Office Action 1: Non-Final Rejection (2023-12-08)
Rejection types: 101 - Patent-Eligible Subject Matter, 103 - Obviousness, 112 - Written Description/Enablement

CLAIMS:
Claims:

1\. A system for measuring and assessing anesthesia of an animal subject based on electroencephalography of the animal subject, the system comprising:a signal pre-processor comprising at least two filters in different filtering frequency and a down-sampler for removing unwanted signals and noise from incoming signal stream of the system to generate an incoming signal for subsequent epoch generation;
an epoch generator for generating an epoch signal containing 1-second and
10% overlapping epochs comprising a two-input multiplexer, an address generator and a memory, the two-input multiplexer receiving the incoming signal from the signal pre-processor and also a selector input from the address generator to feed an input signal stream to the memory after a counter value of a counter at a relatively higher frequency from the address generator reaches 500; the address generator having two counters at different frequencies and generating two counter signals with write address and read address, respectively, to be fed to the memory, and also a control signal to be fed directly for subsequent feature extraction; the memory receiving the input signal stream from the two-input multiplexer and two counter signals with the write address and read address, respectively, from the address generator and then generating the epoch signal containing the 1-second and 10% overlapping epochs for subsequent feature extraction;a feature extractor comprising a derivative calculator and a variance calculator, the derivative calculator receiving the epoch signal containing 1-second and 10% overlapping epochs from the memory of the epoch generator and
calculating a mean of accumulated squared-differences among different epochs; the variance calculator receiving absolute value of each of the epochs accumulated and determining a mean of the accumulated epochs, obtaining a deviation of an epoch by subtracting the mean from one of the absolute values of the epoch, calculating a squared deviation followed by determining square root of an average squared deviation for subsequent classification;a classifier comprising two cascaded units for expanding features extracted by the feature extractor by double the number of the features followed by feature mapping to set a classification boundary, and subsequently using an output of the

feature mapping to obtain a sigmoid function as a decision boundary in order for subsequent prediction;a predictor comprising a predictor circuit for accumulating the classifier's outputs, constraining a decision value of the classifier's outputs between 0 and 1, and determining level of anesthesia of the animal subject in terms of the constrained decision value based on the animal subject's real-time electroencephalogram.

2\. The system of claim 1, wherein a first filter of the signal pre-processor has a filtering frequency to compensate the power-line interference of the incoming signal
from the electroencephalography of the animal subject.

3\. The system of claim 1, wherein a second filter of the signal pre-processor has a filtering frequency comparable to an average frequency of a wide electroencephalography frequency region in a range of 0-250 Hz of the incoming
signal from the animal subject.

4\. The system of claim 1, wherein the incoming signal is down sampled at least ten times after being subject to the second filter using the down-sampler.

5\. The system of claim 1, wherein the epoch generator creates a sliding window to divide each electroencephalography channel into short-overlapped epochs. It is optimally designed to consume lower resources and the smallest possible size of memory unit (RAM).

6\. The system of claim 1, wherein the derivative calculator of the feature extractor comprises a multiplier to square the differences among different epochs before calculating the mean of the accumulated squared-differences, and further comprises an accumulator and divisor for calculating the mean.

7\. The system of claim 1, wherein the variance calculator comprises a multiplier, an accumulator and a divisor circuit to consecutively calculate the average squared deviation; the variance calculator further comprises a register to store the average squared deviation for being fed to the classifier subsequently.

8\. The system of claim 1, wherein the two cascaded units of the classifier comprises a feature mapping unit for said expanding and feature mapping to enhance classification accuracy by setting the classification boundary with an increase in classifier variance, and an exponential and reciprocal computation circuit for
receiving the output of the feature mapping unit to determine the sigmoid function as the decision boundary for being fed to the subsequent predictor.

9\. The system of claim 1, wherein the predictor is configured to accumulate the level of anesthesia in terms of percentage and constrain an output value as a decision
score between 0 and I with respect to the percentage of the anesthesia of the animal subject, wherein a 100% anesthesia corresponds to the decision score of 1, while a 0% anesthesia corresponds to the decision score of 0.

10\. A method for determining depth of anesthesia of an animal subject from a
transient behavior of an electroencephalography using machine learning techniques thereof, the method comprising:recording electroencephalographic (EEG) signal of the animal subject and filtering thereof within a relatively lower frequency band to remove unwanted signals and noise;
down-sampling the filtered EEG signal to at least ten times for reducing data size and accelerating subsequent processing without losing essential features of the EEG for subsequent classification;
generating epochs from the down-sampled EEG signals comprising segmenting each EEG channel with a shortened signal time and overlapping one
EEG signal with a preceding EEG signal thereof;extracting two features from each epoch selected from derivative and variance thereof;
mapping the derivative and variance features to determine a classification boundary followed by using an output of the mapping to determine a sigmoid
function as a decision boundary between awake and anesthetized states:if the output value is lower than 0.5, the epoch is classified as awake; otherwise, the epoch is classified as anesthetized;

accumulating the output value of consecutive awake and anesthetized epochs, respectively, and constraining each of the output value between 0 and 1 in order to predict a likelihood of a successive awake or anesthetized epoch.
5 10 15 20 25 30

OFFICE ACTION:
DETAILED ACTION
Notice of Pre-AIA  or AIA  Status
Note: The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-10 are pending and currently under consideration for patentability under 37 CFR 1.104
Information Disclosure Statement
The information disclosure statements (IDS) submitted on August 23, 2022 has been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA  35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.  Such claim limitation(s) is/are:
Epoch generator for generating an epoch signal” in claim 1 line 9.
Prong A: Epoch generator (non-structural term)
Prong B: Generating an epoch signal (functional language)
Prong C: No sufficient structure for performing the function was recited in the claim. Sufficient structure for performing the function can be found in circuit Fig. 4
“Feature extractor” in claim 1 line 22.
Prong A: Feature extractor (non-structural term)
Prong B: Features extracted (functional language)
Prong C: No sufficient structure for performing the function was recited in the claim. Sufficient structure for performing the function can be found in circuit Fig. 5, 6A and 6B.
“Derivative calculator” in claim 1 line 22.
Prong A: Derivative calculator (non-structural term)
Prong B: Calculating a mean (functional language)
Prong C: No sufficient structure for performing the function was recited in the claim. Sufficient structure for performing the function can be found in circuit Fig. 6A.
“Variance calculator” in claim 1 lines 22-23.
Prong A: Variance calculator (non-structural term)
Prong B: determining a mean (functional language)
Prong C: No sufficient structure for performing the function was recited in the claim. Sufficient structure for performing the function can be found in circuit Fig. 6B.
“Classifier” in claim 1 line 31.
Prong A: Classifier (non-structural term)
Prong B: Expanding features (functional language)
Prong C: No sufficient structure for performing the function was recited in the claim. Sufficient structure for performing the function can be found in circuit Fig. 7.
“Predictor” in claim 1 line 36.
Prong A: Predictor (non-structural term)
Prong B: determining level of anesthesia (functional language)
Prong C: No sufficient structure for performing the function was recited in the claim. Sufficient structure for performing the function can be found in circuit Fig. 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph, applicant may:  (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA  35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b)  CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA  35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, at lines 7-8, the limitations “an epoch signal containing 1-second and 10% overlapping epochs” render the claim indefinite because it is unclear whether the duration “1-second” refers to the value or duration of the epoch and if so, it is further unclear how such value or duration would overlap with another value or duration; for examination purposes, the Office has construed the language to mean --an epoch containing samples recorded for 1 second, wherein the samples of adjacent epochs overlap by 10%--; at line 23, the limitations “accumulated squared-differences among different epochs” render the claim indefinite because it is unclear whether each epoch of the plurality of epochs include squared-differences of the 1-second of samples contained in each epoch; at line 25, the limitations “the epochs accumulated” lack sufficient antecedent basis.
Regarding claim 2, at line 9, the limitation “the power-line interference” lacks sufficient antecedent basis.
Regarding claim 3, at line 12, the limitations “a second filter”  because there is no prior recitation of a first filter in claim 1 from which the claim depends; at lines 2-4, the limitations “a filtering frequency comparable to an average frequency of a wide electroencephalography frequency region in a range of 0-250 Hz of the incoming signal from the animal subject” render the claim indefinite because it is unclear whether the claimed “filtering frequency” is “in a range of 0-250 Hz of the incoming signal from the animal subject”.
Regarding claim 4, at line 18, the limitations “the second filter” lack sufficient antecedent basis.
Regarding claim 5, at line 21-23, the sentence “It is optimally designed to consume lower resources and the smallest possible size of memory unit (RAM).” renders the claim indefinite because the scope cannot be determined. The specification fails to provide a standard by which to judge what is construed as “less resources” and “smallest possible size of memory unit”. The claim is also indefinite because the limitation “It” is unclear as to what “it” is referring to. The claim is also indefinite because it is unclear whether the claim requires just a memory or a random-access memory (RAM). The claim is also indefinite because the claim should only read as one sentence and each element should be separated by a line indentation (see MPEP 608.01(i) part (i)).
Regarding claim 5, line 22, the term “optimally” is a relative term which renders the claim indefinite. The term “optimally” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It has been rendered indefinite by the use of the term optimally.
Regarding claim 6, at line 26, the limitations “a multiplier to square the differences” render the claim indefinite; it is unclear as to which differences the claim refers. The term “difference” in mathematics is defined as “the degree or amount by which things differ in quantity or measure.” The claim saliently fails to disclose the “things” whose difference is referenced in the claim.
Regarding claim 7, at lines 31-32, the limitation “the squared deviation” renders the claim indefinite because it is unclear as to which squared deviation the claim refers. The claim saliently fails to disclose the “things” whose deviation is referenced in the claim.
Regarding claim 8, at line 6, the limitation “for being fed to the subsequent predictor” renders the claim indefinite because it is unclear as to what “thing” is being fed to the predictor in the claim.
Regarding claim 9, at lines 8-9, recites the limitation the level of anesthesia.  There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 10:
At line 18, the term “relatively lower” is a relative term which renders the claim indefinite. The term “relatively lower” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. frequency band has been rendered indefinite by the use of the term relatively lower;
At line 21, the limitations “without losing essential features” render the claim indefinite because it is unclear as to which feature the claim construes as essential;
At lines 26-27, the limitations “extracting two features from each epoch selected from derivative and variance” render the claim indefinite because it is unclear whether the claim requires both a derivative and a variance.
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.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03):
Claims 1-9 are directed to a “machine”, which describes one of the four statutory categories of patentable subject matter.
Claim 10 is directed to a “method”, which describes one of the four statutory categories of patentable subject matter.
Step 2A of the subject matter eligibility test (see MPEP 2106.04):
Prong 1 – Claim 1 recites the abstract ideas of mathematical process and mental processes as follows:
“Derivative calculator” and “Feature extractor” which comprises steps of “calculating a mean of accumulated squared-differences among different epochs”. The determining step can be practically performed by a mathematical process. There is nothing to suggest an undue level of complexity in a mean of accumulated squared-differences.
“Variance calculator” and “Feature extractor” which comprises steps of “determining a mean of the accumulated epochs”,  “calculating a squared deviation” and “determining square root of an average squared deviation”. The determining step can be practically performed in the human mind by a mathematical process, with the aid of a pen and paper. There is nothing to suggest an undue level of complexity in a squared deviation.
“Classifier” which comprises steps of “feature mapping to set a classification boundary”. The determining step can be practically performed in the human mind by a mathematical process, with the aid of a pen and paper.
“Predictor” which comprises steps of “determining level of anesthesia of the animal subject in terms of the constrained decision value based on the animal subject's real-time electroencephalogram”; The determining step can be practically performed by a mathematical process or with the aid of a generic computer to perform the steps. For example, if a user were to obtain the electroencephalography (EEG) signal of the animal subject, said user would be able to filter the signal (generic pre-solution activity using a generic computer), divide the signal into epochs (generic pre-solution activity using a generic computer), extract features from the epochs (generic mathematical processes using a generic computer), determine a classification boundary from the features (human mind/generic computer), and classify epochs between 0 and 1 to determine level of anesthesia of an animal subject (human mind). There is nothing to suggest an undue level of complexity in the level of anesthesia of the animal subject.
Prong 1 – Claim 10 recites the abstract ideas of mathematical processes and mental processes as follows:
“determine a classification boundary”; The determining step can be practically performed in the human mind, with the aid of a pen and paper, by a mathematical process or with the aid of a generic computer to perform the steps. There is nothing to suggest an undue level of complexity in a classification boundary.
“determine a sigmoid function”; The determining step can be practically by a mathematical process or with the aid of a generic computer to perform the steps. There is nothing to suggest an undue level of complexity in a sigmoid function.
“predict a likelihood of a successive awake or anesthetized epoch”; The determining step can be practically performed in the human mind, with the aid of a pen and paper, by a mathematical process or with the aid of a generic computer to perform the steps. There is nothing to suggest an undue level of complexity in a likelihood of a successive awake or anesthetized epoch.
Prong 2 – Claims 1 and 10 do not include additional elements that integrate the mental process or the mathematical process into a practical application. Therefore, the claims are “directed to” the mental process or mathematical process.
The additional elements comprise a signal pre-processor, epoch generator, multiplexer, and steps for outputting EEG data. These additional elements merely add insignificant extra-solution activity or pre-solution activity of obtaining data from an object.
As a whole, the additional elements serve to gather and feed information to the abstract idea. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the determined level of anesthesia of the animal subject is not displayed in any way such that a diagnostic benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05):
Claims 1 and 10 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception for the same reasons as listed above. The additional limitations generically recite sending or receiving EEG data, see for example, Rothman (US20140289172A1) (P.0003-P.0005) for sending or receiving EEG data, Delorme (EEGLAB) (2.1.1) for signal pre-processor, epoch generator, and Matlab (Mux) for multiplexer.
Dependent claims 2-9, merely further define the  additional elements and are thus directed to an abstract idea for similar reasons as above:
Further description of the abstract idea:
Claim 4: the incoming signal is down sampled at least ten times
Claim 9: constrain an output value as a decision score between 0 and 1
Further description of the pre-solution activity or structure used for the activity:
Claim 2: a first filter
Claim 3: a second filter
Claim 5: a sliding window
Claim 6: a multiplier, an accumulator, and divisor
Claim 7:  a multiplier, an accumulator, divisor, and a register
Claim 8: a feature mapping unit and an exponential and reciprocal computation circuit
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 10 is rejected under 35 U.S.C. 103 as being unpatentable over Nagaraj (US20200253544A1) in view of Nierenberg (US8666484B2) and in further view of Abele (WO-2004005895-A1).
Regarding claim 10, Nagaraj discloses a method for determining depth of anesthesia of an animal subject from a transient behavior of an electroencephalography using machine learning techniques thereof (see P.0004), the method comprising:
recording electroencephalographic (EEG) signal of the animal subject (The method can include receiving an electroencephalography (EEG) signal from a sensor electrode attached to the patient, see P.0007) and filtering thereof within a relatively lower frequency band to remove unwanted signals and noise (the band pass filter may have a frequency range of 0.5-25 Hz., see P.0051, 0108 & 0130);
down-sampling the filtered EEG signal to at least ten times for reducing data size and accelerating subsequent processing without losing essential features of the EEG for subsequent classification (the signal is sampled at 5 kHz (P.0041), and it is mentioned that “the collection and processing of EEG data can be performed by the controller 1400”(P.0041). That same controller 1400 may down sample the EEG signals to 250 Hz (P.0051), resulting in a down-sampling of twenty times);
generating epochs from the down-sampled EEG signals comprising segmenting each EEG channel with a shortened signal time (see P.0052);
extracting two features from each epoch selected from derivative and variance thereof (interpreted as the plain meaning as in something that is derived from something else: “several features derived from the frontal EEG, commonly reported for EEG and sedation level analysis, can be used as input to a logistic regression for automatically predicting a patient’s sedation level” see P.0035, P.0054, and Table 1);
followed by using an output of the mapping to determine a sigmoid function as a decision boundary between awake and anesthetized states (see P.0057):
accumulating the output value of consecutive awake and anesthetized epochs (“A continuous sedation level or patient state index can be obtained by developing a binary classifier, which is trained only on awake and sedated epochs, to assign a probability score to all EEG epochs”, in having a continuous sedation level, it is implied that the epochs are consecutive see P.0080-0083, respectively, and constraining each of the output value between 0 and 1 in order to predict a likelihood of a successive awake or anesthetized epoch (see P.0058).
Nagaraj fails to teach
overlapping one EEG signal with a preceding EEG signal thereof.
mapping the derivative and variance features to determine a classification boundary
if the output value is lower than 0.5, the epoch is classified as awake; otherwise, the epoch is classified as anesthetized
However, It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the probability score of Nagaraj to indicate that if the output value is lower than 0.5, the epoch is classified as awake, because a score of 0.6 equates to a 60% chance of sedation as described in P.0081, a score of less than 0.5 would equate to a less than 50% chance of sedation, meaning a more than 50% chance of being awake. “The approach is beneficial because it can reduce the score “annotation-noise” due to interobserver variability during sedation level assessment” (P.0080 Nagaraj).
Nierenberg is pertinent prior art as it is a method for processing EEG data into overlapping signals. Nierenberg teaches overlapping one EEG signal with a preceding EEG signal thereof (see Col. 7, lines 54-67 and Col. 8, lines 1-5)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the EEG signals of Nagaraj to include overlapping as taught by Nierenberg to produce a “continuous processed EEG report” that is “not missing timeframes from stitching or creating discontinuities” (see Nierenberg Col. 7, lines 41-47).
However, the combination of Nagaraj and Nierenberg fails to teach:
mapping the derivative and variance features to determine a classification boundary
Abele is pertinent prior art as it is a system that classifies patient data and outputs diagnostic results. Abele teaches mapping the derivative and variance features to determine a classification boundary (statistical classification techniques including a method based on maximal variance, and non-statistically-derived information P.0026 – P.0028).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the classification of Nagaraj and Nierenberg to include a method based on variance and derived information as described in Abele to achieve “increased diagnostic sensitivity and specificity by combining a plurality of statistical classification techniques” (Abele P.0018).
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b)  CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 and 9-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA  35 U.S.C. 112, the applicant), regards as the invention. Regarding claim 1, the phrase “the down-sampler output signal” in lines 25 and 40 lacks proper antecedent basis. The Examiner suggests amending the phrase to read “the down-sampled filtered incoming measured EEG signal” in order to properly refer back to the previously recited down-sampled filtered incoming measured EEG signal. The phrase “wherein the epoch signal being the memory output data” in line 51 of claim 1 renders the claim indefinite. Is the epoch signal the memory output data (i.e., wherein the epoch signal is the memory output data), or is an action taken upon the epoch signal that is somehow connected to the memory output data? Clarification is requested. Lines 64-65 of claim 1 recite that “the animal subject being awake or anesthetized or that the animal subject being awake or anesthetized”. Duplication of the phrase “being awake or anesthetized” renders the claim indefinite; both output results of the classifier are the same. Furthermore, while not a rejection, it is noted that the term “being” in both phrases should be amended to read “is”. Regarding claim 2, the wording of the claim renders the claim indefinite (it is unclear how a filter compensates a power-life interference). As best understood, the first filter of the signal pre-processor is configured to remove or reduce a power-line interference of the incoming measured EEG signal. Regarding claim 3, the wording of the claim renders the claim indefinite. What does it mean for a filtering frequency to be “comparable” to an average frequency of a wide EEG frequency region? Furthermore, the phrase “wide electroencephalography frequency region” is indefinite. What is to be considered a “wide” EEG frequency region? The same indefiniteness issue is present in claim 13. Regarding claim 5, the phrase “each electroencephalography channel” lacks proper antecedent basis as there is no previous mention of EEG channels. Further regarding claim 5, the phrase “short-overlapped epochs” renders the claim indefinite in that the term “short” is a relative term that is not defined by the claim, and it is unclear if the short-overlapped epochs of claim 5 are the same overlapping epochs of claim 1 or different overlapping epochs. Regarding claim 7, the phrase “computing a difference between each of the absolute epoch values and the mean of the accumulated absolute epoch values and a square of the difference to obtain a squared deviation” renders the claim indefinite. The phrase “the difference” is singular, but performing the steps recited in claims 1 and 7 results in a plurality of difference values (a difference between each of the absolute epoch values and the mean of the accumulated absolute epoch values is determined). Furthermore, in order to compute an average of all of the squared deviations for all of the absolute epoch values, more than one squared deviation would need to be obtained. The Examiner suggests amending the phrase to read “computing a difference between each of the absolute epoch values and the mean of the accumulated absolute epoch values and a square of each difference to obtain squared deviations” in order to clarify the claim. Further regarding claim 7, it is suggested that the term “the” be added before the phrase “squared deviations” in lines 11 and 13. The same indefiniteness issues and suggestions also apply to claim 15. Regarding claim 9, the phrase “the percentage of the anesthesia of the animal subject” lacks proper antecedent basis. It is suggested that the phrase be amended to read “a percentage of the anesthesia level of the animal subject”. Regarding claim 10, the “mapping the derivative feature” and “accumulating a plurality of the classifier outputs” limitations render the claim indefinite. What are the derivative feature and variance featured mapped to? What is the output of the mapping and how is it determined? The phrase “the mapping output value” further renders the claim indefinite. The phrase lacks proper antecedent basis, and it remains unclear how the mapping output value is determined. The accumulating step is indefinite in that it is unclear how accumulating consecutive classifier outputs would result in generating a prediction output of a likelihood of the animal subject being awake or anesthetized in a successive epoch. The indefiniteness issues within claim 10 have rendered a proper prior art search unable to be performed. Regarding claim 11, it is unclear what the phrase “increasing a prediction probability” is referring to as there is no previous mention of determining a prediction probability of the animal subject being either anesthetized or awake. The phrases “being anesthetized” and “being awake” in claim 11 should be amended to read “is anesthetized” and “is awake”, respectively.
Claims not explicitly rejected above are rejected due to their dependence on a rejected base claim.
Response to Arguments
Applicant’s arguments with respect to the rejections of the claims have been considered. The rejection of the claims under 35 U.S.C. 101 has been withdrawn as a particular machine is used to perform the Abstract Idea. Regarding rejections of the claims under 35 U.S.C. 112(b), current indefiniteness issues within the claims are discussed in paragraph 3 above. Regarding the previous prior art rejection of claim 10, as noted in paragraph 3 above, the amendments to the claim have rendered a prior art search of the claim unable to be performed.
Examiner’s Note
Regarding claim 1, none of the prior art teaches or suggests, either alone or in combination a device comprising an epoch generator, wherein the epoch generator comprises a two-input multiplexer, an address generator, and a memory, wherein the two-input multiplexer is configured to receive a down-sampled filtered incoming measured EEG signal, a feedback of an epoch signal, and a selector input from the address generator, and further wherein the address generator is configured to generate the selector input to the multiplexer to generate a multiplexer output from the down-sampler output signal during each of a plurality of slow count cycles and from the feedback of the epoch signal during each of a plurality of fast count cycles, in combination with the other claimed elements.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Tabassum et al. (Novel Multirate…) teaches a device for processing EEG signals, wherein the device comprises a signal pre-processor configured to perform a first filtering technique, a second filtering technique, and a down-sampling technique on acquired EEG signals. Tabassum et al. further teaches use of a two-input multiplexer to process the filtered and down-sampled EEG signals.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.  Accordingly, THIS ACTION IS MADE FINAL.  See MPEP § 706.07(a).  Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action.  In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action.  In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.

CITED REFERENCE SUMMARIES: Ref US20200253544A1: Claims:
A method for generating a sedation level estimate, the method comprising: receiving an electroencephalography (EEG) signal from a sensor electrode attached to the patient, the EEG signal comprising a plurality of channels; segmenting the EEG signal into smaller epochs for each channel; extracting features of the EEG signal in each epoch; determining a median of features among the plurality of channels for each epoch; determining, by a classifier, a probabilistic estimate of a patient sedation; generating, using a determined correlation, a sedation level estimate, the sedation level estimate comprising a continuous sedation score; and displaying an indication of the sedation level estimate.
receiving an electroencephalography (EEG) signal from a sensor electrode attached to the patient, the EEG signal comprising a plurality of channels;
segmenting the EEG signal into smaller epochs for each channel;
extracting features of the EEG signal in each epoch;
determining a median of features among the plurality of channels for each epoch;
determining, by a classifier, a probabilistic estimate of a patient sedation;
generating, using a determined correlation, a sedation level estimate, the sedation level estimate comprising a continuous sedation score; and
displaying an indication of the sedation level estimate.
The method of claim 1 , wherein an epoch comprises 4 seconds.
The method of claim 1 , wherein the features comprise quantitative electroencephalogram (QEEG) features.
The method of claim 3 , wherein extracting features comprises extracting at least 44 QEEG features.
The method of claim 1 , wherein classifier comprises a binary classifier trained by machine learning.
The method of claim 5 , wherein the binary classifier is trained using awake and sedated epoch data, the awake and sedated epoch data comprising a plurality of epochs having sedation scores.
The method of claim 5 , wherein the sedation scores comprise a score on a scale of 0 to 5.
The method of claim 5 , wherein the sedation scores comprise MOAA/S scores.
The method of claim 1 , wherein the determined correlation comprises a correlation between the probabilistic estimate of the patient sedation and a sedation score.
The method of claim 9 , wherein the determined correlation comprises a spearman rank-correlation.
A method of selecting a subset of EEG features for use in an electronic determination of sedation state across a plurality of drugs, said method comprising: receiving EEG signal data from an EEG sensor for a plurality of administered drugs; associating a human determined sedation score corresponding to patient's sedation state with the received EEG signal data; selecting EEG signals with the associated human determined sedation score that has a high degree of confidence; extracting a plurality of features from the selected EEG signals; training the plurality of features with the corresponding human determined sedation scores; and identifying a set of features with a high degree of correlation based on the training.
receiving EEG signal data from an EEG sensor for a plurality of administered drugs;
associating a human determined sedation score corresponding to patient's sedation state with the received EEG signal data;
selecting EEG signals with the associated human determined sedation score that has a high degree of confidence;
extracting a plurality of features from the selected EEG signals;
training the plurality of features with the corresponding human determined sedation scores; and
identifying a set of features with a high degree of correlation based on the training.
A method of determining sedation state of a patient, the method comprising: receiving EEG signal data from an EEG sensor; extracting features from the received EEG signal data; applying non-linear machine learning model to the received EEG signal data; and determining the sedation state based on the application of the non-linear machine learning model.
receiving EEG signal data from an EEG sensor;
extracting features from the received EEG signal data;
applying non-linear machine learning model to the received EEG signal data; and
determining the sedation state based on the application of the non-linear machine learning model.
The method of claim 12 , wherein the non-linear machine learning model comprises ensemble tree with bagging model.
The method of claim 12 , wherein the non-linear machine learning model comprises random forest model.
The method of claim 12 , wherein the non-linear machine learning model comprises support vector machine with Gaussian kernel model.
The method of claim 12 , wherein the non-linear machine learning model comprises elastic net logistic regression.
The method of claim 12 , wherein the features comprise at least power in alpha band and power in beta band.
The method of claim 12 , wherein the features comprise at least BSR, standard deviation of FM, SVDE and FD.
Ref US8666484B2: Claims:
A method for generating an EEG recording, the method comprising: generating an original EEG signal from a machine comprising a plurality of electrodes, an amplifier and a processor; partitioning the original EEG signal from a set of channels into a plurality of epochs, each of the plurality of epochs having an epoch duration length and an overlap increment; performing artifact reduction on the plurality of epochs to generate a plurality of artifact reduced epochs; and combining the plurality of artifact reduced epochs to generate a processed EEG recording, wherein each of the plurality of artifact reduced epochs overlaps an adjacent artifact reduced epoch to produce a continuous processed EEG recording without discontinuities in the processed EEG recording, wherein the plurality of artifact reduced epochs are combined using a weighted average wherein each of the plurality of artifact reduced epochs and the adjacent artifact reduced epoch are combined using a weighted average wherein the weight is proportional to a ratio of distances to epoch centers wherein a value from each of the plurality of artifact reduced epochs is weighted higher for an overlapping portion nearer a center of each of the plurality of artifact reduced epochs and a value from the adjacent artifact reduced epoch is weighted higher for an overlapping portion nearer a center of the artifact reduced epoch.
generating an original EEG signal from a machine comprising a plurality of electrodes, an amplifier and a processor;
partitioning the original EEG signal from a set of channels into a plurality of epochs, each of the plurality of epochs having an epoch duration length and an overlap increment;
performing artifact reduction on the plurality of epochs to generate a plurality of artifact reduced epochs; and
combining the plurality of artifact reduced epochs to generate a processed EEG recording, wherein each of the plurality of artifact reduced epochs overlaps an adjacent artifact reduced epoch to produce a continuous processed EEG recording without discontinuities in the processed EEG recording, wherein the plurality of artifact reduced epochs are combined using a weighted average wherein each of the plurality of artifact reduced epochs and the adjacent artifact reduced epoch are combined using a weighted average wherein the weight is proportional to a ratio of distances to epoch centers wherein a value from each of the plurality of artifact reduced epochs is weighted higher for an overlapping portion nearer a center of each of the plurality of artifact reduced epochs and a value from the adjacent artifact reduced epoch is weighted higher for an overlapping portion nearer a center of the artifact reduced epoch.
The method according to claim 1 wherein the original EEG is subjected to a blind source separation algorithm.
The method according to claim 1 wherein the artifact reduction is for at least one of muscle artifact, eye movement artifact, electrical artifact, heartbeat artifact, tongue movement artifact, and chewing artifact.
The method according to claim 1 further comprising illustrating the processed EEG recording on a display in communication with the processor wherein an operator switches from illustrating the processed EEG recording to illustrating an unprocessed EEG recording.
The method according to claim 1 wherein each of the plurality of epochs has an epoch duration length of four seconds and an increment of two seconds.
A system for filtering artifacts from an EEG signal, the system comprising: a plurality of electrodes for generating a plurality of EEG signals; at least one amplifier connected to each of the plurality of electrodes by a plurality of wires to amplify each of the plurality of EEG signals; a processor connected to the amplifier to generate an EEG recording from the plurality of EEG signals; and a display connected to the processor for displaying an original EEG recording and a processed EEG recording; wherein the processor is configured to partition the original real-time EEG signal from a set of channels into a plurality of epochs, each of the plurality of epochs having an epoch duration length and an overlap increment, perform artifact reduction on the plurality of epochs to generate a plurality of artifact reduced epochs, and combine the plurality of artifact reduced epochs to generate a processed real-time EEG recording, wherein each of the plurality of artifact reduced epochs and an adjacent artifact reduced epoch are combined using a weighted average wherein the weight is proportional to a ratio of distances to epoch centers wherein a value from each of the plurality of artifact reduced epochs is weighted higher for an overlapping portion nearer a center of each of the plurality of artifact reduced epochs and a value from the adjacent artifact reduced epoch is weighted higher for an overlapping portion nearer a center of the artifact reduced epoch, wherein each of the plurality of artifact reduced epochs overlaps the adjacent artifact reduced epoch to produce a processed EEG recording without discontinuities in the processed EEG recording.
a plurality of electrodes for generating a plurality of EEG signals;
at least one amplifier connected to each of the plurality of electrodes by a plurality of wires to amplify each of the plurality of EEG signals;
a processor connected to the amplifier to generate an EEG recording from the plurality of EEG signals; and
a display connected to the processor for displaying an original EEG recording and a processed EEG recording;
wherein the processor is configured to partition the original real-time EEG signal from a set of channels into a plurality of epochs, each of the plurality of epochs having an epoch duration length and an overlap increment, perform artifact reduction on the plurality of epochs to generate a plurality of artifact reduced epochs, and combine the plurality of artifact reduced epochs to generate a processed real-time EEG recording, wherein each of the plurality of artifact reduced epochs and an adjacent artifact reduced epoch are combined using a weighted average wherein the weight is proportional to a ratio of distances to epoch centers wherein a value from each of the plurality of artifact reduced epochs is weighted higher for an overlapping portion nearer a center of each of the plurality of artifact reduced epochs and a value from the adjacent artifact reduced epoch is weighted higher for an overlapping portion nearer a center of the artifact reduced epoch, wherein each of the plurality of artifact reduced epochs overlaps the adjacent artifact reduced epoch to produce a processed EEG recording without discontinuities in the processed EEG recording.
The system according to claim 6 wherein each of the plurality of epochs has an epoch duration length of four seconds and an increment of two seconds.
The system according to claim 6 wherein the artifact reduction is for at least one of muscle artifact, eye movement artifact, electrical artifact, heartbeat artifact, tongue movement artifact, and chewing artifact.
A method for generating a continuous EEG recording from a plurality of artifact reduced epochs, the method comprising: generating an original EEG signal for a patient from a machine comprising a plurality of electrodes attached to the patient, an amplifier and a processor; partitioning the original EEG signal from a set of channels into a plurality of epochs, each of the plurality of epochs having an epoch duration length and an overlap increment; performing a first artifact reduction on the plurality of epochs to remove electrode artifacts; performing a second artifact reduction on the plurality of epochs to remove muscle artifacts; performing a third artifact reduction on the plurality of epochs to remove eye movement artifacts; combining the plurality of epochs subsequent to performing the third artifact reduction to generate a combined plurality of epochs wherein each epoch of the plurality of epochs overlaps an adjacent epoch, wherein each of the plurality of artifact reduced epochs and an adjacent artifact reduced epoch are combined using a weighted average wherein the weight is proportional to a ratio of distances to epoch centers wherein a value from each of the plurality of artifact reduced epochs is weighted higher for an overlapping portion nearer a center of each of the plurality of artifact reduced epochs and a value from the adjacent artifact reduced epoch is weighted higher for an overlapping portion nearer a center of the artifact reduced epoch; and generating a processed EEG recording without discontinuities from the combined plurality of artifact reduced epochs.
generating an original EEG signal for a patient from a machine comprising a plurality of electrodes attached to the patient, an amplifier and a processor;
performing a first artifact reduction on the plurality of epochs to remove electrode artifacts;
performing a second artifact reduction on the plurality of epochs to remove muscle artifacts;
performing a third artifact reduction on the plurality of epochs to remove eye movement artifacts;
combining the plurality of epochs subsequent to performing the third artifact reduction to generate a combined plurality of epochs wherein each epoch of the plurality of epochs overlaps an adjacent epoch, wherein each of the plurality of artifact reduced epochs and an adjacent artifact reduced epoch are combined using a weighted average wherein the weight is proportional to a ratio of distances to epoch centers wherein a value from each of the plurality of artifact reduced epochs is weighted higher for an overlapping portion nearer a center of each of the plurality of artifact reduced epochs and a value from the adjacent artifact reduced epoch is weighted higher for an overlapping portion nearer a center of the artifact reduced epoch; and
generating a processed EEG recording without discontinuities from the combined plurality of artifact reduced epochs.
The method according to claim 9 wherein each of the plurality of epochs has an epoch duration length of two seconds and an increment of one second.
The method according to claim 9 wherein each of the plurality of epochs has an epoch duration length of four seconds and an increment of two seconds.
Ref US20140289172A1: Claims:
A method of building a classifier for assessment of a subject's neurological condition, comprising the steps of: providing a processing device operatively connected to a memory device storing a reference database of clinical results collected from one or more neurocognitive and/or clinical symptoms assessments performed on a plurality of individuals in the presence or absence of brain abnormalities, the processing device comprising a processor configured to perform the steps of: extracting quantitative features from the clinical results; storing the extracted features in a pool of selectable features; selecting a subset of features from the pool of selectable features; constructing a classifier using the subset of features; and determining classification accuracy of the classifier by using it to classify data records having a priori classification information.
providing a processing device operatively connected to a memory device storing a reference database of clinical results collected from one or more neurocognitive and/or clinical symptoms assessments performed on a plurality of individuals in the presence or absence of brain abnormalities, the processing device comprising a processor configured to perform the steps of: extracting quantitative features from the clinical results; storing the extracted features in a pool of selectable features; selecting a subset of features from the pool of selectable features; constructing a classifier using the subset of features; and determining classification accuracy of the classifier by using it to classify data records having a priori classification information.
extracting quantitative features from the clinical results;
storing the extracted features in a pool of selectable features;
selecting a subset of features from the pool of selectable features;
constructing a classifier using the subset of features; and
determining classification accuracy of the classifier by using it to classify data records having a priori classification information.
The method of claim 1 , further comprising the step of applying one or more data reduction criteria to the pool of selectable features to create a reduced pool of features from which the subset of features for constructing the classifier are selected.
The method of claim 1 , wherein the neurocognitive assessment is performed using a dynamic questionnaire designed to change questions based on answers provided to prior questions.
The method of claim 1 , wherein the neurocognitive assessment is performed using a standardized questionnaire.
The method of claim 1 , wherein the quantitative features include one or more of severity ranking of symptoms, binary metric indicating presence or absence of symptoms, and normalized combinations of symptoms.
The method of claim 1 , wherein the subset of features are selected using an evolutionary algorithm.
The method of claim 6 , wherein the evolutionary algorithm applied is a genetic algorithm.
The method of claim 7 , wherein the selected subset of features is optimized using at least one of a Random Mutation Hill Climbing algorithm and a Modified Random Mutation Hill Climbing algorithm.
The method of claim 1 , wherein the subset of features is selected using a Simple Feature Picker algorithm
The method of claim 9 , wherein the selected subset of features is optimized using at least one of a Random Mutation Hill Climbing algorithm and a Modified Random Mutation Hill Climbing algorithm.
The method of claim 1 , wherein the classifier is a Linear Discriminant Function.
The method of claim 1 , wherein the classifier is a Quadratic Discriminant Function.
The method of claim 1 , wherein an objective function is used to evaluate the performance of the classifier.
The method of claim 13 , wherein the objective function used is Area Under the Receiver Operating Curve of the classifier.
The method of claim 1 , wherein the classifier is a binary classifier.
The method of claim 1 , further comprising the step of imputing values for missing clinical results of the plurality of individuals in the population reference database.
The method of claim 1 , wherein the references database further comprises clinical results from physiological tests.
The method of claim 1 , wherein the reference database further comprises clinical results from neurophysiological assessments.
The method of claim 1 , wherein the reference database comprises clinical results from brain electrical signals measurements.
A device for assessing a subject's neurological condition, the device comprising: an input device configured to acquire clinical results of one or more neurocognitive and/or clinical symptoms assessments pe
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