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 .
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 and 25-26 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, and determines a likelihood of mTBI based on the first and second vector. 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).
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, and determines a likelihood of mTBI based on the first and second vector, as taught by Vakorin, because Nenadovic requires detecting brain injury using PSM, but fails to detail specific brain injuries, and Vakorin teaches that PSM vectors can be correlated to likelihood of mTBI in a subject.
Regarding claims 2 and 20, Gerber 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)).
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 and 9-14 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. It is noted that Gerber teaches appropriate statistical test can used for the comparison.
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, because Gerber requires a statistical test be performed for comparing, but fails to provide details, and Geva teaches using a Kullback-Liebler test can be used as a statistical test for comparing.
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.
Response to Arguments
Applicant's arguments filed 03/13/2026 have been fully considered but they are not persuasive.
Applicant’s arguments with respect to 35 U.S.C. 112(b) rejections are persuasive and have been withdrawn.
Applicant’s arguments with respect to newly added claim 29 has been considered but is moot because amendments require new grounds of rejection.
Applicant broadly contends that Vakorin fails to teach first vector and second vector representing a first and second set of phase synchrony measures, on page 10 of the Remarks. It is noted that the argument fails to expressly point out why Vakorin does not teach the limitation. Vakorin discloses that oscillatory phase synchrony measurements can be used to detect and determine mTBI (see abstract). Vakorin further discloses obtaining vectors of the PSM and using them to make the mTBI determination, “[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) and “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: Phase Synchronization Analysis; section: Classification). As such, Vakorin teaches the amended limitation.
Applicant further contends that none of the references cited teach newly added claims 28 and 30, on page 11 of the Remarks. It is noted that the argument fails to expressly point out why Vakorin does not teach the limitation. Valkorin teaches the first and second vectors are in a phase synchrony and that the likelihood of mTBI is 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)). As such, Vakorin teaches newly added claims 28 and 30.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Plenz teaches the topological identification of electrode positions on a microelectrode array with respect to brain region and cortical layer. US 20090036791
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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/MARTIN NATHAN ORTEGA/Examiner, Art Unit 3791 /TSE W CHEN/Supervisory Patent Examiner, Art Unit 3791
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