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 .
Drawings
The drawings are objected to because in Fig. 1A, "110" has been used to indicate a brain wave measuring device which should instead be indicated with "400" per the instant specification.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 Objections
Claim 11 is objected to because of the following informalities:
Claim 11 recites the limitation “the feature data” in line in line 10 of the claim. The limitation should be amended to “the at least one feature data” in accordance with earlier limitations.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: communication unit in claims 14 and 15.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Accordingly, “communication unit” is interpreted according to paragraph 0125 and 0135 of the instant specification “The communication unit 120 is connected to the user mobile device 200, the medical staff device 300, and the brain wave measuring device 400 using wired/wireless communication to transmit and receive various data”.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 12 is 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.
Claim 12 recites the limitation “the brain activity data” in lines 1 and 2 of the claim. There is insufficient antecedent basis for this limitation of the claim. The claim is currently interpreted as depending from claim 11, rather than from claim 1, as claim 11 refers to generating brain activity data based on the brain wave data.
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) "determining whether the subject has the major depressive disorder on the basis of the at least one feature data, by using a classification model trained to output whether the subject has the major depressive disorder on the basis of the at least one feature data as an input, wherein the subject is a subject suspected of suffering from the major depressive disorder without having a history of drug use". 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, method. Following Step 2A Prong one, any judicial exceptions are identified in the claims. In claim 1, the limitations "determining whether the subject has the major depressive disorder on the basis of the at least one feature data, by using a classification model trained to output whether the subject has the major depressive disorder on the basis of the at least one feature data as an input, wherein the subject is a subject suspected of suffering from the major depressive disorder without having a history of drug use" are abstract ideas as they are directed to a mental process or mathematical concept. 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 “being implemented by a processor, comprising: receiving brain wave data of a subject; extracting at least one feature data of power spectrum densities (PSDs), a functional connectivity, and a network index with respect to the brain wave data”. 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 “determining” such that the end result of use of the system is only the generic determination 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 brain wave data and extraction of feature data from the brain wave data, such that the claim does not integrate the judicial exception into any practical application. Regarding “being implemented by 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 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) 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 “being implemented by a processor, comprising: receiving brain wave data of a subject; extracting at least one feature data of power spectrum densities (PSDs), a functional connectivity, and a network index with respect to the brain wave data” 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 which Sun (“Graph Theory Analysis of Functional Connectivity…”) describes as well-understood, routine, or conventional as it describes processing being performed using a commercially available tool (Matlab R2017a) which is utilized in conjunction with a generic computer (Page 431, section II part B--Data
processing tool was Matlab R2017a). It is additionally noted that “receiving brain wave data” implies the data is received from some brain wave sensor, such as an EEG including a plurality of electrodes. Per Sun, the use of EEG signals to determine major depressive disorder is well-understood, routine, or conventional in the art (see Table I—previous studies reporting abnormalities of resting state EEG functional brain networks in MDD, which describes six previous studies utilizing EEG signals including a plurality of channels for this purpose and Page 431, section II, part B—128 channel HydroCel Geodesic Sensor Net with a Cz reference, which were positioned according to the standard international 10/20 system). 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 2-13 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 14 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: claims 2-13 recites the limitation recite further limitations 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. Claim 11 further recites a limitation which comprises further details of the abstract idea which are themselves abstract, specifically “the determining of whether the subject has a major depressive disorder further includes: determining whether the subject has a major depressive disorder using the classification model, based on the feature data for each of the brain wave data and the brain activity data” which like the abstract idea of the independent claim may be seen to be directed to a mental process or mathematical concept. The claim element of claim 1 of a method for providing information on a major depressive disorder 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.
Claim 14 is rejected for similar reasons to claim 1 as described in paragraph 14 of this action above. In addition to a processor as described in the rejection of claim 1, in Step 2B, claim 14 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation of “a communication unit configured to receive brain wave data of a subject” 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 transmitter which Sun (“Graph Theory Analysis of Functional Connectivity…”) implicitly discloses as well-understood, routine, or conventional in its description of previous studies of analyzing EEG data to identify MDD in patients such that the collection of said EEG data to a computer to be analyzed is known in the art (see Table I—previous studies reporting abnormalities of resting state EEG functional brain networks in MDD, which describes six previous studies utilizing EEG signals including a plurality of channels for this purpose) and additionally in its description of using commercially available sensors and processing tools to analyze the sensor data (Page 431, section II part B—128 channel HydroCel Geodesic Sensor Net with a Cz reference, which were positioned according to the standard international 10/20 system…Data processing tool was Matlab R2017a). 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.
Claims 15-20 are additionally rejected under 35 U.S.C. 101 for similar reasons to claims 2-13.
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) 1-2, 8-10, and 13-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sun ("Graph Theory Analysis of Functional Connectivity in Major Depression Disorder With High-Density Resting State EEG Data").
Regarding claim 1, Sun teaches a method for providing information on a major depressive disorder being implemented by a processor (Page 431, section II, part B—Data processing tool was Matlab R2017a.), comprising:
receiving brain wave data of a subject (Page 431, section II, part B-- EEG signals were continuously recorded for approximately 5 min using a 128 channel HydroCel Geodesic Sensor Net (HCGSN) with a Cz reference, which were positioned according to the standard international 10/20 system);
extracting at least one feature data of power spectrum densities (PSDs), a functional connectivity, and a network index with respect to the brain wave data (Page 431-432, section II, part C-- each EEG electrode was defined as a node. The edges represented the connectivity strength between different EEG electrodes. To construct functional connectivity matrix, we used 5 coupling methods including Coh, ICoh, Corr, PLI and PLV… functional connectivity matrices were calculated every 4 s…; Fig. 1-- Coupling methods included: coherence (Coh), imaginary part of coherence (ICoh), Pearson correlation coefficient (Corr), phase lag index (PLI) and phase lock value (PLV). Binarization approaches included: Cluster-Span Threshold (CST), Efficiency Cost Optimization threshold (ECO), Minimum Spanning Threshold (MST), and Density; Page 432, section II, part E--Network matrices were calculated based on the EEG data); and
determining whether the subject has the major depressive disorder on the basis of the at least one feature data, by using a classification model trained to output whether the subject has the major depressive disorder on the basis of the at least one feature data as an input (Page 432-433, section III and section IV, part A, parts A-C--Classification between patients with major depressive disorder (MDD) and normal controls (NC) is based on the network metrics),
wherein the subject is a subject suspected of suffering from the major depressive disorder without having a history of drug use (Page 431, section II, part A-- For MDD patients, the inclusion criteria were the diagnostic criteria of MINI met the criteria for depression…The exclusion criteria for all subjects were abused or dependent alcohol or psychotropic drugs in the past year).
Regarding claim 2, Sun teaches the method of claim 1. Sun additionally teaches wherein: the receiving brain wave data includes:
receiving brain wave data of the subject measured from a plurality of electrode channels selected from FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6,PO8, CB1, O1, OZ, O2, and CB2 (Page 431, section II, part B-- EEG signals were continuously recorded for approximately 5 min using a 128 channel HydroCel Geodesic Sensor Net (HCGSN) with a Cz reference, which were positioned according to the standard international 10/20 system; the particular channels are disclosed by the technical document “Determination of the HydroCel Geodesic Sensor Nets’ Average Electrode Positions…” document from the HCGSN manufacturer, Electrical Geodesics, Inc.) and
extracting at least one feature data includes: extracting the at least one feature data from a plurality of frequency bands selected from low-alpha, high-alpha, low-beta, high-beta, gamma, delta, and theta of the brain wave data measured from the plurality of electrode channels (Page 432, Section II, Part B-- frequency bands of interest were theta (4 - 8 Hz) and alpha (8 - 13 Hz) computed by Hanning Filter, which had been confirmed to make a vital role in identifying depression at resting state; Fig. 1).
Regarding claim 8, Sun teaches the method of claim 1. Sun additionally teaches wherein the at least one feature data is the functional connectivity and extracting at least one feature data includes: determining a connectivity of a phase locking value (PLV) for the brain wave data (Page 431-432, section II, part C-- each EEG electrode was defined as a node. The edges represented the connectivity strength between different EEG electrodes. To construct functional connectivity matrix, we used 5 coupling methods including Coh, ICoh, Corr, PLI and PLV… functional connectivity matrices were calculated every 4 s…; Fig. 1-- Coupling methods included: coherence (Coh), imaginary part of coherence (ICoh), Pearson correlation coefficient (Corr), phase lag index (PLI) and phase lock value (PLV). Binarization approaches included: Cluster-Span Threshold (CST), Efficiency Cost Optimization threshold (ECO), Minimum Spanning Threshold (MST), and Density; Page 432, section II, part E--Network matrices were calculated based on the EEG data).
Regarding claim 9, Sun teaches the method of claim 1. Sun additionally teaches wherein: the at least one feature data is the functional connectivity and the network index and extracting at least one feature data includes: extracting the functional connectivity from the brain wave data; and determining the network index based on network structural feature data of the functional connectivity (Page 431-432, section II, part C-- each EEG electrode was defined as a node. The edges represented the connectivity strength between different EEG electrodes. To construct functional connectivity matrix, we used 5 coupling methods including Coh, ICoh, Corr, PLI and PLV… functional connectivity matrices were calculated every 4 s…; Fig. 1-- Coupling methods included: coherence (Coh), imaginary part of coherence (ICoh), Pearson correlation coefficient (Corr), phase lag index (PLI) and phase lock value (PLV). Binarization approaches included: Cluster-Span Threshold (CST), Efficiency Cost Optimization threshold (ECO), Minimum Spanning Threshold (MST), and Density; Page 432, section II, part E--Network matrices were calculated based on the EEG data).
Regarding claim 10, Sun teaches the method of claim 9. Sun additionally teaches wherein determining the network index includes: determining at least one index of a strength for the functional connectivity, a clustering coefficient, and a path length (Page 431-432, section II, part C-- each EEG electrode was defined as a node. The edges represented the connectivity strength between different EEG electrodes. To construct functional connectivity matrix, we used 5 coupling methods including Coh, ICoh, Corr, PLI and PLV… functional connectivity matrices were calculated every 4 s…; Fig. 1-- Characteristic Path Length (CPL) of EEG-based functional networks in MDD and NC…; Page 432, section II, part E--The network metrics of functional integration included: 1) CPL (Characteristic path length), 2) Edge betweenness centrality (EBC), and 3) Node betweenness centrality (NBC). The network metrics of functional segregation included: 1) CC (clustering coefficient), and 2) Modularity (mathematical formula of the network metrics are in the supplementary material-C)).
Regarding claim 13, Sun teaches the method of claim 1. Sun additionally teaches wherein the brain wave data is defined as brain wave data acquired in a resting state (Page 431, Section II, part B-- eye-closed resting state EEG signals were continuously recorded).
Regarding claim 14, Sun teaches a device for providing information on a major depressive disorder, comprising:
a communication unit configured to receive brain wave data of a subject (Page 431, Section II, part B-- Net Station acquisition software); and
a processor connected to communicate with the communication unit (Page 431, section II, part B—Data processing tool was Matlab R2017a), wherein the processor is configured to extract at least one feature data of power spectrum densities (PSDs), a functional connectivity, and a network index with respect to the brain wave data (Page 431-432, section II, part C-- each EEG electrode was defined as a node. The edges represented the connectivity strength between different EEG electrodes. To construct functional connectivity matrix, we used 5 coupling methods including Coh, ICoh, Corr, PLI and PLV… functional connectivity matrices were calculated every 4 s…; Fig. 1-- Coupling methods included: coherence (Coh), imaginary part of coherence (ICoh), Pearson correlation coefficient (Corr), phase lag index (PLI) and phase lock value (PLV). Binarization approaches included: Cluster-Span Threshold (CST), Efficiency Cost Optimization threshold (ECO), Minimum Spanning Threshold (MST), and Density; Page 432, section II, part E--Network matrices were calculated based on the EEG data) and
determine whether the subject has the major depressive disorder on the basis of the at least one feature data, by using a classification model trained to output whether the subject has the major depressive disorder on the basis of the at least one feature data as an input (Page 432-433, section III and section IV, part A, parts A-C--Classification between patients with major depressive disorder (MDD) and normal controls (NC) is based on the network metrics), and
the subject is a subject suspected of suffering from the major depressive disorder without having a history of drug use (Page 431, section II, part A-- For MDD patients, the inclusion criteria were the diagnostic criteria of MINI met the criteria for depression…The exclusion criteria for all subjects were abused or dependent alcohol or psychotropic drugs in the past year).
Regarding claim 15, Sun teaches the device of claim 14. Sun additionally teaches wherein: the communication unit is configured to receive the brain wave data of the subject measured from a plurality of electrode channels selected from FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6, PO8, CB1, O1, OZ, O2, and CB2 (Page 431, section II, part B-- EEG signals were continuously recorded for approximately 5 min using a 128 channel HydroCel Geodesic Sensor Net (HCGSN) with a Cz reference, which were positioned according to the standard international 10/20 system; the particular channels are disclosed by the technical document “Determination of the HydroCel Geodesic Sensor Nets’ Average Electrode Positions…” document from the HCGSN manufacturer, Electrical Geodesics, Inc.) and
the processor is configured to extract the at least one feature data from a plurality of frequency bands selected from low-alpha, high-alpha, low-beta, high-beta, gamma, delta, and theta of the brain wave data measured from the plurality of electrode channels (Page 432, Section II, Part B-- frequency bands of interest were theta (4 - 8 Hz) and alpha (8 - 13 Hz) computed by Hanning Filter, which had been confirmed to make a vital role in identifying depression at resting state; Fig. 1).
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) 1-3, 5, 7-10, 13-16,18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shim ("Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study") in view of Mumtaz ("Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD)").
Regarding claim 1, Shim teaches a method for providing information on a major depressive disorder being implemented by a processor (Page 1002, section 2.3 EEG recordings and pre-processing--We used Matlab (Mathworks Inc.) while performing network analyses procedures), comprising:
receiving brain wave data of a subject (Page 1001, section 2.3-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system);
extracting at least one feature data of power spectrum densities (PSDs), a functional connectivity (Page 1002, 2.5 Connectivity and network analysis-- The FC between each pair of nodes was evaluated using phaselocking values (PLVs)), and a network index with respect to the brain wave data (Page 1002, Section 2.5 Connectivity and network analysis-- The network measurements are defined as follows: 1) Strength represents the degree of connection strength in the network. A higher strength value means that the whole brain is strongly connected. 2) Clustering coefficients (CC) represent the degree in which a node is clustered with its neighbor's nodes. The enhanced CC indicated the well-segregated network between the relevant brain regions. 3) Path length (PL) is the summation of lengths between two nodes within the network); and
determining whether the subject has the major depressive disorder on the basis of the at least one feature data(Page 1002, section 3 Results—differences between patients with MDD and HCs),
wherein the subject is a subject suspected of suffering from the major depressive disorder without having a history of drug use (Page 1001, section 2.1-- patients were diagnosed based on the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) Axis I Psychiatric Disorders (First et al., 1997) by a psychiatrist…Patients were excluded if they accorded with the following criteria:…2) medical history of alcohol or drug abuse).
However, Shim does not explicitly disclose determining whether the subject has the major depressive disorder on the basis of the at least one feature data, by using a classification model trained to output whether the subject has the major depressive disorder on the basis of the at least one feature data as an input, nor does Shim disclose extracting at least one feature data of power spectrum densities (PSDs).
Mumtaz, in the same field of endeavor of a method for observing differences in EEG signals of users having MDD versus health controls, discloses a method for providing information on a major depressive disorder being implemented by a processor, comprising:
receiving brain wave data of a subject (Page 109, section 2.2 experimental data acquisition-- EEG sensors were placed on the scalp according to the international 10–20 electrode placement standard);
extracting at least one feature data of power spectrum densities (PSDs), a functional connectivity, and a network index with respect to the brain wave data (Page 110, section 2.4 Feature extraction-- Power spectral densities were represented as SLmn and SRmns…); and
determining whether the subject has the major depressive disorder on the basis of the at least one feature data, by using a classification model trained to output whether the subject has the major depressive disorder on the basis of the at least one feature data as an input (Page 112, section 3 results-- specific values of the parameter assigned for each classifier during training and testing…classification models).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Shim to include a classification model as in Mumtaz in order to allow a user to utilize the knowledge of the differences between a patient with MDD and a healthy patient to classify a user and thus improve the diagnostic capability of the method. Furthermore, it would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Shim to additionally include the feature of a power spectrum density of Mumtaz in order to predictably improve the ability of the method to distinguish between a healthy user and a user with MDD by providing an additional metric which is known to differ between users with and without MDD.
Regarding claim 2, the combination of Shim and Mumtaz teaches the method of claim 1. Shim additionally teaches wherein: the receiving brain wave data includes:
receiving brain wave data of the subject measured from a plurality of electrode channels selected from FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6,PO8, CB1, O1, OZ, O2, and CB2 (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well) and
extracting at least one feature data includes: extracting the at least one feature data from a plurality of frequency bands selected from low-alpha, high-alpha, low-beta, high-beta, gamma, delta, and theta of the brain wave data measured from the plurality of electrode channels (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]). It is noted that these frequency bands encompass each of the second and fourth frequency bands).
Regarding claim 3, the combination of Shim and Mumtaz teaches the method of claim 2. Shim additionally teaches wherein: the plurality of electrode channels is a first electrode channel set of FP 1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6, PO8, CB1,O1, OZ, O2, and CB2, a second electrode channel set of FP1, FP2, F7, F3, FZ, F4, F8, FT7, FC3, FCZ, FC4, FT8, T7, C3, CZ, C4, T8, TP7, CP3, CPZ, CP4, TP8, P7, P3, PZ, P4, P8, O1, OZ, and O2, a third electrode channel set of FP1, FP2, F7, F3, FZ, F4, F8, T7, C3, CZ, C4, T8, P7, P3, PZ, P4, P8, O1, and O2, or a fourth electrode channel set of F3, F4, T7, C3, C4, T8, P3, P4, O1, and O2 (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well)
the plurality of frequency bands is a first frequency band set of theta, low-alpha, and high-alpha, a second frequency band set of delta and theta, a third frequency band set of delta, theta, low-alpha, high-alpha, and low-beta, or a fourth frequency band set of theta and low-beta (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]). It is noted that these frequency bands encompass each of the second and fourth frequency bands).
It is additionally noted that the current claim language does not limit the plurality of electrode channels or the plurality of frequency bands to only the respective channels and frequency bands of any given set but rather includes any plurality of electrode channels or plurality of frequency bands which includes all of the claimed electrode channels and frequency bands.
Regarding claim 5, the combination of Shim and Mumtaz teaches the method of claim 3. Shim additionally teaches wherein: the plurality of electrode channels is the second electrode channel set (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well as these sets as claimed are not limited to only the particular electrode channels), and the plurality of frequency bands is the second frequency band set (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]). It is noted that these frequency bands encompass each of the second and fourth frequency bands).
Regarding claim 7, the combination of Shim and Mumtaz teaches the method of claim 2. Shim additionally teaches wherein: the plurality of electrode channels is the fourth electrode channel set (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well as these sets as claimed are not limited to only the particular electrode channels), and the plurality of frequency bands is the fourth frequency band set (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]). It is noted that these frequency bands encompass each of the second and fourth frequency bands).
Regarding claim 8, the combination of Shim and Mumtaz teaches the method of claim 1. Shim additionally teaches wherein the at least one feature data is the functional connectivity and extracting at least one feature data includes: determining a connectivity of a phase locking value (PLV) for the brain wave data (Page 1002, section 2.5 Connectivity and network analysis-- The FC between each pair of nodes was evaluated using phaselocking values (PLVs).).
Regarding claim 9, the combination of Shim and Mumtaz teaches the method of claim 1. Shim additionally teaches wherein: the at least one feature data is the functional connectivity and the network index and extracting at least one feature data includes: extracting the functional connectivity from the brain wave data (Page 1002, section 2.5 Connectivity and network analysis-- The FC between each pair of nodes was evaluated using phaselocking values (PLVs).); and
determining the network index based on network structural feature data of the functional connectivity (Page 1002, section 2.5 Connectivity and network analysis-- The network measurements are defined as follows: 1) Strength represents the degree of connection strength in the network. A higher strength value means that the whole brain is strongly connected. 2) Clustering coefficients (CC) represent the degree in which a node is clustered with its neighbor's nodes. The enhanced CC indicated the well-segregated network between the relevant brain regions. 3) Path length (PL) is the summation of lengths between two nodes within the network.).
Regarding claim 10, the combination of Shim and Mumtaz teaches the method of claim 9. Shim additionally teaches wherein determining the network index includes: determining at least one index of a strength for the functional connectivity, a clustering coefficient, and a path length (Page 1002, section 2.5 connectivity and network analysis--The network measurements are defined as follows: 1) Strength represents the degree of connection strength in the network. A higher strength value means that the whole brain is strongly connected. 2) Clustering coefficients (CC) represent the degree in which a node is clustered with its neighbor's nodes. The enhanced CC indicated the well-segregated network between the relevant brain regions. 3) Path length (PL) is the summation of lengths between two nodes within the network.).
Regarding claim 13, the combination of Shim and Mumtaz teaches the method of claim 1. Shim additionally teaches wherein the brain wave data is defined as brain wave data acquired in a resting state (Page 1001, section 2.3 EEG recordings and pre-processing-- Resting-state EEGs were recorded for 5min with the eyes closed).
Regarding claim 14, Shim teaches a device for providing information on a major depressive disorder, comprising:
a communication unit configured to receive brain wave data of a subject (Page 1001, section 2.3 EEG recordings and pre-processing--EEG signals were recorded using a NeuroScan SynAmps2 amplifier); and
a processor connected to communicate with the communication unit (Page 1002, section 2.3 EEG recordings and pre-processing--We used Matlab (Mathworks Inc.) while performing network analyses procedures) wherein the processor is configured to extract at least one feature data of power spectrum densities (PSDs), a functional connectivity (Page 1002, 2.5 Connectivity and network analysis-- The FC between each pair of nodes was evaluated using phaselocking values (PLVs)), and a network index with respect to the brain wave data (Page 1002, Section 2.5 Connectivity and network analysis-- The network measurements are defined as follows: 1) Strength represents the degree of connection strength in the network. A higher strength value means that the whole brain is strongly connected. 2) Clustering coefficients (CC) represent the degree in which a node is clustered with its neighbor's nodes. The enhanced CC indicated the well-segregated network between the relevant brain regions. 3) Path length (PL) is the summation of lengths between two nodes within the network); and
determine whether the subject has the major depressive disorder on the basis of the at least one feature data (Page 1002, section 3 Results—differences between patients with MDD and HCs),
wherein the subject is a subject suspected of suffering from the major depressive disorder without having a history of drug use (Page 1001, section 2.1-- patients were diagnosed based on the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) Axis I Psychiatric Disorders (First et al., 1997) by a psychiatrist…Patients were excluded if they accorded with the following criteria:…2) medical history of alcohol or drug abuse).
However, Shim does not explicitly disclose determining whether the subject has the major depressive disorder on the basis of the at least one feature data, by using a classification model trained to output whether the subject has the major depressive disorder on the basis of the at least one feature data as an input, nor does Shim disclose extracting at least one feature data of power spectrum densities (PSDs).
Mumtaz, in the same field of endeavor of a method for observing differences in EEG signals of users having MDD versus health controls, discloses providing information on a major depressive disorder, comprising: receiving brain wave data of a subject (Page 109, section 2.2 experimental data acquisition-- EEG sensors were placed on the scalp according to the international 10–20 electrode placement standard); extracting at least one feature data of power spectrum densities (PSDs), a functional connectivity, and a network index with respect to the brain wave data (Page 110, section 2.4 Feature extraction-- Power spectral densities were represented as SLmn and SRmns…); and
determining whether the subject has the major depressive disorder on the basis of the at least one feature data, by using a classification model trained to output whether the subject has the major depressive disorder on the basis of the at least one feature data as an input (Page 112, section 3 results-- specific values of the parameter assigned for each classifier during training and testing…classification models).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the device of Shim to include a classification model as in Mumtaz in order to allow a user to utilize the knowledge of the differences between a patient with MDD and a healthy patient to classify a user and thus improve the diagnostic capability of the method. Furthermore, it would have been obvious to one having ordinary skill in the art at the time of filing to modify the device of Shim to additionally include the feature of a power spectrum density of Mumtaz in order to predictably improve the ability of the method to distinguish between a healthy user and a user with MDD by providing an additional metric which is known to differ between users with and without MDD.
Regarding claim 15, the combination of Shim and Mumtaz teaches the device of claim 14. Shim additionally teaches wherein: the communication unit is configured to receive the brain wave data of the subject measured from a plurality of electrode channels selected from FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6,PO8, CB1, O1, OZ, O2, and CB2 (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well) and
the processor is configured to extract at least one feature data includes: extracting the at least one feature data from a plurality of frequency bands selected from low-alpha, high-alpha, low-beta, high-beta, gamma, delta, and theta of the brain wave data measured from the plurality of electrode channels (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]). It is noted that these frequency bands encompass each of the second and fourth frequency bands).
Regarding claim 16, the combination of Shim and Mumtaz teaches the device of claim 15. Shim additionally teaches wherein: the plurality of electrode channels is a first electrode channel set of FP 1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6, PO8, CB1,O1, OZ, O2, and CB2, a second electrode channel set of FP1, FP2, F7, F3, FZ, F4, F8, FT7, FC3, FCZ, FC4, FT8, T7, C3, CZ, C4, T8, TP7, CP3, CPZ, CP4, TP8, P7, P3, PZ, P4, P8, O1, OZ, and O2, a third electrode channel set of FP1, FP2, F7, F3, FZ, F4, F8, T7, C3, CZ, C4, T8, P7, P3, PZ, P4, P8, O1, and O2, or a fourth electrode channel set of F3, F4, T7, C3, C4, T8, P3, P4, O1, and O2 (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well)
the plurality of frequency bands is a first frequency band set of theta, low-alpha, and high-alpha, a second frequency band set of delta and theta, a third frequency band set of delta, theta, low-alpha, high-alpha, and low-beta, or a fourth frequency band set of theta and low-beta (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]). It is noted that these frequency bands encompass each of the second and fourth frequency bands).
Regarding claim 18, the combination of Shim and Mumtaz teaches the device of claim 16. Shim additionally teaches wherein: the plurality of electrode channels is the second electrode channel set (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well as these sets as claimed are not limited to only the particular electrode channels), and the plurality of frequency bands is the second frequency band set (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]). It is noted that these frequency bands encompass each of the second and fourth frequency bands).
Regarding claim 20, the combination of Shim and Mumtaz teaches the device of claim 16. Shim additionally teaches wherein: the plurality of electrode channels is the fourth electrode channel set (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well as these sets as claimed are not limited to only the particular electrode channels), and the plurality of frequency bands is the fourth frequency band set (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]). It is noted that these frequency bands encompass each of the second and fourth frequency bands).
Claim(s) 4, 6, 17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shim in view of Mumtaz, further in view of Lee (“Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study”).
Regarding claim 4, the combination of Shim and Mumtaz discloses the method of claim 1. Shim additionally discloses the plurality of electrode channels is the first electrode channel set (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well) and the frequency bands include theta and alpha bands (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]).). However, the combination fails to disclose the plurality of frequency bands is the first frequency band set as it fails to explicitly include low-alpha, and high-alpha frequency bands.
Lee, in the same field of endeavor of identifying depressive disorders using brain wave data (Introduction), discloses a method which monitors EEG data in the first frequency band set of theta, low-alpha, and high-alpha (Page 317, Section 2.4 EEG recordings and analysis-- standard frequency bands: delta (1–4Hz), theta (4–8Hz), low-alpha (8–10Hz), high-alpha (10–12Hz) and beta (12–30Hz)).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Shim and Mumtaz to additionally include the frequency bands of Lee as Lee discloses that it is known in the art that these frequency bands are standard (Lee, page 317, 2.4 EEG recordings and analysis-- standard frequency bands) and Lee discloses these additional frequency bands correspond to depressive disorders (Lee, page 318, section 3.2 comparison of power spectrum—Comparison of depressive and euthymic groups revealed higher delta power but decreased theta, low-alpha and high-alpha power in majority of brain regions in depressed participants), such that inclusion of these additional frequency bands may predictably improve the accuracy of a determination of major depressive disorder in a user by providing more data which is known to correspond to MDD for comparison.
Regarding claim 6, the combination of Shim and Mumtaz discloses the method of claim 3. Shim additionally discloses the plurality of electrode channels is the third electrode channel set (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well) and the plurality of frequency bands includes delta, theta, and low-beta (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]).). However, the combination fails to disclose the plurality of frequency bands is the third frequency band set as it fails to explicitly include low-alpha and high-alpha frequency bands.
Lee, in the same field of endeavor of identifying depressive disorders using brain wave data (Introduction), discloses a method which monitors EEG data in the first frequency band set of theta, low-alpha, and high-alpha (Page 317, Section 2.4 EEG recordings and analysis-- standard frequency bands: delta (1–4Hz), theta (4–8Hz), low-alpha (8–10Hz), high-alpha (10–12Hz) and beta (12–30Hz)).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Shim and Mumtaz to additionally include the frequency bands of Lee as Lee discloses that it is known in the art that these frequency bands are standard (Lee, page 317, 2.4 EEG recordings and analysis-- standard frequency bands) and Lee discloses these additional frequency bands correspond to depressive disorders (Lee, page 318, section 3.2 comparison of power spectrum—Comparison of depressive and euthymic groups revealed higher delta power but decreased theta, low-alpha and high-alpha power in majority of brain regions in depressed participants), such that inclusion of these additional frequency bands may predictably improve the accuracy of a determination of major depressive disorder in a user by providing more data which is known to correspond to MDD for comparison.
Regarding claim 17, the combination of Shim and Mumtaz discloses the device of claim 16. Shim additionally discloses the plurality of electrode channels is the first electrode channel set (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well) and the frequency bands include theta and alpha bands (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]).). However, the combination fails to disclose the plurality of frequency bands is the first frequency band set as it fails to explicitly include low-alpha, and high-alpha frequency bands.
Lee, in the same field of endeavor of identifying depressive disorders using brain wave data (Introduction), discloses a method which monitors EEG data in the first frequency band set of theta, low-alpha, and high-alpha (Page 317, Section 2.4 EEG recordings and analysis-- standard frequency bands: delta (1–4Hz), theta (4–8Hz), low-alpha (8–10Hz), high-alpha (10–12Hz) and beta (12–30Hz)).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method of Shim and Mumtaz to additionally include the frequency bands of Lee as Lee discloses that it is known in the art that these frequency bands are standard (Lee, page 317, 2.4 EEG recordings and analysis-- standard frequency bands) and Lee discloses these additional frequency bands correspond to depressive disorders (Lee, page 318, section 3.2 comparison of power spectrum—Comparison of depressive and euthymic groups revealed higher delta power but decreased theta, low-alpha and high-alpha power in majority of brain regions in depressed participants), such that inclusion of these additional frequency bands may predictably improve the accuracy of a determination of major depressive disorder in a user by providing more data which is known to correspond to MDD for comparison.
Regarding claim 19, the combination of Shim and Mumtaz discloses the device of claim 16. Shim additionally discloses the plurality of electrode channels is the third electrode channel set (Page 1001, section 2.3 EEG recordings and pre-processing-- EEG signals were recorded using a NeuroScan SynAmps2 amplifier (Compumedics USA, El Paso, TX, USA) from 62 Ag/AgCl scalp electrodes that were evenly mounted on a QuikCap according to the extended international 10−20 system. It is noted that this arrangement includes the 62 electrode channels of the claimed “first set” and thus encompasses each of the second, third, and fourth sets of channels as well) and the frequency bands include delta, theta, alpha, and low-beta (Page 1002, section 2.4 Source localization-- A time-series of the cortical sources at each of the 66 nodes were bandpass filtered and divided into six frequency bands (delta [1−4 Hz], theta [4−8 Hz], alpha [8−12 Hz], low-beta [12−22 Hz], high-beta [22−30 Hz], and gamma [30−55 Hz]).). However, the combination fails to disclose the plurality of frequency bands is the third frequency band set as it fails to explicitly include low-alpha and high-alpha frequency bands.
Lee, in the same field of endeavor of identifying depressive disorders using brain wave data (Introduction), discloses a method which monitors EEG data in the first frequency band set of theta, low-alpha, and high-alpha (Page 317, Section 2.4 EEG recordings and analysis-- standard frequency bands: delta (1–4Hz), theta (4–8Hz), low-alpha (8–10Hz), high-alpha (10–12Hz) and beta (12–30Hz)).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the device of Shim and Mumtaz to additionally include the frequency bands of Lee as Lee discloses that it is known in the art that these frequency bands are standard (Lee, page 317, 2.4 EEG recordings and analysis-- standard frequency bands) and Lee discloses these additional frequency bands correspond to depressive disorders (Lee, page 318, section 3.2 comparison of power spectrum—Comparison of depressive and euthymic groups revealed higher delta power but decreased theta, low-alpha and high-alpha power in majority of brain regions in depressed participants), such that inclusion of these additional frequency bands may predictably improve the accuracy of a determination of major depressive disorder in a user by providing more data which is known to correspond to MDD for comparison.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20050273017 A1 to Gordon, which discloses a method of diagnosing a psychiatric disorder of a user based on at least one feature of brain waves including functional connectivity.
US 20120296569 A1 to Shahaf, which discloses a method of classifying features of brain wave data in order to diagnose a psychiatric disorder of a user.
US 20200107766 A1 to Liu, which discloses a method of diagnosing a psychiatric disorder of a user based on at least one feature of brain waves including functional connectivity.
US 20230190185 A1 to Dvorak, which discloses a method of diagnosing a psychiatric disorder of a user based on at least one feature of brain waves including power spectral density.
US 20230248295 A1 to Houmani, which discloses a method of diagnosing a brain disorder of a user based on at least one feature of brain waves including functional connectivity.
US 20090054801 A1 to Hinrikus, which discloses a method of determining a depressive disorder of a user based on at least one feature of brain waves including power spectral density.
Claims 11 and 12 are not currently rejected under 35 U.S.C. 102/103.
Regarding claim 11, the most pertinent prior art of the record, Shim, fails to disclose and/or fairly suggest extracting the at least one feature data for each of the brain wave data and the brain activity data. Each of Shim, Sun, and Mumtaz discloses some means of generating brain activity data based on the brain wave data by processing EEG data to remove noises and artifacts which may reflect non-brain activity (see Shim Page 1001, section 2.3 EEG recordings and pre-processing; Sun Page 431, Section II, part B; Mumtaz Page 110, section 2.3 Noise removal from the EEG data). However, each of these references teaches away from extracting features for each of the brain wave data and the brain activity data as the generation of brain activity data was performed to remove sources of inaccuracy that were present in the brain wave data and thus the unprocessed data was not utilized for the extraction of feature data. Claim 12 depends from claim 11 and thus is untaught.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNA ROBERTS whose telephone number is (571)272-7912. The examiner can normally be reached M-F 8:30-4:30 EST.
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/ANNA ROBERTS/Examiner, Art Unit 3791