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 .
Applicant' s arguments, filed 3/2/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 3/2/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1 and 3-16 are the currently pending claims hereby under examination. Claim 2 has been canceled.
Claim Objections
Claims 4 and 15 are objected to because of the following informalities:
In claim 4, line 2: “(ACC)” should be “(dACC)”; and
In claim 15, line 6: “a dorsal cingulate cortex (dACC)” should be "a dorsal anterior cingulate cortex (dACC)”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention.
Claim 8 recites “wherein a first alpha phase is determined before the target alpha phase is determined based on a blood-oxygen-level-dependent (BOLD) response in dorsal anterior cingulate cortex (dACC) evoked by transcranial magnetic stimulation (TMS) pulses before the target alpha phase optimized relative to the first alpha phase is determined, and the first alpha phase is indicative of a strongest activity in the dorsal anterior cingulate cortex (dACC)” in lines 8-12. It is unclear whether the phrase “based on a blood-oxygen-level-dependent (BOLD) response in dorsal anterior cingulate cortex (dACC) evoked by transcranial magnetic stimulation (TMS) pulses” modifies the recited “first alpha phase,” the recited “target alpha phase,” or both. It is further unclear how the phrase “before the target alpha phase optimized relative to the first alpha phase is determined” structurally relates to the earlier-recited determination steps. As written, the ordering and modifier attachment of the recited first alpha phase, target alpha phase, BOLD response basis, and optimized-relative-to relationship are ambiguous. Additionally, claim 8 recites two separate 'before' clauses that each purport to describe temporal ordering of the determination steps, creating ambiguity as to whether these clauses describe the same temporal relationship or two distinct sequential conditions. The Examiner interprets claim 8 as requiring that a first alpha phase is determined from a measured relationship between alpha phase and TMS-evoked BOLD response, and that a later target alpha phase is then determined as optimized relative to that first alpha phase. However, the claim does not clearly recite that relationship, and therefore the scope remains indefinite.
Claims 9 and 10 are rejected by virtue of their dependence from claim 8.
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.
Claims 1 and 3-7 are rejected under 35 U.S.C. 103 as being unpatentable over Peters et al. (Peters, Judith C et al. “Concurrent Human TMS-EEG-fMRI Enables Monitoring of Oscillatory Brain State-Dependent Gating of Cortico-Subcortical Network Activity.” Communications biology 3.1 (2020): n. pag. Web.), hereto referred as Peters, and further in view of George et al. (George, M et al. “Combined TMS-EEG-fMRI. The Level of TMS-Evoked Activation in Anterior Cingulate Cortex Depends on Timing of TMS Delivery Relative to Frontal Alpha Phase.” Brain stimulation 12.2 (2019): 580–580. Web), hereto referred as George.
Regarding claim 1, Peters teaches that a system for identifying an alpha phase in brain of a subject, comprises: a processor configured to: process one or more of functional magnetic resonance imaging (fMRI) data and electroencephalogram (EEG) data, wherein the fMRI data and EEG data are simultaneously acquired (Peters, p. 1, Abstract: "In participants with adequate motor network reactivity, strong pre-TMS alpha power reduced TMS-evokod hemodynamic activations throughout the bilateral cortico-subcortical motor system (including striatum and thalamus), suggesting shunted network connectivity"; p. 2, l. 11: "...simultaneous combination of Transcranial Magnetic Stimulation (TMS), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) as a potential solution, demonstrating its safety and technical feasibility..."); trigger a transcranial magnetic stimulation (TMS) pulse (Peters, p. 2–3, "Results": "Here, triple-pulse TMS was repeatedly applied at jittered intervals throughout the scanning session"); analyze the fMRI data, the EEG data and the TMS pulse (Peters, p. 7, "Concurrent TMS-EEG-fMRI setup": "...TMS-evoked BOLD activity was analyzed ... as a function of the alpha and beta power in the EEG interval immediately prior to TMS application"; see also p. 8–9).
Also regarding claim 1, Peters does not fully teach determining one or more alpha phases based on a blood-oxygen-level-dependent (BOLD) response in a dorsal anterior cingulate cortex (dACC) evoked by the TMS pulses, wherein a first of the one or more alpha phases is indicative of a strongest activity in the dorsal anterior cingulate cortex (dACC). Peters teaches simultaneous TMS-EEG-fMRI acquisition and EEG-informed fMRI analysis of BOLD activity, but does not disclose determining a specific alpha phase corresponding to the strongest BOLD response in the dACC.
George teaches that TMS-evoked BOLD responses in the anterior cingulate cortex (ACC) depend on timing of TMS delivery relative to frontal EEG alpha phase, and further teaches that “we constructed acausal bandpass filters centered around the individual subject alpha frequency (IAF) to extract alpha phase prior to each TMS pulse” and that “for each pulse, TMS-evoked BOLD responses were extracted locked to the pulse onset and averaged over voxels in each subject’s TMS modulated ACC cluster", demonstrating evaluation of alpha phase and corresponding ACC BOLD response on a per-pulse basis across multiple TMS pulse instances (George, p. 580). George further teaches that “BOLD response was largest when TMS was applied on the rising edge of the alpha wave” (George, p. 580), thereby identifying a specific alpha phase corresponding to the strongest ACC activity. George additionally teaches that “We have created a closed-loop EEG-TMS system to deliver TMS pulses synchronized to individual’s instantaneous frontal alpha phase to maximize modulation of ACC", further evidencing phase-synchronized stimulation based on the identified relationship between frontal alpha phase and ACC response (George, p. 580). Thus, George demonstrates a relationship between alpha phase and TMS-evoked BOLD response across multiple pulse instances and identifies a particular phase, namely the rising edge, corresponding to the strongest ACC response. Because George extracts alpha phase prior to each TMS pulse and extracts corresponding TMS-evoked BOLD responses for each pulse, the relationship between phase and neural response is determined from measured neural responses on a per-pulse basis. This supports determining, from the measured relationship between alpha phase and TMS-evoked BOLD response across multiple pulses, one or more alpha phases and identifying a first alpha phase corresponding to the strongest activity.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Peters in view of George to configure the processor to determine one or more alpha phases based on measured TMS-evoked BOLD responses and their relationship to alpha phase, and to select a first alpha phase from the determined alpha phases corresponding to the strongest BOLD response, as evidenced by George identifying the rising edge of the alpha wave as producing the largest BOLD response. The combination would have been feasible because Peters already performs EEG-informed fMRI analysis of BOLD activity, and George provides evidence that alpha phase modulates ACC BOLD response in a predictable, phase-specific manner.
One of ordinary skill in the art would have recognized that identifying, from among multiple determined alpha phases, the alpha phase yielding the strongest response is a result-effective variable once phase-dependent modulation is known, and would have applied this to improve reproducibility and effectiveness of stimulation. Further, George teaches that TMS-evoked BOLD responses are “averaged over voxels in each subject’s TMS modulated ACC cluster”, demonstrating that spatially defined ACC regions are analyzed rather than treating the ACC as a uniform structure (George, p. 580). It would have been prima facie obvious before the effective filing date of the claimed invention to refine the ACC analysis to a specific subregion such as the dACC, as subdivision of the ACC into functionally distinct subregions, including dorsal and ventral regions, was well known, and selection of a specific ACC subregion would have been a predictable refinement of the ACC cluster analysis taught by George to improve sensitivity and specificity of BOLD analysis. The benefit of the combination would be to enable more accurate targeting of stimulation parameters by determining the optimal alpha phase associated with the strongest dACC BOLD response, thereby improving therapeutic efficacy and reliability of neuromodulation.
Regarding claim 3, The modified Peters teaches that the EEG data is acquired using an MR-compatible EEG system (Peters, p. 8, 'EEG and EMG acquisition': "EEG data were recorded via two MR-compatible “BrainAmp MR plus” amplifiers (16-bit A/D conversion; 0.5 μV resolution; ± 16.384 mV operating range; 5000 Hz sampling rate) powered by MR-compatible, rechargeable PowerPacks (Brain Products, Munich, Germany)", Peters expressly teaches using a MR-compatible EEG system for EEG acquisition).
Regarding claim 4, the modified Peters does not teach that the level of activation of the dorsal anterior cingulate cortex (ACC) varies with the TMS pulse applied to dorsal lateral prefrontal cortex (DLPFC). Rather, the modified Peters discloses simultaneous TMS-EEG-fMRI and analysis of BOLD and EEG alpha states, as shown above in claim 1, but does not expressly describe that ACC activity varies with TMS pulses applied to the DLPFC. George fills this gap by explicitly stating that TMS pulses applied at DLPFC evoke responses in ACC that depend on the phase of frontal alpha rhythms (George, p. 580: "TMS pulse delivery to instantaneous electroencephalogram (EEG) rhythms may increase efficacy of rTMS… we used simultaneous TMS-EEG-fMRI to investigate how ongoing brain activity in DLPFC shapes TMS-evoked responses in the anterior cingulate cortex (ACC)"). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Peters in view of George to analyze how ACC activity varies as a function of TMS pulses applied at DLPFC. The combination would have been feasible because Peters already integrates TMS at cortical sites with EEG and fMRI monitoring, and George provides direct evidence that DLPFC stimulation modulates ACC activity. A person of ordinary skill in the art would have recognized that adding this analysis is a predictable use of the combined system and represents routine mapping of cortico-cortical connectivity using TMS-fMRI. The benefit of this combination would be to enable established TMS-fMRI practice of mapping functional connectivity by revealing how stimulation of a prefrontal region (DLPFC) influences activity in a connected region (ACC). Characterizing this pathway would have been understood as a logical and useful application of TMS-fMRI, since it provides insight into inter-regional connectivity between a site of stimulation and a downstream cortical area. Such characterization would have been recognized as valuable for both advancing mechanistic understanding of brain networks and informing therapeutic applications of TMS protocols.
Regarding claim 5, the modified Peters does not teach that the DLPFC depends on timing of the applied TMS pulses relative to the phase of an EEG alpha rhythm of the subject. Rather, the modified Peters teaches concurrent TMS-EEG-fMRI analysis of EEG alpha power, TMS pulses, and fMRI BOLD responses, but does not disclose that DLPFC stimulation effects depend specifically on the timing of TMS pulses relative to alpha phase. George fills this gap by teaching that TMS pulses synchronized to instantaneous alpha phase delivered over the DLPFC alter ACC responses (George, p. 580: "TMS-evoked BOLD response in ACC depends on frontal alpha phase just prior to TMS delivery"). This shows that DLPFC stimulation outcomes vary with alpha phase timing. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Peters in view of George to configure the system such that the effect of TMS pulses on DLPFC is analyzed relative to the phase of EEG alpha rhythm. The combination would have been feasible because Peters already records alpha oscillations and applies TMS during fMRI, and George provides the explicit evidence of alpha-phase dependence at DLPFC. A person of ordinary skill in the art would have recognized that integrating this dependency analysis is a routine, predictable enhancement of EEG-informed TMS-fMRI studies. The benefit of this combination would be to provide more accurate characterization of how alpha-phase timing shapes prefrontal stimulation outcomes, thereby increasing precision of experimental results and improving guidance for therapeutic TMS applications.
Regarding claim 6, the modified Peters does not teach that the one or more alpha phases are determined in the EEG data at the time of each TMS pulse. Rather, the modified Peters discloses determining EEG alpha power just before TMS pulses, but does not explicitly describe extracting the instantaneous alpha phase at each pulse. George fills this gap by describing the use of causal bandpass filters centered on each subject’s alpha frequency to determine the instantaneous phase prior to each TMS pulse (George, p. 580: "we constructed a causal bandpass filters centered around the individual subject alpha frequency (IAF) to extract alpha phase prior to each TMS pulse from averaged EEG signal over the four left frontal electrodes"). In Claim 6, the limitation states that “the alpha phase is determined in the EEG data at the time of each TMS pulse.” George states that “we constructed a causal bandpass filters centered around the individual subject alpha frequency (IAF) to extract alpha phase prior to each TMS pulse from averaged EEG signal over the four left frontal electrodes.” Although the wording differs (“at the time” vs. “prior to”), they describe the same concept from different perspectives. Methodologically, the only way to know the instantaneous phase at the moment of stimulation is to compute it using the EEG signal immediately before the TMS pulse. Thus, George’s “prior to each TMS pulse” is the analytic step that yields the phase “at the time of” the pulse. A person of ordinary skill in the art would have recognized these two as functionally equivalent: the claim uses outcome-focused wording (“at the time”), while George explains the procedural detail (“prior to”) that achieves that outcome. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Peters in view of George to configure the processor to determine the EEG alpha phase at the time of each TMS pulse. The combination would have been feasible because Peters already aligns EEG data with TMS events, and George provides a direct methodological step for phase extraction. A person of ordinary skill in the art would have recognized that incorporating explicit alpha phase determination is a predictable extension of EEG-TMS analysis. The benefit of this combination would be to improve temporal precision in analyzing how brain state influences TMS responses, thereby enhancing both mechanistic understanding and potential therapeutic optimization.
Regarding claim 7, the modified Peters does not fully teach that the TMS pulse is synchronized to the subject's prefrontal quasi-alpha rhythm. Rather, the modified Peters discloses concurrent TMS-EEG-fMRI analysis of EEG alpha power and TMS-evoked BOLD responses but does not teach synchronization of TMS pulses to quasi-alpha rhythms. George fills this gap by explicitly teaching a closed-loop system in which TMS pulses are synchronized to the subject’s instantaneous frontal alpha phase to maximize modulation of ACC (George, p. 580: "TMS-evoked BOLD response in ACC depends on frontal alpha phase just prior to TMS delivery. We have created a closed-loop EEG-TMS system to deliver TMS pulses synchronized to individual's instantaneous frontal alpha phase to maximize modulation of ACC"). George also specifies that the individual alpha frequency (IAF) was selected based on the peak frequency in the 7.5–12.5 Hz range (George, p. 580), which substantially overlaps with the 6–13 Hz ‘quasi-alpha’ rhythm described in the instant specification (Instant Application, [0080]), thereby showing that George’s synchronization method operates within the same frequency range as the claimed quasi-alpha rhythm. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Peters in view of George to synchronize TMS pulses to a patient’s prefrontal quasi-alpha rhythm. The combination would have been feasible because Peters already aligns EEG, fMRI, and TMS, while George provides the methodological detail of using closed-loop synchronization. A person of ordinary skill in the art would have recognized that synchronization to the patient’s alpha rhythm is a predictable enhancement of TMS-EEG-fMRI systems. The benefit of this combination would be to ensure that stimulation is delivered at the physiologically optimal phase of the subject’s quasi-alpha rhythm, thereby producing more consistent modulation of ACC activity across trials and subjects. This improves signal-to-noise in both research measurements and therapeutic outcomes, making the system more reliable and effective for investigating and applying TMS interventions.
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over George et al. (George, M et al. “Combined TMS-EEG-fMRI. The Level of TMS-Evoked Activation in Anterior Cingulate Cortex Depends on Timing of TMS Delivery Relative to Frontal Alpha Phase.” Brain stimulation 12.2 (2019): 580–580. Web), hereto referred as George, and further in view of Katz (US 6488617 B1), hereto referred as Katz.
Regarding claim 8, George teaches that a closed-loop electroencephalogram (EEG)-repetitive transcranial magnetic stimulation (rTMS) system (George, p. 580: “We have created a closed-loop EEG-TMS system to deliver TMS pulses synchronized to individual’s instantaneous frontal alpha phase to maximize modulation of ACC”, George expressly teaches a closed-loop EEG-TMS system; “Synchronizing TMS pulse delivery to instantaneous electroencephalogram (EEG) rhythms may increase efficacy of rTMS...”, demonstrating the application of repetitive TMS) comprises: a processor configured to acquire EEG data (George, p. 580: “Our integrated EEG-fMRI-TMS instrument included...” and “Raw EEG data was processed and we constructed acausal bandpass filters centered around the individual subject alpha frequency (IAF) to extract alpha phase prior to each TMS pulse from averaged EEG signal over the four left frontal electrodes...”, George teaches that the integrated instrument collected raw EEG data before processing, evidencing acquisition and processing of EEG data, which implicitly requires a processor); trigger a rTMS pulse (George, p. 580: “Pseudo randomized TMS (120%MT) inter-pulse interval ranged from four to six TRs. Four to six runs were collected, yielding 184 to 276 TMS pulses per session”, George teaches triggering of rTMS pulses); and process the EEG data and the rTMS pulse (George, p. 580: “For each pulse, TMS-evoked BOLD responses were extracted locked to the pulse onset and averaged over voxels in each subject’s TMS modulated ACC cluster”, George teaches processing EEG data and TMS pulses together with resulting neural activity).
Also regarding claim 8, George partially teaches wherein a first alpha phase is determined before the target alpha phase is determined based on a blood-oxygen-level-dependent (BOLD) response in dorsal anterior cingulate cortex (dACC) evoked by transcranial magnetic stimulation (TMS) pulses, and the first alpha phase is indicative of a strongest activity in the dorsal anterior cingulate cortex (dACC). Specifically, George teaches that “we constructed acausal bandpass filters centered around the individual subject alpha frequency (IAF) to extract alpha phase prior to each TMS pulse” and that “For each pulse, TMS-evoked BOLD responses were extracted locked to the pulse onset and averaged over voxels in each subject’s TMS modulated ACC cluster” (George, p. 580), thereby teaching determination of alpha phase and corresponding TMS-evoked BOLD response on a per-pulse basis. George further teaches that “BOLD response was largest when TMS was applied on the rising edge of the alpha wave” (George, p. 580), thereby identifying a particular alpha phase indicative of the strongest ACC activity. Accordingly, George teaches determining, from the measured relationship between alpha phase and TMS-evoked BOLD response, a first alpha phase indicative of strongest activity. George further teaches phase-synchronized delivery based on that measured relationship, evidencing use of an alpha-phase target for stimulation. The alpha phase to which George synchronizes TMS delivery (i.e., the rising edge of the alpha wave identified as producing the strongest BOLD response) constitutes the target alpha phase for stimulation as taught by George. Further, George teaches that TMS-evoked BOLD responses are “averaged over voxels in each subject’s TMS modulated ACC cluster,” demonstrating that spatially defined ACC regions are analyzed rather than treating the ACC as a uniform structure (George, p. 580). It would have been prima facie obvious before the effective filing date of the claimed invention to refine the ACC analysis to a specific subregion such as the dACC, as subdivision of the ACC into functionally distinct subregions, including dorsal and ventral regions, was well known, and selection of a specific ACC subregion would have been a predictable refinement of the ACC cluster analysis taught by George to improve sensitivity and specificity of BOLD analysis.
However, George does not fully teach that, after the first alpha phase is determined from the measured relationship between alpha phase and TMS-evoked BOLD response and identified as indicative of the strongest activity, a later target alpha phase is then determined as optimized relative to that first alpha phase.
Katz teaches optimizing stimulation relative to an initially measured brain state. Katz teaches that “The system 7 compares the characteristics of the actual brain state to those of the desired brain state”, and that the therapeutic goal determines “how the system 7 adjusts key parameters of the magnetic stimulation in order to reduce the gap between the actual and desired state” (Katz, col. 6, ll. 16-61). Katz further teaches a computer-controlled method including “measuring an electroencephalogram signal”, “comparing the characteristics of the digital electroencephalogram signal to the characteristics of a desired electroencephalogram signal”, “applying a magnetic field having parameters”, “measuring a resulting electroencephalogram signal”, and “comparing the characteristics of the resulting electroencephalogram signal to the characteristics of the desired electroencephalogram signal to determine the need to further alter the brain state”, wherein “the at least one parameter is varied according to a gradient descent algorithm” (Katz, Claim 21). Katz therefore teaches iterative optimization of stimulation parameters relative to an initially measured EEG-defined state. When applied to George’s phase-specific closed-loop EEG-rTMS system, in which the first alpha phase has already been determined from the measured relationship between alpha phase and TMS-evoked BOLD response, Katz’s iterative feedback and gradient-descent adjustment would have taught determining a later target alpha phase as optimized relative to that first alpha phase so as to move stimulation toward a desired neural-response state. Further, one of ordinary skill in the art would have recognized that Katz’s parameter-optimization principle applies equally to phase timing as a stimulation parameter, because George establishes alpha phase as the relevant controllable timing variable for stimulation, and thus phase timing would have been predictably optimized using Katz’s framework. One of ordinary skill in the art would have further recognized that the “desired state” in Katz’s framework could be defined relative to a previously measured optimal response state, such as the first alpha phase identified in George as producing the strongest activity, because using a measured optimal response as the target for further refinement is a natural application of Katz’s feedback framework.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified George in view of Katz so that, after determining a first alpha phase from the measured relationship between alpha phase and TMS-evoked BOLD response and identifying that first alpha phase as indicative of the strongest activity, the system further determines a later target alpha phase optimized relative to the first alpha phase by iteratively adjusting stimulation timing based on measured EEG-state characteristics, as taught by Katz’s feedback and gradient-descent optimization framework. The combination would have been feasible because George already teaches extracting alpha phase prior to each TMS pulse, measuring the corresponding TMS-evoked BOLD response, and identifying a phase associated with strongest ACC activity, thereby establishing a phase-specific EEG-rTMS framework, while Katz teaches the well-known feedback technique of iteratively optimizing stimulation parameters relative to an earlier measured EEG-defined state. One of ordinary skill in the art would have recognized that Katz’s optimization framework, although described in terms of EEG signal characteristics generally, would have been predictably applicable to George’s already-identified first alpha phase in order to determine a later target alpha phase optimized relative to that first alpha phase and thereby improve precision, reproducibility, and treatment effectiveness. The benefit of the combination would have been to provide a more individualized and adaptive target alpha phase for stimulation.
Regarding claim 9, George teaches that the rTMS is synchronized to the EEG alpha phase(George, p. 580: “Synchronizing TMS pulse delivery to instantaneous electroencephalogram (EEG) rhythms may increase efficacy of rTMS... TMS-evoked BOLD response in ACC depends on frontal alpha phase just prior to TMS delivery. We have created a closed-loop EEG-TMS system to deliver TMS pulses synchronized to individual's instantaneous frontal alpha phase to maximize modulation of ACC”, George expressly teaches synchronization of rTMS to EEG alpha phase).
Regarding claim 10, with respect to the first alpha phase being determined by functional magnetic resonance imaging (fMRI) data, electroencephalogram (EEG) data, and a trigger transcranial magnetic stimulation (TMS) pulse, George already performs simultaneous EEG, fMRI, and TMS, and from that data the phase used to drive synchronization can be considered the 'first' phase (George, p. 580). While George does not label it explicitly, the IAF extraction and initial phase measurements function as a calibration or baseline determination. Clarifying that the 'first alpha phase' in the claim corresponds to this implicit baseline step strengthens the logic and shows why it is obvious to view George’s process as comprising both a first and then a target alpha phase. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified George’s simultaneous TMS-EEG-fMRI methodology to frame the extraction of EEG alpha phase, in conjunction with BOLD fMRI and triggered TMS pulses, as determination of a first alpha phase. The combination would have been feasible because George already integrates all three modalities in its analysis pipeline. A person of ordinary skill in the art would have recognized that explicitly defining the initial alpha phase using EEG, fMRI, and TMS together is a routine and predictable refinement of George’s method. The benefit of this refinement would be to provide a clearer procedural step for identifying the first alpha phase, ensuring reproducibility and improving integration of multimodal neuroimaging data.
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over George et al. (George, M et al. “Combined TMS-EEG-fMRI. The Level of TMS-Evoked Activation in Anterior Cingulate Cortex Depends on Timing of TMS Delivery Relative to Frontal Alpha Phase.” Brain stimulation 12.2 (2019): 580–580. Web), hereto referred as George, and further in view of Etkin et al. (US 20190083805 A1), hereto referred as Etkin, and further in view of Katz (US 6488617 B1), hereto referred as Katz.
Regarding claim 11, George teaches a method of determining a target alpha phase in brain of a subject (George, p. 580: "TMS-evoked BOLD response in ACC depends on frontal alpha phase just prior to TMS delivery", George teaches that ACC activity varies depending on alpha phase, thereby establishing a method for determining a target alpha phase in the brain of a subject); wherein the simultaneous scan uses functional magnetic resonance imaging (fMRI) data, electroencephalogram (EEG) data, and a trigger transcranial magnetic stimulation (TMS) pulse (George, p. 580: "we used simultaneous TMS-EEG-fMRI... Raw EEG data was processed... For each pulse, TMS-evoked BOLD responses were extracted locked to the pulse onset and averaged over voxels in each subject's TMS modulated ACC cluster", George expressly teaches the simultaneous use of fMRI, EEG, and triggered TMS pulses).
Also regarding claim 11, with regards to performing a treatment of multiple sessions to the subject, George notes that “two subjects [had] two sessions” (George, p. 580), which evidences that rTMS may be performed across multiple sessions. Etkin more explicitly teaches that “treatment with rTMS is comprised of multiple sessions (either daily across days or multiple times per day and across days) wherein TMS is delivered repetitively in a pattern that is intended to induce plasticity...” (Etkin, ¶[0108]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified George in view of Etkin so that George’s phase-specific simultaneous TMS-EEG-fMRI methodology is implemented in a treatment framework comprising multiple sessions to the subject, as more explicitly taught by Etkin. The combination would have been feasible because both George and Etkin concern rTMS applied to modulate brain activity, and Etkin’s disclosure of repeated sessions represents a known application of rTMS delivery that could be readily applied to George’s phase-guided stimulation approach. The benefit of the combination would have been to use George’s phase-specific targeting in a multi-session treatment regimen so as to improve treatment efficacy and durability over time.
Also regarding claim 11, the modified George teaches performing a first simultaneous scan to determine a first alpha phase indicative of a strongest activity in dorsal anterior cingulate cortex (dACC), and partially teaches performing a second simultaneous scan at the end of the subject’s treatment to determine a target alpha phase. Specifically, the modified George teaches that “we constructed acausal bandpass filters centered around the individual subject alpha frequency (IAF) to extract alpha phase prior to each TMS pulse” and that “For each pulse, TMS-evoked BOLD responses were extracted locked to the pulse onset and averaged over voxels in each subject’s TMS modulated ACC cluster” (George, p. 580), thereby teaching determination of alpha phase and corresponding TMS-evoked BOLD response on a per-pulse basis during a simultaneous scan. It further teaches that “BOLD response was largest when TMS was applied on the rising edge of the alpha wave” (George, p. 580), thereby identifying a particular alpha phase indicative of the strongest ACC activity. Accordingly, the modified George teaches determining, from the measured relationship between alpha phase and TMS-evoked BOLD response, a first alpha phase indicative of strongest activity, and further teaches phase-synchronized delivery based on that measured relationship, evidencing use of a target alpha phase for stimulation. Further, it teaches that TMS-evoked BOLD responses are “averaged over voxels in each subject’s TMS modulated ACC cluster,” demonstrating that spatially defined ACC regions are analyzed rather than treating the ACC as a uniform structure (George, p. 580). It would have been prima facie obvious before the effective filing date of the claimed invention to refine the ACC analysis to a specific subregion such as the dACC, as subdivision of the ACC into functionally distinct subregions, including dorsal and ventral regions, was well known, and selection of a specific ACC subregion would have been a predictable refinement of the ACC cluster analysis taught by George to improve sensitivity and specificity of BOLD analysis. However, the modified George does not fully teach that, after performing the first simultaneous scan to determine the first alpha phase indicative of the strongest activity, and after performing the treatment of multiple sessions to the subject, a second simultaneous scan is performed at the end of the subject’s treatment to determine a target alpha phase optimized relative to the first alpha phase.
Etkin teaches the treatment-course framework in which rTMS treatment is performed over multiple sessions and outcomes are assessed over time. Etkin teaches that “treatment with rTMS is comprised of multiple sessions (either daily across days or multiple times per day and across days) wherein TMS is delivered repetitively in a pattern that is intended to induce plasticity (defined as a change in brain activity)” (Etkin, ¶[0108]). Etkin further teaches outcome assessment across time points, including significant “group by time interaction” effects (Etkin, ¶[0231]-¶[0232]), thereby evidencing reassessment of brain-response effects after treatment over multiple sessions. Etkin therefore teaches the well-known therapeutic framework of performing treatment across multiple sessions and then reassessing neural effects following treatment. It would have been obvious to use George’s simultaneous TMS-EEG-fMRI scan methodology both before and after the multi-session treatment framework taught by Etkin in order to compare phase-dependent neural responses over the course of treatment.
Katz teaches optimizing stimulation relative to an initially measured brain state. Katz teaches that “The system 7 compares the characteristics of the actual brain state to those of the desired brain state”, and that the therapeutic goal determines “how the system 7 adjusts key parameters of the magnetic stimulation in order to reduce the gap between the actual and desired state” (Katz, col. 6, ll. 16-61). Katz further teaches a computer-controlled method including “measuring an electroencephalogram signal”, “comparing the characteristics of the digital electroencephalogram signal to the characteristics of a desired electroencephalogram signal”, “applying a magnetic field having parameters”, “measuring a resulting electroencephalogram signal”, and “comparing the characteristics of the resulting electroencephalogram signal to the characteristics of the desired electroencephalogram signal to determine the need to further alter the brain state”, wherein “the at least one parameter is varied according to a gradient descent algorithm” (Katz, Claim 21). Katz therefore teaches determining an initial measured state and then determining an adjusted or optimized later target state relative to that initial measured state through iterative feedback and refinement. One of ordinary skill in the art would have understood this optimization framework to remain applicable when the later measured state is obtained after an intervening treatment course, because the same comparison of an earlier measured neural state to a later measured neural state would have predictably informed recalibration of the stimulation target after treatment-induced changes in brain activity.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified George in view of Etkin and Katz so that George’s simultaneous scan methodology is used to determine a first alpha phase indicative of strongest activity before treatment, Etkin’s multi-session treatment is administered, and George’s simultaneous scan methodology is then used again after treatment to determine a target alpha phase optimized relative to the first alpha phase using Katz’s feedback-based optimization framework. The combination would have been feasible because George already teaches simultaneous TMS-EEG-fMRI scanning, extraction of alpha phase prior to each TMS pulse, measurement of corresponding TMS-evoked BOLD response, and identification of a phase associated with strongest ACC activity; Etkin teaches the well-known therapeutic structure of administering rTMS over multiple sessions and reassessing effects over time; and Katz teaches the well-known feedback technique of optimizing a later target state relative to an earlier measured state through comparison and parameter adjustment. One of ordinary skill in the art would have recognized that Katz’s optimization framework, although described in a measured-state feedback context, would have been predictably applicable to George’s phase-specific simultaneous scan methodology across the treatment interval taught by Etkin, because treatment-induced changes in neural activity would have made it desirable to reassess the phase/BOLD relationship after treatment and recalibrate the later target alpha phase relative to the earlier phase in order to improve precision, reproducibility, and treatment effectiveness. The benefit of the combination would have been to provide a more individualized and adaptive target alpha phase for stimulation before and after treatment, thereby improving therapeutic efficacy and reliability.
Regarding claim 12, the modified George teaches that a simultaneous EEG is recorded (George, p. 580: “we used simultaneous TMS-EEG-fMRI... Our integrated EEG-fMRI-TMS instrument included a custom 43 channel MR-compatible bipolar EEG system… Raw EEG data was processed and we constructed a causal bandpass filters centered around the individual subject alpha frequency (IAF) to extract alpha phase prior to each TMS pulse”, George expressly teaches that EEG was recorded simultaneously with TMS and fMRI).
Regarding claim 13, the modified George partially teaches that a motor threshold is measured on the subject's left motor cortex. Specifically, George discloses stimulation at 120% of motor threshold, which necessarily requires measuring motor threshold on the motor cortex (George, p. 580), but does not specify the left motor cortex. A person of ordinary skill in the art would not see a substantive scientific distinction between left vs. right motor cortex for determining MT. Standard practice often used the left motor cortex because it controls the dominant right hand in most subjects, making twitch detection easier. With only two hemispheres to choose from, selecting the left side would have been a routine design choice imposing no undue burden. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify George’s methodology to measure motor threshold on the left motor cortex. This refinement would have been feasible because George already describes standard motor thresholding methodology for TMS, and choosing the left hemisphere was a predictable and routine variable. The benefit of this refinement would be to align motor threshold determination with conventional practice of hemisphere-specific motor cortex stimulation (with the dominant thumb twitch being easier to detect), improving reproducibility and ensuring consistency with established neuromodulation protocols.
Claims 14 is rejected under 35 U.S.C. 103 as being unpatentable over George et al. (George, M et al. “Combined TMS-EEG-fMRI. The Level of TMS-Evoked Activation in Anterior Cingulate Cortex Depends on Timing of TMS Delivery Relative to Frontal Alpha Phase.” Brain stimulation 12.2 (2019): 580–580. Web), hereto referred as George, and further in view of Etkin et al. (US 20190083805 A1), hereto referred as Etkin, and further in view of Katz (US 6488617 B1), hereto referred as Katz, and further in view of Rossini et al. (Rossini, P.M et al. “Non-Invasive Electrical and Magnetic Stimulation of the Brain, Spinal Cord, Roots and Peripheral Nerves: Basic Principles and Procedures for Routine Clinical and Research Application. An Updated Report from an I.F.C.N. Committee.” Clinical neurophysiology 126.6 (2015): 1071–1107. Web), hereto referred as Rossini.
The modified George teaches claims 11 and 13 as described above.
Regarding claim 14, the modified George does not fully teach the method further comprises adjusting a TMS output voltage until an involuntary thumb twitch is observed in the subject before the scan. Specifically, George discloses stimulation at 120% of motor threshold (George, p. 580: “Pseudo randomized TMS (120%MT) inter-pulse interval ranged from four to six TRs”), but does not specify adjusting TMS using an involuntary thumb twitch. Rossini provides explicit teaching that motor threshold is determined by adjusting TMS until a visible muscle twitch (e.g., in the thenar/thumb muscles) is observed (Rossini, p. 11, '4. Motor threshold'; p. 86-87, Tables 2-3). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to modify the modified George in view of Rossini to determine motor threshold by observing a thumb twitch and then apply stimulation at the motor cortex. This combination would have been feasible because both references use standard TMS thresholding practices, and the benefit would be improved reproducibility and standardized identification of MT across subjects using a methodology that is standard in the field.
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over George et al. (George, M et al. “Combined TMS-EEG-fMRI. The Level of TMS-Evoked Activation in Anterior Cingulate Cortex Depends on Timing of TMS Delivery Relative to Frontal Alpha Phase.” Brain stimulation 12.2 (2019): 580–580. Web), hereto referred as George, and further in view of Zrenner et al. (Zrenner, Brigitte et al. “Brain Oscillation-Synchronized Stimulation of the Left Dorsolateral Prefrontal Cortex in Depression Using Real-Time EEG-Triggered TMS.” Brain stimulation 13.1 (2020): 197–205. Web.), hereto referred as Zrenner.
Regarding claim 15, George does not fully teach a method for treating depressive disorder, comprising performing a simultaneous scan using electroencephalogram (EEG) data, functional magnetic resonance imaging (fMRI) data, and a transcranial magnetic stimulation (TMS) pulse to determine one or more alpha phases indicative of activity in a dorsal anterior cingulate cortex (dACC), wherein a first of one or more alpha phases is indicative of a strongest activity in the dorsal anterior cingulate cortex (dACC), and partially teaches delivering EEG-triggered repetitive transcranial magnetic stimulation (rTMS) to a subject, wherein the rTMS is synchronized to the subject’s prefrontal EEG quasi-alpha rhythm. Specifically, George teaches “Here, we used simultaneous TMS-EEG-fMRI to investigate how ongoing brain activity in DLPFC shapes TMS-evoked responses in the anterior cingulate cortex (ACC) on a trial-by-trial basis” and further teaches that “we constructed acausal bandpass filters centered around the individual subject alpha frequency (IAF) to extract alpha phase prior to each TMS pulse” and that “For each pulse, TMS-evoked BOLD responses were extracted locked to the pulse onset and averaged over voxels in each subject’s TMS modulated ACC cluster” (George, p. 580), thereby teaching a simultaneous scan using EEG data, fMRI data, and a TMS pulse to determine alpha phases from the measured relationship between alpha phase and TMS-evoked BOLD response. George further teaches that “BOLD response was largest when TMS was applied on the rising edge of the alpha wave” (George, p. 580), thereby identifying a first alpha phase indicative of the strongest ACC activity. Accordingly, George teaches determining, from the measured relationship between alpha phase and TMS-evoked BOLD response across multiple pulses, one or more alpha phases indicative of activity and identifying a first of the alpha phases corresponding to the strongest activity. Further, George teaches that TMS-evoked BOLD responses are “averaged over voxels in each subject’s TMS modulated ACC cluster,” demonstrating that spatially defined ACC regions are analyzed rather than treating the ACC as a uniform structure (George, p. 580). It would have been prima facie obvious before the effective filing date of the claimed invention to refine the ACC analysis to a specific subregion such as the dACC, as subdivision of the ACC into functionally distinct subregions, including dorsal and ventral regions, was well known, and selection of a specific ACC subregion would have been a predictable refinement of the ACC cluster analysis taught by George to improve sensitivity and specificity of BOLD analysis.
George further teaches “We have created a closed-loop EEG-TMS system to deliver TMS pulses synchronized to individual’s instantaneous frontal alpha phase to maximize modulation of ACC” (George, p. 580), thereby teaching phase-synchronized delivery of TMS based on EEG-derived alpha phase.. George also teaches that the individual alpha frequency was selected “based on the peak frequency in 7.5-12.5 Hz range” (George, p. 580), which substantially overlaps the claimed prefrontal EEG quasi-alpha rhythm. Thus, although George does not expressly use the term “quasi-alpha rhythm,” George teaches synchronization within substantially the same alpha-frequency range as the claimed quasi-alpha rhythm. However, George does not fully teach that the method is for treating depressive disorder.
Zrenner teaches delivering EEG-triggered repetitive transcranial magnetic stimulation (rTMS) for treating depressive disorder. Zrenner teaches that the study involved “22 right-handed subjects ... meeting the clinical criteria for a single or recurrent episode of MDD” (Zrenner, p. 198, 'Material and Methods'), and further teaches “For brain oscillation-synchronized stimulation of the left DLPFC ... a EEG-TMS set-up was used, with the capability of analyzing EEG signals in real-time and triggering TMS pulses depending on the instantaneous oscillatory phase of the recorded EEG signal” (Zrenner, p. 198-199, 'Material and Methods'). Zrenner also teaches, “Here we report feasibility, safety and immediate neuromodulatory effects of real-time EEG-triggered alpha-synchronized rTMS of left DLPFC in patients with antidepressant-resistant MDD” (Zrenner, p. 200, 'Discussion'). Zrenner therefore teaches applying EEG-triggered rTMS synchronized to alpha oscillatory activity in patients with major depressive disorder.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified George in view of Zrenner to use George’s simultaneous scan methodology to determine one or more alpha phases indicative of dACC activity, identify a first alpha phase indicative of the strongest activity, and then deliver EEG-triggered rTMS synchronized to the subject’s prefrontal EEG quasi-alpha rhythm for treating depressive disorder, as taught by Zrenner. The combination would have been feasible because George already teaches simultaneous TMS-EEG-fMRI scanning, extraction of alpha phase prior to each TMS pulse, measurement of corresponding TMS-evoked BOLD response, identification of a strongest-response phase, and phase-synchronized stimulation within an alpha-frequency range that substantially overlaps the claimed quasi-alpha rhythm, while Zrenner teaches real-time EEG-triggered alpha-synchronized rTMS of left DLPFC in patients with antidepressant-resistant MDD. One of ordinary skill in the art would have recognized that Zrenner’s therapeutic application of alpha-synchronized rTMS could be applied to George’s phase-specific simultaneous scan framework in order to identify an optimal phase associated with strongest dACC activity and use that phase-guided stimulation in treatment of depressive disorder. The benefit of the combination would have been to provide more individualized and effective treatment of depressive disorder by using measured phase-dependent neural responses to guide EEG-triggered rTMS delivery.
Regarding claim 16, the modified George teaches that the rTMS is applied on a dorsal prefrontal cortex (DLPFC) (George, p. 580: “we used simultaneous TMS-EEG-fMRI to investigate how ongoing brain activity in DLPFC shapes TMS-evoked responses... The TMS coil was placed over subject's DLPFC”, George shows coil placement at DLPFC for repetitive TMS; where “Four to six runs were collected, yielding 184 to 276 TMS pulses per session” with “two sessions”; and as shown above in claim 15, Zrenner makes it applicable to therapy).
Response to Arguments
Objections
Applicant's arguments filed 3/2/2026, page 5, regarding the previous Objections of claims 1-2, 5, 11, and 16 have been fully considered and are persuasive. The previous Objections have been withdrawn. However, there are new objections as shown above.
35 U.S.C. §112(b)
Applicant's arguments filed 3/2/2026, page 5, regarding the previous 112(b) Rejections of claims 2, 4, and 11-14 have been fully considered and are persuasive. The previous 112(b) rejections have been withdrawn. However, there are new 112(b) Rejections as shown above.
35 U.S.C. §112(d)
Although the applicant did not make arguments, the amendments filed on 3/2/2026, regarding the previous 112(d) Rejections of claims 5 and 16 have been fully considered and are persuasive. The previous 112(d) rejections have been withdrawn.
35 U.S.C. §103
Applicant's arguments filed 3/2/2026, pages 6-7, regarding the previous 103 Rejections of claims 1-16 have been fully considered but are not persuasive and are additionally moot in regards to independent claims 8 and 11 because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. That is, there are new grounds of rejection. The arguments are not persuasive for the reasons discussed below.
Applicant's Argument 1: Applicant argues that Peters fails to disclose or suggest determining one or more alpha phases based on a blood-oxygen-level-dependent (BOLD) response in the dorsal anterior cingulate cortex (dACC) evoked by TMS pulses, and further fails to disclose or suggest a first of the one or more alpha phases indicative of strongest activity in the dACC. Applicant further argues that Peters merely performs post-hoc statistical evaluation of correlations between pre-existing brain rhythms and resulting activation patterns, rather than determining alpha phases based on TMS-evoked neural responses.
Response: This argument is not persuasive. Peters teaches simultaneous acquisition and analysis of EEG, TMS, and fMRI data in a single TMS-EEG-fMRI framework, including analysis of TMS-evoked BOLD activity as a function of EEG alpha activity immediately prior to TMS application. Thus, Peters teaches the claimed processing framework for analyzing EEG data, TMS pulses, and corresponding BOLD responses. Peters is not relied upon alone for the further limitation requiring determination of a phase corresponding to strongest activity. Rather, as set forth in the rejection, George is relied upon for the additional teaching of a phase-specific relationship between measured alpha phase and measured TMS-evoked BOLD response, including identification of the phase corresponding to the strongest response.
Applicant's Argument 2: Applicant argues that George fails to disclose or suggest determining alpha phases derived from measured neural responses, and further fails to disclose or suggest determining a phase indicative of a strongest dACC activation.
Response: This argument is not persuasive. Under the broadest reasonable interpretation, the recitation of determining one or more alpha phases “based on” a TMS-evoked BOLD response does not require that the alpha phase itself be mathematically derived from the BOLD signal alone. Rather, the limitation reasonably encompasses determining which alpha phase corresponds to a measured BOLD response, including determining which alpha phase is associated with the strongest measured BOLD response. George expressly teaches that “we constructed acausal bandpass filters centered around the individual subject alpha frequency (IAF) to extract alpha phase prior to each TMS pulse” and that “for each pulse, TMS-evoked BOLD responses were extracted locked to the pulse onset and averaged over voxels in each subject’s TMS modulated ACC cluster” (George, p. 580). Thus, George evaluates a measured relationship between alpha phase and measured TMS-evoked neural response on a per-pulse basis across multiple pulse instances. George further teaches that “BOLD response was largest when TMS was applied on the rising edge of the alpha wave” (George, p. 580), thereby identifying a particular alpha phase corresponding to the strongest measured activation. George additionally teaches that “We have created a closed-loop EEG-TMS system to deliver TMS pulses synchronized to individual’s instantaneous frontal alpha phase to maximize modulation of ACC” (George, p. 580), further evidencing phase-targeted stimulation based on the measured phase-response relationship. Accordingly, George does not merely disclose a general correlation, but teaches determining, from measured neural responses, which alpha phase corresponds to the strongest activity, which satisfies the limitation under the broadest reasonable interpretation of “based on.”
Applicant's Argument 3: Applicant argues that the combination of Peters and George therefore fails to render amended claim 1 obvious.
Response: This argument is not persuasive. Peters and George are properly combined because both references concern simultaneous TMS-EEG-fMRI analysis of neural activity in relation to EEG alpha activity, and George provides the phase-specific response teaching missing from Peters. Peters supplies the simultaneous acquisition and analysis framework, while George teaches that measured TMS-evoked BOLD response varies as a function of measured alpha phase and identifies a particular phase corresponding to the strongest response. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply George’s phase-specific teaching within the Peters framework in order to improve reproducibility and effectiveness of stimulation by identifying the phase associated with the strongest response.
Applicant's Argument 4: Applicant argues that Etkin, Rossini, and Zrenner do not cure the same deficiencies in similarly amended independent claims 8, 11, and 15, and that dependent claims 3-7, 9-10, 12-14, and 16 are likewise not obvious for the same reasons.
Response: This argument is not persuasive for the same reasons discussed above with respect to claim 1. The amendments to claims 8, 11, and 15 incorporate analogous phase-selection limitations, and the cited combinations likewise rely on George for the teaching of a measured relationship between alpha phase and TMS-evoked BOLD response, including identification of the phase corresponding to the strongest response. The amendments to claims 8, 11, and 15 incorporate additional limitations relating to optimization, treatment framework, and/or clinical application. The cited combinations rely on George for the measured relationship between alpha phase and TMS-evoked BOLD response and identification of a strongest-response phase, while additional references (e.g., Katz, Etkin, and Zrenner) are applied to supply the respective optimization, multi-session treatment, and clinical application aspects. Accordingly, the cited combinations, taken together, teach or render obvious the amended limitations. Dependent claims 3-7, 9-10, 12-14, and 16 fall with their respective base claims absent separate persuasive arguments.
Accordingly, Applicant's arguments have been considered but are not persuasive.
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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/AARON MERRIAM/Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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