DETAILED ACTION
This action is pursuant to claims filed on 09/22/2023. Claims 1-11 are pending. A first action on the merits of claims 1-11 is as follows.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1-3 and 9-11 objected to because of the following informalities:
In claim 1, line 13, “cortical spreading depolarization” should read “the cortical spreading depolarization”
In claim 1, line 17, “cortical spreading depolarization” should read “the cortical spreading depolarization”
In claim 2, line 20, “cortical spreading depolarization” should read “the cortical spreading depolarization”
In claim 3, line 27, “cortical spreading depolarization” should read “the cortical spreading depolarization”
In claim 9, line 10, “cortical spreading depolarization” should read “the cortical spreading depolarization”
In claim 10, “line 17, “signals output from the subdural sensor” should read “the signals output from the subdural sensor”
In claim 11, line 15, “cortical spreading depolarization” should read “the cortical spreading depolarization”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the claim recites the limitation “a plurality of sites of the brain” in lines 15-16. It is unclear if this is referring to the plurality of sites of a brain introduced earlier in the claim in line 2, or a different plurality of sites of the brain. If it is referring to the plurality of sites of the brain from earlier in the claim, it needs to refer back to it. If it is referring to a different plurality of sites of the brain, it needs to be distinguished from the candidates from earlier in the claim. For purposes of examination, it is being interpreted as referring to the plurality of sites of the brain introduced earlier. Claims 2-8 are also rejected due to their dependence on claim 1.
Regarding claim 4, the claim recites the limitation “acquiring waveform data relating to a plurality of types of biological information” in lines 2-3. It is unclear if this is referring to the waveform data indicating a temporal change in one or more types of biological information introduced in claim 1, lines 3-4, or different waveform data and different types of biological information. If it is referring to the waveform data and biological information from claim 1, it needs to refer back to it. If it is referring to different waveform data and different biological information, it needs to be distinguished from the candidates from claim 1. For purposes of examination, it is being interpreted as referring to the waveform data and biological information from claim 1.
Regarding claim 9, the claim recites the limitation “a plurality of sites of the brain” in line 9. It is unclear if this is referring to the plurality of sites of a brain introduced earlier in the claim in lines 28-29, or a different plurality of sites of the brain. If it is referring to the plurality of sites of the brain from earlier in the claim, it needs to refer back to it. If it is referring to a different plurality of sites of the brain, it needs to be distinguished from the candidates from earlier in the claim. For purposes of examination, it is being interpreted as referring to the plurality of sites of the brain introduced earlier. Claim 10 is also rejected due to its dependence on claim 9.
Regarding claim 10, the claim recites the limitation “a brain surface” in line 25. It is unclear if this is referring to the brain surface introduced earlier in the claim in line 23, or a different brain surface. If it is referring to the brain surface introduced earlier, it needs to refer back to it. If it is referring to a different brain surface, it needs to be distinguished from the candidate introduced earlier in the claim. For purposes of examination, it is being interpreted as referring to the brain surface introduced earlier.
Regarding claim 11, the claim recites the limitation “a plurality of sites of the brain” in lines 16-17. It is unclear if this is referring to the plurality of sites of a brain introduced earlier in the claim in line 33, or a different plurality of sites of the brain. If it is referring to the plurality of sites of the brain from earlier in the claim, it needs to refer back to it. If it is referring to a different plurality of sites of the brain, it needs to be distinguished from the candidates from earlier in the claim. For purposes of examination, it is being interpreted as referring to the plurality of sites of the brain introduced earlier.
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 1-9 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under the two-step 101 analysis, the claims fail to satisfy the criteria for subject matter eligibility.
Regarding Step 1, claims 1-9 and 11 are all within at least one of the four statutory categories.
Claim 1 and its dependent claims disclose a method (process).
Claim 9 and its dependent claim disclose a system (machine).
Claim 11 discloses a storage medium (machine).
Regarding Step 2A, Prong One, the independent claims 1, 9, and 11 recite an abstract idea. In particular, the claims generally recite the following:
an acquisition step of acquiring, for each of a plurality of sites of a brain, waveform data indicating a temporal change in one or more types of biological information including at least any one of a direct current component of an electroencephalogram, hemoglobin concentration in the brain, cerebral blood flow, and brain temperature;
a feature amount calculation step of setting windows set at predetermined time intervals on the waveform data and calculating a predetermined feature amount for each of the windows;
an event extraction step of performing threshold processing on the feature amounts calculated in the feature amount calculation step to extract a timing of the occurrence of an event in which a value of the waveform data temporarily changes due to cortical spreading depolarization;
a statistical processing step of acquiring a frequency distribution of the timings extracted based on the plurality of waveform data acquired in relation to a plurality of sites of the brain;
a determination step of determining whether cortical spreading depolarization has occurred based on the frequency distribution.
These elements recited in claims 1, 9, and 11 are drawn to abstract ideas since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgement, and opinion and using pen and paper.
An acquisition step of acquiring, for each of a plurality of sites of a brain, waveform data indicating a temporal change in one or more types of biological information including at least any one of a direct current component of an electroencephalogram, hemoglobin concentration in the brain, cerebral blood flow, and brain temperature is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably receive the waveform data on a piece of paper. There is nothing to suggest an undue level of complexity in an acquisition step of acquiring, for each of a plurality of sites of a brain, waveform data indicating a temporal change in one or more types of biological information including at least any one of a direct current component of an electroencephalogram, hemoglobin concentration in the brain, cerebral blood flow, and brain temperature.
A feature amount calculation step of setting windows set at predetermined time intervals on the waveform data and calculating a predetermined feature amount for each of the windows is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably analyze the waveform data and set windows at predetermined time intervals, then perform the necessary calculations for the feature amount mentally or on paper. These techniques are based on algorithms, calculations, and mathematical principles, which can be performed by hand. The mathematics of calculating a predetermined feature amount are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in a feature amount calculation step of setting windows set at predetermined time intervals on the waveform data and calculating a predetermined feature amount for each of the windows.
An event extraction step of performing threshold processing on the feature amounts calculated in the feature amount calculation step to extract a timing of the occurrence of an event in which a value of the waveform data temporarily changes due to cortical spreading depolarization is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably extract the timing of the occurrence from the processed waveform data mentally or on paper. These techniques are based on algorithms, calculations, evaluation, and judgement, which can be performed by hand or in the human mind. The mathematics of extracting the timing of the occurrence are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in an event extraction step of performing threshold processing on the feature amounts calculated in the feature amount calculation step to extract a timing of the occurrence of an event in which a value of the waveform data temporarily changes due to cortical spreading depolarization.
A statistical processing step of acquiring a frequency distribution of the timings extracted based on the plurality of waveform data acquired in relation to a plurality of sites of the brain is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, or with the aid of pen and paper. A person of ordinary skill in the art could reasonably extract a frequency distribution of the timings mentally or on paper. These techniques are based on algorithms, calculations, evaluation, and judgement, which can be performed by hand or in the human mind. The mathematics of extracting a frequency distribution are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas. There is nothing to suggest an undue level of complexity in a statistical processing step of acquiring a frequency distribution of the timings extracted based on the plurality of waveform data acquired in relation to a plurality of sites of the brain.
A determination step of determining whether cortical spreading depolarization has occurred based on the frequency distribution is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind. A person of ordinary skill in the art could reasonably determine whether cortical spreading depolarization has occurred based on the frequency distribution. These techniques are based on evaluation, observation, and judgement, which can be performed in the human mind. There is nothing to suggest an undue level of complexity in a determination step of determining whether cortical spreading depolarization has occurred based on the frequency distribution.
Regarding Step 2A, Prong Two, claims 1, 9, and 11 do not recite additional elements that integrate the exception into a practical application. Therefore, the claims are directed to the abstract idea. 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., the various structural “parts” of claim 9 and “a computer readable storage medium” (claim 11)).
As a whole, the additional elements merely serve to gather information to be used by 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. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. 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.
Regarding Step 2B, claims 1, 9, and 11 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.
The element of a computer readable storage medium in claim 11 and the various structural “parts” of claim 9 and do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above judicial exception. Looking at the limitations as an ordered combination (that is, as a whole) 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 or improves any other technology. Their collective functions merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Regarding the dependent claims, claims 2-8 depend on claim 1. The dependent claims merely further define the abstract idea or are additional data output that is well-understood, routine, and previously known to the industry.
For example, the following are dependent claims reciting abstract ideas and can be performed in the human mind.
(Claim 2): “wherein the determination step includes determining cortical spreading depolarization to have occurred when a peak value of frequency in the frequency distribution is in a predetermined range” further defines the abstract idea as it is based in judgment, evaluation, and calculations which can be performed by hand or in the human mind. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 3): “wherein the determination step includes determining timings of peaks exceeding a first threshold in the frequency distribution except for timings of peaks exceeding a second threshold that is larger than the first threshold as timings of cortical spreading depolarization occurrence” further defines the abstract idea as it is based in judgment, evaluation, and calculations which can be performed by hand or in the human mind. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 4): “wherein the acquisition step includes acquiring waveform data relating to a plurality of types of biological information; and wherein the statistical processing step includes obtaining the frequency distribution based on the timings extracted on the basis of the plurality of types of biological information” further defines the abstract idea as it is based in judgment, evaluation, and calculations which can be performed by hand or in the human mind. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 5): “wherein the feature amount is permutation entropy, and wherein the event extraction step includes extracting a negative peak below a threshold” further defines the abstract idea as it is based in judgment, evaluation, and calculations which can be performed by hand or in the human mind. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 6): “wherein the feature amount is power density, and wherein the event extraction step includes extracting a positive peak exceeding a threshold” further defines the abstract idea as it is based in judgment, evaluation, and calculations which can be performed by hand or in the human mind. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
(Claim 7): “wherein the windows each have a width of longer than 0.5 minutes and shorter than 10 minutes” further defines the abstract idea as it further defines the lengths of the windows, which can be determined mentally;
(Claim 8): “wherein the feature amount calculation step includes calculating the feature amount for data obtained by subjecting the waveform data to filtering process to cut off frequency components less than or equal to a predetermined frequency” further defines the abstract idea as it is based in judgment, evaluation, and calculations which can be performed by hand or in the human mind. The mathematics are not overly complicated to perform using pen and paper given enough time, therefore these are defined as abstract ideas;
The dependent claims do not recite significantly more than the abstract ideas. Therefore, claims 1-9 and 11 are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4 and 7-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Boutelle (US 20200359925).
Regarding independent claim 1, Boutelle teaches a cortical spreading depolarization sensing method (Abstract: “A method of automatically monitoring electrophysiological data in the brain and detecting clinically significant events comprises receiving signal inputs from at least one or more electrophysiological signal channels each indicative of electrical brain activity.”), comprising:
an acquisition step of acquiring, for each of a plurality of sites of a brain, waveform data indicating a temporal change in one or more types of biological information including at least any one of a direct current component of an electroencephalogram, hemoglobin concentration in the brain, cerebral blood flow, and brain temperature ([0015]: “an input module configured to receive signal inputs from at least one or more electrophysiological signal channels indicative of electrical brain activity”; Fig. 4; [0031]: “The clinical datasets may comprise, or be derived from, electrocorticography (ECoG) electrodes placed directly on or into an exposed surface of the brain to record electrical activity from the cerebral cortex and may comprise strip electrodes extending over a surface of the brain tissue or depth electrodes inserted into the brain tissue. Electrode types may include electrode grids or custom electrode arrays having multiple electrode contacts at specific spacings. It may also be possible to use conventional electroencephalography electrodes monitoring electrical activity from outside the skull provided that sufficient signal can be obtained from which to extract the relevant data, to be discussed below.”; [0013]: “The method may further include displaying detected events as a function of time correlated with other signals indicative of one or more of … brain temperature”);
a feature amount calculation step of setting windows set at predetermined time intervals on the waveform data and calculating a predetermined feature amount for each of the windows ([0017]-[0018]: “a detection module configured to: detect the appearance of a succession of correlated, non-synchronous events in the waveforms of the one or more first subchannels”; [0057]: “One approach as described here is based upon a sliding window. The contents of each window (data epoch, step 21) are examined across the various filtered subchannels, looking for (amongst other things) high amplitude, low frequency waves (step 22) and high frequency suppression (step 25). A next data epoch is then loaded (step 21), as the window slides across the dataset. When implementing a real-time system, this approach is still realistic, with detections being based on current events and a set period of time in the past. In one example, this period (and hence the size of the sliding window) may be of the order of 10 minutes, based upon the required proximity of low level events to suggest that a CSD or PID has occurred.”);
an event extraction step of performing threshold processing on the feature amounts calculated in the feature amount calculation step to extract a timing of the occurrence of an event in which a value of the waveform data temporarily changes due to cortical spreading depolarization ([0019]: “detect the suppression of an amplitude of the signal in one or more of the second subchannels correlated with the detected events in the one or more first subchannels”; [0057]: “One approach as described here is based upon a sliding window. The contents of each window (data epoch, step 21) are examined across the various filtered subchannels, looking for (amongst other things) high amplitude, low frequency waves (step 22) and high frequency suppression (step 25). A next data epoch is then loaded (step 21), as the window slides across the dataset. When implementing a real-time system, this approach is still realistic, with detections being based on current events and a set period of time in the past. In one example, this period (and hence the size of the sliding window) may be of the order of 10 minutes, based upon the required proximity of low level events to suggest that a CSD or PID has occurred.”);
a statistical processing step of acquiring a frequency distribution of the timings extracted based on the plurality of waveform data acquired in relation to a plurality of sites of the brain ([0070]: “The location of detected clinically significant events may be marked for the subchannels where they occur and in other subchannels in the data set. A confidence level may be determined for each event. A summary of the events found may be provided including, for example: a type of event, a start and end time, a level of confidence in the classification and, where appropriate, a duration of the suppression in each of the higher frequency subchannels”; [0058]-[0059]: “The detection of CSD and PID events may depend on two lower level types of event: slow potential changes (SPCs—low frequency, large amplitude waves) and high frequency amplitude suppression. The first event required is a slow potential change in the low pass filtered subchannel data (step 22) as discussed below. If no such waves are present in a data epoch, the possibility of CSD and PID events can be safely discounted and the process returns to step 21. If an SPC is present, further investigation is required, as shown in FIG. 2. The system tests for multiple, non-synchronous events in the low frequency subchannels 11. If only a single wave is present in an epoch, this is not sufficient for detection of any CSD or PID events. The presence of such a wave can still be noted as ‘suspicious’ (step 24), but it is likely an artefact. If multiple waves are present (step 23), their synchronicity is examined. If the waves are highly synchronised (e.g. substantially aligned in time) then the event should also be noted only as ‘suspicious’ (step 24), and not as anything more. This is because a clinically significant biological event is highly unlikely to be extremely synchronous due to the slow rate at which the waves travel.”. This method involves monitoring the detected waves and events and determining whether there are multiple events in the same subchannel, which shows the frequency distribution for the subchannels.); and
a determination step of determining whether cortical spreading depolarization has occurred based on the frequency distribution ([0020]: “a classification module configured to classify the detected events as a predetermined type of clinically significant event according to the output of the detection module”; [0065]: “If feature (i) is observed on one or more subchannels 12, the event should be classified as a CSD (step 26). If feature (ii) is observed, the event should be classified as a PID (step 27). If feature (iii) is observed, the event should be labelled only as ‘suspicious’ (step 24).”).
Regarding claim 2, Boutelle teaches the cortical spreading depolarization sensing method according to claim 1, wherein the determination step includes determining cortical spreading depolarization to have occurred when a peak value of frequency in the frequency distribution is in a predetermined range ([0075]: “The series of correlated non-synchronous events in different channels may be checked to see if they comply with an ‘event rate’ (e.g. a number of events per unit time—which is approximated by the angle of the arrows 51-54) that lies within a predetermined range of allowable event rates”).
Regarding claim 3, Boutelle teaches the cortical spreading depolarization sensing method according to claim 1 wherein the determination step includes determining timings of peaks exceeding a first threshold in the frequency distribution except for timings of peaks exceeding a second threshold that is larger than the first threshold as timings of cortical spreading depolarization occurrence ([0075]: “The series of correlated non-synchronous events in different channels may be checked to see if they comply with an ‘event rate’ (e.g. a number of events per unit time—which is approximated by the angle of the arrows 51-54) that lies within a predetermined range of allowable event rates”).
Regarding claim 4, Boutelle teaches the cortical spreading depolarization sensing method according to claim 1, wherein the acquisition step includes acquiring waveform data relating to a plurality of types of biological information ([0015]: “an input module configured to receive signal inputs from at least one or more electrophysiological signal channels indicative of electrical brain activity”; Fig. 4; [0031]: “The clinical datasets may comprise, or be derived from, electrocorticography (ECoG) electrodes placed directly on or into an exposed surface of the brain to record electrical activity from the cerebral cortex and may comprise strip electrodes extending over a surface of the brain tissue or depth electrodes inserted into the brain tissue. Electrode types may include electrode grids or custom electrode arrays having multiple electrode contacts at specific spacings. It may also be possible to use conventional electroencephalography electrodes monitoring electrical activity from outside the skull provided that sufficient signal can be obtained from which to extract the relevant data, to be discussed below.”; [0013]: “The method may further include displaying detected events as a function of time correlated with other signals indicative of one or more of … brain temperature”), and wherein the statistical processing step includes obtaining the frequency distribution based on the timings extracted on the basis of the plurality of types of biological information ([0070]: “The location of detected clinically significant events may be marked for the subchannels where they occur and in other subchannels in the data set. A confidence level may be determined for each event. A summary of the events found may be provided including, for example: a type of event, a start and end time, a level of confidence in the classification and, where appropriate, a duration of the suppression in each of the higher frequency subchannels”; [0058]-[0059]: “The detection of CSD and PID events may depend on two lower level types of event: slow potential changes (SPCs—low frequency, large amplitude waves) and high frequency amplitude suppression. The first event required is a slow potential change in the low pass filtered subchannel data (step 22) as discussed below. If no such waves are present in a data epoch, the possibility of CSD and PID events can be safely discounted and the process returns to step 21. If an SPC is present, further investigation is required, as shown in FIG. 2. The system tests for multiple, non-synchronous events in the low frequency subchannels 11. If only a single wave is present in an epoch, this is not sufficient for detection of any CSD or PID events. The presence of such a wave can still be noted as ‘suspicious’ (step 24), but it is likely an artefact. If multiple waves are present (step 23), their synchronicity is examined. If the waves are highly synchronised (e.g. substantially aligned in time) then the event should also be noted only as ‘suspicious’ (step 24), and not as anything more. This is because a clinically significant biological event is highly unlikely to be extremely synchronous due to the slow rate at which the waves travel.”).
Regarding claim 7, Boutelle teaches the cortical spreading depolarization sensing method according to claim 1, wherein the windows each have a width of longer than 0.5 minutes and shorter than 10 minutes ([0057]: “this period (and hence the size of the sliding window) may be of the order of 10 minutes”).
Regarding claim 8, Boutelle teaches the cortical spreading depolarization sensing method according to claim 1, wherein the feature amount calculation step includes calculating the feature amount for data obtained by subjecting the waveform data to filtering process to cut off frequency components less than or equal to a predetermined frequency ([0052]: “FIG. 1 illustrates a schematic example of the above process, in which raw data signals 1 are received to provide electrophysiological data signal channels 10, which are fed to a low pass filter 2 and a high pass filter 3 to generate first subchannels 11 and second subchannels 12”).
Regarding independent claim 9, Boutelle teaches a sensing system ([0029]: “An analysis tool as described herein is configured to detect clinically significant events in clinical datasets being received.”) comprising:
a waveform data acquisition part configured to acquire, for each of a plurality of sites of a brain, waveform data indicating a temporal change in one or more types of biological information including at least any one of a direct current component of an electroencephalogram, hemoglobin concentration in the brain, cerebral blood flow, and brain temperature ([0015]: “an input module configured to receive signal inputs from at least one or more electrophysiological signal channels indicative of electrical brain activity”; Fig. 4; [0031]: “The clinical datasets may comprise, or be derived from, electrocorticography (ECoG) electrodes placed directly on or into an exposed surface of the brain to record electrical activity from the cerebral cortex and may comprise strip electrodes extending over a surface of the brain tissue or depth electrodes inserted into the brain tissue. Electrode types may include electrode grids or custom electrode arrays having multiple electrode contacts at specific spacings. It may also be possible to use conventional electroencephalography electrodes monitoring electrical activity from outside the skull provided that sufficient signal can be obtained from which to extract the relevant data, to be discussed below.”; [0013]: “The method may further include displaying detected events as a function of time correlated with other signals indicative of one or more of … brain temperature”);
a feature amount calculation part configured to set windows set at predetermined time intervals on the waveform data and calculate a predetermined feature amount for each of the windows ([0017]-[0018]: “a detection module configured to: detect the appearance of a succession of correlated, non-synchronous events in the waveforms of the one or more first subchannels”; [0057]: “One approach as described here is based upon a sliding window. The contents of each window (data epoch, step 21) are examined across the various filtered subchannels, looking for (amongst other things) high amplitude, low frequency waves (step 22) and high frequency suppression (step 25). A next data epoch is then loaded (step 21), as the window slides across the dataset. When implementing a real-time system, this approach is still realistic, with detections being based on current events and a set period of time in the past. In one example, this period (and hence the size of the sliding window) may be of the order of 10 minutes, based upon the required proximity of low level events to suggest that a CSD or PID has occurred.”);
an event extraction part configured to perform threshold processing on the feature amounts calculated in the feature amount calculation part to extract a timing of the occurrence of an event in which a value of the waveform data temporarily changes due to cortical spreading depolarization ([0019]: “detect the suppression of an amplitude of the signal in one or more of the second subchannels correlated with the detected events in the one or more first subchannels”; [0057]: “One approach as described here is based upon a sliding window. The contents of each window (data epoch, step 21) are examined across the various filtered subchannels, looking for (amongst other things) high amplitude, low frequency waves (step 22) and high frequency suppression (step 25). A next data epoch is then loaded (step 21), as the window slides across the dataset. When implementing a real-time system, this approach is still realistic, with detections being based on current events and a set period of time in the past. In one example, this period (and hence the size of the sliding window) may be of the order of 10 minutes, based upon the required proximity of low level events to suggest that a CSD or PID has occurred.”);
a statistical processing part configured to acquire a frequency distribution of the timings extracted based on the plurality of waveform data acquired in relation to a plurality of sites of the brain ([0070]: “The location of detected clinically significant events may be marked for the subchannels where they occur and in other subchannels in the data set. A confidence level may be determined for each event. A summary of the events found may be provided including, for example: a type of event, a start and end time, a level of confidence in the classification and, where appropriate, a duration of the suppression in each of the higher frequency subchannels”; [0058]-[0059]: “The detection of CSD and PID events may depend on two lower level types of event: slow potential changes (SPCs—low frequency, large amplitude waves) and high frequency amplitude suppression. The first event required is a slow potential change in the low pass filtered subchannel data (step 22) as discussed below. If no such waves are present in a data epoch, the possibility of CSD and PID events can be safely discounted and the process returns to step 21. If an SPC is present, further investigation is required, as shown in FIG. 2. The system tests for multiple, non-synchronous events in the low frequency subchannels 11. If only a single wave is present in an epoch, this is not sufficient for detection of any CSD or PID events. The presence of such a wave can still be noted as ‘suspicious’ (step 24), but it is likely an artefact. If multiple waves are present (step 23), their synchronicity is examined. If the waves are highly synchronised (e.g. substantially aligned in time) then the event should also be noted only as ‘suspicious’ (step 24), and not as anything more. This is because a clinically significant biological event is highly unlikely to be extremely synchronous due to the slow rate at which the waves travel.” This method involves monitoring the detected waves and events and determining whether there are multiple events in the same subchannel, which shows the frequency distribution for the subchannels.); and
a determination part configured to determine whether cortical spreading depolarization has occurred based on the frequency distribution ([0020]: “a classification module configured to classify the detected events as a predetermined type of clinically significant event according to the output of the detection module”; [0065]: “If feature (i) is observed on one or more subchannels 12, the event should be classified as a CSD (step 26). If feature (ii) is observed, the event should be classified as a PID (step 27). If feature (iii) is observed, the event should be labelled only as ‘suspicious’ (step 24).”).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Boutelle as applied to claim 1 above, and further in view of Yang (“Epileptic Seizure Prediction based on Permutation Entropy”).
Regarding claim 5, Boutelle teaches the cortical spreading depolarization sensing method according to claim 1.
However, Boutelle does not teach wherein the feature amount is permutation entropy, and wherein the event extraction step includes extracting a negative peak below a threshold.
Yang discloses using permutation entropy to determine if a user has an abnormality in their brain. Specifically, Yang teaches wherein the feature amount is permutation entropy, and wherein the event extraction step includes extracting a negative peak below a threshold (“PE measures the departure of the time series from a completely random process: a smaller PE indicates a more regular time series.”; Fig. 4). Boutelle and Yang are analogous arts as they are both related to systems and methods that are used to measure information and determine irregularities in the brain.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to use permutation entropy to analyze the measured signals from Yang into the method from Boutelle, since permutation energy has been shown to be a good indicator of irregularities in the brain, which can be used to correctly identify different conditions or events.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Boutelle as applied to claim 1 above, and further in view of Hartings (US 20180085047).
Regarding claim 6, Boutelle teaches the cortical spreading depolarization sensing method according to claim 1.
However, Boutelle does not teach wherein the feature amount is power density, and wherein the event extraction step includes extracting a positive peak exceeding a threshold.
Hartings discloses a method for automatically detection of spreading depolarizations. Specifically, Hartings teaches wherein the feature amount is power density, and wherein the event extraction step includes extracting a positive peak exceeding a threshold (Fig. 2A). Boutelle and Hartings are analogous arts as they are both related to systems and methods that are used to measure information and determine irregularities in the brain.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the feature amount being power density from Hartings into the method from Boutelle as power density is a suitable technique used for determining spreading depolarization and provides important information during waveform analysis.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Boutelle in further view of Hartings.
Regarding independent claim 11, Boutelle teaches an acquisition step of acquiring, for each of a plurality of sites of a brain, waveform data indicating a temporal change in one or more types of biological information including at least any one of a direct current component of an electroencephalogram, hemoglobin concentration in the brain, cerebral blood flow, and brain temperature ([0015]: “an input module configured to receive signal inputs from at least one or more electrophysiological signal channels indicative of electrical brain activity”; Fig. 4; [0031]: “The clinical datasets may comprise, or be derived from, electrocorticography (ECoG) electrodes placed directly on or into an exposed surface of the brain to record electrical activity from the cerebral cortex and may comprise strip electrodes extending over a surface of the brain tissue or depth electrodes inserted into the brain tissue. Electrode types may include electrode grids or custom electrode arrays having multiple electrode contacts at specific spacings. It may also be possible to use conventional electroencephalography electrodes monitoring electrical activity from outside the skull provided that sufficient signal can be obtained from which to extract the relevant data, to be discussed below.”; [0013]: “The method may further include displaying detected events as a function of time correlated with other signals indicative of one or more of … brain temperature”);
a feature amount calculation step of setting windows set at predetermined time intervals on the waveform data and calculating a predetermined feature amount for each of the windows ([0017]-[0018]: “a detection module configured to: detect the appearance of a succession of correlated, non-synchronous events in the waveforms of the one or more first subchannels”; [0057]: “One approach as described here is based upon a sliding window. The contents of each window (data epoch, step 21) are examined across the various filtered subchannels, looking for (amongst other things) high amplitude, low frequency waves (step 22) and high frequency suppression (step 25). A next data epoch is then loaded (step 21), as the window slides across the dataset. When implementing a real-time system, this approach is still realistic, with detections being based on current events and a set period of time in the past. In one example, this period (and hence the size of the sliding window) may be of the order of 10 minutes, based upon the required proximity of low level events to suggest that a CSD or PID has occurred.”);
an event extraction step of performing threshold processing on the feature amounts calculated in the feature amount calculation step to extract a timing of the occurrence of an event in which a value of the waveform data temporarily changes due to cortical spreading depolarization ([0019]: “detect the suppression of an amplitude of the signal in one or more of the second subchannels correlated with the detected events in the one or more first subchannels”; [0057]: “One approach as described here is based upon a sliding window. The contents of each window (data epoch, step 21) are examined across the various filtered subchannels, looking for (amongst other things) high amplitude, low frequency waves (step 22) and high frequency suppression (step 25). A next data epoch is then loaded (step 21), as the window slides across the dataset. When implementing a real-time system, this approach is still realistic, with detections being based on current events and a set period of time in the past. In one example, this period (and hence the size of the sliding window) may be of the order of 10 minutes, based upon the required proximity of low level events to suggest that a CSD or PID has occurred.”);
a statistical processing step of acquiring a frequency distribution of the timings extracted based on the plurality of waveform data acquired in relation to a plurality of sites of the brain ([0070]: “The location of detected clinically significant events may be marked for the subchannels where they occur and in other subchannels in the data set. A confidence level may be determined for each event. A summary of the events found may be provided including, for example: a type of event, a start and end time, a level of confidence in the classification and, where appropriate, a duration of the suppression in each of the higher frequency subchannels”; [0058]-[0059]: “The detection of CSD and PID events may depend on two lower level types of event: slow potential changes (SPCs—low frequency, large amplitude waves) and high frequency amplitude suppression. The first event required is a slow potential change in the low pass filtered subchannel data (step 22) as discussed below. If no such waves are present in a data epoch, the possibility of CSD and PID events can be safely discounted and the process returns to step 21. If an SPC is present, further investigation is required, as shown in FIG. 2. The system tests for multiple, non-synchronous events in the low frequency subchannels 11. If only a single wave is present in an epoch, this is not sufficient for detection of any CSD or PID events. The presence of such a wave can still be noted as ‘suspicious’ (step 24), but it is likely an artefact. If multiple waves are present (step 23), their synchronicity is examined. If the waves are highly synchronised (e.g. substantially aligned in time) then the event should also be noted only as ‘suspicious’ (step 24), and not as anything more. This is because a clinically significant biological event is highly unlikely to be extremely synchronous due to the slow rate at which the waves travel.” This method involves monitoring the detected waves and events and determining whether there are multiple events in the same subchannel, which shows the frequency distribution for the subchannels.); and
a determination step of determining whether cortical spreading depolarization has occurred based on the f