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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/20/2026 has been entered.
Response to Amendment
This action is in response to the remarks filed on 1/20/2026.
The amendments filed on 1/20/2026 have been entered. Accordingly claims 1-22 remain pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5, 7-13 and 17-21 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy (US20230390556A1 and or US20210370064A1) in view of Tyler et al (US20150151142A1).
Regarding the claims 1 and 18, Murphy teaches a transcranial ultrasound device (“a transcranially mounted neuromodulation device and a stimulation control” abst; also see figs. 1, 3, 5, 6 and the associated pars.) comprising:
an ultrasound transducer (“neuromodulation device comprising at least one ultrasound transducer” abst), configured to transmit stimulation signals (“acoustic simulations performed on brain” abst) to one or more brain regions associated with at least one of the amygdala or the posterior cingulate cortex (“A neuromodulation device disclosed herein could be used to prevent or treat a depression or anxiety which arise from or could be remedied by sites in the deep brain. This could also include modulating activity of the amygdala for treating and/or altering emotional states such as depression or anxiety” [0100]);
an electroencephalography sensor array, wherein the ultrasound transducer receives feedback in response to the stimulation signals (“neuromodulation device comprising at least one ultrasound transducer and at least one EEG electrode and the disclosed stimulation control computing environment comprises a stimulation control unit and offline computing device” abst; “EEG electrode disclosed herein results in a continuous initiation and/or adjustment from a stimulation control unit disclosed herein, which regulates the application of ultrasound by an ultrasound transducer array disclosed herein. This results in a feedback mechanism that enables timely, effective, and accurate ultrasound stimulation to one or more specific brain region requiring such stimulation” [0070]); and
at least one of an electrooculography (EOG) or an electromyography sensor (EMG) providing additional physiological feedback (“as shown in FIGS. 7A-B, a deep learning model for sleep stage prediction comprises one multi-branch that uses four signal (EEG, EOG-R, EOG-L, and EMG) input architecture.” [0083]); and
a controller configured to adjust at least one subsequent stimulation signal in response to the feedback received by the electroencephalography sensor array and (“stimulation control unit comprising a deep learning model for regulating ultrasound stimulation parameters. Such an application of artificial intelligence systems involves automatically determining and adjusting in real time the ultrasound stimulation parameters required for modulating brain activity. A deep learning model for regulating ultrasound stimulation parameters can read information from the deep brain at an individualized level, and in real time instruct a neuromodulation device disclosed herein to deliver the required ultrasound stimulation to achieve its desired outcome. In some embodiments, the deep learning model automatically determines and adjusts in real time the ultrasound stimulation parameters required for modulating brain activity in order to improve sleep quality of sleep. In some embodiments, a deep learning model for regulating ultrasound stimulation parameters comprises 1) reinforcement learning to adapt in real time to changes in the device position and other external factors; 2) subsystem routines that control an EEG electrode; and 3) a data logging module used for training long-term personalization and for improving the other modules.” [0089]) the at least one of the EOG sensor or the EMG sensor (“as shown in FIGS. 7A-B, a deep learning model for sleep stage prediction comprises one multi-branch that uses four signal (EEG, EOG-R, EOG-L, and EMG) input architecture.” [0083]).
Murphy does not point out the specifics of additional physiological feedback during a single subject session for correlation with electroencephalography (EEG) data or to validate the targeting of an ultrasound field by confirming modulation of reflexive movements; and dynamically adjust at least one subsequent stimulation signal within the single subject session; and the EOG sensor or the EMG sensor, by adjusting at least one treatment parameter comprising at least one of: a spatial extent of the ultrasound field, a waveform duration, a waveform intensity, a waveform phase, or a frequency of ultrasonic modulation.
However, in the same field of endeavor, Tyler teaches the relative position of the one or more targeted brain regions is determined as part of a combined transcranial ultrasound neuromodulation and aTCD session. A 3-dimensional blood vessel map can be determined by aTCD and one or more assessments about the efficacy of a transcranial ultrasound neuromodulation protocol for an intended neuromodulatory effect is made relative to locations defined within this three-dimensional map. The targeting of neuromodulation can be improved or optimized based on the results of the one or more assessments of transcranial ultrasound neuromodulation efficacy, including by fTPI imaging, another functional assessment of neuronal activity, a self-report of an altered state by the subject, or cognitive assessment. The aTCD data acquired concurrently or near in time within about 5 minutes to the transcranial ultrasound neuromodulation stimulation can be used to define a target location fixed relative to the three-dimensional map of brain blood vessels generated by aTCD. The one or more measurements of brain activity, physiology, cognitive function, or other changes in the brain or body induced by transcranial ultrasound neuromodulation may include one or more of: (1) brain activity measured by one or more techniques chosen from the group of: electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), positron emission tomography (PET), single-photon emission computed tomography (SPECT), computed tomography (CT), functional tissue pulsatility imaging (fTPI), or other techniques for measuring brain activity known to one skilled in the art; (2) physiology measured by one or more techniques chosen from the group of: electromyogram (EMG) [0136].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with additional physiological feedback during a single subject session for correlation with electroencephalography (EEG) data or to validate the targeting of an ultrasound field by confirming modulation of reflexive movements; and dynamically adjust at least one subsequent stimulation signal within the single subject session; and the EOG sensor or the EMG sensor, by adjusting at least one treatment parameter comprising at least one of: a spatial extent of the ultrasound field, a waveform duration, a waveform intensity, a waveform phase, or a frequency of ultrasonic modulation as taught by Tyler because adjusting targeting of transcranial ultrasound neuromodulation based on movements of the user, brain, or transcranial ultrasound neuromodulation system. Ideally, such a system would be comfortably worn by a user, and automatically adjust targeting of the transcranial ultrasound ([0014] of Tyler).
Regarding the claim 2, Murphy teaches wherein the ultrasound transducer, the electroencephalography sensor array, and the controller are configured in a closed loop configuration (“and as shown in FIGS. 1A-B, an exemplary neurostimulator device 110 comprises a wearable device housing 120, which supports two array housings 130 each containing an ultrasound transducer 140, and two EEG electrodes 150,” [0030]; “conductive wiring disclosed herein powers an EEG amplification stage for each EEG electrode 150, each ultrasound transducer array 140, stimulation control unit 200 and its associated processing elements and functions, and other components of neuromodulation device 110 and can be bundled together.” [0060]).
Regarding the claim 3, Murphy teaches wherein the stimulation signals are phase-locked to a periodic signal measured by the electroencephalography sensor array (“the current slow wave phase will be determined by the ending phase of the sine wave fit to the EEG signal. If the signal acquisition is delayed, the sine wave may be extended beyond the data to predict future phase. In another method a phase locked loop may be used. A phase locked loop is a type of control system which detects the phase difference of a reference signal and an input signal,” [0072]).
Regarding the claim 5, Murphy teaches the feedback is derived from a plurality of brain regions (“modulating operation of a neuromodulation device disclosed herein is shown in FIG. 3 . Brain activity data, including frequency, amplitude, waveform, is continuously being collected by an EEG electrode… stimulation control unit then executes brain activity software to analyze this EEG information in order to categorize the brain activity and if specified criteria are met, initiate protocols for the administration of ultrasound stimulation.” [0070]).
Regarding the claims 7 and 8, Murphy teaches controller is further coupled to an electrooculography (EOG) sensor ([0084] “as shown in FIGS. 7A-B, a deep learning model for sleep stage prediction comprises one multi-branch that uses four signal (EEG, EOG-R, EOG-L, and EMG) input architecture”; [0021] “brainwave frequencies, amplitudes, and waveform type, along with other distinguishable biologic rhythms including eye movements (EOG) and muscle movements (EMG)”).
Regarding the claim 9, Murphy teaches wherein the ultrasound transducer stimulates a selected brain region associated with mental health treatment, and the feedback is derived from physiological signals or medical imaging data are obtained during a single subject session by the electroencephalography sensor array (“a stimulation control unit disclosed herein then instructs an ultrasound transducer array disclosed herein to administer ultrasound signal to these mapped brain regions for a certain period of time and with or without signal amplification. Constant input from an EEG electrode disclosed herein results in a continuous initiation and/or adjustment from a stimulation control unit disclosed herein, which regulates the application of ultrasound by an ultrasound transducer array disclosed herein. This results in a feedback mechanism that enables timely, effective, and accurate ultrasound stimulation to one or more specific brain region requiring such stimulation” [0070]).
Regarding the claims 10 and 19, Murphy teaches wherein the physiological signals are used to modify the stimulation signals during the subject session (“EEG brain activity shows, as measured using an electroencephelogram (EEG), frequencies of between 15 Hz to 50 Hz, amplitudes of less than 50 mV and faintly discernable waveform types” [0020]; “Collectively, enhancing slow waves is an opportunity to improve both cognitive and physiological functions” [0025]; “a neuromodulation system comprising a wearable neuromodulation device integrated with EEG electrodes and one or more integrated ultrasound transducer arrays. The disclosed neuromodulation system further includes a stimulation control unit comprising one or more processors, and software that operates and controls the features and functionality of the ultrasound stimulation when executed by such processors.” [0026]).
Regarding the claim 11, Murphy teaches wherein the selected brain region is the amygdala (“A neuromodulation device disclosed herein could be used to prevent or treat a depression or anxiety which arise from or could be remedied by sites in the deep brain. This could also include modulating activity of the amygdala for treating and/or altering emotional states such as depression or anxiety” [0101]).
Regarding the claim 12, Murphy teaches wherein the selected brain region is the posterior singulate cortex (PCC) (“direct interaction with the cortex, focused ultrasound stimulation of the thalamus ultimately engages greater volumes of cortex through the corticothalamic loop” [0090]).
Regarding the claim 13, Murphy teaches wherein the feedback is further obtained via a functional magnetic resonance imaging (fMRI) device (“[0015] FIGS. 5A-J show an exemplary MRI-based methodology utilized for computational measurements for determining and applying focused ultrasound to a specific targeted neural region during a pre-use calibration step, with FIG. 5A showing an image created by MRI scanning taken from an axial plane with anterior commissure marked as control point”).
Regarding the claim 17, Murphy teaches all the limitations except for the feedback is derived from Doppler ultrasound.
However, in the same field of endeavor, Tyler teaches methods and systems for transcranial ultrasound neuromodulation as well as targeting such neuromodulation in the brain are disclosed. Automated transcranial Doppler imaging (aTCD) of blood flow in the brain is performed and one or more 3-dimensional maps of the neurovasculature are generated. Ultrasound energy is delivered transcranially in conjunction to induce neuromodulation (abst).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with Doppler ultrasound as taught by Tyler because ideally, such a system would be comfortably worn by a user, and automatically adjust targeting of the transcranial ultrasound (0014).
Regarding the claim 20, Murphy teaches wherein generating the adjustment of the transcranial stimulation or analysis of the subject’s physiological responses is enhanced using machine learning or artificial intelligence (“machine learning elements such as a deep learning model for sleep stage prediction. Such an application of artificial intelligence systems in the present invention involves automatically monitoring, classifying, quantifying EEG information to predict the sleep stages of a user in real time. A deep learning model for sleep stage prediction will first use representation learning to extract useful features from the raw EEG data using convolutional neural networks (CNN) to detect features on the raw data, such as, e.g., using 1D convolutions on the raw EEG or 2D convolutions on the Spectrograms” [0083]).
Regarding the claim 21, Murphy teaches further comprising generating efficacy data based upon an analysis of the physiological measurements or medical imaging data (“systems and methods for controlling the neuromodulation device functionality using acoustic simulations performed on brain image data as well as methods and uses of such neuromodulation systems in modulating brain activity using focused ultrasound stimulation of the thalamus and thalamic sub regions during certain phases of slow wave brain oscillations in order to treat various neural-based disorders or conditions including sleep disorders.” abst).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy in view of Tyler and further in view of Aharonovitch et al (US20200139112A1).
Regarding the claim 6, the above noted combination teaches all the limitations except for feedback comprises modulation of evoked potentials.
However, in the same field of endeavor, Aharonovitch teaches [0135] The device is adapted to induce certain stimuli such as (but not only) electric current, magnetic field, lights, sounds, smells, ultrasound, vibrations, administration of liquid (external/internal) such as neurotransmitters, substances which have biological functions, air flow (5 sense stimuli). [0136] The invention discloses special electrodes that are designed to achieve monitoring and induction in the best optimal way. [0137] The inducer might be a standalone unit that could fit different types of EEGs or other wearable/stationery devices. Also see [0171], [0220], [0221]
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with evoked potentials as taught by Aharonovitch because that will help achieve and maintain results for the average user (in medical, wellness and recreational fields), and will help enhance everyday experiences ([0011] of Aharonovitch).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy in view of Tyler, Aharonovitch et al (US20200139112A1) and Li et al (Noninvasive Ultrasonic Neuromodulation in Freely Moving Mice, IEEE Transactions on Biomedical Engineering (Volume: 66, Issue: 1, January 2019).
Regarding the claim 4, the above noted combination teaches all the limitations except for wherein the controller causes the ultrasound transducer to utilize transcranial focused ultrasound evoked potentials occurring 1-200 milliseconds after ultrasound stimulation in the stimulation signals in response to the feedback in response to the stimulation signals obtained by the electroencephalography sensor array to enable plasticity changes between brain regions associated with at least one of the amygdala or the posterior cingulate cortex.
However, in the same field of endeavor, Aharonovitch teaches [0135] The device is adapted to induce certain stimuli such as (but not only) electric current, magnetic field, lights, sounds, smells, ultrasound, vibrations, administration of liquid (external/internal) such as neurotransmitters, substances which have biological functions, air flow (5 sense stimuli). [0136] The invention discloses special electrodes that are designed to achieve monitoring and induction in the best optimal way. [0137] The inducer might be a standalone unit that could fit different types of EEGs or other wearable/stationery devices. Also see [0171], [0220], [0221].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with evoked potentials as taught by Aharonovitch because that will help achieve and maintain results for the average user (in medical, wellness and recreational fields), and will help enhance everyday experiences ([0011] of Aharonovitch).
Further, Li teaches ultrasound stimulator for inducing neuromodulation in freely moving mice. The main components of the stimulator include a miniature piezoelectric ceramic, a concave epoxy acoustic lens, and housing and connection components. The device was able to induce action potentials recorded in situ and evoke head-turning behaviors by stimulating the primary somatosensory cortex barrel field of the mouse (abst).
For the US-SPIKE group (n=4), a head stage (NeuroStudio, Bio-Signal Technologies) was plugged into the electrode socket of each external electrode device for recording neuronal signals at 16-bit resolution with a 250-Hz high-pass filter and a sampling frequency of 30 kHz. A series of 2-MHz ultrasound stimuli (P0=1.2MPa, T1=0.3ms, T2=1ms, T3=300ms, T4=6sec, duty cycle = 30%, Isppa=46W/cm2, Ispta=0.70 W/cm2) was delivered to each mouse in a 360 seconds trial (F. Electrophysiological Assays section).
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It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with transcranial focused ultrasound evoked potentials occurring 1-200 milliseconds after ultrasound stimulation in the stimulation signals in response to the feedback in response to the stimulation signals obtained by the electroencephalography as taught by Li because it could potentially lead to the application of ultrasonic neuromodulation in more-extensive neuroscience investigations. (Abst of Li).
Claims 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy in view of Tyler and further in view of Vortman et al (US20210170205A1).
Regarding the claim 14, the above noted combination teaches all the limitations except for the fMRI comprises Blood Oxygen Level Dependent (BOLD) contrast.
However, in the same field of endeavor, Vortman teaches stimulating neural activity in one or more target regions associated with one or more brain diseases or disorders include transmitting the first sequence of ultrasound pulses to the target region(s); measuring a physiological parameter indicative of the neural activity at the target region(s) resulting from the ultrasound pulses (abst). The MRI apparatus 202 is utilized in conjunction with a blood oxygen level-dependent (BOLD) contrast agent for detecting changes in the blood flow at the target/non-target region(s) in real time (this technique is often termed “functional magnetic resonance imaging” or “fMRI”) during the ultrasound-mediated stimulation [0033].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with BOLD as taught by Vortman because there is a need for a noninvasive approach that facilitates brain stimulation at multiple (e.g., more than two) target locations with the ability to change the stimulated locations if desired ([0004] of Vortman).
Regarding the claim 15, the above noted combination teaches all the limitations except for the fMRI comprises Arterial Spin Labeling (ASL) contrast.
However, in the same field of endeavor, Vortman teaches stimulating neural activity in one or more target regions associated with one or more brain diseases or disorders include transmitting the first sequence of ultrasound pulses to the target region(s); measuring a physiological parameter indicative of the neural activity at the target region(s) resulting from the ultrasound pulses (abst). Arterial spin labeling (ASL) MRI that uses magnetically labeled arterial-blood water protons as an endogenous tracer may be implemented to directly measure the blood flow change. The arterial blood water may be magnetically labeled by applying an RF pulse that inverts or saturates the water protons in the flowing blood supplying the imaged target/non-target regions. After a period of delay time, the labeled blood flows into the imaged region; the inflowing inverted spins within the labeled blood water may alter total tissue magnetization and, consequently, the MR signal and image intensity [0037].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with ASL as taught by Vortman because there is a need for a noninvasive approach that facilitates brain stimulation at multiple (e.g., more than two) target locations with the ability to change the stimulated locations if desired ([0004] of Vortman).
Regarding the claim 16, the above noted combination teaches all the limitations except for the feedback is derived from functional near infra-red spectroscopy (fNIRS).
However, in the same field of endeavor, Vortman teaches stimulating neural activity in one or more target regions associated with one or more brain diseases or disorders include transmitting the first sequence of ultrasound pulses to the target region(s); measuring a physiological parameter indicative of the neural activity at the target region(s) resulting from the ultrasound pulses (abst). FIG. 2, multiple electrodes 216 may be placed along the patient's scalp to monitor the electrical activity of the brain (this technique is often termed “electroencephalograph” or “EEG”) during the ultrasound-mediated neurostimulation. Alternatively, functional near-infrared spectroscopy (fNIRS) may be employed [0039].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with fNIRS as taught by Vortman because there is a need for a noninvasive approach that facilitates brain stimulation at multiple (e.g., more than two) target locations with the ability to change the stimulated locations if desired ([0004] of Vortman).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Murphy in view of Tyler and further in view of Simon et al (US20200054872A1).
Regarding the claim 22, the above noted combination teaches all the limitations except for the disruption of the Default Mode Network (DMN).
However, in the same field of endeavor, Simon teaches the default mode network corresponds to task-independent introspection (e.g., daydreaming), or self-referential thought. When the DMN is activated, the individual is ordinarily awake and alert, but the DMN may also be active during the early stages of sleep and during conscious sedation. During goal-oriented activity, the DMN is deactivated and one or more of several other networks, so-called task-positive networks (TPN), are activated. DMN activity is attenuated rather than extinguished during the transition between states, and is observed, albeit at lower levels, alongside task-specific activations. Strength of the DMN deactivation appears to be inversely related to the extent to which the task is demanding. Thus, DMN has been described as a task-negative network, given the apparent antagonism between its activation and task performance [0142].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with DMN as taught by Simon because it allows for a non-invasive procedures that are generally painless and may be performed without the dangers and costs of surgery. They are ordinarily performed even without the need for local anesthesia. Less training may be required for use of non-invasive procedures by medical professionals. In view of the reduced risk ordinarily associated with non-invasive procedures, some such procedures may be suitable for use by the patient or family members at home or by first-responders at home or at a workplace. Furthermore, the cost of non-invasive procedures may be significantly reduced relative to comparable invasive procedures ([0009] of Simon).
Response to Arguments
Applicant’s arguments have been considered but are moot 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.
Conclusion
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/SERKAN AKAR/ Primary Examiner, Art Unit 3797