SYSTEM AND METHOD FOR CONCUSSIVE IMPACT MONITORING

§101 §103

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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/20/2026 has been entered. 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 1-35 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 18 follows. STEP 1 Regarding claim 18, the claim recites a series of steps or acts, including determining a likelihood of mTBI based on the first and second vectors. 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 step of determining a likelihood of mTBI based on the first and second vectors sets forth a judicial exception. This step describes a mathematical calculation that is a mathematical operation or an act of calculating using mathematical methods to determine a variable, e.g. performing an arithmetic operation such as calculating a probability. Thus, the claim is drawn to a Mathematical Concept, 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 probability of an mTBI 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 probability, nor does the method use a particular machine to perform the Abstract Idea. 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 accessing brain electrical activity data, collecting current brain data, generating sets of PSM vectors that include data related to the electrical activity between electrode pairs. Retrieving and Obtaining data to generate vectors that include data related to electrical activity is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the retrieving, obtaining, and comparing 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. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps 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 1, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The recited memory arrangement is configured to perform pre-solutional data gathering and the computer system is configured to perform the Abstract Idea. 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. The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to data gathering and processing. The retrieving, obtaining, and calculating steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims. However, claim 16, and 15 since it is dependent therefrom, seems to contain a particular device. Specifically, the helmet placed over the wearable garment in combination with the electrodes of claim 8 and the accelerometer of claim 11. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claims 1-4, 17-18, 20-22, 25-26, and 31-33 are rejected under 35 U.S.C. 103 as being unpatentable over Nenadovic et al. (US 20190059769- Previously cited), hereinafter Nenadovic, and further in view of Vakorin et al. (Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity 2016- Previously cited), hereinafter Vakorin. Regarding claims 1 and 18, Nenadovic teaches a system for detecting brain injury (see abstract and ¶ [0056]), comprising: a memory arrangement (216) including stored brain data corresponding to an electrical activity of a brain of a subject at a plurality of locations in the brain during a first time period (see fig. 2 and ¶ [0112], storage device 216 comprises current and historical brain signals); and a processor receiving, the stored brain data, and current brain data corresponding to the electrical activity of the brain of the subject at the plurality of locations during a second time period, wherein the second time period is after the first time period (¶ [0112] and fig. 17A, stores historical (first period) and current data (second period)), the processor generating a first set of phase synchrony measures (PSM) corresponding to frequency band-specific oscillatory phase synchrony of the stored brain data, and a second set of PSM corresponding to frequency band-specific oscillatory phase synchrony of the current brain data (¶ [0062], the obtain brainwaves are used to “calculate the phase synchrony.”), Nenadovic fails to teach wherein the processor generates a first vector and second vector representing a first and second set of PSM for each of a plurality of frequency bands and determines a likelihood of mTBI based on the first and second vector, wherein each set of PSM includes data related to electrical activity between each of a plurality of pairs of the locations. It is noted, Nenadovic is directed to monitoring the progression of brain injury and treatment thereof using PSM (see abstract). Vakorin teaches that oscillatory phase synchrony measurements can be used to detect and determine mTBI (see abstract). That is, “[t]he phase differences can be projected as a series of two-dimensional vectors onto the unit circle, one per time point τ = τ1, …, τN. The phase-locking value PLVx, y(f), which reflects the amount of phase-synchrony between two signals across time, is computed as the length of the resultant (mean) vector” (emphasis added) (section: Phase Synchronization Analysis). Moreover, “Support Vector Machine (SVM) learning was used to predict the clinical status Y of a subject (mTBI or control) from a set of features X obtained from the subject[]” (section: Classification). The vectors, e.g. first, second, etc., represent a set of PSM for each of a plurality of frequency bands (pg. 16-17, “Thirty frequency points equally spaced on alogarithmic scale were selected to cover the range between 1Hz and 75Hz” and “For each epoch, for all pairs of 90 regions of interest (ROIs), frequency-specific phase differences were computed as functions of time. The phase-locking value, PLV x, y (f), was calculated as relative stability of the phase differences between two signals at a given frequency, subsequently averaging across epochs. Thus, 30 90-by-90 matrices were produced for each subject, representing functional connectivity in terms of phase-locking between 90 neuromagnetic sources at 30 frequency points.” That is, there are 30 frequency points from which 30 sets of PSMs in a 90 x 90 matric are produced. Moreover, “[f]or each epoch, for all pairs of 90 regions of interest (ROIs), frequency-specific phase differences were computed as functions of time” and “Each of the samples (subjects) i = 1, ..., m, where m = 41, can be treated as a point x in a n dimensional feature space, where n is the total number of features—unique combinations of all the connections and frequencies of interest” indicates that set of PSM includes data related to the electrical activity between each of the pairs). Additionally, the limitations does not require separately measuring electrical activity data, instead, merely that PSM data is related to electrical activity between pairs of electrodes. One of ordinary skill in the art understands that phase synchrony measured between two pairs of electrodes necessarily requires information of the neuromagnetic (electrical) activity at the two positions. Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic, such that the processor generates a first vector and second vector representing a first and second set of PSM for each of a plurality of frequency bands to determine a likelihood of mTBI and each set of PSM data includes electrical activity between each of a plurality of pairs, as taught by Vakorin, because Nenadovic requires detecting brain injury using PSM, but fails to detail specific brain injuries, and Vakorin teaches that PSM vector sets and electrical activity data between the pairs can used to determine a likelihood of mTBI in a subject. Regarding claims 2 and 20, Nenadovic teaches wherein the first time period during which the subject is physically inactive (¶ [0140], “results obtained with recordings under conscious states are compared with those taken during unconscious states, which included sleep (all the stages) and seizures.”) Regarding claim 3 and 21, Nenadovic teaches wherein the second time period corresponds to a time period during which the subject is engaged in physical activity (¶ [0140], “results obtained with recordings under conscious states are compared with those taken during unconscious states, which included sleep (all the stages) and seizures.” Conscious being the physical activity, e.g., awake vs asleep). Regarding claims 4 and 22, Nenadovic teaches wherein the processor is further configured to generate a first statistical distribution of the first set of PSM and a second statistical distribution of the second set of PSM and compare the first and second statistical distributions to determine the likelihood of mTBI (¶ [0132] and 17A, “The phase synchrony index (R) may be calculated using a 1-second running window, obtained from the phase differences using the mean phase coherence statistic,” requires that statistical distribution of the phase synchrony, which is computed for each input of at least two brain signals). Regarding claim 17, Nenadovic teaches wherein the stored data corresponds to a normative distribution of data of the electrical activity determined from a set of standards (¶ [0112,0124,0130,0148] and fig. 3, stored data comprises “normal” data, e.g. healthy). Regarding claim 25, Nenadovic teaches wherein the time epochs each have a duration of about one second (¶ [0132], “The phase synchrony index (R) may be calculated using a 1-second running window”). Regarding claim 26, Nenadovic teaches wherein the second time period has a predetermined duration, but does not explicitly teach the duration is two hours (¶ [0135-136], “The longer the time epoch is that is being analyzed, generally the lower the phase synchrony value” and “Phase synchrony is always calculated between 2 electrodes for the specified time epoch”). “Where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation.” In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955). As such, it would have been within the skill of the art, through routine optimization, to determine the second time period duration is two hours as disclosed above. Regarding claim 28, Valkorin teaches the first and second vectors are in a phase synchrony (section: Phase Synchronization Analysis, “The phase-locking value PLVx, y(f), which reflects the amount of phase-synchrony between two signals across time, is computed as the length of the resultant (mean) vector” (emphasis added)). Regarding claim 30, Valkorin teaches wherein determine the likelihood of mTBI based on a timing corresponding to the PSM values in the first and second sets of PSM (section: Phase Synchronization Analysis, “The phase-locking value PLVx, y(f), which reflects the amount of phase-synchrony between two signals across time, is computed as the length of the resultant (mean) vector” (emphasis added)). Regarding claim 31, Vakorin teaches wherein each of the first and second vectors has a dimension n(n-1)/2 for each frequency band (pg. 20, “To quantitatively compare the contribution of individual frequency bands to the contrast depicted in Fig 6A, we performed a series of steps testing difference in proportions. First, we identified the wavelets closest to the central frequencies of five canonical frequency bands: 2 Hz(delta), 6 Hz (theta), 11 Hz (alpha), 23 Hz (beta), 48 Hz (lower gamma). Then, for a given z-sore threshold (1% tails), at each central wavelet frequency, we counted connections (out of the total 90 89/2 = 4005) within the positive and negative tails of the overall distribution of z scores (see Fig 6C and 6D).” (emphasis added) indicating the n(n-1)/2. In this case, “n” representing the matrix at each frequency). Regarding claim 32, Vakorin teaches wherein the PSM values are computed based on a phase locking value (PLV) measuring the degree to which a phase of a first oscillating signal estimates a phase of another concurrent oscillating signal (pg. 17, “The phase-locking value PLV x, y (f), which reflects the amount of phase-synchrony between two signals across time” (emphasis added)). Regarding claim 33, Vakorin teaches wherein PLV is calculated based on the equation PNG media_image1.png 31 120 media_image1.png Greyscale , wherein t corresponds to time samples, j is imaginary, T is the total length of the signal samples, and PNG media_image1.png 31 120 media_image1.png Greyscale corresponds to the instantaneous phase difference between the two signals (pg. 16-17, see equation 2, which represents “relative phase Δϕx(τ,f) of two signals, x(t)and y(t),is defined as a time series of the difference between the instantaneous phase of each signal.” PNG media_image2.png 50 342 media_image2.png Greyscale Equation 2. See equation 4, for the PLV determination that is “phase differences can be projected as a series of two-dimensional vectors onto the unit circle, one per time point τ = τ 1 , . . ., τ N . The phase-locking value PLV x, y (f), which reflects the amount of phase-synchrony between two signals across time, is computed as the length of the resultant (mean) vector” and “i” is imaginary. PNG media_image3.png 103 547 media_image3.png Greyscale Equation 4). Claims 5, 6, 15-16, 23, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Nenadovic in view of Vakorin, as applied to claim 1, and further in view of Chu et al. (US 10105076- Previously cited), hereinafter Chu. Regarding claim 5 and 23, Nenadovic-Vakorin fail to teach wherein the processor further receives (iii) motion data corresponding to an acceleration motion of a head of the subject during the second time period and wherein the processor is further configured to generate a cumulative recent head impact (CRHI) value based on the motion data, and to generate the second set of PSM using only those portions of the current brain data corresponding to time epochs within the second time period when the CRHI value exceeds a predetermined threshold value. Chu teaches a system and method for monitoring physiological parameters of a subject (see ABSTRACT) comprising electrodes (see col. 11-12 [56-67, 1-20]), to determine mTBI (see col. 19 [33-44]). Chu teaches determining likelihood of mTBI is based on comparing the data of a subject engaged in a physical activity with a predetermined threshold, the threshold based on data gathered in a first time period (see col. 13-14 [57-67, 1-12], “The single impact over-exposure threshold may be at the level that is inclusive of diagnosed concussion” therefore indicating likelihood of mTBI, and the under threshold is indicative of no mTBI, e.g., normal). Chu further teaches wherein the processor further receives motion data corresponding to an acceleration motion of a head of the subject during the second time period (col. 6 [36-41]) and wherein the processor is further configured to generate a cumulative recent head impact (CRHI) value based on the motion data (“monitoring at least one physiological parameter of players engaged in a sports activity, such as pressure or force on a body part (e.g., the head) and/or the acceleration of a body part (e.g., linear acceleration or rotational acceleration)”), and to generate the second set of PSM using only those portions of the current brain data corresponding to time epochs within the second time period when the CRHI value exceeds a predetermined threshold value (see col. 7-9, 15 and 22 [58-64; 61-67; 1-27; 51-53; 12-16], “If the calculated value is not peak magnitude alertable (i.e., it is below the alert threshold), then the calculated value is evaluated to determine if it should be added to the cumulative calculation. If the calculated value is added to the cumulative calculation, the processed cumulative value is compared to the predetermined cumulative impact alert threshold. If the processed cumulative value exceeds that alert threshold, then a cumulative alert (or multiple impact alert) is sent to the remote alert unit 1120” (emphasis added) a severity level is then computed to aid in indicating mTBI; see col. 15 [44-50] “The cumulative impacts correspond to multiple impacts over a defined period of time. The defined period of time corresponds to seven days in one specific example. The accumulation process may assign a “weight” to older impacts. The accumulation process also allows for removal of older impacts that are beyond the time period to allow for newer impacts to be added” indicating that measurements at a specific time epoch are used to generate cumulative reports and likelihood of mTBI). Chu further teaches determining likelihood of mTBI is based on comparing the impact data of a subject engaged in a physical activity with a predetermined threshold, the threshold based on data gathered in a first time period (see col. 13-14 [57-67, 1-12], “The single impact over-exposure threshold may be at the level that is inclusive of diagnosed concussion” therefore indicating likelihood of mTBI, and the under threshold is indicative of no mTBI, e.g., normal). It would have been obvious to one ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin, acceleration motion of the users head is received, as taught by Chu, to aid in determining the likelihood of mTBI. It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin, such that a cumulative recent head impact value is generated based on CRHI values that exceed a predetermined threshold, as taught by Chu, to aid in determining likelihood of concussion. It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin, such that the second set data, e.g., PSM, brain data, generated corresponds to time epochs when CRHI exceeds a threshold, as taught by Chu, to aid in determining likelihood of concussion (see col. 15 [44-47]). The combination of Nenadovic-Vakorin therefore teaches, generating second set of PSM (¶ [0140] of Nenadovic) using only those portions of the current brain data corresponding to time epochs within the second time period when the CRHI value exceeds a predetermined threshold value (see col. 7-9, 15 and 22 [58-64; 61-67; 1-27; 51-53; 12-16] of Chu). Regarding claim 6, Chu teaches wherein the CRHI values is generated based on absolute magnitudes of acceleration motion of the head of subject (see col. 6 and 20 [39-42,19-29]“pressure or force on a body part (e.g., the head) and/or the acceleration of a body part (e.g., linear acceleration or rotational acceleration), both resulting from an impact or series of impacts to the player(s)” and “The player helmet 1110, includes an in-helmet unit (or monitoring unit) 1200 that is configured to monitor and analyze both single and cumulative impacts to the player wearing the player helmet 1110 (see FIG. 1B). The impact data may be correlated to a multi dimensional severity measure (e.g., weighted principal component score such as Head Impact Technology Severity Profile (HIT.sub.SP) that takes into account linear acceleration, Head Injury Criterion (HIC), Gadd Severity Index (GSI), and impact direction. The impact severity may also be weighted by impact location”). Regarding claims 15 and 27, Nenadovic-Vakorin-Chu teach wherein the likelihood of mTBI is a probability that the subject suffered from mTBI (see abstract Vakorin), and the processor is further configured to activate an external signal when the probability that the subject suffered from mTBI is above a predetermined threshold probability value (see col. 15 [61-64] of Chu, “Once the cumulative impact recorded in the database exceeds the multiple impact over-exposure threshold, the alert is generated and sent to the alert unit 1120”). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin, such that an external signal is activated, as taught by Chu, as it would merely be combining prior art elements (devices for monitoring brain injury) according to known methods (sending external alarm signals) to yield predictable results. Regarding claim 16, Nenadovic-Vakorin-Chu teach a helmet (1110) placed over a wearable garment (1200) onto the head of the subject (see col. 7 [17-23] and fig. 1B-1C of Chu), wherein the external signal is sending an alarm signal to a remote monitoring device 1120 (see fig. 1A of Chu). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin-Chu, such that the device comprises a helmet placed over a wearable garment onto the head of the subject, as taught by Chu, as it would merely be combining prior art elements (devices for monitoring brain injury) according to known methods (device comprising a helmet and wearable garment) to yield predictable results. Claims 7 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Nenadovic in view of Vakorin and Chu, as applied to claims 5 and 18, and further in view of Greenwald et al. (US 20060074338 A1- Previously cited). Regarding claims 7 and 24, Nenadovic-Vakorin-Chu fail to teach wherein the predetermined threshold corresponds to the acceleration motion of the head when the subject is substantially physically inactive. Greenwald teaches a system and method for quantifying the severity of head impact based on monitoring head acceleration exceeding a threshold, the threshold set when the head is substantially physically inactive (¶ [0008,0010,0016,0030,0041], “For example, the sensors 22 can be single-axis accelerometers that measure head acceleration but only generate signals when the sensed head acceleration surpasses 10 G's”). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin-Chu, such that the predetermined threshold corresponds to the acceleration motion of the head when the subject is substantially physically inactive, as taught by Greenwald, because Chu requires a threshold but fails to provide details, and Greenwald teaches that the threshold corresponds to physically inactive head acceleration motion. Claims 8-14 are rejected under 35 U.S.C. 103 as being unpatentable over Nenadovic in view of Vakorin and Chu, as applied to claim 1, and further in view of Turner et al. (US 20060088619 A1- Previously cited). Regarding claim 8, Nenadovic-Vakorin-Chu fail teach wherein a plurality of electrodes coupled to a wearable garment for a head of the subject so that, when the garment is worn in a desired configuration, each of the electrodes is positioned on a portion of scalp adjacent to a corresponding one of the plurality of locations, the electrodes being couplable to the processor to transfer data corresponding to the detected electrical activity thereto. Turner teaches a wearable garment comprising a plurality of EEG electrodes 18, positioned at plurality of locations on the users scalp, to record brain activity and communicate the recoded data to a remote location e.g., computer, server, etc. (see ABSTRACT and ¶ [0010] and Figs. 2-10). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin-Chu, such the EEG device comprises a wearable garment and a plurality of electrodes positioned thereon, as taught by Turner, because Nenadovic requires capturing brainwave signals via electrodes, but fails to provide details, and Tuner teaches it can be accomplished with an electrode head device with specific positions. Regarding claims 9-10, Turner teaches wherein a portion of the plurality of electrodes 18 are positioned along a transverse, coronal, sagittal band of the head of the subject, the transverse band approximating a circumference of the head of the subject (see figs. 2-10). Regarding claims 11-12, Chu teaches at least one accelerometer coupled to the helmet, the at least one accelerometer is configured to detect linear acceleration and rotational acceleration in pitch, yaw, and roll, and the at least one accelerometer being couplable to the processor to transfer data corresponding to the detected acceleration motion of the head of the subject (see col. 7 and 14 [36-39; 13-46]). Regarding claim 13, Nenadovic-Vakorin-Chu teach a data communications device operably connected to the plurality of electrodes and the at least one accelerometer so that the electrical activity detected by the electrodes and the acceleration motion detected by the at least one accelerometer are communicated via the data communications device to the processor (see fig. 2 of Nenadovic, processing device 232 is in communication with sensor 102). Regarding claim 14, Nenadovic teaches wherein the data communications device communicates with the processor via a wireless connection (¶ [0191]). Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Nenadovic in view of Vakorin, as applied to claim 1, and further in view of Geva et al. (US 20040230105 A1- Previously cited). Regarding claim 19, Nenadovic-Vakorin fail to teach wherein the first and second sets of PSM are compared using a Kullback-Liebler method to determine the likelihood of mTBI. Geva teaches a system and method for predicting changes of physiological states in a subject (see ABSTRACT). Geva further teaches comparing two series samples can be performed using a Kullback-Leibler divergence method (¶ [00367-368]). It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin, such that the statistical comparison is performed using Kullback-Liebler divergence, as taught by Geva, to aid in predicting changes of physiological states in subjects. Claim 29 is rejected under 35 U.S.C. 103 as being unpatentable over Nenadovic in view of Vakorin, as applied to claim 1, and further in view of D’Arcy et al. (US 20210196182 A1), hereinafter D’Arcy. Regarding claim 29, Nenadovic-Vakorin fail to teach wherein the processor determines the likelihood of mTBI based on identities of the electrodes corresponding to the first and second sets of PSM. D’Arcy teaches a system and method for measuring brain activity to assess brain function (abstract and ¶[0031,0034]). The invention further comprises electrodes and identifying groups of those electrodes to maximize optimization of the desired brain activity signal (¶[0082-87], “an optimization technique to retain only the desired type of activity (e.g. motor) and remove all the others”). Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin, such that the processor determines the identities of the electrodes, as taught by D’Arcy, to aid in optimizing the desired brain activity signal and subsiding the rest. Claim 34 is rejected under 35 U.S.C. 103 as being unpatentable over Nenadovic in view of Vakorin, as applied to claim 1, and further in view of Vinck et al. (An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias- 2011), hereinafter Vinck. Regarding claim 34, Vakorin contemplates using weighted phase lag index (WPLI) to minimize the volume conduction effects (pg. 21), but Nenadovic-Vakorin fail to teach that the WPLI equation is PNG media_image4.png 30 116 media_image4.png Greyscale wherein X is a cross-spectrum of signals coming from a pair of the locations, E is a mean, s{X} is an imaginary component of the cross-spectrum and |3{X}| is a magnitude of the cross-sepectrum. Vinck teaches that applying WPLI of recorded neural signals to increase sensitivity to detect phase synchronization and changes (abstract and pg. 1561-62). The WPLI, equation 8, is as follows. PNG media_image5.png 74 482 media_image5.png Greyscale Equation 8 (pg. 1550). This equation is based on “the imaginary component of the cross-spectrum” and “let ℑ{X}and ℜ{X} denote the imaginary and the real component of the cross-spectrum” (pg. 1550). Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin, such that it teaches the PSM values are computed based on the particulars of the WPLI equation of Vinck to minimize the volume conduction effects (pg. 21 of Vakorin). Claim 35 is rejected under 35 U.S.C. 103 as being unpatentable over Nenadovic in view of Vakorin, as applied to claim 1, and further in view of Radwan et al. (Impaired cognitive discrimination and discoordination of coupled theta-gamma oscillations in Fmr1 knockout mice- 2017), hereinafter Radwan, and Thatcher et al. (An EEG Severity Index of Traumatic Brain Injury- 2001), hereinafter Thatcher. Regarding claim 35, Nenadovic-Vakorin fail to teach wherein the PSM values are computed based on a debiased Weighted Phase Lag Index (dWPLI) according to the equation PNG media_image6.png 55 142 media_image6.png Greyscale , wherein X is a cross spectrum of signals from a pair of the locations, E is a mean, ℑ{X} is an imaginary component of the cross-spectrum and |ℑ{X}| is a magnitude of the cross-spectrum and a coherence computed according to equation PNG media_image7.png 55 150 media_image7.png Greyscale where Gij(f) is a cross-spectral density between signals i and j, and Gii(f) and Gjj(f) are two autospectral densities of the i and j signals. Radwan teaches a debiased WPLI to estimate the phase synchrony because it minimizes the influence of volume conduction to a small sample size (pg. 19). Radwan’s debiased WPLI is expressly based on Vinck’s debiased WPLI, structured “where X is the cross-spectrum of signals coming from a pair of electrodes, E is the mean, X} is the imaginary component of the cross-spectrum and [] is its magnitude. We used the debiased weighted phase lag index (dWPLI) WPLI-squared estimator, which removes the positive bias in the estimation process according to equation 2 (Vinck et al., 2010).” PNG media_image8.png 63 397 media_image8.png Greyscale Equation 2 of Radwan. Noticeably, the equation is the same as the claimed equation. Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin, such that the particular dWPLI is used to compute PSM, as taught by Radwan, to minimize the influence of volume conduction to a small sample size and remove the positive bias in the estimation process. Thatcher teaches an EEG severity index of traumatic brain injury that relies on EEG variables entered into a multivariate analysis (abstract). The variables were found to discriminate mild and severe TBI groups (abstract). The EEG variables included coherence (pg. 79, “The EEG variables were grouped into five categories: 1) relative power, 2) total power, 3) coherence,4) phase, and 5) amplitude asymmetry”). Coherence was computed using magnitude-squared coherence, a well-known formula in statistic for computing coherence, reproduce below (pg. 79). PNG media_image9.png 61 241 media_image9.png Greyscale Fig. of Magnitude-squared coherence. Where, “G xy(f ) is the cross-power spectral density and Gxx(f ) and Gyy (f ) are the respective autopower spectral densities” (pg. 79). Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Nenadovic-Vakorin-Radwan, such that the particular dWPLI is used to compute PSM, as taught by Radwan, to aid in assessing the severity of traumatic brain injuries. Response to Arguments Applicant's arguments filed 08/20/2026 have been fully considered but they are not persuasive. Applicant contends that Vakorin only teaches a single two dimensional vector for each phase difference, and not a set of PSM including data related to electrical activity between each of a plurality of pairs of the locations, on page 12 of the Remarks. Examiner disagrees. While the text in Vakorin gives an example of how to calculate one phase based on one phase synchrony measure between two brain locations, in that “phase differences can be projected as a series of two-dimensional vectors onto the unit circle, one per time point” (pg. 17). Vakorin further goes on to disclose that “For each epoch, for all pairs of 90 regions of interest (ROIs), frequency-specific phase differences were computed as functions of time. The phase-locking value, PLV x, y (f), was calculated as relative stability of the phase differences between two signals at a given frequency, subsequently averaging across epochs. Thus, 30 90-by-90 matrices were produced for each subject, representing functional connectivity in terms of phase-locking between 90 neuromagnetic sources at 30 frequency points” (pg. 17 of Vakorin, emphasis added). That is, a plurality of PLV values (set of PSMs) at multiple frequencies (30) based on multiple pairs (90 ROIs). Features are then obtained and used to create a vector xi (pg. 17, “Support Vector Machine (SVM) learning was used to predict the clinical status Y of a subject (mTBI or control) from a set of features X obtained from the subject’s MEG data[52]. These features are represented by frequency-specific phase-locking values (PLV) between the neuromagnetic activity reconstructed for 90 regions of interest (ROIs). Each of the samples (subjects) i = 1, ..., m, where m = 41, can be treated as a point xi in a n dimensional feature space, where n is the total number of features—unique combinations of all the connections and frequencies of interest.”) Subsequently, Applicant contends that Vakorin’s PSM does not include electrical activity between each of a plurality of pairs of the locations. Examiner disagrees. In the quote above of Vakorin, “where n is the total number of features—unique combinations of all the connections and frequencies of interest” indicates data is related to “the neuromagnetic activity reconstructed for 90 regions of interest,” (emphasis added) that is, the electrical activity (pg. 17). Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lachaux teaches a method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. Measuring Phase Synchrony in Brain Signals- 1999 Kaniwa teaches an EEG steady state synchrony patterns sonification using a magnitude-squared coherence equation, PNG media_image10.png 89 371 media_image10.png Greyscale . EEG Steady State Synchrony Patterns Sonification- 2012. Hardmeir teaches deriving phase lag index and weight phase lag index from EEG. Reproducibility of Functional Connectivity and Graph Measures Based on the Phase Lag Index (PLI) and Weighted Phase Lag Index (wPLI) Derived from High Resolution EEG- 2014 Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARTIN NATHAN ORTEGA whose telephone number is (571)270-7801. The examiner can normally be reached M-F 7:10 am - 5:00 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert (Tse) Chen can be reached at (571) 272-3672. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MARTIN NATHAN ORTEGA/ Examiner, Art Unit 3791 /TSE CHEN/Supervisory Patent Examiner, Art Unit 3791