelet transform, selecting a scale of the multilevel discrete wavelet transform and, for the selected scale: set wavelet coefficient for non-selected scales to 0; and perform a multilevel inverse discrete wavelet transform to obtain a time domain sub-response for the selected scale; and return a plurality of sets of sub-responses, each set comprising sub-responses having the same scale.
An apparatus for detection of claim 9, wherein the statistic is covariance and the statistic matrix is a covariance matrix.
An apparatus for detection of claim 9, wherein the statistic matrix is an array of greater than two dimensions.
An apparatus for detection of claim 9, wherein the statistic is root mean square and the statistic matrix is a root mean square array of greater than two dimensions.
An apparatus for detection of claim 9, wherein the divide the noisy signal into a plurality of responses to the plurality of stimuli is based on the plurality of responses being synchronized with the plurality of stimuli.
A system for detection of an evoked response signal in noise, the system comprising: a stimulus generator configured to generate a plurality of stimuli; a plurality of sensors configured to receive a noisy signal including an evoked response signal to the plurality of stimuli; an input device configured to receive data related to the plurality of stimuli and the noisy signal; a processor configured to: receive the noisy signal from the input device and divide the noisy signal into a plurality of responses to the plurality of stimuli; estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; and apply weights to the plurality of responses to construct a final response representing the evoked response signal; and an output device to output the final response received from the processor.
A system for detection of claim 17, wherein when shrinking the statistic matrix the processor is further configured to: calculate a correlation between combinations of responses; create a list of negatively correlated pairs; for each negatively correlated pair in the list: determine if one of the responses in the pair is in a shrinkage list, if so, remove the pair from the list of negatively correlated pairs, otherwise, add both responses of the pair to the shrinkage list; when the list of negatively correlated pairs is empty, set all non-diagonal elements of the statistic matrix corresponding to responses in the shrinkage list to 0 to provide a shrunk statistic matrix; and return the shrunk statistic matrix.
A system for detection of claim 17, wherein the processor is further configured to decompose each response into a plurality of sub-responses to create a plurality of sets of sub-responses and performing the: estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; apply weights to the plurality of responses to construct a final response; of claim 17 for each of the plurality of sets of sub-responses.
A system for detection of claim 17, wherein the processor is further configured to decompose each response into a plurality of sub-responses to create a plurality of sets of sub-responses and performing the: estimate a statistic matrix for the plurality of responses; shrink the statistic matrix; calculate weights based on an inverse of the shrunk statistic matrix; apply weights to the plurality of responses to construct a final response;
of claim 17 for each of the plurality of sets of sub-responses.
A system for detection of claim 19, wherein the processor, when decomposing each response, is further configure to: perform a multilevel discrete wavelet transform on individual responses in a loop for each scale of the multilevel discrete wavelet transform, selecting a scale of the multilevel discrete wavelet transform and, for the selected scale: set wavelet coefficients for non-selected scales to 0; and perform a multilevel inverse discrete wavelet transform to obtain a time domain sub-response for the selected scale; and return a plurality of sets of sub-responses, each set comprising sub-responses having the same scale.
A system for detection of claim 17, wherein the stati
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