DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/28/2026 has been entered.
Claims Accounting
Applicant's arguments, filed 04/28/2026, have been fully considered.
The following rejections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 04/28/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1 and 15 have been amended.
Claims 6, 14, 20 and 25 have been canceled.
Claims 26 and 27 are newly presented.
Claims 1, 3-5, 8-13, 15, 17-18, 21, and 26-27 are the current claims hereby under examination.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-5, 8-11, 15, 17-18, 21, and 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over Graph Theory Analysis of Functional Connectivity… by Sun et al. (2019) – previously cited, hereinafter “Sun” in view of US Patent Publication 2019/0172232 by Penatti et al., hereinafter “Penatti” in view of US Patent Publication 2016/0029919 by Hebert et al. – previously cited, hereinafter “Hebert”.
Regarding claim 1, Sun teaches a method for providing information on a major depressive disorder (MDD) (Introduction, par. 6; This study focused on functional brain network analysis of EEG signals that can effectively and reliably identify MDD); implemented by a processor (Section II.B, data processing tool was MATLAB, therefore must be implemented on a computer having a processor), the method comprising: receiving brain wave data of an individual from a brain wave measurement device configured to be in close contact with the individual’s scalp and to measure brain waves of the individual (Section II.B., EEG signals were continuously recorded from HyroCel Geodesic Sensor Net); generating brain activity data based on the brain wave data, the brain wave data including a plurality of pieces of brain activity data (Section II.B., generating amplified EEG signals from 128 channels); generating main data by extracting features of the brain activity data (Section II.E; Network metrics were calculated based on the generated and filtered EEG data) and determining the main data based on a statistical scoring method for each extracted feature of the plurality of pieces of brain activity data, the main data exhibiting a statistically significant between-group difference as determined by the statistical scoring method (Section IV.D.; the main data is identified by using t-tests between groups to identify the metrics (i.e., extracted features) that have the greatest discriminatory power between MDD and healthy controls using a significance level of p<0.05); determining whether a major depressive disorder of the individual is present by using a classification model that is run by the processor and is configured to classify the major depressive disorder based on the brain activity data and the main data (Section IV.D.; Classification between patients with major depressive disorder (MDD) and normal controls (NC) is based on the network metrics. The best performing network metrics were used and are considered the main data and are based on the brain activity data), wherein the statistical scoring method includes a statistical hypothesis test (Section IV.D, the main data is identified by using t-tests between groups to identify the metrics (i.e., features) that have the greatest discriminatory power between MDD and healthy controls using a significance level of p<0.05).
Sun does not teach wherein, in response to the statistical hypothesis test, the main data is provided together with the plurality of pieces of brain activity data as inputs to the classification model.
Fig. 2 of Penatti teaches a method of training a classifier by generating enriched data (i.e., features) from the data collected from the sensors (i.e., raw data). Penatti teaches that the enriched data can be used together with the sensor data as input for the feature/classification learning process ([0060-0062, 0078]). Including richer data as inputs in conjunction with raw sensor data enables obtaining classification models with higher accuracy and less complexity ([0062]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Sun such that in response to the statistical hypothesis test, the main data is provided together with the plurality of pieces of brain activity data as inputs to the classification model, in order to obtain a classification model with higher accuracy and less complexity, as taught by Penatti ([0062]). It is noted that the training process as taught by Penatti teaches inputting both enriched data (i.e., features) and sensor data into the classification model. Although Penatti directly mentions using the enriched data and raw data in the training of the model, it is noted that models are trained using the same type of data as used when implementing the models. Therefore, the inputs to the model would consist of both the enriched data and the raw data. The method of Sun uses a hypothesis test in order to determine the features to be used in a classification model, therefore in the combination of Sun and Penatti, the identified features of Sun are input into the classification model after the statistical test (after and in response to the features being identified) along with the sensor data (i.e., brain activity data).
Sun in view of Penatti does not teach outputting the determination to a medical team device configured to continuously monitor the individual.
Hebert teaches a method of using a classifier to confirm a diagnosis of a patient suspected of, or deemed to be predisposed to having or developing a mental disorder, including MDD ([0069, 0306]). One possible method of communicating the results includes displaying (i.e., outputting) them on a monitor and/or to a health care provider to choose the appropriate course of action based on the data/results ([0516]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Sun in view of Penatti to include outputting the determination to a medical team device configured to continuously monitor the individual, in order to provide a health care provider the determination and they can choose the appropriate course of action based on the data/results, as taught by Hebert ([0516]).
Regarding claim 3, the combination of Sun, Penatti, and Hebert teaches the method of claim 1, wherein the features of the brain activity are extracted by determining a functional connectivity between the plurality of pieces of brain activity data (Sun; Section II.C. Each EEG electrode is defined as a node and the functional connectivity matrices were calculated between each node), and determining the features of the brain activity data based on a network structural characteristic of the functional connectivity (Sun; Section II.E.; Network metrics used for the classification are based on the functional connectivity matrices).
Regarding claim 4, the combination of Sun, Penatti, and Hebert teaches the method of claim 3, but does not teach wherein the determining of the functional connectivity includes, determining a connectivity of a phase locking value (PLV) for each of the plurality of pieces of brain activity data, wherein the features of the brain activity data are determined based on strength and a clustering coefficient of the connectivity of the PLV for the each of the plurality of pieces of brain activity data.
Sun further teaches multiple ways to determine connectivity. In the scenario relied upon by the combination of Sun and Hebert as applied to claim 3, functional connectivity is determined by the ICoh method, as this method produced the most discriminatory results between MDD and healthy groups. However, Sun also teaches using the PLV to determine functional connectivity for each of the plurality of pieces of brain activity data (Section II.C.; “To construct functional connectivity matrix, PLV was used as a coupling method”). Sun further teaches the analysis of network metrics (i.e., features) such as Clustering coefficient (CC) for use in discriminating between MDD and healthy groups (Section IV.A; Section II.C; Network Metrics included Clustering coefficient (CC) and the edges of the network represent the connectivity strength between electrodes).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Sun in view of Hebert, such that the determining of the functional connectivity includes, determining a connectivity of a phase locking value (PLV) for each of the plurality of pieces of brain activity data, wherein the features of the brain activity data are determined based on strength and a clustering coefficient of the connectivity of the PLV for the each of the plurality of pieces of brain activity data. This combination comprises combining prior art elements according to known methods to yield predictable results. See MPEP 2143.I.A. Sun teaches the potential use of PLV and any of the identified network metrics (including clustering coefficients).
Regarding claim 5, the combination of Sun, Penatti, and Hebert teaches the method of claim 1, wherein the classification model is further configured to output 0 or 1 depending on whether the major depressive disorder is present in the individual (Sun; Classification into the MDD or NC group is equivalent to an output of 0 or 1. Classification into the NC group can be equivalent to 0 and MDD can be equivalent to 1), wherein the output of the classification model is provided to the medical team device (See the rejection of claim 1).
Regarding claim 8, the combination of Sun, Penatti, and Hebert teaches the method of claim 1, wherein the main data includes: brain activity data of a right isthmus of cingulate, and brain activity data of a left postcentral area (Sun; Section II.B; Data from standard international 10/20 system would include data from a right isthmus of cingulate and a left postcentral area).
Regarding claim 9, the combination of Sun, Penatti, and Hebert teaches the method of claim 8, wherein the brain activity data of the right isthmus of cingulate is at least one of theta strength, alpha strength, a theta clustering coefficient, and an alpha clustering coefficient (Sun; Section III.A, Fig. 4; Network metrics of Clustering Coefficient (CC) in the alpha and theta bands were calculated), and wherein the brain activity data of the left postcentral area is at least one of delta strength, alpha strength, and an alpha clustering coefficient (Sun; Section III.A, Fig. 4; Network metrics of Clustering Coefficient (CC) in the alpha band were calculated).
Regarding claim 10, the combination of Sun, Penatti, and Hebert teaches the method of claim 1, further comprising: filtering the brain activity data based on a band pass filter (Sun; Section II.B; after receiving and amplifying the data, the recordings are band pass filtered between 0.5 and 40 Hz).
Regarding claim 11, the combination of Sun, Penatti, and Hebert teaches the method of claim 1, wherein the brain wave data is defined as brain wave data obtained in a resting state (Section II.B.; for each subject, resting state EEG signals were recorded).
Regarding claim 15, the combination of Sun, Penatti, and Hebert teaches a device for providing information on a major depressive disorder (MDD), the device comprising: a brain wave measurement device configured to receive brain wave data of an individual, the brain wave measurement device further configured to be in close contact with the individual’s scalp and to measure brain waves of the individual (Sun; Section II.B; EEG signals are collected using a 128 channel HydroCel Geodesic Sensor Net (HCGSN)); and a processor coupled to the receiver to communicate therewith (Sun; Section II.B, data processing tool was MATLAB, therefore must be implemented on a computer having a processor), wherein the processor is further configured to generate brain activity data based on the brain wave data, the brain activity data including a plurality of pieces of brain activity data (See the rejection of claim 1), generate main data by extracting features of the brain activity data and determining the main data based on a statistical scoring method for each extracted feature of the plurality of pieces of brain activity data, the main data exhibiting a statistically significant, between-group difference as determined by the statistical scoring method (See the rejection of claim 1); determine whether a major depressive disorder of the individual is present by using a classification model that is run by the processor and is configured to classify a major depressive disorder based on the brain activity data and the main data (See the rejection of claim 1), and output the determination to a medical team device configured to continuously monitor the individual (See the rejection of claim 1), wherein the statistical scoring method includes a statistical hypothesis test (See the rejection of claim 1), and wherein, in response to the statistical hypothesis test, the main data is provided together with the plurality of pieces of brain activity data as inputs to the classification model (See the rejection of claim 1).
Regarding claim 17, the combination of Sun, Penatti, and Hebert teaches the device of claim 15, wherein the processor is further configured to determine a functional connectivity between the plurality of pieces of brain activity data (Sun; Section II.C; Each EEG electrode is defined as a node and the functional connectivity matrices were calculated between each node) and determine the features of the brain activity data based on a network structural characteristic of the functional connectivity (Sun; Section II.E.; Network metrics used for the classification are based on the functional connectivity matrices).
Regarding claim 18, the combination of Sun, Penatti, and Hebert teaches the device of claim 17, wherein the processor is further configured to determine a connectivity of a phase locking value (PLV) for each of the plurality of pieces of brain activity data and determine the feature based on strength and a clustering coefficient of the connectivity of the PLV for the each of the plurality of pieces of brain activity data (See the rejection of claim 4).
Regarding claim 21, the combination of Sun, Penatti, and Hebert teaches the device of claim 15, wherein the main data includes brain activity data of a right isthmus of cingulate, and brain activity data of a left postcentral area (Sun; Section II.B; Data from standard international 10/20 system would include data from a right isthmus of cingulate and a left postcentral area), wherein the brain activity data of the right isthmus of cingulate is at least one of theta strength, alpha strength, a theta clustering coefficient (Sun; Section III.A, Fig. 4; Network metrics of Clustering Coefficient (CC) in the alpha and theta bands were calculated), and an alpha clustering coefficient, and wherein the brain activity data of the left postcentral area is at least one of delta strength, alpha strength, and an alpha clustering coefficient (Sun; Section III.A, Fig. 4; Network metrics of Clustering Coefficient (CC) in the alpha band were calculated).
Regarding claim 26, the combination of Sun, Penatti, and Hebert teaches the device of claim 15, wherein the statistical scoring method orders the extracted features according to a degree of association with a class corresponding to major depressive disorder or normal (Sections III.B. and IV.D.; The statistical scoring method determines which measures show statistical differences between groups. To further differentiate the measures that effectively detect depression, the relationships between the network metrics having significant differences were determined using receiver operating characteristic (ROC) plots and the resulting area under the ROC curve (AUC) values. The AUC is used to determine the precision of the diagnostic test (between MDD and NC), so AUC can be considered a degree of association with a class corresponding to MDD or NC, because it represents the ability to discriminate between the two. Sun further teaches that an AUC value less than 0.70 indicates that the precision of the diagnostic test is poor, and therefore the metric would not be a suitable discriminatory feature (i.e., main data). Sun identifies 0.70 as a threshold value for the AUC (Section IV.D.) to determine which features have sufficient (better than poor) precision. Sun orders the extracted features by those having an AUC above the 0.70 threshold and those having an AUC below the 0.70 threshold) and wherein the main data is determined based on an order of the extracted features according to the degree of association (The main data is chosen based on the extracted features having an AUC ordered above the 0.70 threshold).
Regarding claim 27, the combination of Sun, Penatti, and Hebert teaches the method of claim 1, wherein the statistical scoring method orders the extracted features according to a degree of association with a class corresponding to major depressive disorder or normal (See the rejection of claim 26), and wherein the main data is determined based on an order of the extracted features according to the degree of association (See the rejection of claim 26).
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Sun in view of Penatti in view of Hebert, as applied to claim 1, in view of EEG source functional connectivity reveals abnormal… by Whitton et al. (2018) – previously cited, hereinafter “Whitton”.
Regarding claim 12, the combination of Sun, Penatti, and Hebert teaches the method of claim 1, but does not teach wherein the generating of the brain activity data includes converting the brain wave data into the brain activity data, by using at least one among low-resolution brain electromagnetic tomography (LORETA), standardized low-resolution brain electromagnetic tomography (sLORETA), exact resolution brain electromagnetic tomography (eLORETA), minimum-norm estimate (MNE) and dynamic statistical parametric mapping (dSPM).
Whitton teaches that eLORETA can be applied to EEG to estimate functional connectivity in regions of interest of the brain. This method produces functional connectivity measures corrected for the effects of volume conduction as it represents the connectivity of two signals after the potentially artifactual zero-lag contribution has been excluded (Pg. 3, par. 1).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method taught by Sun, Penatti, and Hebert such that the generating of the brain activity data includes converting the brain wave data into the brain activity data, by using exact resolution brain electromagnetic tomography (eLORETA), to correct for the effects of volume conduction as it represents the connectivity of two signals after the potentially artifactual zero-lag contribution has been excluded, as taught by Whitton (Pg. 3, par. 1).
Regarding claim 13, the combination of Sun, Penatti, Hebert, and Whitton (see the rejection of claim 12 above) teaches the method of claim 1, wherein the brain activity data includes a current source density (CSD) in at least one brain area among banks of the superior temporal sulcus, caudal anterior cingulate, caudal middle frontal, cuneus, entorhinal, frontal pole, fusiform, inferior parietal, inferior temporal, insula, isthmus cingulate, lateral occipital, lateral orbitofrontal, lingual, medial orbitofrontal, middle temporal, para central, para hippocampal, pars opercularis, pars orbitalis, pars triangularis, pericalcarine, post central, posterior cingulate, precentral, precuneus, rostral anterior cingulate, rostral middle frontal, superior frontal, superior parietal, superior temporal, supramarginal, temporal pole, and transverse temporal.
The combination of Sun, Penatti, Hebert, and Whitton calculates a current source density across voxels during the eLORETA method (Whitton; Pg. 3, par. 1). The regions of interest include the middle temporal gyrus, para hippocampal gyrus, posterior cingulate, frontal pole, supramarginal gyrus, cingulate gyrus, and the precuneus cortex (Whitton; Pg. 5, par. 1; Table 1).
Response to Arguments
Applicant’s arguments, filed 04/28/2026 have been fully considered.
The amendments to the claims overcome the rejections under 35 U.S.C. 101.
Applicant’s assertions regarding the rejection of the independent claims under 35 U.S.C. 103 are acknowledged. These assertions are moot as they are based on amendments to the claims not entered at the time of the previous Office action. The newly presented limitations are rejected on new grounds above.
Conclusion
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/NELSON ALEXANDER GLOVER/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791
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