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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/06/2025 has been entered.
Response to Amendment
This Office Action is responsive to the amendment filed 10/06/2025 (“Amendment”). Claims 1-6 and 8-22 are currently under consideration. The Office acknowledges the amendments to claims 1, 9, 12, 19, and 22.
The objection(s) to the drawings, specification, and/or claims, the interpretation(s) under 35 USC 112(f), and/or the rejection(s) under 35 USC 101 and/or 35 USC 112 not reproduced below has/have been withdrawn in view of the corresponding amendments.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 9 and 11 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 claim 9, there is no disclosure of a sleep or wakefulness or level of arousal stage or state that is different from the already claimed awake, drowsy, REM sleep, etc. stages or states.
Regarding claim 11, there is no disclosure of what is contemplated by and what defines the “other” indication. E.g. there is no disclosure of what an other indication of a neurological event is.
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 9, 11, and 22 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 pre-AIA the applicant regards as the invention.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claims 9 and 11 recite broad recitations using the terms “a sleep or wakefulness or level of arousal stage or state” and “other indication,” and the claims also recite narrower examples. E.g. in claim 9, the narrow example is “Stage 3 sleep.” What other stages/states are contemplated? Scope is unclear. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Regarding claim 22, antecedent basis for the recitations of “power spectral density (PSD) circuitry,” “baseline-adjustment circuitry,” and “classifier circuitry” in the method portions of the claim is unclear, at least because these elements are part of the apparatus already described. Is reference being made to the same or new/different elements? For purposes of examination, they will be interpreted as the same elements.
Further regarding claim 22, it is unclear whether the signal processor circuitry of line 5 is the same as the signal processor circuitry of line 2, as least because the recitation in line 5 is not “the signal processor circuitry.”
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 1-6 and 8-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claims 1-6, 8-18, and 22 are directed to an “apparatus” and a “tangible physical computer readable medium,” which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Claims 19-21 are directed to a “method,” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claims 1, 19, and 22 recite (“set forth” or “describe”) the abstract ideas of a mental process and a mathematical concept, substantially as follows:
receiving an acquired EEG signal acquired via a channel coupled to a skin electrode of the one or more EEG skin electrodes, computing a monitored PSD EEG signal representing a monitored power spectral density across a spectral range of frequencies of at least 1.0 Hz through 20 Hz using the acquired EEG signal; selecting a baseline PSD EEG signal in the range of frequencies from stored multiple non-stroke baseline PSD EEG signals; receiving a stored same-channel and same-subject baseline PSD EEG signal in the range of frequencies, forming a baseline-adjusted monitored PSD EEG signal using the monitored PSD EEG signal and the selected baseline PSD EEG signal; and classifying a temporal shift in the baseline-adjusted monitored PSD EEG signal, over the range of frequencies, to produce a real-time detection.
The receiving, computing, selecting, receiving, forming, and classifying steps can be practically performed in the human mind, with the aid of a pen and paper, but for performance on a generic computer, in a computer environment, or merely using the computer as a tool to perform the steps. If a person were to see a printout of an EEG signal, they would be able to receive it visually, and then manipulate it, by using a visually-received baseline signal, to obtain a classification. There is nothing to suggest an undue level of complexity in the manipulations. Therefore, a person would be able to perform the calculations mentally or with pen and paper.
The steps also involve the mathematical concepts of manipulating data inputs via a transformation, and performing selection and comparison steps to obtain a classification. These steps correspond to “[w]ords used in a claim operating on data to solve a problem [that] can serve the same purpose as a formula.” See MPEP 2106.04(a)(2)(I).
Prong Two: Claims 1, 19, and 22 do not include additional elements that integrate the mental process or mathematical concept into a practical application. Therefore, the claims are “directed to” the mental process and mathematical concept. The additional elements merely:
recite the words “apply it” (or an equivalent) with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g. signal processor circuitry, power spectral density (PSD) circuitry, baseline-adjustment circuitry, memory circuitry, and classifier circuitry), and
add insignificant extra-solution activity (the pre-solution activity of: receiving data via an EEG skin electrode channel; and the post-solution activity of: producing a real-time detection and associated alert).
As a whole, the additional elements merely serve to gather and feed information to the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1, 19, and 22 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above.
Dependent Claims
The dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely
further describe the abstract idea (e.g. periodically forming a signal (claims 2 and 20), generating stroke probability metrics (claims 2 and 20), basing the alert on particular conditions (claims 2, 4, 5, and 20), using partially overlapping periods (claim 3), details of forming the baseline (claims 6, 16, and 21), details of selection (claims 8 and 10), updating the baseline (claim 11), alert suppression (claim 12), multi-channel classification of magnitude (claim 14), classification based on left and right channels (claims 17 and 18), alert certainty (claim 18), etc.), and
further describe the extra-solution activity (or the structure used for such activity) (e.g. storing multiple baselines (claims 8 and 9), model training (claim 13), filtering (claim 15), etc.).
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way (e.g. no alert is actually outputted, and nobody needs to see or act on the alert). They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves another technology or technical field, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 4-6, 9, 11, 13-17, and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication 2021/0030299 (“Naber”) and US Patent Application Publication 2012/0150545 (“Simon”).
Regarding claim 1, Naber teaches [a] wearable real-time stroke detector apparatus (Abstract, Fig. 1A), configured to be coupled to one or more EEG skin electrodes located on a subject (¶¶s 0008, 0059, 0060, sensor assemblies disposed on the skin) for performing real-time stroke detection (¶¶s 0008, 0096, 0106), the stroke detector apparatus comprising: signal processor circuitry (Fig. 2A, stroke detection processing system 256), comprising: power spectral density (PSD) circuitry (Fig. 2A, feature extraction engine 260 – also see ¶¶s 0082, 0092), coupled to receive an acquired EEG signal acquired via a channel coupled to a skin electrode of the one or more EEG skin electrodes (Fig. 2A, from sensor system 252 – also see ¶ 0073), the PSD circuitry configured to compute a monitored PSD EEG signal … using the acquired EEG signal (¶¶s 0082, 0092); baseline-adjustment circuitry, coupled to the PSD circuitry to receive a baseline of the monitored PSD EEG signal …, and coupled to memory circuitry to receive a stored same-channel and same-subject baseline PSD EEG signal (although Naber is not explicit that the baseline is same-channel and same-subject, it does contemplate monitoring temporal as opposed to spatial changes in the same individual (¶ 0082), which suggests this feature. It would have been obvious to use a same-channel and same-subject baseline to have a most representative baseline from which to determine changes (see the next element and Simon below)), and to form a baseline-adjusted monitored PSD EEG signal … using the monitored PSD EEG signal and the baseline PSD EEG signal, … (¶¶s 0082, 0092, temporal differences of PSD features are calculated, which requires use of a stored PSD signal that can be considered a baseline PSD signal. The temporal differences of PSD features are regarded as the baseline-adjusted monitored PSD EEG signal); and classifier circuitry, coupled to the PSD circuitry to receive the baseline-adjusted monitored PSD EEG signal, the classifier circuitry using a trained model to classify a temporal shift (e.g. a non-zero value) … in the baseline-adjusted monitored PSD EEG signal (e.g. the temporal difference), to produce a real-time detection indicating Detected Stroke based on the classified temporal shift as determined by the classifier circuitry using the trained model and, in response to the real-time detection, providing an alert indicated Detected Stroke (¶¶s 0083, 0094-0096, a machine learning engine for stroke classification based on PSD features, which are defined over a specific range of frequencies; ¶ 0064, an alert concurrent with the stroke).
Naber does not appear to explicitly teach wherein the memory circuitry is configured to (it is noted that these elements are not actually limiting because the memory circuitry is not part of the apparatus. Nonetheless, to facilitate compact prosecution, these elements have been found) store multiple non-stroke baseline PSD EEG signals for selection by the baseline-adjustment circuitry to form the baseline-adjusted monitored PSD EEG signal using the monitored PSD EEG signal and the selected baseline PSD EEG signal (although it does contemplate comparison with a known control baseline state (¶ 0081) and use of multiple PSD features (¶ 0082)). Naber further does not appear to explicitly teach the monitored PSD EEG signal representing a monitored power spectral density across a spectral range of frequencies of at least 1.0 Hz through 20 Hz, representing a baseline power spectral density across the spectral range of frequencies of at least 1.0 Hz through 20 Hz, forming the baselined-adjusted signal across this range, and classifying a temporal shift across this range.
Simon teaches establishing a normal/baseline EEG PSD signature for an individual so that changes therefrom can be detected and used for diagnosis (¶¶s 0016, 0073, 0175, etc.). There can be different or multiple signatures under baseline conditions (¶ 0083). Simon also teaches monitoring and processing based on a PSD spanning at least the 1.0 Hz through 20 Hz range of frequencies (Figs. 9B, 10A, 10B, 11, ¶ 0089, etc.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to monitor changes from a same-channel and same-subject baseline in Naber as in Simon, the baseline selected from multiple non-stroke baselines depending on e.g. the particular PSD feature being compared, for the purpose of being able to detect deviations and thereby track health conditions and deterioration (Simon: ¶¶s 0073, 0175, etc.). It would have been obvious to work at least with the 1.0 Hz to 20 Hz frequency range, as in Simon, since this is identified as the relevant range for monitoring brain/EEG activity (Simon: ¶¶s 0070, 0089, 0091, etc.).
Regarding claim 19, Naber teaches [a] method of stroke detection, the method comprising: using a wearable real-time stroke detector apparatus (Abstract, Fig. 1A), configured to be coupled to one or more EEG skin electrodes located on a subject (¶¶s 0008, 0059, 0060, sensor assemblies disposed on the skin) for performing real-time stroke detection (¶¶s 0008, 0096, 0106), the stroke detector apparatus comprising: signal processor circuitry (Fig. 2A, stroke detection processing system 256), comprising: power spectral density (PSD) circuitry (Fig. 2A, feature extraction engine 260 – also see ¶¶s 0082, 0092), coupled to receive an acquired EEG signal acquired via a channel coupled to a skin electrode of the one or more EEG skin electrodes (Fig. 2A, from sensor system 252 – also see ¶ 0073), the PSD circuitry configured to compute a monitored PSD EEG signal … using the acquired EEG signal (¶¶s 0082, 0092); baseline-adjustment circuitry, coupled to the PSD circuitry to receive a baseline of the monitored PSD EEG signal …, and coupled to memory circuitry to receive a stored same-channel and same-subject baseline PSD EEG signal (although Naber is not explicit that the baseline is same-channel and same-subject, it does contemplate monitoring temporal as opposed to spatial changes in the same individual (¶ 0082), which suggests this feature. It would have been obvious to use a same-channel and same-subject baseline to have a most representative baseline from which to determine changes (see the next element and Simon below)), and to form a baseline-adjusted monitored PSD EEG signal … using the monitored PSD EEG signal and the baseline PSD EEG signal, … (¶¶s 0082, 0092, temporal differences of PSD features are calculated, which requires use of a stored PSD signal that can be considered a baseline PSD signal. The temporal differences of PSD features are regarded as the baseline-adjusted monitored PSD EEG signal); and classifier circuitry, coupled to the PSD circuitry to receive the baseline-adjusted monitored PSD EEG signal, the classifier circuitry using a trained model to classify a temporal shift (e.g. a non-zero value) … in the baseline-adjusted monitored PSD EEG signal (e.g. the temporal difference), to produce a real-time detection indicating Detected Stroke based on the classified temporal shift as determined by the classifier circuitry using the trained model (¶¶s 0083, 0094-0096, a machine learning engine for stroke classification based on PSD features, which are defined over a specific range of frequencies; ¶ 0064, alert), the method further comprising: receiving an acquired EEG signal acquired via a channel coupled to a skin electrode of the one or more EEG skin electrodes and, using the power spectral density (PSD) circuitry, computing a monitored PSD EEG signal … using the acquired EEG signal (as noted above); …; receiving a stored same-channel and same-subject baseline PSD EEG signal, and, using the baseline-adjustment circuitry, forming a baseline-adjusted monitored PSD EEG signal … using the monitored PSD EEG signal and the selected baseline PSD EEG signal (as noted above); and classifying a temporal shift … in the baseline-adjusted monitored PSD EEG signal, over a specified range of frequencies, using the classifier circuitry, to produce a real-time detection indicating Detected Stroke based on the classified temporal shift as determined by the classifier circuitry using the trained model and, in response to the real-time detection, providing an alert indicating Detected Stroke (as noted above – and see ¶ 0064, an alert concurrent with the stroke).
Naber does not appear to explicitly teach wherein the memory circuitry is configured to (it is noted that these elements are not actually limiting because the memory circuitry is not part of the apparatus. Nonetheless, to facilitate compact prosecution, these elements have been found) store multiple non-stroke baseline PSD EEG signals for selection by the baseline-adjustment circuitry to form the baseline-adjusted monitored PSD EEG signal using the monitored PSD EEG signal and the selected baseline PSD EEG signal, or selecting a selected baseline PSD EEG signal from stored multiple non-stroke baseline PSD EEG signals (although it does contemplate comparison with a known control baseline state (¶ 0081) and use of multiple PSD features (¶ 0082)). Naber further does not appear to explicitly teach the monitored PSD EEG signal representing a monitored power spectral density across a spectral range of frequencies of at least 1.0 Hz through 20 Hz, representing a baseline power spectral density across the spectral range of frequencies of at least 1.0 Hz through 20 Hz, forming the baselined-adjusted signal across this range, and classifying a temporal shift across this range.
Simon teaches establishing a normal/baseline EEG PSD signature for an individual so that changes therefrom can be detected and used for diagnosis (¶¶s 0016, 0073, 0175, etc.). There can be different or multiple signatures under baseline conditions (¶ 0083). Simon also teaches monitoring and processing based on a PSD spanning at least the 1.0 Hz through 20 Hz range of frequencies (Figs. 9B, 10A, 10B, 11, ¶ 0089, etc.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to monitor changes from a same-channel and same-subject baseline in Naber as in Simon, the baseline selected from multiple non-stroke baselines depending on e.g. the particular PSD feature being compared, for the purpose of being able to detect deviations and thereby track health conditions and deterioration (Simon: ¶¶s 0073, 0175, etc.). It would have been obvious to work at least with the 1.0 Hz to 20 Hz frequency range, as in Simon, since this is identified as the relevant range for monitoring brain/EEG activity (Simon: ¶¶s 0070, 0089, 0091, etc.).
Regarding claim 22, Naber teaches [a] tangible physical computer readable medium including stored instructions (¶ 0110) for instructing signal processor circuitry for performing a method of stroke detection using a wearable real-time stroke detector apparatus (Abstract, Fig. 1A), configured to be coupled to one or more EEG skin electrodes located on a subject (¶¶s 0008, 0059, 0060, sensor assemblies disposed on the skin) for performing real-time stroke detection (¶¶s 0008, 0096, 0106), the stroke detector apparatus comprising: signal processor circuitry (Fig. 2A, stroke detection processing system 256), comprising: power spectral density (PSD) circuitry (Fig. 2A, feature extraction engine 260 – also see ¶¶s 0082, 0092), coupled to receive an acquired EEG signal acquired via a channel coupled to a skin electrode of the one or more EEG skin electrodes (Fig. 2A, from sensor system 252 – also see ¶ 0073), the PSD circuitry configured to compute a monitored PSD EEG signal … using the acquired EEG signal (¶¶s 0082, 0092); baseline-adjustment circuitry, coupled to the PSD circuitry to receive a baseline of the monitored PSD EEG signal, and coupled to memory circuitry to receive a stored same-channel and same-subject baseline PSD EEG signal (although Naber is not explicit that the baseline is same-channel and same-subject, it does contemplate monitoring temporal as opposed to spatial changes in the same individual (¶ 0082), which suggests this feature. It would have been obvious to use a same-channel and same-subject baseline to have a most representative baseline from which to determine changes (see the next element and Simon below)) …, and to form a baseline-adjusted monitored PSD EEG signal … using the monitored PSD EEG signal and the baseline PSD EEG signal, … (¶¶s 0082, 0092, temporal differences of PSD features are calculated, which requires use of a stored PSD signal that can be considered a baseline PSD signal. The temporal differences of PSD features are regarded as the baseline-adjusted monitored PSD EEG signal); and classifier circuitry, coupled to the PSD circuitry to receive the baseline-adjusted monitored PSD EEG signal, the classifier circuitry using a trained model to classify a temporal shift (e.g. a non-zero value) … in the baseline-adjusted monitored PSD EEG signal (e.g. the temporal difference), to produce an alert indicating Detected Stroke based on the classified temporal shift as determined by the classifier circuitry using the trained model (¶¶s 0083, 0094-0096, a machine learning engine for stroke classification based on PSD features, which are defined over a specific range of frequencies; ¶ 0064, alert), the method comprising: receiving an acquired EEG signal acquired via a channel coupled to a skin electrode of the one or more EEG skin electrodes and, using power spectral density (PSD) circuitry, computing a monitored PSD EEG signal using the acquired EEG signal (as noted above); …; receiving a stored same-channel and same-subject baseline PSD EEG signal, and, using baseline-adjustment circuitry, forming a baseline-adjusted monitored PSD EEG signal using the monitored PSD EEG signal and the selected baseline PSD EEG signal (as noted above); and classifying a temporal shift in the baseline-adjusted monitored PSD EEG signal, over a specified range of frequencies, using classifier circuitry, to produce a real-time detection indicating Detected Stroke based on the classified temporal shift as determined by the classifier circuitry using the trained model and, in response to the real-time detection, providing an alert indicating Detected Stroke (as noted above – also see ¶ 0064, an alert concurrent with the stroke).
Naber does not appear to explicitly teach wherein the memory circuitry is configured to (it is noted that these elements are not actually limiting because the memory circuitry is not part of the apparatus. Nonetheless, to facilitate compact prosecution, these elements have been found) store multiple non-stroke baseline PSD EEG signals for selection by the baseline-adjustment circuitry to form the baseline-adjusted monitored PSD EEG signal using the monitored PSD EEG signal and the selected baseline PSD EEG signal, or selecting a selected baseline PSD EEG signal from stored multiple non-stroke baseline PSD EEG signals (although it does contemplate comparison with a known control baseline state (¶ 0081) and use of multiple PSD features (¶ 0082)). Naber further does not appear to explicitly teach the monitored PSD EEG signal representing a monitored power spectral density across a spectral range of frequencies of at least 1.0 Hz through 20 Hz, representing a baseline power spectral density across the spectral range of frequencies of at least 1.0 Hz through 20 Hz, forming the baselined-adjusted signal across this range, and classifying a temporal shift across this range.
Simon teaches establishing a normal/baseline EEG PSD signature for an individual so that changes therefrom can be detected and used for diagnosis (¶¶s 0016, 0073, 0175, etc.). There can be different or multiple signatures under baseline conditions (¶ 0083). Simon also teaches monitoring and processing based on a PSD spanning at least the 1.0 Hz through 20 Hz range of frequencies (Figs. 9B, 10A, 10B, 11, ¶ 0089, etc.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to monitor changes from a same-channel and same-subject baseline in Naber as in Simon, the baseline selected from multiple non-stroke baselines depending on e.g. the particular PSD feature being compared, for the purpose of being able to detect deviations and thereby track health conditions and deterioration (Simon: ¶¶s 0073, 0175, etc.). It would have been obvious to work at least with the 1.0 Hz to 20 Hz frequency range, as in Simon, since this is identified as the relevant range for monitoring brain/EEG activity (Simon: ¶¶s 0070, 0089, 0091, etc.).
Regarding claims 2 and 20, Naber-Simon teaches all the features with respect to the corresponding claims 1 and 19, as outlined above. Regarding claim 2, Naber-Simon further teaches wherein: the baseline-adjustment circuitry is configured to periodically form the baseline-adjusted monitored PSD EEG signal over a series of time periods (Naber: ¶ 0096, on a rolling basis); the classifier circuitry is configured to generate a time-series of stroke probability metrics for corresponding ones of the time periods using the trained model to classify the temporal shift in the baseline-adjusted monitored PSD EEG signal, over the spectral range of frequencies (Naber: ¶ 0011, stroke detection score corresponding to likelihood of a stroke); and wherein the alert indicating Detected Stroke is generated at least in part based on a plurality of successive indications of stroke probability metrics meeting at least one first criterion (Naber: ¶ 0096, evaluating multiple consecutive windows before a determination is made).
Claim 20 is rejected in like manner.
Regarding claims 4 and 5, Naber-Simon teaches all the features with respect to claim 2, as outlined above. Naber-Simon further teaches wherein the alert indicating Detected Stroke is generated at least in part based on a plurality of indications of consecutive stroke probability metrics meeting at least one second criterion, wherein the alert indicating Detected Stroke is generated at least in part based on a plurality of non-consecutive indications of stroke probability metrics meeting at least one third criterion (Naber: ¶ 0096, based on some of the 5-10 rolling time windows (continuous or non-continuous) indicating a stroke. Since they are a subset, the criteria are different (e.g. windows 2, 3, and 4 out of 3 windows, or windows 1, 3, 5, and 7 out of 4 windows)).
Regarding claims 6 and 21, Naber-Simon teaches all the features with respect to the corresponding claims 1 and 19, as outlined above. Regarding claim 6, Naber-Simon further wherein the baseline-adjustment circuitry is configured to form the baseline-adjusted monitored PSD EEG signal by dividing the monitored PSD EEG signal by the stored baseline PSD EEG signal at individual spectral frequencies within the spectral range of frequencies (Naber: ¶¶s 0082, 0092, etc., ratios of PSD features).
Claim 21 is rejected in like manner.
Regarding claim 9, Naber-Simon teaches all the features with respect to claim 1, as outlined above. Naber-Simon further teaches wherein the memory circuitry is configured to store multiple non-stroke baseline PSD EEG signals individually associated with at least one of: (a) time-of-day characteristic; (b) awake, drowsy, REM sleep, Stage 1 sleep, Stage 2 sleep, Stage 3 sleep, or Stage 4 sleep, or a sleep or wakefulness or level of arousal stage or state of thesubject; (c) medication prescription or usage status or characteristic of the subject; (d) a migraine, post-migraine, seizure, post-seizure, post-ictal or stroke mimic confounding condition from the subject or from a different subject; or (e) a physical activity or muscle activity status of the subject (the longitudinal baseline aspects contemplated by e.g. ¶ 0064 of Simon include these features. Further, the eye opening and closing baselines (¶ 0175) can be considered associated with muscle activity status).
Regarding claim 11, Naber-Simon teaches all the features with respect to claim 1, as outlined above. Naber-Simon further teaches wherein the baseline-adjustment circuitry is configured to update the stored baseline PSD EEG signal in response to or at a specified time interval after a trigger event, wherein the trigger event includes at least one of: occurrence of a detected stroke or other indication of a neurological event (after a stroke, it would have been obvious to establish a new baseline for the purpose of measuring recovery – Simon: ¶ 0064); elapsing of an update clock timer; when a sampled duration of the PSD EEG signal deviates from the stored baseline PSD EEG signal by at least a specified amount; or a user input provided via a user or application interface.
Regarding claim 13, Naber-Simon teaches all the features with respect to claim 1, as outlined above. Naber-Simon further teaches wherein the model is trained using baseline-adjusted PSD EEG signal data corresponding to ground truth stroke determinations performed in a time domain using at least one of a raw EEG signal, an artifact-filtered EEG signal, or a noise-filtered artifact-filtered EEG signal from a same or a different subject undergoing an intrinsic or induced brain ischemia event (Naber: ¶ 0011, ground truth labeling, ¶ 0083, support vector machines are based on supervised learning; Abstract, classification based on EEG data (time domain data)).
Regarding claim 14, Naber-Simon teaches all the features with respect to claim 2, as outlined above. Naber-Simon further teaches wherein the classifier is multi-channel corresponding to a number of different skin electrodes (Naber: Fig. 1A shows different sensors/channels, ¶ 0022 describes channel connectivity metrics, etc.), and wherein the classifier is further configured to indicate a stroke magnitude based at least in part on a number of channels respectively concurrently indicating detected stroke based on a corresponding plurality of consecutive stroke probability metrics meeting the at least one first criterion (Naber: ¶¶s 0081, 0091, 0104, metrics related to amplitude; ¶¶s 0056, 0065, severity of the stroke. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine magnitude/severity based on the number of channels affected since they correspond to many of the metrics determined to be relevant (e.g. spatial differences, asymmetry, and connectivity metrics (¶¶s 0081, 0082), and indicate that effects of the stroke are more widespread).
Regarding claim 15, Naber-Simon teaches all the features with respect to claim 1, as outlined above. Naber-Simon further teaches artifact filter circuitry, configured to be coupled to the skin electrodes to receive a raw EEG signal from the skin electrodes and to remove or attenuate a non-EEG signal artifact comprising at least one of high electrode impedance, muscle activation, or eye movement, so as to produce an artifact-filtered EEG signal (Naber: ¶ 0067, filtering motion induced artifacts); and lowpass or bandpass filter circuitry, coupled to the artifact filter circuitry to receive the artifact-filtered EEG signal, and configured to remove or attenuate high frequency noise including at least one of AC utility line noise or switching power supply line noise, so as to provide a noise-filtered artifact-filtered EEG signal as the acquired EEG signal for use by the PSD circuitry (Naber: ¶ 0087, low-pass filter; Simon: ¶ 0198, removing interference from the 60 Hz line frequency).
Regarding claim 16, Naber-Simon teaches all the features with respect to claim 1, as outlined above. Naber-Simon further teaches wherein the baseline-adjustment circuitry is configured to form a baseline-adjusted monitored PSD EEG signal by normalizing the monitored PSD EEG signal by the stored baseline PSD EEG signal at individual spectral frequencies within a specified range of frequencies (Nader: ¶ 0082, ratio).
Regarding claim 17, Naber-Simon teaches all the features with respect to claim 1, as outlined above. Naber-Simon further teaches wherein the EEG signal includes Left and Right channels respectively corresponding to one or more skin electrodes located on one of a Left side of a brain of the subject or a Right side of a brain of the subject, and wherein the classifier circuitry is configured to produce at least one of the alert indicating detected stroke, or an indication of certainty of the alert, based on at least one of (1) a change between contralateral Left and Right channel baseline-adjusted PSD EEG signals; or (2) a relative temporal shift between contralateral Left and Right channel baseline-adjusted PSD EEG signals (Naber: Fig. 1A, ¶ 0081, asymmetry, change in connectivity).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Naber-Simon in view of US Patent Application Publication 2014/0031711 (“Low”).
Regarding claim 3, Naber-Simon teaches all the features with respect to claim 2, as outlined above. Naber-Simon does not appear to explicitly teach wherein the series of time periods includes partially overlapping time periods.
Low teaches using overlapping windows as part of EEG analysis (¶¶s 0041, 0069).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use overlapping windows in the combination as in Low, as the simple substitution of one known analysis technique for another with predictable results (classification of the window), and for the purpose of increasing temporal registration (Low: ¶ 0069).
Claims 8 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Naber-Simon in view of US Patent Application Publication 2022/0044821 (“Eichler”).
Regarding claim 8, Naber-Simon teaches all the features with respect to claim 1, as outlined above. Naber-Simon does not appear to explicitly teach wherein the memory circuitry is configured to store multiple non-stroke baseline PSD EEG signals individually associated with different non-stroke sampled time periods from the same channel of the same subject, and wherein the baseline-adjustment circuitry is configured to select a particular stored baseline PSD EEG signal based on a similarity characteristic between the particular stored baseline PSD EEG signal and the monitored PSD EEG signal.
Eichler teaches obtaining a subject-specific baseline dataset that includes multiple features obtained during non-stroke periods (Fig. 5 and related description, ¶ 0070 (e.g. lip movements compared to subject’s baseline), etc.). Eichler also teaches matching measured features with templates and evaluating degree of match (¶ 0055).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to obtain and store multiple non-stroke baselines in the combination as in Eichler, for the purpose of being able to detect and evaluate stroke more comprehensively (Eichler: Fig. 5, features 160, 162, 164, 166, etc. – also see ¶¶s 0070, 0042, etc.). It would have been obvious to select a baseline based on its match (similarity) with the current data, for the purpose of using the most representative baseline of the current user state to thereby improve classification (Eichler: ¶ 0042).
Regarding claim 10, Naber-Simon teaches all the features with respect to claim 1, as outlined above. Naber-Simon does not appear to explicitly teach wherein the baseline-adjustment circuitry is configured to select a particular stored baseline PSD EEG signal based at least in part on at least one sensor signal from at least one of an accelerometer, a gyroscope, a sleep sensor, a temperature sensor, a blood flow sensor, a blood oxygenation sensor, a tissue oxygenation sensor, or sensor including the one or more EEG skin electrodes.
Eichler teaches identifying a baseline of e.g. lip movement using an image sensor (¶ 0070).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to select e.g. a lip movement baseline based on an image sensor in the combination as in Eichler, for the purpose of being able to evaluate a relevant stroke feature and thereby improve stroke detection (Eichler: ¶ 0070).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Naber-Simon in view of non-patent publication Hand, Peter J., et al. "Distinguishing between stroke and mimic at the bedside: the brain attack study." Stroke 37.3 (2006): 769-775 (“Hand”).
Regarding claim 12, Naber-Simon teaches all the features with respect to claim 1, as outlined above. Naber-Simon does not appear to explicitly teach wherein the classifier circuitry includes an alert-blanking, alert-attenuation, or alert-suppression module to at least one of blank, attenuate, or suppress generation of the alert in response to a migraine, post-migraine, seizure, post-seizure, post-ictal or stroke mimic confounding condition.
Hand teaches the importance of distinguishing between stroke and mimic, as well as features that can be used to make the distinction (Abstract, Table 1).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to distinguish between stroke and mimic in the combination as in Hand, for the purpose of appropriately treating a user (Hand: page 769, left column, page 774, right column). It would have been obvious to suppress the stroke alert of the combination when a mimic was detected, for the purpose of reducing false alarms, and for making the alarms more accurate (Hand: page 769, left column).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Naber-Simon in view of US Patent Application Publication 2021/0251497 (“Schulhauser”).
Regarding claim 18, Naber-Simon teaches all the features with respect to claim 1, as outlined above. Naber-Simon further teaches wherein the classifier is multi-channel corresponding to left-head and right-head skin electrodes (Naber: Fig. 1A, ¶ 0081, asymmetry, change in connectivity, etc.), but does not appear to explicitly teach wherein the classifier is further configured to provide an alert certainty indication based at least in part on a change between respective left-head and right-head temporal shifts in contralateral baseline-adjusted monitored PSD EEG signals, over a specified range of frequencies ().
Schulhauser teaches providing a confidence score associated with a determination of a patient stroke (¶ 0054).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to make a confidence determination (which is an alert certainty indication) in the combination as in Schulhauser, based on e.g. metrics including asymmetry, change in connectivity, etc., for the purpose of helping a user make more informed decisions (Schulhauser: ¶ 0054).
Response to Arguments
Applicant’s arguments filed 10/06/2025 have been fully considered.
With respect to the rejections under 35 USC 112, it is noted that claim 9 continues to include unsupported scope, and that not all other issues have been addressed.
Regarding 35 USC 101, claim 22 is only "eligible subject matter" in the sense that it is now directed to one of the four statutory categories of patentable subject matter, as opposed to a transitory form of signal transmission. It is still ineligible because it is directed to an abstract idea without a practical application. Real-time detection of stroke is part of the abstract idea, and it is unclear why this could not be performed mentally. The phrase “real-time” does not imply a speed unachievable by a human.
In response to the arguments regarding the rejections under 35 USC 103, they are not persuasive. The combination teaches computation of power spectral density. Because Naber teaches using features of power spectral density, it also teaches computing power spectral density. Power spectral density represents the distribution of power across different frequencies. Use of a hand-designed feature of PSD is not a teaching away from the PSD representing distribution of power across a range. In any case, Naber teaches that for which it is cited, and Simon makes up the other teachings, including teachings regarding power spectral density. Simon is not irrelevant because it is analogous art (EEG power spectral density calculations for medical diagnosis purposes). Applicant does not address the teachings of Simon that were actually used, or the motivations for combination found in the prior art itself. Even if Simon monitors a different condition, its teachings on how to evaluate the condition based on a baseline are relevant. But Simon also mentions application to detection of strokes (¶¶s 0003, 0142, 0175, etc.). All claims remain rejected in light of the prior art.
Conclusion
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/ANDREY SHOSTAK/Primary Examiner, Art Unit 3791