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 .
Claim Objections
Claim 10 is objected to because of the following informalities: “selected peak with is” is grammatically incorrect. Appropriate correction is required.
Claim 18 is objected to because of the following informalities: “abased” should read “based”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 7 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 13 recites the limitation, “which classifies the baseline potential as either monitorable or not monitorable based without engineering features of the two SSEP potentials”. However, applicant’s disclosure does not state how the neural network performs this feature without using engineering features. Paras [00138] of applicant’s specification merely restate the claim without further explanation. For the purpose of examination, the claim will be interpreted as a wavelet convolution neural network.
Claim 13 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 13 recites the limitation, “compensating for the artifacts to mitigate unnecessary alerting.”. However, applicant’s disclosure does not state how compensation occurs. Paras [00126] and [00144] of applicant’s specification merely restate the claim without further explanation.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-14 and 17-18 is/are rejected under 35 U.S.C. 101 because the claimed invention, considering all claim elements both individually and in combination as a whole, do not amount to significantly more than a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea).
Claim 1 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 1 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “determining presence and characteristics of the evoked potential in the at least one SSEP recording to determine presence of a monitorable baseline potential”, “comparing the ongoing evoked potentials to the monitorable baseline potential;”, and “upon the ongoing evoked potentials deviating from the monitorable baseline potential according to a defined criteria, executing an alert.” This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more. The additional elements are “acquiring at least one/ongoing SSEP recording” and “acquiring ongoing SSEP recordings from the subject to determine ongoing evoked potentials”. However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0002]: “A common EEG analysis technique includes evoked potentials (EP), which involves averaging the EEG activity time-locked to the presentation of a stimulus (e.g… somatosensory… in real-time” of Desai et al. (US 20230320669 A1). Regarding claim 14, a generic computer structure such as “a non-transitory computer readable medium” is not significantly more according to Alice v. CLS. Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception.
Additionally, the ordered combination of elements do not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements do not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 1 fails to recite patent-eligible subject matter under 35 U.S.C. 101. Dependent claim(s) 2-14 and 17 fail to cure the deficiencies of independent claim 1 by merely reciting additional abstract ideas, further limitations on abstract ideas already recited, and/or additional elements that are not significantly more. Claim 15 recites the additional elements “stimulating electrodes” and “recording electrodes” However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0006]: “traditional recording and stimulating electrodes” of Meng et al (US 20200206496 A1). Regarding claim 16, a generic computer structure such as a “processor” or “non-transitory computer readable medium” is not significantly more according to Alice v. CLS. Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception. Thus, claim(s) 1-17 is/are rejected under 35 U.S.C. 101.
Claim 18 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 18 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “identifying one or more candidate peaks in an ongoing SSEP recording with the same polarity as a primary peak of an evoked potential in a predetermined baseline SSEP recording”, “filtering the one or more candidate peaks abased on an analysis range;”, “comparing an amplitude of the evoked potential in a previous ongoing SSEP recording to a threshold;” , “when the amplitude exceeds the threshold, applying a peak tracking by selecting as the primary peak the candidate peak that is closest in latency to the primary peak of the potential from the previous ongoing SSEP recording”, “selecting the peak with the greatest prominence within an ongoing analysis range as the primary peak;”, “identifying potential reference peaks in a region around the primary peak;”, and “selecting a reference that maximizes the amplitude of the ongoing SSEP evoked potential.”. This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more.
Additionally, the ordered combination of elements do not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements do not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 18 fails to recite patent-eligible subject matter under 35 U.S.C. 101. Thus, claim(s) 18 is/are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
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 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) 1-14 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Higgins et al. ( US 20110230785 A1, as cited by applicant’s IDS filed 10/11/2023), hereinafter Higgins, in view of Snow et al. (US 20180078210 A1, as cited by applicant’s IDS filed 10/11/2023), hereinafter Snow.
Regarding claim 1, Higgins discloses a method for determining the presence, absence, and/or monitorability of an evoked potential within one or more SSEP recordings (abstract: “SEP is analyzed”), comprising: acquiring an SSEP recordings from the subject to determine ongoing evoked potentials ([0017]: “registering a sensory evoked potential waveform in response to the applied peripheral nerve stimulation”, [0038]; “Otherwise, the process is continually repeated until the surgery is complete”, Fig 6 steps 618 back to steps 602-606); comparing the ongoing evoked potentials to the monitorable baseline potential ([0017]: “The waveform is analyzed to determine a slope of a portion of the sensory evoked potential waveform that is compared to a baseline value”); and upon the ongoing evoked potentials deviating from the monitorable baseline potential according to a defined criteria, executing an alert ([0017]: “Indicia may be provided to the surgeon when the comparison indicates the slope of the portion of the sensory evoked potential waveform deviates from the baseline value a predetermined amount.”).
Higgins fails to disclose acquiring at least one SSEP recording from a subject; determining presence and characteristics of the evoked potential in the at least one SSEP recording to determine presence of a monitorable baseline potential.
Snow discloses a method for determining the presence, absence, and/or monitorability of an evoked potential within one or more SSEP recordings, comprising ([0005]: “automated EP analysis apparatus… wherein the apparatus is adapted to identify in an electrophysiological response at least one characteristic representative of non-physiological artifact noise to classify the signal as an artifact signal”, wherein if the signal is an artifact signal it is not monitorable): acquiring at least one SSEP recording from a subject ([0039]: “a collection of approximately 1000 ERs obtained during a surgery”); determining presence and characteristics of the evoked potential in the at least one SSEP recording to determine presence of a monitorable baseline potential ([0040]: “the algorithm first looks for any ER that contain sudden increases in amplitude (signal having a steep rising edge), characteristic of non-physiological artifact. If none is present, no further processing is required. If this characteristic is present, the algorithm looks for the presence of a sudden decrease in amplitude occurring within a specified time or window length from initial increase….If all defined characteristics are present, it rejects the signal from the ensemble averaging”, wherein if the signal is not rejected, it is “monitorable”, and wherein a monitorable signal may be determined based on amplitude per applicant’s specification para [0043]).
As Higgins discloses a method of obtaining a baseline ([0038]: “Baseline value Z may be, for example, the average slope of the SSEP taken from an individual when not in a surgical position to get the normal SSEP waveform.”), it would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method of determining a baseline disclosed by Higgins to the method of determining a baseline disclosed by Snow in order to improve accuracy by accounting for noise in the baseline potential (Snow [0004]).
Regarding claim 2, Higgins as modified by Snow discloses the method of claim 1, and Snow further discloses wherein acquiring at least one SSEP recording comprises acquiring two independent SSEP recordings comprising a first and a second SSEP recording ([0038]: “processes the individual signals… shows five individual ERs”), and identifying two SSEP evoked potentials (Fig 4), including a first evoked potential from the first SSEP recording and a second evoked potential from the second SSEP recording ([0038]); determining a baseline recording based on a grand ensemble average of the first and second of the two independent SSEP recordings ([0036]: “. Ensemble averaging of the electrophysiological responses may be performed”); and determining whether the baseline recording is monitorable based on features of the two independent SSEP recordings, two evoked potentials identified from the two independent sets of SSEP recordings, and/or the potential identified from the grand ensemble average ([0037]: “The methods and algorithms are applied to every ER signal recoded after each stimulation that survives any initial frequency and amplitude rejection filtering, and prior to summation of those timed signals into a EA. The algorithm establishes the presence or absence of a pacemaker or similar artifact in each ER, and then excludes those in which it is present from the EA.”).
Regarding claim 3, Higgins as modified by Snow discloses the method of claim 2, and Snow further discloses calculating the features of the first evoked potential and the second evoked potential ([0040]: “analyze each ER through a series of characteristics”), wherein the features comprise: an amplitude of a first potential of the two SSEP recordings ([0040]: “which may include but are not limited to amplitude, rise time, peak duration and pre and post peak slope”), an amplitude of a second potential of the two SSEP recordings, an absolute value of a slope between the primary and reference peaks in the baseline potential, an absolute value of a difference in peak latency of the potentials in the first and second SSEP recordings, a SNR (signal to noise ratio) of the first and second potentials in comparison to an entire respective recordings, and a ratio of a peak amplitude of the potentials to the RMS (root mean square) of the entire respective recordings for the two SSEP recordings and the grand ensemble baseline recording.
Regarding claim 12, Higgins as modified by Snow discloses the method of claim 1, and Higgins further discloses wherein the alert comprises an auditory alert, a visual alert, and/or a haptic alert ([0039]: “Indicia being provided to the surgeon should be understood to generically refer to any type of alert or indication provided to the surgeon. For example, the indicia may be an audible alarm emitted from a device 416 on processor 402 such as, for example, a speaker, a buzzer, or the like. Indicia may alternatively be a visual indication on monitor 404.”).
Regarding claim 13, Higgins as modified by Snow discloses the method claim 1. Snow further discloses identifying artifacts on the ongoing SSEP recordings and potentials due to presence of anesthesia in the subject and/or noise from a surrounding environment ([0015]: “the apparatus can obtain information from an anesthesia or blood pressure machine or pacemaker to calculate when changes in EP waveforms are due to anesthesia or physiologic or non-physiologic changes.”); compensating for the artifacts to mitigate unnecessary alerting (claim 18).
Regarding claim 14, Higgins as modified by Snow discloses the method claim 1. Higgins further discloses identifying a non-transitory computer readable medium having recorded thereon statements and instructions that, when executed by a processor of an SSEP (Somatosensory Evoked Potentials) system, configure the processor to implement a method according to claim 1 ([0043]: “The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), flash memory, Read Only Memory (ROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.”).
Regarding claim 15, Higgins further discloses An SSEP (Somatosensory Evoked Potentials) system (abstract), comprising: stimulating electrodes configured to generate electric responses from a subject's nervous system (Fig 4 element 410); recording electrodes configured to sense the electric potentials generated by stimulation upon their traversing the nervous system ([0013]: “The waveform 100 was the measured response by an electrode placed on the skin surface or subdermally of the patient's head.”); a nerve injury detection device coupled to the recording electrodes and the recording electrodes and configured to implement a method according to claim 1 (Fig 4 element 404).
Regarding claim 16, Higgins as modified by Snow discloses the SSEP system of claim 15, and Higgins further discloses wherein the nerve injury detection device comprises: a processor ([0016]: “a processor including an input port to receive a waveform from a sensor is provided”); and a non-transitory computer readable medium having recorded thereon statements and instructions that, when executed by the processor of the SSEP system, configure the processor to implement the nerve injury detection device ([0043]: “The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), flash memory, Read Only Memory (ROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.”).
Regarding claim 17, Higgins as modified by Snow discloses the method of claim 2, and Snow further discloses determining whether the baseline recording is deemed monitorable by identifying primary and reference peaks of the two SSEP potentials ([0006]: “minimum amplitude, a maximum rise time, a maximum fall time, a minimum peak duration,”, wherein the minimum and maximum peaks are identified).
Claim(s) 4, 5, 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Higgins in view of Snow in view of Torres (US 20250204842 A1), in further view of Parker (US 20230310864 A1).
Regarding claim 4, Higgins as modified by Snow discloses the method of claim 3, but fails to disclose discloses identifying candidate peaks in an upright representation and an inverted representation of the baseline SSEP recordings, identifying which candidate peak has greatest prominence based on how much the candidate peak stands out due to its intrinsic height and its location relative to other candidate peaks, and assigning the candidate peak with the greatest prominence as the primary peak.
Torres discloses a method of identifying which candidate peak has greatest prominence based on how much a candidate peak stands out due to its intrinsic height and its location relative to other candidate peaks ([0062]: “properties of a primary peak in the electrical measurements associated with a set of vertices… The trace 414 indicates essentially a correlation with the multiple vertices I to VII expected in this electrical signal and thus the primary peak represents a Dirac delta function at the time of receipt of the expected signal”), and assigning the candidate peak with the greatest prominence as the primary peak ([0062]: “The location of the primary peak is considered to be at frame 25 (1 ms along the axis) and the properties of this primary peak include a prominence 425 of about 2.5 mV above a zero electrical amplitude and a width 426 at half height of about 6 frames (about 240 microseconds).”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Higgins and Snow to include identification of a candidate peak as disclosed by Torres in order to distinguish between background neural activity and relevant conditions (Torres [0061]).
Higgins as modified by Snow and Torres fails to disclose identifying candidate peaks in an upright representation and an inverted representation of the baseline SSEP recordings.
Parker discloses a method of identifying candidate peaks in an upright representation ([0059]: “The ECAP 600 generated from the synchronous depolarisation of a group of similar fibres comprises a positive peak P1, then a negative peak N1, followed by a second positive peak P2.”) and an inverted representation of the baseline SSEP recordings ([0060]: “Depending on the polarity of recording, a differential ECAP may take an inverse form to that shown in FIG. 6, i.e. a form having two negative peaks N1 and N2, and one positive peak P1.”).
Parker and Higgins are considered analogous art as they pertain to the analysis of evoked potentials. It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Higgins as modified by Snow and Torres to include the upright and inverse waveforms as disclosed by Parker in order to account for measurements with different electrode types (Parker [0060]).
Regarding claim 5, Higgins as modified by Snow, Torres, and Parker discloses the method of claim 4. Torres further discloses identifying onset and offset peaks surrounding the reference peak ([0088]; “inclusive of activity prior and after the primary peak. The variations in the fluctuations were examined, including micropeak widths, prominences, and full amplitudes”), and selecting an onset or offset peak that maximizes the amplitude of the potential as the reference peak of the baseline potential ([0065]: “Where Pn is the prominence of the nth peak, Pn* is the normalized prominence of the nth peak, and Avg (Pmin1, Pmin2) is the average of all the points between the two local minima Pmin1 and Pmin2, including the local maximum (micropeak) Pn When the Avg (Pmin1, Pmin2) is small compared to the prominence of the nth peak, such as for the primary peak, the value of Pn* approaches 1”).
Regarding claim 10, Higgins as modified by Snow discloses the method of claim 1, and Snow further discloses for each ongoing SSEP recording, calculating peak/trough markers of the ongoing SSEP recordings ([0009]: “a minimum slope, minimum amplitude, a maximum rise time, a maximum fall time, a minimum peak duration, and a maximum activity between a rising edge and a falling edge of the signal.”) and comparing an amplitude of the potential in the previous ongoing SSEP recording to a threshold ([0044]: “threshold values for each piece of data is set… An amplitude threshold describes the minimum amplitude of the artifact, to confirm that processing to determine presence of a pacing artifact should continue on the current signal.”). However, Higgins as modified by Snow fails to disclose identifying candidate peaks in an upright representation or an inverted representation of the ongoing SSEP recording depending on a polarity of the baseline SSEP potential; selecting the candidate peak based on which peak has the latency nearest the latency of the potential in the previous ongoing SSEP potential or which has greatest prominence based on how much the candidate peak stands out due to its intrinsic height and its location relative to other candidate peaks depending on the amplitude comparison to the previous ongoing SSEP potential; wherein the selected peak with is used in the comparing of the ongoing SSEP potential to the monitorable baseline potential.
Torres discloses a method of selecting the candidate peak based on which peak has the latency nearest the latency of the potential in the previous ongoing SSEP potential ([0055]: “the latency, or latencies for various vertices, or the distribution of latencies for one vertex, or the distributions of latencies for various vertices, are compared to the predetermined latencies.”) or which has greatest prominence based on how much the candidate peak stands out due to its intrinsic height and its location relative to other candidate peaks depending on the amplitude comparison to the previous ongoing SSEP potential; wherein the selected peak with is used in the comparing of the ongoing SSEP potential to the monitorable baseline potential ([0081]: “The ABR latencies are compared at millisecond time scale along with the fluctuations in the waveform's primary peak and micropeaks' amplitude (μV) and width (ms) reflecting these responses.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Higgins and Snow to include identification of a candidate peak as disclosed by Torres in order to distinguish between background neural activity and relevant conditions (Torres [0061]).
Higgins as modified by Snow and Torres fails to disclose identifying candidate peaks in an upright representation and an inverted representation of the baseline SSEP recordings.
Parker discloses a method of identifying candidate peaks in an upright representation ([0059]: “The ECAP 600 generated from the synchronous depolarisation of a group of similar fibres comprises a positive peak P1, then a negative peak N1, followed by a second positive peak P2.”) and an inverted representation of the baseline SSEP recordings ([0060]: “Depending on the polarity of recording, a differential ECAP may take an inverse form to that shown in FIG. 6, i.e. a form having two negative peaks N1 and N2, and one positive peak P1.”).
Parker and Higgins are considered analogous art as they pertain to the analysis of evoked potentials. It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Higgins as modified by Snow and Torres to include the upright and inverse waveforms as disclosed by Parker in order to account for measurements with different electrode types (Parker [0060]).
Regarding claim 11, Higgins as modified by Snow, Torres, and Parker discloses the method of claim 10. Snow further discloses the defined criteria comprises a defined decrease in amplitude based on decreased prominence and/or a defined increase in latency based on delay of peak ([0053]: “may detect changes in the EPs, such as, e.g., but not limited to, changes in latency, changes in amplitude or changes in morphology.”).
Claim(s) 6 and 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Higgins in view of Snow in view of Stashuk et al. (US 20230329948A1, as cited by applicant’s IDS filed 10/11/2023), hereinafter Stashuk.
Regarding 6, Higgins as modified by Snow discloses the method of claim 2 but fails to disclose determining whether the baseline recording is deemed monitorable using a machine learning classification algorithm which classifies the baseline SSEP potential as either monitorable or non-monitorable based on the features of the two SSEP evoked potentials.
Stashuk discloses a method for determining the presence, absence, and/or monitorability of an evoked potential within one or more SSEP recordings (abstract), comprising determining whether the baseline recording is deemed monitorable using a machine learning classification algorithm ([0010]: “computer algorithm for characterization and classification of electrophysiological EPs… algorithms establish the characteristics of a baseline/normal EP and then characterize subsequent EPs relative to the baseline EP”) which classifies the baseline SSEP potential as either monitorable or non-monitorable based on the features of the two SSEP evoked potentials ([0034]: “EPs may be classified into four possible categories: Good, Bad, Undetermined and Unreliable based on the characterization… An unreliable classification may indicate the EP includes too much noise to be properly characterized and classified.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Higgins as modified by Snow to include the machine learning classification algorithm disclosed by Stashuk in order to minimize false positives and negatives (Stashuk [0010]).
Regarding claim 8, Higgins as modified by Snow and Stashuk discloses the method of claim 6. Stashuk further discloses determining a confidence value of whether the baseline potential is monitorable ([0040-0042]: “default width analysis range by adjusting the location of the range until a minimum congruity… the current baseline may be set to 25% of the previous response and 75% of the previous baseline. If the previous response is not classified as good, the current baseline may be set to the previous baseline.”).
Regarding claim 9, Higgins as modified by Snow and Stashuk discloses the method of claim 6. Stashuk further discloses adapting size of the two corresponding independent sets of SSEP data depending on the confidence value ([0040]: “Using the initial Good responses, the width of the analysis range is then adjusted by increasing it to the left or right until a minimum congruity value is obtained”).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Higgins in view of Snow in further view of Cecotti (“A time–frequency convolutional neural network for the offline classification of steady-state visual evoked potential responses”) in view of Xue et al. (“Feature Extraction and Classification of EEG Signal Based on Deep Learning”), hereinafter Xue.
Higgins as modified by Snow discloses the method of claim 2, but fails to disclose determining whether the baseline SSEP potential is deemed monitorable using a wavelet convolution neural network which classifies the baseline potential as either monitorable or not monitorable based without engineering features of the two SSEP potentials.
Cecotti discloses a method for determining whether the baseline SSEP potential is deemed monitorable using a convolution neural network which classifies the baseline potential as either monitorable or not monitorable (Sec 2. Spatial Filtering para 1: “Some electrodes may contain the same kind of noise that their combination may eliminate. Some electrodes contain more information than noise, a weight on their information power shall translate this behavior… classifying EEG signals, which correspond to different kinds of SSVEP responses… The goal is to determine the optimal set of weights for Ns channels, which can improve the final classification.”, wherein the classification determine which signals contain more noise than information which indicates monitorability) based without engineering features of the two SSEP potentials (introduction para 3: “directly classify the raw signal and to integrate the signal processing functions within the discriminant steps when it is needed. enables all the processing steps (preprocessing, feature extraction and classification) to be performed and tuned in a single and unified way.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Higgins as modified by Snow to include the method disclosed by Cecotti in order to improve the efficiency of signal analysis (Cecotti abstract).
Higgins as modified by Snow and Cecotti fails to disclose a wavelet convolution neural network.
Xue discloses a wavelet (section IV paras 1-3) convolution neural network (section V para 1: “Convolutional neural networks”) for the analysis of a evoked potential (introduction para 2: “steady-state visual evoked potential (SSVEP)”).
As Cecotti discloses that the method may be improved by the addition of wavelet analysis (Cecotti discussion para 4: “Further works will deal with other methods like wavelets, to add new knowledge and directions about the neural processing.”), it would have been obvious to a person of ordinary skill in the art prior to the effective filing date to apply the known wavelet convolution neural network disclosed by Xue to the known convolution neural network disclosed ready for improvement disclosed by Higgins, Cecotti, and Snow to yield the predictable result of classifying an evoked potential.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Torres in view of D’arcy et al. (US 20130245422 A1), hereinafter D’arcy.
Torres discloses a method of identifying one or more relevant peaks of an evoked potential (abstract), the method comprising the steps of: identifying one or more candidate peaks in an ongoing SSEP recording ([0062]: “a primary peak in the electrical measurements associated with a set of vertices”); comparing an amplitude of the evoked potential in a previous ongoing SSEP recording to a threshold ([0036]: “by comparing those electrical signals to electrical signals associated with one or more populations of individuals with known neurodevelopmental conditions”, wherein the electrical signals are collected prior to the acquired signal, [0074]: “the distributions of one or more peak properties, are compared to the predetermined distributions of peak properties stored for the certain neurodevelopmental categories… the z-test or t-test or Anderson-Darling statistic or a similarity statistic, or Earth-movers distance can be used”); selecting the peak with the greatest prominence within an ongoing analysis range as the primary peak ([0061- 0062]: “One temporal region, or portion, 415 of the trace 414 spans the large electrical signals of the vertices described above, at about 550 ms… trace 414 indicates essentially a correlation with the multiple vertices I to VII expected in this electrical signal and thus the primary peak represents a Dirac delta function at the time of receipt of the expected signal. The location of the primary peak is considered to be at frame 25 (1 ms along the axis) and the properties of this primary peak include a prominence 425 of about 2.5 mV above a zero electrical amplitude and a width 426 at half height of about 6 frames (about 240 microseconds).”, wherein the temporal region constitutes the analysis range); identifying potential reference peaks in a region around the primary peak ([0088]; “inclusive of activity prior and after the primary peak. The variations in the fluctuations were examined, including micropeak widths, prominences, and full amplitudes”); and selecting a reference that maximizes the amplitude of the ongoing SSEP evoked potential ([0065]: “Where Pn is the prominence of the nth peak, Pn* is the normalized prominence of the nth peak, and Avg (Pmin1, Pmin2) is the average of all the points between the two local minima Pmin1 and Pmin2, including the local maximum (micropeak) Pn When the Avg (Pmin1, Pmin2) is small compared to the prominence of the nth peak, such as for the primary peak, the value of Pn* approaches 1”).
While Torres discloses comparing the primary peak the candidate peak that is closest in latency to the primary peak of the potential from the previous ongoing SSEP recording ([0081]: “The ABR latencies are compared at millisecond time scale along with the fluctuations in the waveform's primary peak and micropeaks' amplitude (μV) and width (ms) reflecting these responses… assess several empirical parameterizations related to the micro-fluctuations of the waveforms' peaks features (amplitude, prominence, and width)”, they fail to disclose applying a peak tracking by selecting as the primary peak the candidate peak that is closest in latency to the primary peak of the potential from the previous ongoing SSEP recording, filtering the one or more candidate peaks based on an analysis range, and identifying one or more candidate peaks in an ongoing SSEP recording with the same polarity as a primary peak of an evoked potential in a predetermined baseline SSEP recording.
D'arcy discloses a method of identifying one or more candidate peaks in an ongoing SSEP recording with the same polarity as a primary peak of an evoked potential in a predetermined baseline SSEP recording ([0101]: “the evoked response identification algorithm determines whether a candidate peak is an evoked response based on identification criteria selected from the group consisting of experimental condition, polarity,”, [0164]: “identification criteria were established based on the existing experimental literature, which primarily deals with group averages of many individuals”), filtering the one or more candidate peaks based on an analysis range ([0162]: “amplitude thresholding method whereby small peaks are rejected from further analysis. Specifically, if a small peak does not reach a certain percentage of the larger peak's amplitude, it is rejected.”), and applying a peak tracking by selecting as the primary peak the candidate peak that is closest in latency to the primary peak of the potential from the previous ongoing SSEP recording ([0101]: “determines whether a candidate peak is an evoked response based on identification criteria selected from the group consisting of … latency”, [0164]: “ identification criteria are applied that describe generic ERP component characteristics based on several basic features, such as … latency”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Torres to include the identifying one or more candidate peaks with the same polarity of a primary peak in a predetermined baseline, filtering based on an analysis range, and peak tracking as disclosed by D’arcy in order to obtain a more accurate primary peak value by accounting for unique variations in an individual’s waveforms (D’arcy [0164]).
Conclusion
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/KAVYA SHOBANA BALAJI/Examiner, Art Unit 3791
/DANIEL L CERIONI/Primary Examiner, Art Unit 3791