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 .
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 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 1-20 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.
Claims 1, 8, and 15 recite the limitation “generate mean waveforms of signals from at least one condition aligned by the temporal positions assigned to at least one of extracted feature types to indicate a brain state of the at least one animal”. It is unclear if “extracted feature types” is meant to refer to the extrema and time-domain features, only the time-domain features, or the feature characteristics referred to earlier in the claim, as “feature types” is not referred to at any other point in the claim. This part of the limitation is interpreted as referring to the extrema and time-domain features.
Furthermore, the limitation is unclear as to whether the “generate mean waveforms of signals from at least one condition” refers to signals from the brain of the at least one animal or signals from the brain of some other animals, whether multiple mean waveforms are created from each of the “signals” for a given “at least one condition” or if multiple mean waveforms are created and each mean waveform represents only one condition, whether the “at least one condition” refers to a condition of the waveforms (such as signals exceeding a certain peak amplitude or frequency of oscillation) or a condition relating to a brain state (such as the presence of Alzheimer’s disease). The limitation is currently interpreted as referring to a mean waveform of brain signals from the brains of a group of animals, generated for each condition of at least one condition, where the condition relates to a brain state.
Claims 2-7, 9-14, and 16-20 are additionally rejected under 35 U.S.C. 112(b) as indefinite due to their dependence on claims 1, 8, or 15, which have been rejected as indefinite.
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.
Utilizing the two step process adopted by the Supreme Court (Alice Corp vs CLS Bank Int'l, US
Supreme Court, 110 USPQ2d 1976 (2014) and the recent 101 guideline Federal Register Vol. 84, No., Jan
2019)), determination of the subject matter eligibility under the 35 U.S.C. 101 is as follows: Specifically, the Step 1 requires claim belongs to one of the four statutory categories (process, machine, manufacture, or composition of matter). If Step 1 is satisfied, then in the first part of Step 2A (Prong One), identification of any judicial recognized exceptions in the claim is made. If any limitation in the claim is identified as judicial recognized exception, then in the second part of Step 2A (Prong Two), determination is made whether the identified judicial exception is being integrated into practical application. If the identified judicial exception is not integrated into a practical application, then in Step 2B, the claim is further evaluated to see if the additional elements, individually and in combination provide "inventive concept" that would amount to significantly more than the judicial exception. If the element and combination of elements do not amount to significantly more than the judicial recognized exception itself, then the claim is ineligible under the 35 U.S.C. 101.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in this case an abstract idea, without significantly more. The claim recite(s) " compare statistical distributions of a plurality of feature characteristics of the beta wave events based upon extracted extrema and time-domain features and the first and second representations, and generate mean waveforms of signals from at least one condition aligned by the temporal positions assigned to at least one of extracted feature types to indicate a brain state of the at least one animal". This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 1 satisfies Step 1, namely the claim is directed to one of the four statutory classes, machine. Following Step 2A Prong one, any judicial exceptions are identified in the claims. In claim 1, the limitations " compare statistical distributions of a plurality of feature characteristics of the beta wave events based upon extracted extrema and time-domain features and the first and second representations, and generate mean waveforms of signals from at least one condition aligned by the temporal positions assigned to at least one of extracted feature types to indicate a brain state of the at least one animal" are abstract ideas as they are directed to a mental process or mathematical concept, as the comparison of statistical distributions can be performed mentally or via mathematical operations such as by comparing values of peak amplitudes or frequency, among other characteristics, and generating mean waveforms may similarly be performed in the human mind with the aid of pen and paper using basic mathematical transforms. With the identification of an abstract idea, the next phase is to proceed Step 2A, Prong Two, wherewith additional elements and taken as a whole, evaluation occurs of whether the identified abstract idea is integrated into a practical application.
In Step 2A, Prong Two, the claim does not recite any additional elements or evidence that amounts to significantly more than the judicial exception. Besides the abstract idea, the claim recites the additional elements “a first computing device comprising: a non-transitory machine-readable storage medium storing instructions; and a processor coupled to the non-transitory computer-readable storage medium and configured to execute the instructions to: obtain recordings of electrical activity arising from a brain of at least one animal, detect beta wave events from the recordings by at least extracting extrema and time-domain features in unnormalized representations of the recordings, process the beta wave events to generate a first representation of short-time segments representing a plurality of amplitude fluctuations indicating the extrema and a second representation for identifying temporal positions of the extrema in the first representation”. However, these components may be seen as the use of well-understood, routine, or conventional elements to perform a non-mental process in order to gather data for the mental process step, much like the example given in MPEP 2106.04(d)(2)(c), such that these limitations are extra-solution activity and thus do not integrate the judicial exception into a practical application. The measurement step leads to the final limitation of “indicat[ing]” such that the end result of use of the system is only the generic determined indicator which may be any generic output, or no output at all. As this determination is not defined as requiring any further action, such as a form of prophylaxis or treatment or an improvement to a computer or other technology, the claim limitations constitute mere generation of data, in this case the measurement of data relating to extrema and temporal positions of extrema of beta wave events, such that the claim does not integrate the judicial exception into any practical application. Regarding “a processor”, the limitation amounts to nothing more than an instruction to apply the abstract idea using a generic computer, which does not render an abstract idea eligible. The steps performed by the a processor are, as claimed, capable of being performed in the human mind similar to the examples given in MPEP 2106.04(a)(2)(III)(A)-(C), wherein it is described that “a claim to ‘collecting information, analyzing it, and displaying certain results of the collection and analysis’ where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind” recites a mental process and that claims which merely use a computer as a tool to perform a mental process are not eligible when “there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper” such as “mental processes of parsing and comparing data” when the steps are recited at a high level of generality and a computer is used merely as a tool to perform the processes. Under the broadest reasonable interpretation, the claim elements are recited with a high level of generality (as written, each claimed step of the process may be performed by a person in an undefined manner including observing the sensor readings for extrema and corresponding temporal information and then performing mathematical transformations and/or comparisons) that there are no meaningful limitations to the abstract idea. Consequently, with the identified abstract idea not being integrated into a practical application, the next step is Step 2B, evaluating whether the additional elements provide "inventive concept" that would amount to significantly more than the abstract idea.
In Step 2B, claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation of “a first computing device comprising: a non-transitory machine-readable storage medium storing instructions; and a processor coupled to the non-transitory computer-readable storage medium and configured to execute the instructions to: obtain recordings of electrical activity arising from a brain of at least one animal, detect beta wave events from the recordings by at least extracting extrema and time-domain features in unnormalized representations of the recordings, process the beta wave events to generate a first representation of short-time segments representing a plurality of amplitude fluctuations indicating the extrema and a second representation for identifying temporal positions of the extrema in the first representation” constitutes extra-solution activity to the judicial exception, which does not amount to an inventive concept when the activity is well-understood, routine, or conventional, and are thus not indicative of integration into a practical application. The claim limitation constitutes adding a generic processor and memory, which Lee (US 20240382162 A1) describes as both well-understood, routine, or conventional in its description of a generic processor and memory (Paragraph 0072-- a dedicated processor (for example, an embedded processor) configured to perform the corresponding operation or a generic-purpose processor (for example, a CPU or an application processor) which is capable of performing the operations by executing one or more software programs stored in a memory device). As discussed above with respect to integration of the abstract idea into a practical application, the present elements amount to no more than mere indications to apply the exception.
In Summary, Claim 1 recites abstract idea without being integrated into a practical application, and does not provide additional elements that would amount to significantly more. As such, taken as a whole, the claim and is ineligible under the 35 U.S.C. 101.
Claims 8 and 15 are rejected for similar reasons.
Claims 2-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in this case an abstract idea, without significantly more. As each of these claims depends from claim 1, which was rejected under 35 U.S.C. 101 in paragraph 9 of this action, these claims must be evaluated on whether they sufficiently add to the practical application of claim 1, or comprise significantly more than the limitations of claim 1.
Besides the abstract idea of claim 1: claim 2 constitutes the addition of further elements which does not amount to an inventive concept when the activity is well-understood, routine, or conventional, and are thus not indicative of integration into a practical application. The claim limitation constitutes adding a generic processor, memory, and sensors, which Lee (US 20240382162 A1) describes as both well-understood, routine, or conventional in its description of a generic processor and memory and an electroencephalogram (Paragraph 0072-- a dedicated processor (for example, an embedded processor) configured to perform the corresponding operation or a generic-purpose processor (for example, a CPU or an application processor) which is capable of performing the operations by executing one or more software programs stored in a memory device; Paragraphs 0079-0082); claims 3-7 recite additional limitations which may be seen as abstract ideas which can be performed as mental processes using basic mathematical concepts, such as determining various extrema and time-domain features or template matching mentally or with the aid of pen and paper, and additional elements of extra-solution activity, such as the storing of data which is a generic computer function.
The claim element of claim 1 of a system is recited with a high level of generality (as written, the actions of the processor may be carried out by a person alone or with a generic computer in any undefined manner). This limitation provides no practical application, nor does it provide meaningful limitations to the abstract idea.
Claims 9-14 and 16-20 are additionally rejected under 35 U.S.C. 101 for similar reasons.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 6-10, 13-18, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Brady ("Periodic/Aperiodic parameterization of transient oscillations (PAPTO)–Implications for healthy ageing").
Regarding claims 1, 8, and 15, Brady teaches a system deployed within a communication network, the system comprising:
a first computing device comprising: a non-transitory machine-readable storage medium storing instructions; and a processor coupled to the non-transitory computer-readable storage medium and configured to execute the instructions (Page 3, 2.3 MEG Pre-processing-- All subsequent MEG processing was completed in the Python programming environment…NOTE: this would necessitate the use of a computer having a memory and processor which receives the data from the MEG sensors) to:
obtain recordings of electrical activity arising from a brain of at least one animal (Page 3, 2.1 Participants and experimental paradigm and 2.2 Data acquisition--Six hundred and forty-seven participants had eyes-closed resting- state MEG data recorded for 8 min and 40 s…MEG data were acquired at 1000 Hz with inline band-pass filtering between 0.03 and 330 Hz using a 306- channel Vectorview system with continuous head position monitoring… NOTE: as the MEG sensor data is received from these sensors for processing, the system must include both the sensors for obtaining the recordings and the computing device having a processor and memory to receive the recordings from the sensors),
detect beta wave events from the recordings by at least extracting extrema and time-domain features in unnormalized representations of the recordings (Page 4-5, 2.7 detecting transient events-- transient events are defined as short-lasting local maxima of signal power in single-trial recordings in time-frequency representation (TFR)…; Page 5, 2.8 characterizing and classifying transient events--All transient events were then characterized in the time-frequency domain. The time and frequency coordinates of the local maxima in the single-trial TFRs represent the peak time and peak frequency of the events, respectively…),
process the beta wave events to generate a first representation of short-time segments representing a plurality of amplitude fluctuations indicating the extrema and a second representation for identifying temporal positions of the extrema in the first representation (Fig. 6a-f--scatterplots showing participant-average beta event parameters (occurrence rate, peak frequency, frequency span, event duration, event peak times, and event peak amplitudes, respectively); Fig. 3a-c—representations of beta event signal amplitude over time and spectral characteristics),
compare statistical distributions of a plurality of feature characteristics of the beta wave events based upon extracted extrema and time-domain features and the first and second representations (Fig. 3a-3c and 6a-f; Fig. 7; Page 8--Average characteristics over all PAPTO events, all med-norm events, PAPTO-only events, and all events detected by both methods are all similar with only marginal differences…; Page 10--We found a significant linear decrease in the peak frequency of PAPTO events from about 22.1 Hz to 21.2 Hz across the age-range (18–88) of the Cam-CAN cohort ( Fig. 6 b). Such an age-related decrease suggests that older participants exhibit more low-beta events and less high-beta events compared to younger participants…n age-related increase in PAPTO event amplitude by a factor of about 1.6 across the adult lifespan in M1 (both hemispheres) but not in S1…), and
generate mean waveforms of signals from at least one condition aligned by the temporal positions assigned to at least one of extracted feature types to indicate a brain state of the at least one animal (Fig. 3c shows average beta event waveforms across participants which are aligned by temporal positions assigned to the relevant extrema; Fig. 7--visual depiction of age-related changes in sensorimotor extracranial neurophysiological signals. Each timecourse shown is a compilation of age-related changes for each beta event characteristic NOTE: in this instance, age may be seen as a condition where the visual depiction created from the ‘compilation’ of data demonstrates aligned temporal positions of the peaks and troughs to show changes in a brain state based on age).
Regarding claims 2, 9, and 16, Brady teaches the system of claim 1. Brady additionally teaches further comprising a second computing device configured to collect the recordings of the at least one animal via a plurality of sensors, wherein the recordings comprise at least one of magneto- or electroencephalograph (M/EEG) recordings, local field potential (LFP) recordings, or electrocorticogram (ECoG) recordings (Page 3, 2.1 participants and experimental paradigm and 2.2 Data acquisition-- eyes-closed resting- state MEG data recorded for 8 min and 40 s…).
Regarding claims 3, 10, and 17, Brady teaches the system of claim 1. Brady additionally teaches wherein the extrema and time-domain features comprise characteristics selected from the group consisting of: a peak time, a trough time, a peak amplitude, a trough amplitude, an oscillation period, a peak width, a trough width, an inter-peak timing, and an inter-trough timing (Page 5, 2.8 characterizing and classifying transient events-- The time and frequency coordinates of the local maxima in the single-trial TFRs represent the peak time and peak frequency of the events, respectively…; Fig. 6a-f and Table 2-- beta event parameters (occurrence rate, peak frequency, frequency span, event duration, event peak times, and event peak amplitudes, respectively)).
Regarding claims 6, 13, and 20, Brady teaches the system of claim 1. Brady additionally teaches wherein the extrema and time-domain features include a plurality of peaks and troughs between rising and falling zero-crossings of the beta wave events, wherein the at least one of the extrema includes a plurality of central troughs detected from the beta wave events (Page 5, 2.8 characterizing and classifying transient events-- The time and frequency coordinates of the local maxima in the single-trial TFRs represent the peak time and peak frequency of the events, respectively…; Fig. 3a-c and Fig. 6a-f and Table 2-- beta event parameters (occurrence rate, peak frequency, frequency span, event duration, event peak times, and event peak amplitudes, respectively) where Fig. 3a-c and 6a demonstrates a plurality of peaks between rising and falling zero-crossings).
Regarding claims 7, 14, and 18, Brady teaches the system of claim 1. Brady additionally teaches wherein the processor is further configured to execute the instructions to display the mean waveforms aligned by at least one of the extrema to inform predictive biomarkers and targeted interventions (Fig. 3c shows average beta event waveforms across participants which are aligned by temporal positions assigned to the relevant extrema; Fig. 7--visual depiction of age-related changes in sensorimotor extracranial neurophysiological signals. Each timecourse shown is a compilation of age-related changes for each beta event characteristic NOTE: these representations can be displayed to show changes in a typical brain state which correspond to a given age, and thus may inform predictive biomarkers and targeted interventions when a user’s measurements correspond to a different age than their real age).
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.
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Intrator (US 20170347906 A1).
Regarding claims 5, 12, and 19, Brady teaches the system of claim 1. Brady additionally discloses wherein the processor is further configured to execute the instructions to: store normative data of waveform features of the beta wave events and empirical or probability distributions or likelihoods thereof (Fig. 3a-3c and 6a-f; Fig. 7; Table 2; Page 10--We found a significant linear decrease in the peak frequency of PAPTO events from about 22.1 Hz to 21.2 Hz across the age-range (18–88) of the Cam-CAN cohort ( Fig. 6 b). Such an age-related decrease suggests that older participants exhibit more low-beta events and less high-beta events compared to younger participants…n age-related increase in PAPTO event amplitude by a factor of about 1.6 across the adult lifespan in M1 (both hemispheres) but not in S1…NOTE: it may be seen that the system can store normative data and empirical distributions or likelihoods thereof in the form of the determined average or most likely features of the beta wave events according to age as shown).
However, Brady does not explicitly disclose employ one or more time-domain feature learning and extraction methods to perform prediction of clinical indication and functional brain states of the at least one animal, wherein the one or more time-domain feature learning and extraction methods based upon at least one of: convolutional dictionary learning (CDL), convolutional sparse coding, translation invariant dictionary learning, cycle-by-cycle analysis, phase estimation or Hilbert transform methods, sliding window matching, template matching, empirical mode decomposition, recurrent neural networks or time delay neural networks or convolutional neural networks trained on features or short segments, dynamic time warping, motif learning or discovery, adaptive time-domain signal processing, temporal representation learning, or one or more unsupervised or semi-supervised machine learning algorithms.
Intrator, in the same field of endeavor of assessing a brain state based on brain wave analysis (Abstract), discloses a system which stores normative data of waveform features of the beta wave events and empirical or probability distributions or likelihoods thereof (Paragraph 0005-- continuously normalizing, in real time, the particular set of projections of the individual using a pre-determined set of normalization factors to form a set of normalized projections of the individual) and employ one or more time-domain feature learning and extraction methods to perform prediction of clinical indication and functional brain states of the at least one animal (Paragraph 0005-- determining, in real time, at least one personalized mental state of the individual by assigning at least one specific brain state to the individual based on applying at least one machine learning algorithm to the set of normalized projections of the individual, where the at least one specific brain state is associated with a mental state, a neurological condition, or a combination of the mental state and the neurological condition; paragraph 0106-0110-- the assignment of at least one specific brain state to the visual indication of at least one personalized mental state of the particular individual identifies an abnormality in at least one neural network in the brain of the particular individual associated with a particular neurological condition), wherein the one or more time-domain feature learning and extraction methods based upon at least one of:
convolutional dictionary learning (CDL), convolutional sparse coding, translation invariant dictionary learning, cycle-by-cycle analysis, phase estimation or Hilbert transform methods, sliding window matching, template matching, empirical mode decomposition, recurrent neural networks or time delay neural networks or convolutional neural networks trained on features or short segments, dynamic time warping, motif learning or discovery, adaptive time-domain signal processing, temporal representation learning, or one or more unsupervised or semi-supervised machine learning algorithms (Paragraph 0005—machine learning algorithm; paragraph 0016, 0105-- the at least one machine learning algorithm is one of: logistic regression modeling algorithm, support vector machine modeling algorithm, and a deep learning modeling algorithm).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Brady to further include the learning and extraction methods of Intrator to perform prediction of clinical indication and functional brain states in order to determine not just a general brain state but additionally to determine a clinical indication of said state in order to predictably improve the system by allowing it to be used in treating or enabling prevention of various brain conditions which may correspond to a given brain state.
Claim(s) 5, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brady in view of Nakae (US 20230229977 A1).
Regarding claims 5, 12, and 19, Brady teaches the system of claim 1. Brady additionally discloses wherein the processor is further configured to execute the instructions to: store normative data of waveform features of the beta wave events and empirical or probability distributions or likelihoods thereof (Fig. 3a-3c and 6a-f; Fig. 7; Table 2; Page 10--We found a significant linear decrease in the peak frequency of PAPTO events from about 22.1 Hz to 21.2 Hz across the age-range (18–88) of the Cam-CAN cohort ( Fig. 6 b). Such an age-related decrease suggests that older participants exhibit more low-beta events and less high-beta events compared to younger participants…n age-related increase in PAPTO event amplitude by a factor of about 1.6 across the adult lifespan in M1 (both hemispheres) but not in S1…NOTE: it may be seen that the system can store normative data and empirical distributions or likelihoods thereof in the form of the determined average or most likely features of the beta wave events according to age as shown).
However, Brady does not explicitly disclose employ one or more time-domain feature learning and extraction methods to perform prediction of clinical indication and functional brain states of the at least one animal, wherein the one or more time-domain feature learning and extraction methods based upon at least one of: convolutional dictionary learning (CDL), convolutional sparse coding, translation invariant dictionary learning, cycle-by-cycle analysis, phase estimation or Hilbert transform methods, sliding window matching, template matching, empirical mode decomposition, recurrent neural networks or time delay neural networks or convolutional neural networks trained on features or short segments, dynamic time warping, motif learning or discovery, adaptive time-domain signal processing, temporal representation learning, or one or more unsupervised or semi-supervised machine learning algorithms.
Nakae, in the same field of endeavor of assessing a brain state based on brain wave analysis (Paragraph 0001-0005), discloses a system which stores normative data of waveform features of the beta wave events and empirical or probability distributions or likelihoods thereof (Paragraph 0010, 0060-- a storage means that stores a plurality of feature templates extracted from a plurality of biosignals acquired from a plurality of modeling target objects including a first modeling target object and a second modeling target object, or a plurality of models that have learned the plurality of feature templates, each of the plurality of feature templates associating pieces of feature data of a plurality of samples sampled from a biosignal with values indicating subjective assessments…) and employs one or more time-domain feature learning and extraction methods to perform prediction of clinical indication and functional brain states of the at least one animal (Paragraph 0060-0061-- receiving feature data of a biosignal acquired from the estimation target object; and estimating the subjective assessment made by the estimation target object, based on the feature data and the plurality of feature templates or the plurality of models…; paragraph 0132-- “pains” can be clearly categorized based on the concept of “treatment”. For example, it can be said that “quantitative” classification of pains, such as “comfortable/uncomfortable” and “unbearable” pains, can be led. For example, positioning of a “pain index”, a baseline and a relationship therebetween can be defined, and it is assumed that the case of n=3 or more is also possible in addition to the case of n=2. In the case of three or more, classification into “not painful”, “comfortably painful” and “painful” can be performed. For example, discrimination among a pain that is “unendurable and requiring treatment”, “middle” and “painful but not bothering” can be made…), wherein the one or more time-domain feature learning and extraction methods based upon at least one of:
convolutional dictionary learning (CDL), convolutional sparse coding, translation invariant dictionary learning, cycle-by-cycle analysis, phase estimation or Hilbert transform methods, sliding window matching, template matching, empirical mode decomposition, recurrent neural networks or time delay neural networks or convolutional neural networks trained on features or short segments, dynamic time warping, motif learning or discovery, adaptive time-domain signal processing, temporal representation learning, or one or more unsupervised or semi-supervised machine learning algorithms (Paragraph 0112-0118-- machine learning, linear regression, logistic regression, support vector machine or the like can be used…).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Brady to further include the learning and extraction methods of Nakae to perform prediction of clinical indication and functional brain states in order to determine not just a general brain state but additionally to determine a clinical indication of said state in order to predictably improve the system by allowing it to be used in treating or enabling prevention of various brain conditions which may correspond to a given brain state.
Conclusion
Claims 4 and 11 are not currently rejected under 35 U.S.C. 102/103. The prior art of the record fails to teach and/or fairly suggest, in combination with all other recited limitations, “simulating, via resampling, an effect of alterations of empirical distributions of at least one of the characteristics on the mean waveforms”.
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/ANNA ROBERTS/ Examiner, Art Unit 3791
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