Office Action 3: Non-Final Rejection (2025-01-02)
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 band, a second filter with a second filtering frequency band, 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; and generate a multiplexer output from the down- sampled filtered incoming measured EEG 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-sampled filtered incoming measured EEG 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 epoch generator is configured to:receive the multiplexer output, the write addresses, and the read addresses;
store the received multiplexer output as data according to the received write addresses; and
retrieve data according to the received read addresses and output the retrieved data as a memory output;
wherein the epoch generator is configured to generate and output the epoch signal from the memory output ;
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 to their original values and their squared values; and
set a classification boundary from output of the feature mapping to generate a classifier output, the classifier output being either that the animal subject is awake or that the animal subject is anesthetized; and
a predictor comprising a predictor circuit configured to:accumulate a plurality of the classifier's outputs;
compute a decision value from 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.

2\. (Currently Amended) The system of claim 1, wherein the first filter of the signal pre- processor is configured to filter the incoming measured EEG signal at the first filtering frequency band to reduce a power-line interference of the incoming measured EEG signal.

3\. (Currently Amended) The system of claim 1, wherein the second filter of the signal pre- processor is configured to band-pass filter the incoming measured EEG signal at the second filtering frequency band to remove signal components in the incoming measured EEG signal that do not fall within a electroencephalography frequency band of interest ;
and wherein the second filtering frequency band is within a range of 0-250 Hz.

4\. (Previously Presented) 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\. (Canceled)

6\. (Previously Presented) The system of claim 1, wherein the computing of a mean of accumulated squared-differences among different epochs in the received epoch signal comprises: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 each difference to obtain \[\[a\]\] squared deviations;
computing an average of all of the squared deviations for all of the absolute epoch values;
and computing a square root of the average of all of the 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 to a percentage of the anesthesia level 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 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 their original values and their squared values to generate mapped derivative features and mapped variance features for enhancing distinguishability of \[\[a\]\] classification boundary and classification accuracy;;
 classifying the epoch signal using the mapped derivative feature and the mapped variance feature as either that the animal subject is awake or that the animal subject is anesthetized, the classifier output value 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 and using the plurality of the classifier output values to predict a likelihood percentage of the animal subject being under anesthetized state.

11\. (Currently Amended) The method of claim 10, further comprising:

increasing \[\[a\]\] the prediction probability of likelihood that the animal subject will be under anesthetized state in a successive epoch if the animal subject has been under anesthetized state continuously in previous consecutive epochs;
and decreasing the prediction probability of likelihood that the animal subject with be under anesthetized state in a successive epoch if ~~dietionweutput~isthatbeingaadajustiag--sueeesivepreietioneatpatpbased-n the~pre4ietionprobabihity~~.

12\. (Currently Amended) The method of claim 10, further comprising:generating a confident prediction of the animal subject beingwill be under awake state if the prediction probability is at or below 25% likelihood of the animal subject will be under awake state; and
generating a confident prediction of the animal subject being will be under anesthetized state if the prediction probability is at or above 75% likelihood of the animal subject will be under anesthetized state.

13\. (Currently Amended) 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 band to remove signal components in the EEG signal that do not fall within a electroeneephalography frequency band of interest ; and
wherein the filteringfrequency band is within a range of 0-250 Hz.

14\. (Previously Presented) 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 differencefor 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\. (Currently Amended) 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 each difference to obtain \[\[a\]\] squared deviations;
computing an average of all of the squared deviations for all of the absolute epoch values;
and computing a square root of the average of all of the 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 .
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.

APPLICANT REBUTTAL: Remarks:
The above Amendments and these Remarks are in reply to the Non-final Office Action mailed January 2, 2025. 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 3, 6, 11, and 13 are objected for containing informalities. Claims 1-4, 6, 7, and 9- 15 are rejected under 35 U.S.C. 112 as being indefinite for failing to particularly point out and distinctly claim the subject matter.
II.
Summary of
Applicant's Amendment
By the present amendments, Claims 1, 3, 6, and 9-13 are amended, leaving for the Examiner' s consideration Claims 1-4, 6-7, 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 Objections and Claim Rejections under 35 U.S.C.
In the Office Action, Claims 3, 6, 11, and 13 are objected for containing informalities; and Claims 1-4, 6, 7, and 9-15 are 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 informalities, raised indefiniteness and antecedent basis issues, Claims 1, 3, 6, and 9-13 are amended. Applicant respectfully submits that the amended claims are now clear and contain no informalities; thus, the claim objections and claim rejections under 35 U.S.C. G 112 should be withdrawn.
IV.
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 1-4, 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 added limitations of “and a derivative of each of the epochs of the received epoch signal” and “and a variance of each of the epochs of the received epoch signal” render the claim indefinite. In combination with the other elements of their respective limitations, it is unclear what the addition of each of the newly added phrases means. For example, what is meant by “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 a derivate of each of the epochs of the received epoch signal”? Are two different features extracted – (1) a derivative feature, and (2) a derivative of each of the epochs? If so, how is the “derivative of each epoch” different than the “derivative feature”? Or, is the derivative feature extracted by the entirety of “computing a mean of accumulated squared-differences among the epochs in the received epoch signal and a derivative of each of the epochs of the received epoch signal”? If the “derivative of each of the epochs” is used to extract the “derivative feature”, it is still unclear how the “derivative of each of the epochs” is different than the “derivative feature”. The same indefiniteness issues apply to the “extract a variance feature” limitation of the claim. Claim 1 further recites generating a classifier output for “each of the epochs of the received epoch signal”. As a single derivative feature and a single variance feature are extracted for each epoch signal (not for each epoch of the epoch signal), it is unclear how a classifier output would be generated for each of the epochs of the epoch signal. It is further unclear how a plurality of the classifier outputs corresponding to the epochs of the epoch signal would be accumulated. With a single extracted derivative feature and a single extracted variance feature, each mapped to their original value and their square value, a classifier would output a single classifier output for the epoch signal as a whole. The same indefiniteness issues are also present in claim 10. Further regarding claim 10, the claim recites classifying each of the epochs using “the mapped derivate feature and the mapped variance feature” (emphasis added). If each epoch is classified using the same mapped derivate feature and the same mapped variance feature, each epoch would be classified exactly the same. Claim 10 also recites the phrase “the classifier output value”, which lacks proper antecedent basis. Regarding claim 3, the phrase “the frequency band is within 250 Hz” renders the claim indefinite. It is unclear what is meant by a frequency band being “within 250 Hz”. The same indefiniteness issue applies to claim 13. Regarding claim 9, the phrases “the plurality of the determined levels of anesthesia” and “[an] a value of each of the determined levels of anesthesia” lack proper antecedent basis. Regarding claim 11, it is unclear what further limitation the claim provides as the claim fails to recite a further step; the claim recites a result of performing the method. Regarding claim 12, it is unclear what the claimed “confident prediction” is. How is the “confident prediction” different than the predicted likelihood of claim 10?
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 3 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.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.