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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/20/2025 has been entered.
Claims Accounting
Applicant's arguments, filed 11/20/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicant has amended claims 1, 8, and 17-18, filed 11/20/2025.
Claims 15 and 16 have been canceled.
Claims 1-4, 7-14, and 17-18 are the current claims hereby under examination.
Claim Objections
Claims 1, 8, and 18 are objected to because of the following informalities:
Claim 1 recites “of each channel” in line 22. This should read “of each channel;”.
Claim 8 recites “spectral feature comprises one of: power ratio index, PRI=(δ+θ)/(α+β); delta alpha ratio, DAR=δ/α; theta alpha ratio, TAR=θ/α; and theta beta ratio, TBAR=θ/(α+β)” in lines 1-4. This should read “spectral feature comprises one of: a power ratio index, PRI=(δ+θ)/(α+β); a delta alpha ratio, DAR=δ/α; a theta alpha ratio, TAR=θ/α; and a theta beta ratio, TBAR=θ/(α+β)”.
Claim 18 recites “displaying cerebral dysfunction” in line 29. This should read “displaying potential cerebral dysfunction”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 7-14, and 17-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1, 17, and 18, claim 1 recites “generating an EEG-level scalp heatmap based on the channel-level percentage of segments exhibiting slowing” in lines 25-26. There is insufficient support in the written description for this limitation. The closest identified recitation in the written description is in par. [0104], which reads: “FIG. 14 illustrates five different examples of scalp heatmaps of the percentage of slowing, generated using classification results obtained using a deep learning model as the first classifier for channel-level prediction.” Inspection of Fig. 14 shows a plurality of heatmaps with varying colors corresponding to varying percentages, indicating multiple percentages of slowing are used. It is unclear how these heatmaps can be formed using a single channel-level percentage of segments exhibiting slowing. Similar recitations are present in lines 30-31 of claim 17 and in lines 26-27 of claim 18.
All claims not explicitly addressed above are rejected under 35 U.S.C. 112(a) are rejected by virtue of their dependency on a rejected base claim.
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, 7-14, and 17-18 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 claims 1, 17, and 18, claim 1 recites “obtaining a second classifier that is trained to classify the one or more segments of each said channel, as containing abnormal slow waves based on a second feature set” in lines 15-17. This recitation indicates that second classifier is only trained to classify the segments as containing abnormal slow waves. It is therefore unclear how the classifier may be trained or how it might use the second feature set if there is only one possible classification output. Similar recitations are present in lines 20-22 of claim 17 and in lines 16-18 of claim 18 and render the claims indefinite for the same reasons. Clarification is requested.
For the purposes of examination, the claims are interpreted as “obtaining a second classifier that is trained to classify the one or more segments of each said channel, as containing abnormal slow waves or not containing abnormal slow waves based on a second feature set”.
Further regarding claims 1, 17, and 18, claim 1 recites “obtaining a segment-level slowing classification for the respective one or more segments, by passing the second feature set to the second classifier to generate the slowing classification whereby a segment-level classification is obtained for each segment of each channel” in lines 19-22. It is unclear based on this recitation how many segment-level classifications are obtained. The recitation of “obtaining a segment-level slowing classification for the respective one or more segments” indicates that a segment-level classification is obtained for each of the one or more segments. However, the recitation of “by passing the second feature set to the second classifier to generate the slowing classification” is unclear as there are multiple previously recited slowing classifications, therefore it is unclear which slowing classification is being referred to. The first classifier is recited as generating channel-wise classifications, which would result in a different number of classifications and classifications based on different data (data in a single channel or data in a single segment). Finally, is it unclear if the recitation of “whereby a segment-level classification is obtained for each segment of each channel” is a conclusory statement, reiterating “obtaining a segment-level slowing classification for the respective one or more segments”, or if this imparts additional limitations to the claim. Similar recitations are present in lines 24-27 of claim 17 and in lines 20-23 of claim 18 and render the claims indefinite for the same reasons. Clarification is requested.
For the purposes of examination, the claims are interpreted as “obtaining a segment-level slowing classification for each of the respective one or more segments, by passing the second feature set to the second classifier to generate the segment-level slowing classifications for each segment of each channel;”.
Further regarding claim 1, the claim recites “aggregating the segment-level slowing classifications to determine, for each channel, a channel-level percentage of segments” in lines 23-24. It is unclear what the channel-level percentage of segments is representative of and how the percentage is calculated. Clarification is requested.
For the purposes of examination, the claim is interpreted as “aggregating the segment-level slowing classifications to determine, for each channel, a channel-level percentage of segments exhibiting slowing”.
Further regarding claim 1, the claim recites the limitation of “the channel-level percentage of segments exhibiting slowing” in lines 25-26. There is insufficient antecedent basis for this limitation in the claim.
For the purposes of examination, the limitation of “a channel-level percentage of segments” in line 24 is interpreted as “a channel-level percentage of segments exhibiting slowing”.
Further regarding claims 1, 17, and 18, claim 1 recites “generating an EEG-level scalp heatmap based on the channel-level percentage of segments exhibiting slowing” in lines 25-26. The previous recitation of “aggregating the segment-level slowing classifications to determine, for each channel, a channel-level percentage of segments” indicates that there is a channel-level percentage of segments for each channel, and line 2 of the claim establishes that there are a plurality of channels. Therefore, it is unclear which of the plurality of channel-level percentages of segments exhibiting slowing is being referred to. It is further unclear how an EEG-level scalp heatmap can be constructed out of a single percentage. Similar recitations are present in lines 30-31 of claim 17 and in lines 26-27 of claim 18 and render the claims indefinite for the same reasons. Clarification is requested.
For the purposes of examination, the claim is interpreted as “generate an EEG-level scalp heatmap based on the channel-level percentages of segments exhibiting slowing”.
All claims not explicitly addressed above are rejected under 35 U.S.C. 112(b) are rejected by virtue of their dependency on a rejected base claim.
Further regarding claims 1, 17, and 18, claim 1 recites the limitation of “displaying the EEG-level scalp heatmap representing the percentages of segments exhibiting slowing on a user interface” in lines 27-29. There is insufficient antecedent basis for this limitation in the claim. Similar recitations are present in lines 32-34 of claim 17 and in lines 28-30 of claim 18.
For the purposes of examination, the preceding limitation of “an EEG-level scalp heatmap based on the channel-level percentage of segments exhibiting slowing” is interpreted as “an EEG-level scalp heatmap based on the channel-level percentages of segments exhibiting slowing”.
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 1-4, 7-14, and 17-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
STEP 1
Regarding claim 1, the claim recites a series of steps or acts, including generating a channel-level classification and obtaining a segment-level slowing classification. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The steps of obtaining a first classifier, generating a channel-level classification using the first classifier based on the first feature set, obtaining a second classifier, and obtaining a segment-level slowing classification for the respective one or more segments set forth a judicial exception. These steps describe the use of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations. Thus, the claim is drawn to a Mathematical Concept, which is an Abstract Idea.
Further, the steps of extracting a first feature set and aggregating the segment-level slowing classifications sets forth a judicial exception. These steps describe a concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites displaying potential cerebral dysfunction by displaying the EEG-level scalp heatmap representing the percentages of segments exhibiting slowing on a user interface, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The displaying potential cerebral dysfunction by displaying the EEG-level scalp heatmap on a user interface does not provide an improvement to the technological field, the displaying does not effect a particular treatment or effect a particular change, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of performing a sequence of artifact removal processes, generating an EEG-level scalp heatmap, and displaying cerebral dysfunction. Filtering and pre-processing (i.e., performing a sequence of artifact removal processes) EEG data is well-understood, routine and conventional activity for those in the field of medical diagnostics and are considered insignificant pre-solution activity, e.g., mere data gathering steps necessary to perform the Abstract Idea. The steps of generating an EEG-level scalp heatmap and displaying the potential cerebral dysfunction amount to mere insignificant post-solution activity, e.g., insignificant application of the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes them from well-understood, routine, and conventional data processing and display activity engaged in by medical professionals prior to Applicant's invention.
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
The dependent claims also fail to add significantly more to the abstract independent claims as they generally recite method steps pertaining to the details of data gathering and pre-processing the data. The obtaining, performing, extracting, generating, aggregating, and displaying steps recited in the independent claim maintain a high level of generality even when considered in combination with the dependent claims.
Regarding claims 17 and 18, it is well established that the mere physical or tangible nature of additional elements do not automatically confer eligibility on a claim directed to an abstract idea. Claim 17 recites the abstract ideas of claim 1 stored as computer-readable instructions in a memory with a processor to perform the abstract idea. Claim 18 recites the abstract ideas of claim 1 stored as instruction on a non-transitory computer-readable storage. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Examiner’s Note
The following is a statement of reasons for the lack of prior art rejections:
Claim 1 recites: A method for detecting presence of slowing patterns in an EEG sample comprising a plurality of channels of EEG signals, each channel comprising one or more segments, the method comprising: obtaining a first classifier that is trained to classify EEG samples as containing abnormal slow waves or not, wherein each abnormal slow wave is a waveform present in the EEG sample exhibiting spectral slowing; performing a sequence of artifact removal processes on the EEG sample to generate a preprocessed EEG sample; extracting a first feature set from the preprocessed EEG sample; generating a channel-level classification, using the first classifier and based on the first feature set, of whether the EEG sample contains abnormal slow waves or not, wherein the first classifier classifies the EEG sample by channel-wise classification of the one or more segments, of each of said channels in the EEG sample, as containing slow waves or not containing slow waves, based on the first feature set; obtaining a second classifier that is trained to classify the one or more segments of each said channel, as containing abnormal slow waves based on a second feature set that is extracted from the first feature set and/or from the plurality of channel-wise classifications of the one or more segments of each said channel; obtaining a segment-level slowing classification for the respective one or more segments or for the EEG sample as a whole, by passing the second feature set to the second classifier to generate the slowing classification whereby a segment-level classification is obtained for each segment of each channel aggregating the segment-level slowing classifications to determine, for each channel, a channel-level percentage of segments; generating an EEG-level scalp heatmap based on the channel-level percentage of segments exhibiting slowing; and displaying potential cerebral dysfunction by displaying the EEG-level scalp heatmap slowing classification representing the percentages of segments exhibiting slowing on a user interface, wherein the sequence of artifact removal processes comprises removal of one or more ocular artifacts and removal of one or more electrode artifacts.
The closest prior art is identified as US Patent Publication 2015/0038869 by Simon et al. – previously cited (hereinafter “Simon”) in view of US Patent Publication 2012/0101401 by Faul et al. – previously cited (hereinafter “Faul”) in view of US Patent Publication 2018/0049896 by Connolly et al. – previously cited, (hereinafter “Connolly”).
Simon teaches a method for detecting presence of slowing patterns in an EEG sample comprising a plurality of channels of EEG signals, each channel comprising one or more segments (Epochs of the time-series [0083, 0085]) the method comprising: obtaining a first classifier that is trained to classify EEG samples as containing abnormal slow waves or not (logistic regression using an optimal cut-point as a classifier [0088]; AD brain exhibits a spectral slowing relative to CTL subjects [0182]), wherein each abnormal slow wave is a waveform present in the EEG sample exhibiting spectral slowing (AD brain exhibits a spectral slowing relative to CTL subjects [0182]); performing a sequence of artifact removal processes on the EEG sample to generate a preprocessed EEG sample (Pre-processing artifact detection algorithm detects the epochs to be removed an analysis is only performed on epochs not identified as containing artifacts [0083]); extracting a first feature set from the preprocessed EEG sample (“Once the spectral analysis code has transformed each epoch of artifact free time series EEG data, a feature extraction algorithm can assess each block of transformed data to create a list of features or variables or biomarkers extracted from each block of EEG data conducted during an individual task.”, [0085]); generating a classification, using the first classifier and based on the first feature set, of whether the EEG sample contains abnormal slow waves or not (EEG features can be used in combination as an input to a statistical predictive model, which can classify the state of the brain [0064; 0090-0116]), wherein the sequence of artifact removal processes comprises removal of one or more ocular artifacts (“Excessive Signal segments occur during eye blink”, [0161]) and removal of one or more electrode artifacts (“or non-physiological electrical noise including movement of the EEG dry electrode” [0161]).
Simon does not teach wherein the first classifier classifies the EEG sample by channel-wise classification of the one or more segments, of each of said channels in the EEG sample, as containing slow waves or not containing slow waves, based on the first feature set; obtaining a second classifier that is trained to classify the one or more segments of each said channel, as containing abnormal slow waves or not containing abnormal slow waves based on a second feature set that is extracted from the first feature set and/or from the plurality of channel-wise classifications of the one or more segments of each said channel; and obtaining a slowing classification for the respective one or more segments, by passing the second feature set to the second classifier to generate the slowing classification whereby a segment-level classification is obtained for each segment of each channel.
Fig. 3 of Faul teaches a method for generating features for each segment in each channel of a plurality of EEG channels and passing the features to a classifier in which the classifier generates a classification for each segment in each channel relating to the state of the EEG signal ([0033-0037]). Faul further teaches a channel fusion step (step 312; i.e., second classifier) that uses the output from the plurality of channel classifications (i.e., second feature set) to obtain a binary result (i.e., classification) for each epoch ([0188-0189]). Faul also teaches outputting the indication (i.e., outputting the classification) of the classification in step 316. Applying the classification to each channel and fusing the combined classifications provides increased classification accuracy ([0043]). It is noted that Faul teaches that a classification is obtained for each epoch (i.e., segment).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Simon such that the first classifier classifies the EEG sample by channel-wise classification of the one or more segments, of each of said channels in the EEG sample, as containing slow waves or not containing slow waves, based on the first feature set, and to have modified the method to include obtaining a second classifier that is trained to classify the one or more segments, of each said channel, as containing abnormal slow waves based on a second feature set that is extracted from the first feature set and/or from the plurality of channel-wise classifications of the one or more segments of each said channel; obtaining a slowing classification for the respective one or more segments, by passing the second feature set to the second classifier to generate the slowing classification whereby a segment-level classification is obtained for each segment of each channel.; as applying the classifier to each channel separately and then fusing the combined predictions provides increased classification accuracy ([0043]).
The combination of Simon and Faul do not teach generating an EEG-level scalp heatmap and displaying potential cerebral dysfunction by displaying the EEG-level heatmap representing percentages on a user interface.
Figs. 12-14 of Connolly teach an EEG-level heatmap generated from features derived from each channel of the EEG, illustrating the spatial distribution of the features. The values corresponding to the shades are used to represent the percentage of features of that channel that are related to abnormal brain activity normalized to the maximum number of features. The EEG-level scalp heatmaps can be used to distinguish spatial differences in EEG activity that can help localize the regions where abnormalities exist ([0097-0099]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Simon in view of Faul to include generating an EEG-level scalp heatmap and displaying potential cerebral dysfunction by displaying the EEG-level heatmap representing percentages on a user interface, as generating and displaying EEG-level heatmaps can help to distinguish spatial differences in EEG activity that can help localize the regions where abnormalities exist, as taught by Connolly ([0097-0099]).
The combination of Simon, Faul, and Connolly fails to teach aggregating the segment-level slowing classifications to determine, for each channel, a channel-level percentage of segments exhibiting slowing; generating an EEG-level scalp heatmap based on the channel-level percentage of segments exhibiting slowing, or displaying potential cerebral dysfunction by displaying the EEG-level scalp heatmap representing the percentages of segments exhibiting slowing on a user interface.
Similar limitations are present in independent claims 17 and 18. The limitations of these claims are patentably distinct over the prior art cited in this Office action and any other prior art.
Response to Arguments
Applicant’s arguments, filed 11/20/2025 have been fully considered.
The amendments to the claims overcome the objections of record, however new objections have been applied to the claims.
The amendments to the claims overcome the rejections of record of claims 1, 8, 17, and 18 under 35 U.S.C. 112(b). However, new rejections have been applied under 35 U.S.C. 112(b) and 112(a).
Applicant’s arguments regarding the rejections of the claims under 35 U.S.C. 101 are acknowledged, but are not found persuasive. Applicant’s arguments that the steps of performing artifact removal, extracting feature sets, generating channel-wise classifications, obtaining a second classifier, and displaying cerebral dysfunction are concrete signal-processing steps is acknowledged, however, these steps are comprised of mathematical concepts, mental processes (i.e., the judicial exceptions) and pre-solution activity that do not amount to significantly more than the judicial exception. Applicant’s arguments that the scalp heatmap facilitates direct visualization of spectral slowing patterns that enhances the ability to diagnose underlying brain conditions or monitor brain activity during anesthesia is acknowledged, but is not commensurate in scope with the claims. The claims do not contain any limitations related to relying on the display of the data to diagnose underlying brain conditions or monitor brain activity during anesthesia. Applicant’s arguments that the claims are drawn to a system of monitoring the abnormal slowing of EEG in real-time clinical environments is acknowledged, but is not commensurate in scope with the claims. The claims do not contain any limitations related to real-time monitoring.
Applicant’s assertions regarding the rejections of claims 1, 17, and 18 under 35 U.S.C. 103 is acknowledged. These assertions are found persuasive and the amended claims are addressed above in the Examiner’s Note section.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WIPO Publication 2018/178333 by Pyrzowski et al. teaches a method of obtaining an EEG sample comprising a plurality of channels, each channels comprising a plurality of segments and classifying segments of data independently.
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/NELSON ALEXANDER GLOVER/ Examiner, Art Unit 3791
/ADAM J EISEMAN/ Primary Examiner, Art Unit 3791