Office Action 2: Final Rejection (2024-05-07)
Rejection types: 112 - Written Description/Enablement

CLAIMS:
Listing of claims:

Amendments to Claims:

This listing of claims will replace all prior versions, and listings, of claims in the Application.

1\. (Currently Amended) A device for assessing anesthesia of an animal subject in real-time from electroencephalography (EEG) measurements of the animal subject during application of anesthesia, the device comprising:a signal pre-processor comprising a first filter with a first filtering frequency, a second filter with a second filtering frequency, and a down-sampler to pre-process sequentially an incoming measured EEG signal from the EEG measurements of the animal subject;wherein the pre-processing comprises: removing unwanted signals and noise from the incoming measured EEG signal using the first filter and the second filter to generate a filtered incoming measured EEG signal; and down-sampling the filtered incoming measured EEG signal by the down-sampler to generate a down-sampled filtered incoming measured EEG signal ;
an epoch generator configured to generate an epoch signal containing epochs of 1-second epoch duration each and 10% overlapping of consecutive epochs;wherein the epoch generator comprises a two-input multiplexer, an address generator, and a memory\[\[,\]\];
wherein the two-input multiplexer is configured to:receive the down-sampled filtered incoming measured EEG signal , a feedback of the epoch signal,
and a selector input from the address generator; andgenerate a multiplexer output from the down-sampler output signal or the feedback of the epoch signal based on the selector input;

wherein the address generator comprises:a slow counter configured to generate write addresses, and count from a slow counter start count to a slow counter end count in repeating slow count cycles; and
a fast counter configured to generate read addresses, and count from a fast counter start count to a fast counter end count in repeating fast count cycles that starts after each of the slow count cycles;
 wherein the slow counter is further configured to repeat each of the slow count cycles after each of the fast count cycles;
wherein the fast counter is further configured to repeat each of the fast count cycles after each of the slow count cycles;
wherein the address generator is further configured to:generate the selector input to the multiplexer to generate the multiplexer output from the down-sampler output signal during each of the slow count cycles and from the feedback of the epoch signal during each of the fast count cycles;
wherein the memory is configured to: receive the multiplexer output, the write addresses,and the read addresses\[\[,\]\];
store the received multiplexer output according to the received write addresses; and
output a memory output data according to the received read addresses;
wherein the epoch signal being the memory output data;

a feature extractor configured to:
receive the epoch signal from the epoch generator;
extract a derivative feature ofthe received epoch signal by computing a mean ofaccumulated sqiuared-differences among the epochs in the received epoch signal; and
extract a variance feature ofthe received epoch signal by computing a square root ofan average squared-deviation ofthe epochs in the received epoch signal;
a classifier configured to:perform a feature mapping on the derivative feature and the variancefetr;and
set a decision boundary from output ofthe feature mapping to generate a classifier output, the classifier output being either that the animal subject being awake or anesthetized or that the animal subject being awake or anesthetized; and
a predictor comprising a predictor circuit configured to: accumulate a plurality of the classifier's outputs\[\[,\]\];

 compute a decision value effrom the accumulated classifier's outputs
 determine a level of anesthesia of the animal subject i-n from the decision value ' ;and
predict the animal subject's anesthesia condition from a plurality of determined levels of anesthesia of the animal subject.

2\. (Currently Amended) The system of claim 1, wherein \[\[a\]\] the first filter of the signal pre-processor is configured with the first filtering frequency for compensating a power-line interference of the incoming measured EEG signal .

3\. (Currently Amended) The system of claim 1, wherein \[\[a\]\] the second filter of the signal pre-processor is configured with the second filtering frequency being comparable to an average frequency of a wide electroencephalography frequency region of the incoming measured EEG signal
wherein the average frequency of the wide electroencephalography band of the incoming measured EEG signal is in the range of 0-250 Hz.

4\. (Currently Amended) The system of claim 1, wherein the incoming measured EEG signal is down sampled at least ten times after being subjected to the second filter using the down-sampler.

5\. (Currently Amended) The system of claim 1, wherein the epoch generator creates a sliding window to divide each electroencephalography channel into short-overlapped epochs.
6\. (Currently Amended) The system of claim 1, wherein the computing of a mean of accumulated squared-differences among different epochs in the received epoch signal comprises: Docket No. P2096US00 5 computing a difference between two consecutive epochs of the epochs in the received epoch signal and a square of the difference to obtain a squared difference;
repeating the computation to obtain a squared difference for each pair of consecutive epochs in the received epoch signal;
accumulating all of the squared differences obtained; and
determining the mean of the accumulated squared differences.
7\. (Currently Amended) The system of claim 1, wherein the computing of a square root of an average squared-deviation of the epochs in the received epoch signal comprises: obtaining an absolute epoch value of each of the epochs in the received epoch signal;
accumulating all of the absolute epoch values obtained;
determining a mean of the accumulated absolute epoch values;
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;
computing an average of all of squared deviations for all of the absolute epoch values; and
computing a square root of the average of all of squared deviations.

8\. (Canceled)
9\. (Currently Amended) The system of claim 1, wherein the predictor is configured to accumulate the plurality of the determined levels of anesthesia in terms of percentage and constrain an output value as a decision score between 0 and 1 with respect Docket No. P2096US00 6 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\. (Currently Amended) A method for determining depth of anesthesia (DOA) of an animal subject in real-time from electroencephalography (EEG) measurements , the method comprising:detecting and measuring an EEG signal of the animal subject using an EEGelectrode;
 filtering the EEG signal to remove unwanted signals and noise;
down-sampling the filtered EEG signal for reducing data size and accelerating subsequent processing without losing essential features of the EEG signal for subsequent classification;
generating from the down-sampled EEG signal an epoch signal containing epochs of 1-second epoch duration each and 10% overlapping of consecutive epochs ;
extracting a derivative feature of the epoch signal by computing a mean of accumulated squared-differences among the epochs in the epoch signal;
extracting a variance feature of the epoch signal by computing a square root of an average squared-deviation of the epochs in the epoch signal;mapping the derivative feature and the variance feature 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 state comprising:if the mapping output value is lower than 0.5 or 50%, a classifier output is set to that the animal subject being awake during a time duration of the epochs; otherwise, the classifier Docket No. P2096US00 7 output is set to that the animal subject being anesthetized during a time duration of the epochs;and
accumulating a plurality of the classifier outputs of consecutive awake and anesthetized epochs, respectively to generate a prediction output of a likelihood of the animal subject being awake or anesthetized in a successive epoch.

11\. (New) The method of claim 10, further comprising:increasing a prediction probability of the animal subject being anesthetized if the prediction output is that the animal subject being anesthetized, or decreasing the probability of the animal subject being anesthetized if the prediction output is that the animal subject being awake; and
adjusting a successive prediction output based on the prediction probability.

12\. (New) The method of claim 10, further comprising:generating a confident prediction output of the animal subject being awake if the prediction output is at or below 25% likelihood of the animal subject being anesthetized;
and generating a confident prediction output of the animal subject being anesthetized if the prediction output is at or above 75% likelihood of the animal subject being anesthetized.

13\. (New) The method of claim 10, wherein the filtering of the EEG signal to remove unwanted signals and noise comprising:filtering the EEG signal at a filtering frequency being comparable to an average frequency of a wide electroencephalography frequency band of the incoming measured EEG signal;
wherein the average frequency of the wide electroencephalography band of the incoming measured EEG signal is in the range of 0-250 Hz.

14\. (New) The method of claim 10, wherein the computing of a mean of accumulated squared-differences among different epochs in the epoch signal comprises:computing a difference between two consecutive epochs of the epochs in the epoch signal and a square of the difference to obtain a squared difference;
repeating the computation to obtain a squared difference for each pair of consecutive epochs in the epoch signal;
accumulating all of the squared differences obtained; and
determining the mean of the accumulated squared differences.

15\. (New) The method of claim 10, wherein the computing of a square root of an average squared-deviation of the epochs in the epoch signal comprises:obtaining an absolute epoch value of each of the epochs in the epoch signal;
accumulating all of the absolute epoch values obtained;
determining a mean of the accumulated absolute epoch values;
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;
computing an average of all of squared deviations for all of the absolute epoch values; and
computing a square root of the average of all of squared deviations.

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.

APPLICANT REBUTTAL: Remarks:
The above Amendments and these Remarks are in reply to the non-final Office Action mailed December 8, 2023. Reconsideration and allowance of the application in view of the foregoing amendments and in view of the remarks to follow are respectfully requested.
I.
Summary of Examiner's Rejections
Claims 1-10 are rejected under 35 U.S.C. 112 as being indefinite for failing to particularly point out and distinctly claim the subject matter. Claims 1-10 were rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 10 was rejected under 35 U.S.C. 103 as being unpatentable over Nagaraj et al. (U.S. Patent Application Publication No. 2020/0253544A1; hereinafter referred to as Nagaraj) in view of Nierenberg et al. (U.S. Patent No. 8,666,484B2; hereinafter referred to as Nierenberg) and further in view of Abele et al. (PCT Application Publication No. W02004005895A1; hereinafter referred to as Abele).
II.
Summary of A
ppli
cant's Amendment
By the present amendments, Claims 1-7, 9, and 10 are amended, Claim 8 is cancelled, and new Claims 11-15 are added leaving for the Examiner's consideration Claims 1-7 and 9-15. The Applicant respectfully submits that the proposed amendments contain no new matter. The Applicant respectfully reserves the right to prosecute any originally presented, canceled, or withdrawn claims in a continuing or future application.
III.
Claim rejections under 35 U.S.C.
Claims 1-10 were rejected under 35 U.S.C. 112 as being indefinite for failing to particularly point out and distinctly claim the subject matter.
In correcting the raised indefiniteness and antecedent basis issues, Claim 1 is amended to more clearly define a signal pre-processor comprising
a first filter
with a first filtering frequency,
a second
f
ilter
with a second filtering frequency; an epoch generator configured to generate
an epoch si
g
nal containin
g
epochs of
i
-second epoch duration each and 10
%
overlapping of consecutive epochs.
Claims 2-7, 9, and 10 are also amended to correct all indefiniteness and antecedent basis issues. Claims 8 is canceled, rendering their respective rejections mooted.
By the amendments, Applicant respectfully submits that Claims 1-7 and 9 are clear and thus the claim rejections under 35 U.S.C. § 112 should be withdrawn.
IV.
Claim re
j
ections under 35 U.S.C.
Claims 1-10 were rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Claim 1 as amended defines:
A device for assessing anesthesia of an animal subject in real-time from
electroencephalography (EEG) measurements of the animal subject during application of anesthesia, the device comprising:
a signal pre-processor comprising a first filter with a first filtering frequency, a second filter with a second filtering frequency, and a down-sampler to pre-process sequentially an incoming measured EEG signal from the EEG measurements of the animal subject;
wherein the pre-processing comprises:
removing unwanted signals and noise from the incoming measured EEG signal using the first filter and the second filter to generate a filtered
incoming measured EEG signal; and
down-sampling the filtered incoming measured EEG signal by the down-sampler to generate a down-sampled filtered incoming measured
EEG signal;
an epoch generator configured to generate an epoch signal containing epochs of 1-second epoch duration each and 10% overlapping of consecutive epochs;
wherein the epoch generator comprises a two-input multiplexer, an
address generator, and a memory;
wherein the two-input multiplexer
r
ece
iving
is configured to:
receive the down-sampled filtered incoming measured EEG signal,
a feedback of the epoch signal, and a selector input from the address
generator; and
generate a multiplexer output from the down-sampler output signal or the feedback of the epoch signal based on the selector input;
wherein the address generator comprises:
a slow counter configured to generate write addresses, and count from a slow counter start count to a slow counter end count in repeating
slow count cycles; and
a fast counter configured to generate read addresses, and count from a fast counter start count to a fast counter end count in repeating fast
count cycles that starts after each of the slow count cycles;
wherein the slow counter is further configured to repeat each of the slow
count cycles after each of the fast count cycles;
wherein the fast counter is further configured to repeat each of the fast
count cycles after each of the slow count cycles;
wherein the address generator is further configured to:
generate the selector input to the multiplexer to generate the multiplexer output from the down-sampler output signal during each of the
slow count cycles and from the feedback of the epoch signal during each
of the fast count cycles;
wherein the memory is configured to:
receive the multiplexer output, the write addresses, and the read addresses;
store the received multiplexer output according to the received write addresses; and
output a memory output data according to the received read addresses;
wherein the epoch signal being the memory output data;
a feature extractor configured to:
receive the epoch signal from the epoch generator;
extract a derivative feature of the received epoch signal by computing a
mean of accumulated squared-differences among the epochs in the received epoch
signal; and
extract a variance feature of the received epoch signal by computing a
square root of an average squared-deviation of the epochs in the received epoch
signal;
a classifier configured to:
perform a feature mapping on the derivative feature and the variance
feature; and
set a decision boundary from output of the feature mapping to generate a
classifier output, the classifier output being either that the animal subject being
awake or anesthetized or that the animal subject being awake or anesthetized;
and
a predictor comprising a predictor circuit configured to:
accumulate a plurality of the classifier's outputs;
compute a decision value effrom the accumulated classifier's outputs; determine a level of anesthesia of the animal subject from the decision
value; and
predict the animal subject's anesthesia condition from a plurality of
determined levels of anesthesia of the animal subject.
In the Office Action, it was alleged that Claim 1 recited the abstract ideas of mathematical process and mental processes under Step 1 Prong 1 of the subject matter eligibility test. The Applicant respectfully disagrees. Claim 1 as amended defines
a device
for assessing anesthesia of an animal subject in
real-time
from EEG measurements of the animal subject during application of anesthesia. The device, as described with exemplarily
embodiments in the Specification, is implemented with specially configured electronic components, i.e., FPGAs. It is an optimized complete system on chip circuit design. Further, because the specific use and application (assessing the anesthesia condition of an animal subject) of the claimed invention mandate
real-time
measurement of signals, processing of data, and computation of assessment results, none of the steps can be performed by human. Therefore, the Applicant respectfully submits that invention as claimed in the amended Claim 1 is a patent-eligible subject matter.
In the Office Action, it was alleged that Claims 1-10 do not include additional elements that integrate the mental process or the mathematical process into a practical application under Step 1 Prong 2 of the subject matter eligibility test. By the amendments to Claim 1, Claim 1 as amended now more clearly defines a device for
assessing the anesthesia condition of an animal subject in real-time
from EEG measurements of the animal subject during application of anesthesia, which comprises not only a signal pre- processor, an epoch generator, but also
a feature extractor, a classifier, and a predictor for predicting the animal subject's anesthesia condition
from an accumulation of specifically processed and transformed EEG measurement data.
Prior to the present invention, real-time assessment of an animal subject's anesthesia condition during an application of anesthesia is at best the anesthesiologist's guess work based on extrinsic measurements such as blood pressure, heart-rate, change in respiratory patterns, ability to respond to verbal commands, eyelash reflex, pupillary responses, etc. with no systematic approach. Thus, the invention as claimed in the amended Claim 1 provides a significant advancement to the state of the art.
In the Office Action, it was alleged that Claims 1-10 do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B of the subject matter eligibility test. By the amendments to Claim 1, Claim 1 as amended more clearly defines a signal pre-processor, which comprises
a first filter with a first filtering frequency,
a second filter with a second filtering frequency,
and a down-sampler to pre-process sequentially an incoming measured EEG signal from the EEG measurements of the animal subject. In the amended Claims 2 and 3, it is further defined that the first filter is configured with the first filtering frequency for
compensating a power- line interference of the incoming measured
E
EG signal;
and the second filter of the signal pre-processor is configured with the second filtering frequency
being comparable to an
average fre
q
uency of a wide electroencephalo
g
raphy fre
q
uency region of the incoming measured
E
EG signal.
While simple noise filtering techniques are well known in the art of electronic signal processing, using two filters with two different filtering frequencies to filter different types of noise in applications involving EEG measurements is not routinely done.
Further, the use of an epoch generator configured to generate an epoch signal containing epochs of 1-second epoch duration each and 10% overlapping of consecutive epochs, for which one of the purposes is to create a sliding window to divide each EEG channel into short-overlapped epochs as an optimal design for
lower resources consumption, the smallest possible memory usage, and increased processing speed
is not routinely done.
The Applicant submits that at least the aforementioned elements, without limitation, are necessary and significant due to the strict requirements of the real-time and in-field use of the claimed invention (assessing the anesthesia condition of an animal subject in real- time).
Therefore, the claims as amended now meet the requirements of the subject matter eligibility test, reconsideration therefore is respectfully requested.
Claims 2-7 and 9
Claims 2-7 and 9 are not addressed separately, but it is respectfully submitted that the claims are allowable as depending from an allowable independent claim, and further in view of the comments provided above. Reconsideration thereof is respectfully requested.
Claim 10
Claim 10 is not addressed separately, but it is respectfully submitted that Claim 10 as amended defines features that are substantially similar to those of Claim 1 as amended. As such, in view of the comments provided above, the claim should also be allowable. Reconsideration thereof is respectfully requested.
V.
Claim re
j
ections under 35 U.S.C.
&
Claim 10 was rejected under 35 U.S.C. 103 as being unpatentable over Nagaraj in view of Nierenberg and further in view of Abele.
Claim 10
Claim 10 as amended defines:
A method for determining depth of anesthesia (DOA) of an animal subject in real- time from electroencephalography (EEG) measurements, the method comprising:
detecting and measuring an EEG signal of the animal subject using an EEG electrode;
filtering the EEG signal to remove unwanted signals and noise;
down-sampling the filtered EEG signal for reducing data size and accelerating
subsequent processing without losing essential features of the EEG signal for subsequent
classification;
generating from the down-sampled EEG signal an epoch signal containing
epochs of 1-second epoch duration each and 10% overlapping of consecutive epochs;
extracting a derivative feature of the epoch signal by computing a mean of accumulated squared differences among the epochs in the epoch signal;
extracting a variance feature of the epoch signal by computing a square root of an average squared deviation of the epochs in the epoch signal;
mapping the derivative feature and the variance feature 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 state comprising:
if the mapping output value is lower than 0.5 or 50%, a classifier output is
set to that the animal subject being awake during a time duration of the epochs;
otherwise, the classifier output is set to that the animal subject being anesthetized
during a time duration of the epochs; and
accumulating a plurality of the classifier outputs of consecutive awake and anesthetized epochs, respectively generate a prediction output of a likelihood the animal subject being awake or anesthetized in a successive epoch.
In the Office Action, it is alleged that although Nagaraj has not, Nierenberg teaches overlapping one EEG signal with a preceding EEG signal. Claim 10 as amended now more clearly defines the feature of generating from the down-sampled EEG signal an epoch
signal containing epochs of 1-second epoch duration each and 10% overlapping of consecutive epochs. The Applicant submits that none of the cited references and the combinations thereof has disclosed, taught, or suggested the feature.
In the Office Action, it is also alleged that although the combination of Nagaraj and Nierenberg has not, Abele teaches mapping the derivative and variance features to determine a classification boundary. Claim 10 as amended now more clearly defines the feature of mapping the derivative feature and the variance feature 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 state comprising: if the mapping output value is lower than 0.5 or 50%, a classifier output is set to that the animal subject being awake during a time duration of the epochs; otherwise, the classifier output is set to that the animal subject being anesthetized during a time duration of the epochs.
In addition, Claim 10 as amended now also more clearly defines that the derivative feature is extracted by computing a mean of accumulated squared differences among the epochs in the epoch signal; and the variance feature is extracted by computing a square root of an average squared deviation of the epochs in the epoch signal. The Applicant submits that none of the cited references and the combinations thereof has disclosed, taught, or suggested the features as defined in Claim 10 as amended.
Furthermore, the Applicant respectfully submits that Nagaraj has not disclosed, taught, or suggested the presently claimed invention. Nagaraj discloses a method for determining the DOA of a human subject, while presently claimed invention is a method for determining the DOA of an animal subject. The sedation methodology, technique, anesthetic agent, and EEG recording are entirely different between human and animal subjects, leading to different brain dynamics and EEG signatures.
For example, one of the prominent aspects of anesthetic agents is that the uses of different sedative drugs produce different EEG signatures. With humans, PropofolTM, SevofluraneTM, DexmedetomidineTM, and so on are usually used for human anesthesia. In contrast, IsofluraneTM is widely used on animals as an inhalation anesthetic agent, which cannot be used on humans as it produces severe side effects on human subjects. The EEG electrodes used on animals and humans are also very different. One is to be attached to the scalps of the human subjects, while the other may be screwed into the skulls of small animals, such as mice.
Nagaraj discloses a method that is based on logit transform and correlation to have a continuous sedation level estimation (Nagarj, para. [0080] - [0081]), which only relies only on the current epoch (of length of 4 second) for the classifier's decision. In contrast, the presently claimed invention includes a prediction mechanism based on accumulating classifier outputs for consecutive epochs.  Also, the presently claimed invention's prediction mechanism relies on the epochs of length of only 1 second, thus faster, while maintaining high accuracy.
Nagaraj discloses a method that is based on down-sampling the EEG signals to 250Hz and filtering the EEG signals within the range of 0.5 - 25Hz. Meanwhile, the presently claimed invention includes a down-sampler to 500 Hz and low pass filter within the range 0.1 - 250 Hz. The high frequency oscillations, namely Gamma band (25-250 Hz), play a significant role in distinguishing between the awake and sedated states wherein the critical features between awake and anesthesia states are mainly located within high frequency oscillations. In Nagaraj's disclosure, Gamma band oscillations are totally disregarded.
New Claims 11-15
The Applicant respectfully submits that the cited references have not disclosed, taught, or otherwise suggested the features of the invention claimed in the newly added Claims 11-15. Notwithstanding the above, it is respectfully submitted that the claims are allowable as depending from an allowable independent claim. Reconsideration thereof is respectfully requested.
VI.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.  Applicant's submission filed on 30 July 2024 has been entered.
Claim Objections
Claims 3, 6, 11, and 13 are objected to because of the following informalities: the phrase “within a” in line 4 of claim 3 should be amended to read “within an”; the phrase “different epochs” in line 2 of claim 6 should be amended to read “the epochs”; the phrase “with be” in line 6 of claim 11 should be amended to read “will be”; the term “comprising” in line 2 of claim 13 should be amended to read “comprises”; the phrase “within a” in line 4 of claim 13 should be amended to read “within an”.  Appropriate correction is required.
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 1-4, 6, 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 claim recites that “the epoch generator is configured to generate and output the epoch signal from the memory output”. This limitation renders the claim indefinite. As currently written, the claim requires the epoch generator to generate the epoch signal from the memory output. It is unclear what is meant by this recitation. For this examination, the limitation is being interpreted as “the epoch generator is configured to generate the epoch signal and output the epoch signal from the memory output”. Further regarding claim 1, it is unclear how the predictor accumulates “a plurality of the classifier’s outputs” and predicts the animal subject’s anesthesia condition from “a plurality of determined levels of anesthesia” if there is one output and determined level of anesthesia per epoch signal and only one epoch signal. As currently written, claim 1 extracts a derivative feature and a variance feature “of the received epoch signal”, and provides a classifier output of the epoch signal. As best understood by the specification, while the claim recites performing the extracting steps and the classifying step for an epoch signal, the steps are actually performed for each epoch of the epoch signal. The same indefiniteness is present in claim 10. Claim 10 extracts features “of the epoch signal”, and classifies “the epoch signal”, but also recites “the classifier output is a probability value of the animal subject is under anesthetized state given the current epoch” and “accumulating a plurality of the classifier output values for consecutive classified epochs”. This provides further confusion as to whether the steps are being performed per epoch signal or per epoch of the epoch signal. Clarification is requested. As noted above, according to the specification, the extracting and classifying steps recited in claims 1 and 10 are meant to be performed on each epoch of an epoch signal such that multiple classifications per epoch signal are determined. Regarding claim 3, it is unclear how a bandpass filter would operate within a range of 0-250Hz. It is unclear how a bandpass filter would filter out signals below 0Hz. Regarding claim 9, the phrase “an output value” lacks proper antecedent basis. Clarification is requested as to what the output value of claim 9 is. Regarding claim 10, the phrase “the classifier output value” lacks proper antecedent basis. Regarding claim 11, the wording of the claim makes it difficult to understand what the claim is reciting. For example, it is unclear what is meant by “increasing the prediction probability of likelihood” and “decreasing the prediction probability of likelihood”. Clarification is requested. Regarding claim 12, the wording of the claim renders the claim indefinite (i.e., grammatical errors and verb tense errors). Further regarding claims 11 and 12, the phrase “the prediction probability” lacks proper antecedent basis.
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. Regarding rejections of the claims under 35 U.S.C. 112(b), current indefiniteness issues within the claims are discussed in paragraph 5 above. While no prior art was found to read on the claims as currently written, the claims are not in condition for allowance due to the indefiniteness issues within the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Tabassum et al. (Novel Multirate… – previously cited) 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.