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 .
Claims Accounting
Applicant' s arguments, filed 10/24/2025, 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 10/24/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1, 3-6, 8-10, 13-15, 17, and 20-21 have been amended.
Claims 3, 7, and 19 have been canceled.
Claim 25 is newly presented.
Claims 1, 3-6, 8-15, 17-18, 20-21, and 25 are the current claims hereby under examination.
Claim Objections
Claims 6 and 20 objected to because of the following informalities:
Claims 6 and 20 recite “brain-activity data” in lines 11 and 10, respectively. This should read “brain activity data”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 14 and 25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 14 recites “analyzing whether a major depressive disorder has occurred in one individual of a plurality of individuals; and outputting a result of the analysis to a user mobile device configured to request information on whether a major depressive disorder has occurred in each of the plurality of individuals” in lines 2-6. There is insufficient support for these limitations in the specification, specifically the limitations relating to the information on major depressive disorder in a plurality of individuals, and therefore the limitations are considered new matter. The closest recitation in the published specification of the instant application is identified as par. [0111] and par. [0166]. Par. [0111] discloses that a user mobile device may request information on whether a major depressive disorder has occurred in the individual. Par. [0166] discloses a group of 50 individuals with MDD and 50 individuals in a control group being used to evaluate the classification model. These paragraphs do not disclose a user mobile device configured to request information on major depressive disorder in a plurality of individuals.
A similar recitation is present in claim 25, and claim 25 is rejected under 35 U.S.C. 112(a) for the same reasons as stated above.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-5, 8-15, 17-18, 21, and 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Step 1
Regarding claim 1, the claim recites a series of steps or acts, including determining whether the individual’s major depressive disorder is present by using a classification model configured to classify the major depressive disorder based on the brain activity data. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
Step 2A, Prong One
The claim is then analyzed to determine whether it is directed to any judicial exception. The steps of generating main data by extracting features of the brain activity data and determining whether a major depressive disorder of the individual patient 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 sets forth a judicial exception. These steps describes mathematical calculations (an act of calculating using mathematical methods to determine a variable or number). Thus, the claim is drawn to a Mathematical Concept and/or a mental process, which is an Abstract Idea.
Step 2A, Prong Two
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. The step of outputting the determination to a medical team device configured to continuously monitor the individual does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the determination, nor does the method use a particular machine to perform the Abstract Idea. It is noted that the outputting step is a display of the information, and while it may enable the monitoring of the individual, the outputting of the determination does not effect a particular treatment or particular change. Further, a brain wave measurement device configured to be in close contact with the individual’s scalp and to measure brain waves of the individual is not considered a particular machine, as this recitation describes a generic electroencephalogram (EEG) device.
Step 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of receiving brain wave data, generating brain activity data based on the brain wave data, and outputting the determination to a medical team device. Receiving brain wave data and generating brain activity data based on the brain wave data are well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the receiving and generating steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. The outputting step is recited at such a high level of generality that is amounts to insignificant extra-solution activity. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the receiving step from a brain wave measurement device or the method being performed on a processor do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claim 15, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The recited brain wave measurement device is a generic component (EEG device) configured to perform pre-solutional data gathering activity and the recited processor is a general purpose computer component configured to perform the Abstract Idea and related data gathering steps. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
Dependent claims 3-5, 8-14, 16-18, 21, and 25 also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to data gathering. The determining step recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
It is noted that claims 6 and 20 are not rejected under 35 U.S.C. 101, as these claims recite the use of both the brain activity data and main data as inputs to the classification model, resulting in a two-tier input structure with a significance gate that has the benefit of mitigating overfitting in a small-sample physiological classification.
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, and 21 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 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 (Sections 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); 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).
Sun 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 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, Sun in view of 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, Sun in view of 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, Sun in view of 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, Sun in view of 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, Sun in view of 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, Sun in view of 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, Sun in view of 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, Sun in view of 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).
Regarding claim 17, Sun in view of 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, Sun in view of 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, Sun in view of 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).
Claims 6 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sun in view of Hebert, as applied to claim 1, in view of US Patent Publication 2012/0089330 by Hesch et al., hereinafter “Hesch”.
Regarding claim 6, Sun in view of Hebert teaches the method of claim 1, wherein the statistical scoring method includes a statistical test (Sections 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), but does not teach wherein, in response to the statistical test, the main data is provided together with the plurality of pieces of brain-activity data as inputs to the classification model.
Hesch teaches a method of selecting features for use in a classification model as biomarkers for a mental disease. Hesch teaches using base feature (analogous to the brain activity data) and including features (analogous to main data) along with the base feature if the features result in a statistically significant decrease in the value of the loss function ([0040]; a statistically significant decrease in the loss function comprises using a statistical hypothesis test with the value of the loss functions to determine the main data). Decreasing the value of the loss function indicates a more accurate model, and therefore the inclusion of the additional features (i.e., main data) improve the classification performance of the model.
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 in response to the statistical test, the main data is provided together with the plurality of pieces of brain-activity data as inputs to the classification model, as taught by Hesch ([0040]), in order to improve the classification performance of the model.
Regarding claim 20, Sun in view of Hebert in view of Hesch teaches the device of claim 15, wherein the statistical scoring method includes a statistical test, and wherein, in response to the statistical 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 6).
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Sun 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, Sun in view of 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 Sun 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, 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, 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).
Claims 14 and 25 is rejected under 35 U.S.C. 103 as being unpatentable over Sun in view of Hebert, as applied to claim 1, in view of US Patent Publication 2002/0019748 by Brown, hereinafter “Brown”.
Regarding claim 14, Sun in view of Hebert teaches the method of claim 1, further comprising: analyzing whether a major depressive disorder has occurred in one individual (See the rejection of claim 1); and outputting a result of the analysis to a user mobile device configured to request information on whether a major depressive disorder has occurred (output of the data as applied in claim 1) when a risk of occurrence of the major depressive disorder for the individual is determined, according to a treatment plan, repeatedly performing the receiving of the brain wave data; the generating of the brain activity data; and the determining of whether the individual's major depressive disorder is present. Applicant is reminded that in a method claim, the steps following and dependent from a conditional limitation (i.e. when a risk of occurrence of the major depressive disorder for the individual is determined) do not have to be performed in the method, if the condition precedent recited is not met. An examiner does not have to provide evidence for the method steps that are not required to be performed (MPEP 2111.04 II. “[if] the condition for performing a contingent step is not satisfied, the performance recited by the step need not be carried out in order for the claimed method to be performed.”). See also: Cybersettle, Inc. v. National Arbitration Forum, Inc., 243 Fed.Appx. 603, 606–07 (Fed.Cir.2007).
Sun in view of Hebert does not teach the one individual being one of a plurality of individuals or outputting the result of the analysis to a user mobile device configured to request information of whether a major depressive disorder has occurred in each of the plurality of individuals.
Brown teaches a multiple patient monitoring system that allows a clinician to view and manage the medical status of an entire group of patients with a chronic health condition, such as mental health disorders ([0068]). This system allows a clinician to monitor multiple patients which can optimize efforts and minimize costs in managing the medical needs of the entire group ([0016]).
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 one individual is one of a plurality of individuals or outputting the result of the analysis to a user mobile device configured to request information of whether a major depressive disorder has occurred in each of the plurality of individuals, in order to optimize efforts and minimize costs in managing the medical needs of the entire group, as taught by Brown ([0016]).
The following rejection of claim 14 is being provided in the case that the conditional “when” statement is met.
The combination of Sun, Hebert and Brown teaches the method of claim 1, but does not teach: when a risk of occurrence of the major depressive disorder for the individual is determined, according to a treatment plan, repeatedly performing the receiving of the brain wave data; the generating of the brain activity data; and the determining of whether the individual's major depressive disorder is present.
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]). Patients may be suspected to be suffering from or have a predisposition to a mental disorder, therefore it is likely to develop, or to suffer from, a disorder or disease ([0069, 0077]). The method of diagnosing may be performed multiple times to monitor the susceptible individuals to detect evidence of the development or evolution of major psychiatric disorder. Upon discovery of such evidence, early treatment can be undertaken to combat the disease ([0522]).
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 the combination of Sun, Hebert, and Brown to include when a risk of occurrence of the major depressive disorder for the individual is determined, according to a treatment progress, repeatedly performing the receiving of the brain wave data; the generating of the brain activity data; and the determining of whether the individual's major depressive disorder is present to monitor development of a mental disorder and provide early treatment to combat the disease upon detection, as taught by Hebert ([0522]).
Regarding claim 25, Sun in view of Hebert teaches the device of claim 15, further comprising: analyzing whether a major depressive disorder has occurred in one individual of a plurality of individuals (See the rejection of claim 14); and outputting a result of the analysis to a user mobile device configured to request information on whether a major depressive disorder has occurred in each of the plurality of individuals (See the rejection of claim 14), wherein when a risk of occurrence of the major depressive disorder for the individual is determined, according to a treatment plan, repeatedly performing the receiving of the brain wave data; the generating of the brain activity data; and the determining of whether the individual's major depressive disorder is present (See both conditional rejections of claim 14).
Response to Arguments
Applicant’s arguments, filed 10/24/2025 have been fully considered.
The amendments to claim 13 overcomes the rejections of record, however the amendments to the claims require new objections of claims 6 and 20.
The amendments to the claims overcome the rejections of record under 35 U.S.C. 112(b) of claims 1, 6, 14-15, and 20. However, the amendments to the claims necessitate new rejections of claims 14 and 25 under 35 U.S.C. 112(a).
Applicant’s assertion regarding the rejections under 35 U.S.C. 101 of amended claims 1 and 15 are acknowledged, but are not found persuasive. Applicant’s assertions are not commensurate in scope with the claims. Amended claims 1 and 15 do not require the significance gate or the two-tier input as recited in claims 6 and 20. Further, the recited brain wave measurement device is analogous to a generic EEG device, and does not constitute specialized hardware. The output of the determination to a medical team device configured to continuously monitor the individual is analogous to outputting the determination on a screen. Applicant’s argument that the continuous monitoring constitutes a real-world clinical use and effects a particular change in patient management is not persuasive as the monitoring itself does not result in any particular treatment or particular change. It is noted that the amendments to claims 6 and 20 require the significance gate and result in the two-tier input that mitigates overfitting, and therefore these claims are not rejected under 35 U.S.C. 101.
Applicant’s assertion regarding the rejection of claim 1 under 35 U.S.C. 102 is acknowledged. This assertion is moot as it is 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. It is noted that Applicant’s assertions regarding claims 1 and 15 that the classification model accepts multiple inputs comprising the brain activity data and the main data are not commensurate in scope with the claims. Claims 1 and 15 recite the classification model is configured to “classify the major depressive disorder based on the brain activity data and the main data”. If a classification model is configured to use only the main data as an input to make a classification, and the main data is based on the brain activity data, then the classification model makes classifications based on both the main data and the brain activity data, even if the brain activity data is not an input. It is noted that claims 6 and 20 require that the brain activity data is also an input to the classification model.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NELSON ALEXANDER GLOVER/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791