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 § 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.
Claim 28 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim 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.
With regards to claim 28, the claim is indefinite as being both incomplete, by its dependence on cancelled claim 26; and for lack of antecedent basis for its limitation ("The neuromodulation system...") which is not present in cancelled base claim 26. Amending claim 28 to refer to a claim which recites the stimulation control unit, or deleting the claim, would obviate the rejection.
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.
Claim(s) 16, 18, 22-24, 27, 30, 37 and 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. (US Pub No. 2020/0139113) in view of Vortman et al. (US Pub No. 2021/0170204), Grossman et al. (US Pub No. 2019/0143073 and Onarheim et al. (US Pub No. 2019/0001133).
With regards to claim 16, Shin et al. disclose a neuromodulation system comprising:
a neuromodulation device (100) including a wearable device housing (i.e. 610), and one or more ultrasound-emitting elements (i.e. elements of ultrasonic generator array (630)), and one or more EEG electrodes (paragraph [0001], referring to the brain-stimulating device comprising an EEG measurement unit that measures an EEG signal and a stimulation unit that applies stimulation to a brain in response to the generation of a slow oscillation included in the EEG signal; paragraphs [0057]-[0060], [0098], referring to the brain-stimulating device (100) which includes a cap (610), an electrode array (620) and an ultrasonic generator array (630); paragraph [0099], referring to the electrode array corresponding to the EEG measurement unit; paragraphs [0101], [0109]-[0110]; Figures 1, 5-6 and 8) and
a stimulation control computing environment comprising a stimulation control unit (130, 340), the stimulation control unit comprising at least one processor (i.e. 640) coupled to the one or more ultrasound-emitting elements, and configured with one or more data processing functions to focus ultrasound emission to a target brain area that includes/comprises at least a portion of a thalamus [claim 19] (paragraphs [0072]-[0074], referring to the stimulation including ultrasonic wave stimulation, wherein the stimulation may apply the stimulation to a partial brain region which may be the thalamic reticular nucleus, which is a specific portion of the thalamus; in particular, note paragraph [0074] which sets forth that a “…focused ultrasound device may be used to apply the stimulation to the partial region of the brain. The focused ultrasound device is configured to allow ultrasound to be focused onto the partial brain region to which the stimulation is to be applied…”, and thus ultrasound emission is focused to a partial brain region of the thalamus; paragraphs [0079]-[0083], paragraphs [0101], referring to the processor (640) which is electrically connected to the ultrasonic generator array and can perform functions of the stimulation element and the function of the control unit (130); Figures 1, 3, 6 and 8), the one or more data processing functions configured to:
acquire real-time data by the one or more EEG electrodes (paragraphs [0059]-[0061], [0084]-[0086], [0089], referring to the measurement of a scalp EEG, and determining the generation of slow oscillation in the EEG signal and referring to the sleep state determination unit (360) which may determine whether or not the sleep state is a slow wave sleep state based on the frequency of generation of the slow oscillation; paragraphs [0048]-[0049], [0060], [0064], [0084], referring to the “slow oscillation” referring to an EEG generated during slow wave sleep and may have a frequency (i.e. spectral component) of about 1Hz or less; Figures 1, 3);
detect a phase of at least one slow wave of the target brain region, wherein the phase detection is performed in real time and is dynamically updated based on the real-time data acquired by the one or more EEG electrodes (paragraphs [0059]-[0061], [0084]-[0086], [0089], referring to the measurement of a scalp EEG, and determining the generation of slow oscillation in the EEG signal and referring to the sleep state determination unit (360) which may determine whether or not the sleep state is a slow wave sleep state based on the frequency of generation of the slow oscillation; paragraphs [0048]-[0049], [0060], [0064], [0084], referring to the “slow oscillation” referring to an EEG generated during slow wave sleep and may have a frequency (i.e. spectral component) of about 1Hz or less; paragraph [0135], referring to in-phase mice receiving stimulation during NREM sleep, which occurred in synchrony with “up-states of online (i.e., in real time from the brain)-detected slow oscillations”, and therefore phase detection (i.e. detected slow oscillations) is performed in real time and is dynamically updated based on the real-time data; paragraphs [0002], [0055], referring to the EEG assessing cerebral function in a “continuous” manner and referring to slow oscillation being applied “continuously” to mice and mice receiving the stimulation with the slow oscillation during slow wave sleep; Figures 1, 3),
control the ultrasound emissions from the one or more ultrasound-emitting elements, such that the ultrasound emissions constructively interfere at the target brain area to form at least one ultrasound pulse focused on the target brain area during a certain slow wave phase range for enhancing the at least one slow wave based on the detected phase of the at least one slow wave (paragraphs [0050] and [0054], referring to the “spindle-like stimulation” comprising a spindle component and a slow oscillation component and further referring to thalamic spindles inducing up-state cortical slow oscillation, etc., and thus the stimulation enhances at least one slow wave; paragraphs [0062]-[0065], [0074], [0087]-[0088], referring to applying the stimulation, which can be focused ultrasound (i.e. at least one US pulse focused on the target brain area would be formed due to inherent constructive interferences of the ultrasound emissions which are transmitted to and stimulating the same focused target brain area), to the brain in response to the generation of the slow oscillation; paragraph [0089], referring to the sleep state determination unit (360) turning on the stimulation unit (300) when the determined sleep state is the slow wave sleep; paragraph [0100], referring to emitting ultrasonic waves toward the brain; Figures 1, 3, 6 and 8).
However, Shin et al. do not specifically disclose the one or more processing functions are further configured to identify the target brain area based on brain image data and at least one biometric feature of a user, and perform one or more acoustic simulations to determine information for use in focusing ultrasound emissions from the one or more ultrasound-emitting elements to the target brain area.
Further, though Shin et al. do disclose applying the stimulation to the brain in response to the generation of a slow oscillation included in the EEG signal, Shin et al. do not specifically disclose that the one or more data processing functions are further configured to transform the acquired real-time data into a frequency domain and, using the frequency domain, determine a dominant frequency within a slow-wave frequency band of the acquired real-time data and that the detected phase is associated with the determined dominant frequency.
Additionally, Shin et al. do not specifically set forth that the ultrasound emissions from the one or more ultrasound-emitting elements are specifically controlled “in accordance with the determined information” and wherein the ultrasound emissions are adaptively modulated in one or more of timing, amplitude, duration, and spatial focus in response to changes in the detected phase or dominant frequency.
Vortman et al. disclose system and methods for measuring focusing properties of ultrasound beams for ultrasound therapy and, based thereon, adjusting parameters of the ultrasound in order to optimize focusing properties (Abstract; paragraph [0002]). Prior to treatment, an MRI apparatus or other imaging devices are activated to acquire anatomic characteristics and/or material characteristics of the patient’s skull, wherein a patient-specific 3D skull replica (402) may be created based on the acquired anatomic/material characteristics (paragraph [0049], Figure 4B, note that brain image data (i.e. MRI data) is used to identify one or more parameters (i.e. anatomic/material characteristics) representing one or more brain regions). An ultrasound wave may be applied to the target region (101) via traversing the 3D skull replica (paragraph [0049], Figure 4B, note that a target region (101) is thus identified and applying an ultrasound wave to the target region inherently requires that ultrasound-emitting element(s) are positioned relative to the target region). A detector device (404) deployed at the target region (101) may be activated to measure the focusing properties created by the applied ultrasound wave (paragraph [0049]). In addition, the detection system may measure the acoustic signals from the target region (101) and the ultrasound parameters can be again adjusted and the transducer is then activated based on the adjusted parameters and the acoustic signals and focusing properties in response to the adjusted parameter values may be measured using the detection system and second detector device, respectively (paragraph [0049]; Figure 4B). The steps (416-422) are repeated until sufficient data has been acquired to reliably establish the relationship between the value of the acoustic signals and the focusing properties at the target region (101) (paragraph [0049]; Figure 4B). The relationship may then be stored in memory accessible to the control (108) (paragraph [0049], note that for the result of the steps to be stored in memory and which are performed prior to treatment, the steps are performed offline relative to the steps that are performed during treatment). A controller (108) may adjust the phase shifts (i.e. phase offsets) associated with the transducer elements (104) to increase the acoustic power at the target region, and thus a high-power ultrasound focus may be reliably generated at the target region (101) (paragraph [0050], Figure 1). Alternatively or additionally, the controller (108) may shape the focus using a physical model that simulates acoustic beams from the transducer regions to the target 101 (paragraph [0051], note that the ultrasound emissions may therefore correspond to acoustic simulations of ultrasound emissions). Once the relationships between the parameter value of the acoustic signals and the focusing properties at the target region are established, the ultrasound focusing properties at the target region (101) may be monitored in real time during a treatment procedure (paragraph [0054]). In particular, acoustic signals from the target region (101) are obtained and, based thereon, one or more parameters (e.g., waveform parameters, such as phase shifts (i.e. “phase offsets”), frequencies and/or amplitudes) of the transducer elements (104) are inferred and may be adjusted in order to improve the focusing properties (paragraph [0054]; Figures 1, 4B, 7, note that during treatment (i.e. online algorithmic stimulation application element), ultrasound emissions are focused to the target region using waveform parameter (i.e. phase offset (i.e. phase shift)) information generated by the offline algorithmic mapping element (i.e. steps that were performed prior to treatment and which result is stored in a memory)).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the one or more processing functions of Shin et al. be further configured identify the target brain area based on brain image data, and perform one or more acoustic simulations to determine information for use in focusing ultrasound emissions from the one or more ultrasound-emitting elements to the target brain area and further have the ultrasound emissions from the one or more ultrasound-emitting elements be specifically controlled “in accordance with the determined information, as taught by Vortman et al., in order to optimize focusing properties of the ultrasound emissions to the one or more target regions (Abstract; paragraph [0002]).
However, the above combined references do not disclose that the one or more data processing functions are further configured to transform the acquired real-time data into a frequency domain and, using the frequency domain, determine a dominant frequency within a slow-wave frequency band of the acquired real-time data and that the detected phase is associated with the determined dominant frequency and further that the ultrasound emissions are adaptively modulated in one or more of timing, amplitude, duration, and spatial focus in response to changes in the detected phase or dominant frequency.
Further, the above combined references do not specifically disclose that the identification of the target brain area is further based on at least one biometric feature of a user.
Grossman et al. disclose a neuromodulator which may output stimuli that causes a user to fall asleep faster than the user would in the absence of the stimuli or may modify a sleep state or behavior associated with a sleep state, wherein the neuromodulator may take EEG measurements and the stimuli/neuromodulation signal may comprise an ultrasound signal (Abstract; paragraphs [0271], [0284], referring to an ultrasound transducer (1505) delivering ultrasound stimulation to the brain of a user; paragraph [0058], referring to the measurement of endogenous electrical activity of the brain via EEG electrodes; Figures 1, 15B). A method for accelerating sleep onset comprises of using an EEG sensor to take EEG measurements of the endogenous neural signal, wherein the endogenous neural signal may be the dominant (highest amplitude) neural signal in a frequency band, such as in the delta band (paragraphs
[0083],[0013],[0014]). A neuromodulator may, based on the EEG measurements, calculate instantaneous phase and instantaneous amplitude of the endogenous neural signal by a calculation that involves use of an endpoint-corrected Hilbert Transform (ECHT) (Step 102), wherein the ECHT algorithm can comprise a “frequency domain” version of ECHT which comprises performing a Fast Fourier transform (FFT) on the EEG signal (paragraphs [0083], [0086], note that the real-time EEG data is thus transformed into a frequency domain; Figure 1A). A neuromodulator may, based on the detected instantaneous phase (e.g., instantaneous phase estimated based on the EEG measurements) calculate stimulation that is phase-locked with the endogenous neural signal, such as the dominant neural signal (i.e. dominant frequency) in a delta frequency band (i.e. a slow-wave frequency band), and then physically output this stimulation in a manner perceptible to the human subject (Step 103) (paragraphs [0013]-[0014], [0083], [0173], [0176], note that a dominant/peak frequency within a slow-wave frequency band (i.e. delta band; see paragraph [0013] which refers to the “slow wave (delta) activity”) and a phase of the slow wave (i.e. delta band) with the determined dominant/peak frequency of the acquired real time data is determined and used to control the stimulation to be phase-locked with the dominant neural signal of the slow wave (i.e. delta band); Figure 1A). The neuromodulator may dynamically compute parameters that specify a pulse’s start phase, end phase, minimum duration and maximum duration, as well as dynamically compute other parameters, such as the type(s) of stimulation, location(s) of stimulation, maximum and minimum amplitude of stimulation and duration of stimulation session, such as dynamically computing the start phase as corresponding to the phase of an endogenous neural signal when the pulse ends (paragraphs [0142]-[0143], [0147], note that the “dynamic computation”/dynamic adjustment in real time of parameters that specify a pulses start/end phases/duration and amplitude corresponds to the stimulation (i.e. ultrasound emissions in the above combined references) being adaptively modulated in one or more of timing, amplitude, duration, etc., in response to changes in the detected phase/dominant frequency). The method may be performed iteratively to calculate, in each iteration, the instantaneous phase and instantaneous amplitude for the most recent sample in the sample window and to control stimulation based on the instantaneous phase and instantaneous amplitude (paragraph [0132]; Figure 1A, wherein steps 101, 102, 103, 100 are iteratively/continuously performed).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the one or more data processing functions of the above combined references be further configured to transform the acquired real-time data into a frequency domain and, using the frequency domain, determine a dominant frequency within a slow-wave frequency band of the acquired real-time data and that the detected phase is associated with the determined dominant frequency and further that the ultrasound emissions are adaptively modulated in one or more of timing, amplitude, duration, and spatial focus in response to changes in the detected phase or dominant frequency, as taught by Grossman et al., in order to cause a user to fall asleep faster or modify a sleep state or behavior associated with a sleep state or hinder a transition from a waking state to a sleep state or from a sleep state to another sleep state (Abstract; paragraphs [0014]-[0015]).
However, the above combined references do not specifically disclose that the identification of the target brain area is further based on at least one biometric feature of a user.
Onarheim et al. disclose an a stimulation device for achieving various cognitive effects, wherein a ’10-20 system’ is a method that is used to describe and apply the location of scalp electrodes (i.e. stimulation emitting elements) (Abstract; paragraphs [0001], [0292]). The method was developed to ensure standardized reproducibility so that a subject’s studies could be compared over time and subjects could be compared to each other, wherein the system is based on the relationship between the location of an electrode and the underlying area of cerebral cortex (paragraph [0292]). The “10” and “20” refer to the fact that the actual distances between adjacent electrodes are either 10% or 20% of the total front-back or right-left distance of the skull (i.e. biometric distance) (paragraph [0292], paragraphs [0434]-[0440], Figures 8-10). Earlobes, nasopharyngeal and frontal polar sites are identified and two anatomical landmarks are used for the essential positioning of the EEG electrode: first, the nasion, which is the distinctly depressed area between the eyes; second, the inion, which is the lowest part of the skull from the back of the head and is normally indicated by a prominent bump; further, the purpose of the electrode placement is to target certain predefined brain area inside the skull, wherein the use of the 10-20 system coordinates is a way of seeking to achieve the correct placement on the outside of the skull to target the desired brain areas (paragraphs [0292], [0296], note that a target brain area (i.e. targeted “certain predefined brain area inside the skull”) is thus identified based on at least one biometric feature (i.e distance from anatomical landmarks (i.e. nasion, inion); Figures 9-10).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the identification of the target brain area of the above combined references be further based on at least one biometric feature of a user, as taught by Onarheim et al., in order to achieve the correct placement on the outside of the skull to target the desired brain areas and ensure standardized reproducibility so that a subject’s studies could be compared over time and subjects could be compared to each other (paragraphs [0292], [0296]).
With regards to claim 18, Shin et al. disclose that enhancing the at least one slow wave thereby improves a quality of sleep by the user during a sleep stage (paragraphs [0054]-[0056], referring to inducing up-state cortical slow oscillation and promote consolidation of hippocampus-dependent memory during sleep, thereby providing at least an improvement of memory recall quality of sleep during a sleep stage; further/alternatively, the improvement of sleep quality appears to be an inherent result that follows when the at least one slow wave is enhanced, and therefore, since Shin et al. does disclose enhancing the at least one slow wave, it would follow that the quality of sleep would inherently be improved during a sleep stage).
With regards to claim 22, Vortman et al. disclose that the stimulation control unit adjusts power of the one or more uoltrasound-emitting elements based on estimated acoustic attenuation processed from cranial anatomy (i.e. skull anatomy) and/or bone density (paragraph [0009], [0050], referring to the ultrasound focusing properties including acoustic power); paragraph [0049], referring to the image data being used to adjust the power being based on anatomic characteristics, including density, of the skull/bone).
With regards to claim 23, the above combined references disclose that the stimulation control unit is configured to determine acoustic impedance and a beam steering parameter (i.e. transmission phases and amplitude apodization from the transducer regions, etc.) using ultrasound generated data and to target the centromedian thalamus with the ultrasound emissions from the one or more ultrasound-emitting elements based on the acoustic impedance and the beam steering parameter (see Vortman et al., paragraph [0057], referring to optimizing the ultrasound frequency including measuring the acoustic pressure using the forming intensity=P^2/Z, where Z = acoustic impedance, and thus determination of acoustic impedance is required; paragraphs [0003], [0050]-[0051], referring to adjusting the ultrasound parameter values based on the determined relative energy contributions so as to improve the focusing properties at the target region (101, avoid damage to non-target tissue and/or shape the acoustic beam at the target, wherein such adjusted ultrasound parameters would correspond to beam steering parameters; Figures 4A, 5; see Schiff et al., paragraphs [0106]-[0108], referring to the centromedian (i.e. centromedian thalamus) being the target for stimulation).
With regards to claim 24, Vortman et al. disclose that the beam steering parameter determination optimizes a power distribution ratio between a point relative to the one or more target regions and off-target regions across different steering angles of the one or more ultrasound emitting elements (paragraph [0051], referring to analyzing the relative amplitudes of the reflected acoustic signals received at different regions of the transducer in order to determine the relative contributions of the energy at the target region (101) from the respective regions of the transducer, wherein the controller may then adjust the ultrasound parameter values based on the determined relative energy contributions so as to improve the focusing properties at the target region, wherein, for example, if the amplitudes of the acoustic signals received at the transducer regions (502, 504) are larger than those received at the transducer regions (506, 508), the transducer regions 502, 504 is contributing more energy at the target region 101 than the transducer regions 506, 508, and thus the controller increases the energy (i.e. power) emitted from transducer regions 506, 508 to compensate for the energy lost along the beam paths to the target 101, and thus a power distribution ratio between a point relative to one or more target regions and off-target regions across different steering angles/paths of the ultrasound emitting elements is optimized; Figure 5).
With regards to claim 27, Shin et al. disclose that the stimulation control unit controls the one or more ultrasound-emitting elements to deliver the ultrasound emissions during one or more specific sleep stages (i.e. slow wave sleep state during the non-rapid eye movement (NREM) sleep period) (paragraphs [0007], [0060], [0071]).
With regards to claim 30, Shin et al. disclose that the stimulation control unit optimizes spatial, temporal, and/or intensity of ultrasound emissions based on a current slow wave amplitude reading relative to a baseline (i.e. threshold) slow wave amplitude reading (paragraph [0061], referring to a slow oscillation being determined by comparing the EEG signal (i.e. current slow wave amplitude reading) relative to a threshold (i.e. baseline slow wave amplitude reading); paragraph [0062], referring to the stimulation being applied in response to generation of the slow oscillation, and thus a temporal of ultrasound emissions is optimized by the comparison of the current reading to the baseline/threshold reading).
With regards to claim 37, Onarheim et al. disclose that the at least one biometric feature of the user is selected from one or more eyes of the user, one or more ears of the user, an eyebrow ridge of the user, a nose of the user, a mouth of the user, a jawline of the user, and combinations thereof (paragraph [0292], referring to the 10-20 coordinate system relying upon identification of the earlobes (i.e. ears), nasopharyngeal (i.e. nose) sites and nasion [which is associated with the eyes and nose (i.e. area between eyes, just above the bridge of the nose)]; Figures 9-10).
With regards to claim 39, Grossman et al. disclose that transforming the acquired real-time data into the frequency domain includes applying a Fast Fourier Transform to the acquired real- time data (paragraph [0086], referring to performing a split-radix FFT (Fast Fourier Transform) (116) for the “frequency domain” version of ECHT; Figure 1A).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. in view of Vortman et al., Grossman et al. and Onarheim et al. as applied to claim 16 above, and further in view of Tyler et al. (US Pub No. 2016/0038770).
With regards to claim 17, as discussed above, the above combined references meet the limitations of claim 16. Further, Shin et al. disclose that the ultrasound emissions are phase locked to the at least one slow wave (paragraph [0065]-[0066], referring to the slow oscillation component (240) of the stimulation (220) is substantially in-phase (and thus “phase locked”) with the slow oscillation (i.e. “slow wave”) of the EEG; Figure 1). Grossman et al. further teaches this limitation (paragraphs [0013]-[0014], [0083], [0086], [0173], [0176]).
However, the above combined references do not specifically disclose that the ultrasound pulses have a frequency in a range below 2 Hertz.
Tyler et al. disclose an apparatus and method for focusing transcranial ultrasound, wherein continuous wave (CW) ultrasound waveforms can be used for neuromodulation and have the advantages of employing a lower acoustic intensity and/or a slow pulse repetition frequency of less than about 1 Hz and further have the ability to drive activity robustly (Abstract; paragraph [0078]). Alternative pulsing protocols for CW US stimulus waveform can include those with slower pulse repetition frequencies of less than about 0.5 Hz or less than about 0.1 Hz or less than about 0.01 Hz or less than about 0.0001 Hz are also beneficial (paragraph [0078], note that the ultrasound pulses have a frequency in a range below 2 Hertz).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the ultrasound pulses of the above combined references have a frequency in a range below 2 Hertz, as taught by Tyler et al., in order to employ a lower acoustic intensity and/or a slow pulse repetition frequency and to drive activity robustly (Abstract; paragraph [0078]).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. in view of Vortman et al., Grossman et al. and Onarheim et al. as applied to claim 16 above, and further in view of Schiff et al. (WO 2014/116850).
With regards to claim 20, as discussed above, the above combined references meet the limitations of claim 16. However, they do not specifically disclose the at least a portion of a thalamus is speciifcally at least a portion of the “centromedian” thalamus.
Schiff et al. disclose a deep brain stimulation system and method for multi-site activation of the thalamus, wherein the thalamus brain regions that are stimulated include intraluminar nuclei, specifically including the centromedian, and wherein the disease or condition that is treated is impaired cognitive function (paragraphs [0056]-[0057], [0106]-[0108], note that the “centromedian” of the intraluminar nuclei corresponds to the centromedian thalamus; paragraph [00174]-[0175], referring to impaired cognitive function manifested in deficits including working memory, etc, wherein central thalamic stimulation compensates and supports activity from the central thalamus to widespread cerebral regions typically provided by intrinsic arousal regulation in the normal brain; Figure 7). The stimulation may occur during slow wave sleep in order to enhance synaptic plasticity effects of slow waves (paragraph [00158]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the at least a portion of the thalamus specifically correspond to the “centromedian thalamus”, as taught by Schiff et al., in order to treat impaired cognitive function, provide central thalamic stimulation which compensates and supports activity from the central thalamus to widespread cerebral regions typically provided by intrinsic arousal regulation in the normal brain, and enhance synaptic plasticity effects of slow waves (paragraphs [0107], [0175], [00158]).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. in view of Vortman et al., Grossman et al. and Onarheim et al. as applied to claim 16 above, and further in view of Brown et al. (US Pub No. 2014/0187973).
With regards to claim 21, as discussed above, the above combined references meet the limitations of claim 16. However, though Shin et al. do disclose that the stimulation control unit is further configured to process real-time information and the real-time information processed by the stimulation control unit includes processing of the EEG signal to identify sleep stage (paragraph [0089]), they do not specifically disclose that the real-time information processed by the stimulation control unit includes brainwave power spectral distribution and brainwave spectral amplitude to identify the sleep stage.
Brown et al. disclose a method and system for determining the state of a patient’s brain, wherein EEG waveforms in the time domain have a spectrum and can be translated into a spectrogram (Abstract; paragraphs [0029]-[0030]). Spectral analysis of the EEG recordings yields power spectrum and phase-amplitude (i.e. brainwave spectral amplitude) coupling information which may be complementary sources of information about brain dynamics (paragraphs [0047]-[0049], [0058]). The combination of both measures may reveal greater structure than either analysis alone (paragraphs [0058], [0094]-[0095]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the real-time information processed by the stimulation control unit includes brainwave power spectral distribution and brainwave spectral amplitude to identify sleep stage, as taught by Brown et al., in order to provide complementary sources of information about brain dynamics which may reveal greater structure than either analysis alone (Abstract; paragraph [0058]).
Claim(s) 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. in view of Vortman et al., Grossman et al. and Onarheim et al. as applied to claim 26 above, and further in view of Heeger et al. (US Pub No. 2018/0014784).
With regards to claim 28, as discussed above, the above combined references meet the limitations of claim 26. However, they do not specifically disclose that the stimulation control unit classifies the one or more specific sleep stages using a gradient boosted decision tree machine learning algorithm.
Heeger et al. disclose improved system and method for the acquisition and analysis of physiological data, such as EEG data, to provide further insight to the person’s health and/or behavior (Abstract; paragraph [0006]). A processing arrangement performs processing of the EEG signals, such as performing pattern recognition and pattern classification operations to recognize or identify various physiological states, such as different stages of sleep (paragraphs [0052], [0056]). The pattern recognition operations may include classification trees (i.e. decision trees), gradient boosting and machine learning, etc.. (paragraph [0056]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the stimulation control unit of the above combined references classify the one or more specific sleep stages using a gradient boosted decision tree machine learning algorithm, as taught by Heeger et al., in order to provide an improved system for analyzing EEG signals and thus provide further insight to the person’s health and/or behavior (Abstract; paragraph [0006]).
Claim(s) 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. in view of Vortman et al., Grossman et al. and Onarheim et al. as applied to claim 16 above, and further in view of Kabrams et al. (US Pub No. 2020/0188697).
With regards to claim 29, as discussed above, the above combined references meet the limitations of claim 16. Further, Shin et al. disclose that their stimulation control unit further comprises providing sleep stage prediction and regulating ultrasound emissions (paragraphs [0079], [0089], [0098]-[0100]).
However, they do not specifically disclose that a deep learning model is used for the sleep stage prediction or that a deep learning model is used for regulating the ultrasound emissions.
Kabrams et al. disclose a device wearable by a person which includes an EEG sensor configured to detect a signal from the brain of the person and a transducer configured to apply to the brain an acoustic signal (Abstract). The wearable device may provide ECG signals detected form the brain to a deep learning network to track sleep patterns of the person and the deep learning network may be used to predict the next symptom for the person (Abstract; paragraph [0148]). A machine learning algorithm that senses the brain state may be connected to a beam steering algorithm to make closed-loop systems, including a deep learning network (paragraphs [0157], [0167]). These machine learning algorithms may perform several tasks, including outputting a location to aim the stimulating ultrasound beam (paragraphs [0157], [0167]). Deep learning networks may be used to predict one or more symptoms of a neurological disorder (paragraph [0190]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the sleep stage prediction and the regulation of ultrasound emissions of the above combined references be performed using deep learning models, as taught by Kabrams et al., in order to provide an alternative technique for performing the prediction and regulation and to be able to predict one or more symptoms of a neurological disorder (Abstract; paragraph [0190]).
Claim(s) 31, 33-34 and 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. in view of Vortman et al., Tyler et al. and Grossman et al.
With regards to claim 31, Shin et al. disclose a neuromodulation system comprising:
a neuromodulation device (100) including a wearable device housing (i.e. 610), and one or more ultrasound-emitting elements (i.e. elements of ultrasonic generator array (630)) (paragraphs [0057]-[0060], [0098], referring to the brain-stimulating device (100) which includes a cap (610), an electrode array (620) and an ultrasonic generator array (630); paragraphs [0101], [0109]-[0110]; Figures 1, 5-6 and 8) and
at least one processor (i.e. 640; 130, 340) coupled to the one or more ultrasound-emitting elements, and configured with one or more data processing functions to focus ultrasound emission to a target brain area of a user that includes at least a portion of a thalamus (paragraphs [0072]-[0074], referring to the stimulation including ultrasonic wave stimulation, wherein the stimulation may apply the stimulation to a partial brain region which may be the thalamic reticular nucleus, which is a specific portion of the thalamus; in particular, note paragraph [0074] which sets forth that a “…focused ultrasound device may be used to apply the stimulation to the partial region of the brain. The focused ultrasound device is configured to allow ultrasound to be focused onto the partial brain region to which the stimulation is to be applied…”, and thus ultrasound emission is focused to a partial brain region of the thalamus; paragraphs [0079]-[0083], paragraphs [0101], referring to the processor (640) which is electrically connected to the ultrasonic generator array and can perform functions of the stimulation element and the function of the control unit (130); Figures 1, 3, 6 and 8), the one or more data processing functions configured to:
acquire real-time data by the one or more EEG electrodes (paragraphs [0059]-[0061], [0084]-[0086], [0089], referring to the measurement of a scalp EEG, and determining the generation of slow oscillation in the EEG signal and referring to the sleep state determination unit (360) which may determine whether or not the sleep state is a slow wave sleep state based on the frequency of generation of the slow oscillation; paragraphs [0048]-[0049], [0060], [0064], [0084], referring to the “slow oscillation” referring to an EEG generated during slow wave sleep and may have a frequency (i.e. spectral component) of about 1Hz or less; Figures 1, 3);
detect a phase of at least one slow wave (paragraphs [0059]-[0061], [0084]-[0086], [0089], referring to the measurement of a scalp EEG, and determining the generation of slow oscillation in the EEG signal and referring to the sleep state determination unit (360) which may determine whether or not the sleep state is a slow wave sleep state based on the frequency of generation of the slow oscillation; paragraphs [0048]-[0049], [0060], [0064], [0084], referring to the “slow oscillation” referring to an EEG generated during slow wave sleep and may have a frequency (i.e. spectral component) of about 1Hz or less; Figures 1, 3), wherein the phase detection is performed in real time and is dynamically updated based on the real-time data acquired by the one or more EEG electrodes (paragraph [0135], referring to in-phase mice receiving stimulation during NREM sleep, which occurred in synchrony with “up-states of online (i.e., in real time from the brain)-detected slow oscillations”, and therefore phase detection (i.e. detected slow oscillations) is performed in real time and is dynamically updated based on the real-time data; paragraphs [0002], [0055], referring to the EEG assessing cerebral function in a “continuous” manner and referring to slow oscillation being applied “continuously” to mice and mice receiving the stimulation with the slow oscillation during slow wave sleep; Figures 1, 3); and
control the ultrasound emissions from the one or more ultrasound-emitting elements, such that the ultrasound emissions constructively interfere at the target brain region of a user to form at least one ultrasound pulse focused on the target brain area during a certain slow wave phase range for enhancing the at least one slow wave based on the detected phase of the at least one slow wave for a specified period of time (paragraphs [0050] and [0054], referring to the “spindle-like stimulation” comprising a spindle component and a slow oscillation component and further referring to thalamic spindles inducing up-state cortical slow oscillation, etc., and thus the stimulation enhances at least one slow wave; paragraphs [0062]-[0065], [0074], [0087]-[0088], referring to applying the stimulation, which can be focused ultrasound (i.e. at least one US pulse focused on the target brain area would be formed due to inherent constructive interferences of the ultrasound emissions which are transmitted to and stimulating the same focused target brain area), to the brain in response to the generation of the slow oscillation; paragraph [0089], referring to the sleep state determination unit (360) turning on the stimulation unit (300) when the determined sleep state is the slow wave sleep; paragraph [0100], referring to emitting ultrasonic waves toward the brain; Figures 1, 3, 6 and 8),
wherein the ultrasound emissions are phase locked to the at least one slow wave (paragraph [0065]-[0066], referring to the slow oscillation component (240) of the stimulation (220) is substantially in-phase (and thus “phase locked”) with the slow oscillation (i.e. “slow wave”) of the EEG; Figure 1).
However, Shin et al. do not specifically disclose the one or more processing functions are further configured to use brain image data to identify the target brain area, and perform one or more acoustic simulations to determine waveform parameters for use in focusing ultrasound emissions from the one or more ultrasound-emitting elements to the target brain region.
Additionally, Shin et al. do not specifically set forth that the waveform parameters of the ultrasound emissions from the one or more ultrasound-emitting elements are controlled.
Furthermore, Shin et al. do not specifically disclose that the ultrasound pulses have a spectral frequency component in a range below 2 Hertz.
Further, though Shin et al. do disclose that the one or more data processing functions are further configured to determine a slow frequency band in order to determine a slow wave sleep state (paragraphs [0089]-[0091], referring to the slow oscillation detection signal and the sleep state determination unit determining whether or not the sleep state is a slow wave sleep state based on the frequency of generation of the slow oscillation), Shin et al. do not specifically disclose that the one or more processing functions are further configured to transform the acquired real-time data into a frequency domain, using the frequency domain, determine a dominant frequency within the slow wave frequency band of the acquired real-time data and that the detected phase is associated with the determined dominant frequency and that the detected phase is associated with the determined dominant frequency.
Additionally, Shin et al. do not specifically set forth that the ultrasound emissions from the one or more ultrasound-emitting elements are specifically controlled “in accordance with the determined information” and wherein the ultrasound emissions are adaptively modulated in one or more of timing, amplitude, duration, and spatial focus in response to changes in the detected phase or dominant frequency.
Vortman et al. disclose system and methods for measuring focusing properties of ultrasound beams for ultrasound therapy and, based thereon, adjusting parameters of the ultrasound in order to optimize focusing properties (Abstract; paragraph [0002]). Prior to treatment, an MRI apparatus or other imaging devices are activated to acquire anatomic characteristics and/or material characteristics of the patient’s skull, wherein a patient-specific 3D skull replica (402) may be created based on the acquired anatomic/material characteristics (paragraph [0049], Figure 4B, note that brain image data (i.e. MRI data) is used to identify one or more parameters (i.e. anatomic/material characteristics) representing one or more brain regions). An ultrasound wave may be applied to the target region (101) via traversing the 3D skull replica (paragraph [0049], Figure 4B, note that a target region (101) is thus identified and applying an ultrasound wave to the target region inherently requires that ultrasound-emitting element(s) are positioned relative to the target region). A detector device (404) deployed at the target region (101) may be activated to measure the focusing properties created by the applied ultrasound wave (paragraph [0049]). In addition, the detection system may measure the acoustic signals from the target region (101) and the ultrasound parameters can be again adjusted and the transducer is then activated based on the adjusted parameters and the acoustic signals and focusing properties in response to the adjusted parameter values may be measured using the detection system and second detector device, respectively (paragraph [0049]; Figure 4B). The steps (416-422) are repeated until sufficient data has been acquired to reliably establish the relationship between the value of the acoustic signals and the focusing properties at the target region (101) (paragraph [0049]; Figure 4B). The relationship may then be stored in memory accessible to the control (108) (paragraph [0049], note that for the result of the steps to be stored in memory and which are performed prior to treatment, the steps are performed offline relative to the steps that are performed during treatment). A controller (108) may adjust the phase shifts (i.e. phase offsets) associated with the transducer elements (104) to increase the acoustic power at the target region, and thus a high-power ultrasound focus may be reliably generated at the target region (101) (paragraph [0050], Figure 1). Alternatively or additionally, the controller (108) may shape the focus using a physical model that simulates acoustic beams from the transducer regions to the target 101 (paragraph [0051], note that the ultrasound emissions may therefore correspond to acoustic simulations of ultrasound emissions). Once the relationships between the parameter value of the acoustic signals and the focusing properties at the target region are established, the ultrasound focusing properties at the target region (101) may be monitored in real time during a treatment procedure (paragraph [0054]). In particular, acoustic signals from the target region (101) are obtained and, based thereon, one or more parameters (e.g., waveform parameters, such as phase shifts (i.e. “phase offsets”), frequencies and/or amplitudes) of the transducer elements (104) are inferred and may be adjusted in order to improve the focusing properties (paragraph [0054]; Figures 1, 4B, 7, note that during treatment (i.e. online algorithmic stimulation application element), ultrasound emissions are focused to the target region using waveform parameter (i.e. phase offset (i.e. phase shift)) information generated by the offline algorithmic mapping element (i.e. steps that were performed prior to treatment and which result is stored in a memory)).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the one or more processing functions of Shin et al. be further configured to use brain image data to identify the target brain area, and perform one or more acoustic simulations to determine waveform parameters for use in focusing ultrasound emissions from the one or more ultrasound-emitting elements to the target brain region and have the waveform parameters of the ultrasound emissions from the one or more ultrasound-emitting elements be controlled, as taught by Vortman et al., in order to optimize focusing properties of the ultrasound emissions to the one or more target regions (Abstract; paragraph [0002]).
However, the above combined references do not specifically disclose that the ultrasound pulses have a spectral frequency component in a range below 2 Hertz.
Further, the above combined references do not specifically disclose that the one or more processing functions are further configured to transform the acquired real-time data into a frequency domain, using the frequency domain, determine a dominant frequency within the slow wave frequency band of the acquired real-time data and that the detected phase is associated with the determined dominant frequency.
Tyler et al. disclose an apparatus and method for focusing transcranial ultrasound, wherein continuous wave (CW) ultrasound waveforms can be used for neuromodulation and have the advantages of employing a lower acoustic intensity and/or a slow pulse repetition frequency of less than about 1 Hz and further have the ability to drive activity robustly (Abstract; paragraph [0078]). Alternative pulsing protocols for CW US stimulus waveform can include those with slower pulse repetition frequencies of less than about 0.5 Hz or less than about 0.1 Hz or less than about 0.01 Hz or less than about 0.0001 Hz are also beneficial (paragraph [0078], note that the ultrasound pulses have a frequency in a range below 2 Hertz).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the ultrasound pulses of the above combined references have a frequency in a range below 2 Hertz, as taught by Tyler et al., in order to employ a lower acoustic intensity and/or a slow pulse repetition frequency and to drive activity robustly (Abstract; paragraph [0078]).
However, the above combined references do not specifically disclose that the one or more processing functions are further configured to transform the acquired real-time data into a frequency domain, using the frequency domain, determine a dominant frequency within the slow wave frequency band of the acquired real-time data and that the detected phase is associated with the determined dominant frequency. Further, the above combined references do not specifically disclose that the ultrasound emissions are adaptively modulated in one or more of timing, amplitude, duration, and spatial focus in response to changes in the detected phase or dominant frequency
Grossman et al. disclose a neuromodulator which may output stimuli that causes a user to fall asleep faster than the user would in the absence of the stimuli or may modify a sleep state or behavior associated with a sleep state, wherein the neuromodulator may take EEG measurements and the stimuli/neuromodulation signal may comprise an ultrasound signal (Abstract; paragraphs [0271], [0284], referring to an ultrasound transducer (1505) delivering ultrasound stimulation to the brain of a user; paragraph [0058], referring to the measurement of endogenous electrical activity of the brain via EEG electrodes; Figures 1, 15B). A method for accelerating sleep onset comprises of using an EEG sensor to take EEG measurements of the endogenous neural signal, wherein the endogenous neural signal may be the dominant (highest amplitude) neural signal in a frequency band, such as in the delta band (paragraphs
[0083],[0013],[0014]). A neuromodulator may, based on the EEG measurements, calculate instantaneous phase and instantaneous amplitude of the endogenous neural signal by a calculation that involves use of an endpoint-corrected Hilbert Transform (ECHT) (Step 102), wherein the ECHT algorithm can comprise a “frequency domain” version of ECHT which comprises performing a Fast Fourier transform (FFT) on the EEG signal (paragraphs [0083], [0086], note that the real-time EEG data is thus transformed into a frequency domain; Figure 1A). A neuromodulator may, based on the detected instantaneous phase (e.g., instantaneous phase estimated based on the EEG measurements) calculate stimulation that is phase-locked with the endogenous neural signal, such as the dominant neural signal (i.e. dominant frequency) in a delta frequency band (i.e. a slow-wave frequency band), and then physically output this stimulation in a manner perceptible to the human subject (Step 103) (paragraphs [0013]-[0014], [0083], [0173], [0176], note that a dominant/peak frequency within a slow-wave frequency band (i.e. delta band; see paragraph [0013] which refers to the “slow wave (delta) activity”) and a phase of the slow wave (i.e. delta band) with the determined dominant/peak frequency of the acquired real time data is determined and used to control the stimulation to be phase-locked with the dominant neural signal of the slow wave (i.e. delta band); Figure 1A). The neuromodulator may dynamically compute parameters that specify a pulse’s start phase, end phase, minimum duration and maximum duration, as well as dynamically compute other parameters, such as the type(s) of stimulation, location(s) of stimulation, maximum and minimum amplitude of stimulation and duration of stimulation session, such as dynamically computing the start phase as corresponding to the phase of an endogenous neural signal when the pulse ends (paragraphs [0142]-[0143], [0147], note that the “dynamic computation”/dynamic adjustment in real time of parameters that specify a pulses start/end phases/duration and amplitude corresponds to the stimulation (i.e. ultrasound emissions in the above combined references) being adaptively modulated in one or more of timing, amplitude, duration, etc., in response to changes in the detected phase/dominant frequency). The method may be performed iteratively to calculate, in each iteration, the instantaneous phase and instantaneous amplitude for the most recent sample in the sample window and to control stimulation based on the instantaneous phase and instantaneous amplitude (paragraph [0132]; Figure 1A, wherein steps 101, 102, 103, 100 are iteratively/continuously performed).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the one or more data processing functions of the above combined references be further configured to transform the acquired real-time data into a frequency domain, using the frequency domain, determine a dominant frequency within the slow wave frequency band of the acquired real-time data and have the detected phase be associated with the determined dominant frequency and have the ultrasound emissions be adaptively modulated in one or more of timing, amplitude, duration, and spatial focus in response to changes in the detected phase or dominant frequency, as taught by Grossman et al., in order to cause a user to fall asleep faster or modify a sleep state or behavior associated with a sleep state or hinder a transition from a waking state to a sleep state or from a sleep state to another sleep state (Abstract; paragraphs [0014]-[0015]).
With regards to claim 33, Shin et al. disclose that the at least one processor is configured to control the timing of the ultrasound emissions based on the detected phase of the at least one slow wave such that cells of the thalamus are excited by the ultrasound emissions during an up state of the at least one slow wave (paragraphs [0022], [0067], [0086]-[0087], referring to the stimulation being applied to the brain during the up-state period of the slow oscillation/slow wave period).
With regards to claim 34, Shin et al. disclose that the at least one processor is configured to control the one or more ultrasound-emitting elements to focus a single one of the ultrasound pulses to the thalamus during the up state (paragraphs [0022], [0067], [0086]-[0087]; paragraphs [0074], referring to the focused ultrasound device used to apply the stimulation, wherein a single one of the ultrasound pulses would be applied during one instantaneous time point; Figure 24).
With regards to claim 35, Shin et al. disclose that their system further comprises one or more EEG electrodes (i.e. 620) (paragraphs [0057]-[0060], [0098], referring to the brain-stimulating device (100) which includes a cap (610), an electrode array (620) and an ultrasonic generator array (630); paragraphs [0101], [0109]-[0110]; Figures 1, 5-6 and 8), wherein detecting a phase of the at least one slow wave is based on processing real time data acquired by the one or more EEG electrodes (paragraphs [0059]-[0061], [0084]-[0086], [0089], referring to the measurement of a scalp EEG, and determining the generation of slow oscillation in the EEG signal and referring to the sleep state determination unit (360) which may determine whether or not the sleep state is a slow wave sleep state based on the frequency of generation of the slow oscillation; paragraphs [0048]-[0049], [0060], [0064], [0084], referring to the “slow oscillation” referring to an EEG generated during slow wave sleep and may have a frequency (i.e. spectral component) of about 1Hz or less; Figures 1, 3). Grossman et al. further disclose these limitations (see Grossman et al., paragraphs [0013]-[0014], [0083], [0086], [0173], [0176]; Figure 1A).
With regards to claim 40, Grossman et al. disclose that transforming the acquired real-time data into the frequency domain includes applying a Fast Fourier Transform to the acquired real- time data (paragraph [0086], referring to performing a split-radix FFT (Fast Fourier Transform) (116) for the “frequency domain” version of ECHT; Figure 1A).
Claim(s) 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. in view of Vortman et al., Tyler et al. and Grossman et al. as applied to claim 31 above, and further in view of Schiff et al..
With regards to claim 32, as discussed above, the above combined references meet the limitations of claim 31. However, they do not specifically disclose the at least a portion of a thalamus is speciifcally at least a portion of the “centromedian” thalamus.
Schiff et al. disclose a deep brain stimulation system and method for multi-site activation of the thalamus, wherein the thalamus brain regions that are stimulated include intraluminar nuclei, specifically including the centromedian, and wherein the disease or condition that is treated is impaired cognitive function (paragraphs [0056]-[0057], [0106]-[0108], note that the “centromedian” of the intraluminar nuclei corresponds to the centromedian thalamus; paragraph [00174]-[0175], referring to impaired cognitive function manifested in deficits including working memory, etc, wherein central thalamic stimulation compensates and supports activity from the central thalamus to widespread cerebral regions typically provided by intrinsic arousal regulation in the normal brain; Figure 7). The stimulation may occur during slow wave sleep in order to enhance synaptic plasticity effects of slow waves (paragraph [00158]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the at least a portion of the thalamus specifically correspond to the “centromedian thalamus”, as taught by Schiff et al., in order to treat impaired cognitive function, provide central thalamic stimulation which compensates and supports activity from the central thalamus to widespread cerebral regions typically provided by intrinsic arousal regulation in the normal brain, and enhance synaptic plasticity effects of slow waves (paragraphs [0107], [0175], [00158]).
Claim(s) 38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. in view of Vortman et al., Tyler et al. and Grossman et al. as applied to claim 31 above, and further in view of Onarheim et al..
With regards to claim 38, as discussed above, the above combined references meet the limitations of claim 31. Further, Vortman et al. disclose that the one or more data processing functions are further configured to identify the target brain area based on brain image data (paragraph [0049], Figure 4B, note that brain image data (i.e. MRI data) is used to identify one or more parameters (i.e. anatomic/material characteristics) representing one or more brain regions, and a target region (101) is thus identified and applying an ultrasound wave to the target region inherently requires that ultrasound-emitting element(s) are positioned relative to the target region).
However, the above combined references do not specifically disclose that identification of the target brain area is further based on at least one biometric feature of a user.
Onarheim et al. disclose an a stimulation device for achieving various cognitive effects, wherein a ’10-20 system’ is a method that is used to describe and apply the location of scalp electrodes (i.e. stimulation emitting elements) (Abstract; paragraphs [0001], [0292]). The method was developed to ensure standardized reproducibility so that a subject’s studies could be compared over time and subjects could be compared to each other, wherein the system is based on the relationship between the location of an electrode and the underlying area of cerebral cortex (paragraph [0292]). The “10” and “20” refer to the fact that the actual distances between adjacent electrodes are either 10% or 20% of the total front-back or right-left distance of the skull (i.e. biometric distance) (paragraph [0292], Figures 9-10). Earlobes, nasopharyngeal and frontal polar sites are identified and two anatomical landmarks are used for the essential positioning of the EEG electrode: first, the nasion, which is the distinctly depressed area between the eyes; second, the inion, which is the lowest part of the skull from the back of the head and is normally indicated by a prominent bump; further, the purpose of the electrode placement is to target certain predefined brain area inside the skull, wherein the use of the 10-20 system coordinates is a way of seeking to achieve the correct placement on the outside of the skull to target the desired brain areas (paragraphs [0292], [0296], note that a target brain area (i.e. targeted “certain predefined brain area inside the skull”) is thus identified based on at least one biometric feature (i.e distance from anatomical landmarks (i.e. nasion, inion); Figures 9-10).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the identification of the target brain area of the above combined references be further based on at least one biometric feature of a user, as taught by Onarheim et al., in order to achieve the correct placement on the outside of the skull to target the desired brain areas and ensure standardized reproducibility so that a subject’s studies could be compared over time and subjects could be compared to each other (paragraphs [0292], [0296]).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 16-18 and 20-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 5-15 of U.S. Patent No. 11,730,960 in view of Onarheim et al. and Grossman et al..
With regards to claims 16-18, 20, 25-26, 31-32 and 35, claims 1 and 13 of the Patent meet most of the limitations of the claims (i.e. a neuromodulation device including a wearable device and ultrasound-emitting elements and one or more EEG electrodes, a stimulation control computing environment/at least one processor configured to use brain image data to identify the target brain area, perform one or more acoustic simulations, detect a phase of at least one slow wave, and control the ultrasound emissions/waveform parameters such that the ultrasound emissions constructively interference, process real-time data acquired by the one or more EEG electrodes to detect a phase of at least one slow wave, etc.).
However, with regards to claims 16 and 31, though claim 1 of the Patent does disclose identifying the target brain area based on brain image data (see claim 1, referring to “use brain image data to identify the centromedian thalamus [i.e. target brain area]), the Patent does not specifically disclose that identification of the target brain area is further based on at least one biometric feature of a user. Further, claim 1 of the Patent does not specifically disclose transforming the acquired real-time data into a frequency domain and using the frequency domain, determine a dominant frequency within a slow-wave frequency band of the acquired real-time data.
Onarheim et al. disclose an a stimulation device for achieving various cognitive effects, wherein a ’10-20 system’ is a method that is used to describe and apply the location of scalp electrodes (i.e. stimulation emitting elements) (Abstract; paragraphs [0001], [0292]). The method was developed to ensure standardized reproducibility so that a subject’s studies could be compared over time and subjects could be compared to each other, wherein the system is based on the relationship between the location of an electrode and the underlying area of cerebral cortex (paragraph [0292]). The “10” and “20” refer to the fact that the actual distances between adjacent electrodes are either 10% or 20% of the total front-back or right-left distance of the skull (i.e. biometric distance) (paragraph [0292], Figures 9-10). Earlobes, nasopharyngeal and frontal polar sites are identified and two anatomical landmarks are used for the essential positioning of the EEG electrode: first, the nasion, which is the distinctly depressed area between the eyes; second, the inion, which is the lowest part of the skull from the back of the head and is normally indicated by a prominent bump; further, the purpose of the electrode placement is to target certain predefined brain area inside the skull, wherein the use of the 10-20 system coordinates is a way of seeking to achieve the correct placement on the outside of the skull to target the desired brain areas (paragraphs [0292], [0296], note that a target brain area (i.e. targeted “certain predefined brain area inside the skull”) is thus identified based on at least one biometric feature (i.e distance from anatomical landmarks (i.e. nasion, inion); Figures 9-10).
At the time of the invention, it would have been obvious to one of ordinary skill in the art to have the identification of the target brain area of the Patent be further based on at least one biometric feature of a user, as taught by Onarheim et al., in order to achieve the correct placement on the outside of the skull to target the desired brain areas and ensure standardized reproducibility so that a subject’s studies could be compared over time and subjects could be compared to each other (paragraphs [0292], [0296]).
However, with regards to claims 16 and 31, the Patent does not specifically disclose transforming the acquired real-time data into a frequency domain and using the frequency domain, determine a dominant frequency within a slow-wave frequency band of the acquired real-time data and that the detected phase is associated with the determined dominant frequency and that the detected phase is associated with the determined dominant frequency and further that the ultrasound emissions are adaptively modulated in one or more of timing, amplitude, duration, and spatial focus in response to changes in the detected phase or dominant frequency.
Grossman et al. disclose a neuromodulator which may output stimuli that causes a user to fall asleep faster than the user would in the absence of the stimuli or may modify a sleep state or behavior associated with a sleep state, wherein the neuromodulator may take EEG measurements and the stimuli/neuromodulation signal may comprise an ultrasound signal (Abstract; paragraphs [0271], [0284], referring to an ultrasound transducer (1505) delivering ultrasound stimulation to the brain of a user; paragraph [0058], referring to the measurement of endogenous electrical activity of the brain via EEG electrodes; Figures 1, 15B). A method for accelerating sleep onset comprises of using an EEG sensor to take EEG measurements of the endogenous neural signal, wherein the endogenous neural signal may be the dominant (highest amplitude) neural signal in a frequency band, such as in the delta band (paragraphs
[0083],[0013],[0014]). A neuromodulator may, based on the EEG measurements, calculate instantaneous phase and instantaneous amplitude of the endogenous neural signal by a calculation that involves use of an endpoint-corrected Hilbert Transform (ECHT) (Step 102), wherein the ECHT algorithm can comprise a “frequency domain” version of ECHT which comprises performing a Fast Fourier transform (FFT) on the EEG signal (paragraphs [0083], [0086], note that the real-time EEG data is thus transformed into a frequency domain; Figure 1A). A neuromodulator may, based on the detected instantaneous phase (e.g., instantaneous phase estimated based on the EEG measurements) calculate stimulation that is phase-locked with the endogenous neural signal, such as the dominant neural signal (i.e. dominant frequency) in a delta frequency band (i.e. a slow-wave frequency band), and then physically output this stimulation in a manner perceptible to the human subject (Step 103) (paragraphs [0013]-[0014], [0083], [0173], [0176], note that a dominant/peak frequency within a slow-wave frequency band (i.e. delta band; see paragraph [0013] which refers to the “slow wave (delta) activity”) and a phase of the slow wave (i.e. delta band) with the determined dominant/peak frequency of the acquired real time data is determined and used to control the stimulation to be phase-locked with the dominant neural signal of the slow wave (i.e. delta band); Figure 1A). The neuromodulator may dynamically compute parameters that specify a pulse’s start phase, end phase, minimum duration and maximum duration, as well as dynamically compute other parameters, such as the type(s) of stimulation, location(s) of stimulation, maximum and minimum amplitude of stimulation and duration of stimulation session, such as dynamically computing the start phase as corresponding to the phase of an endogenous neural signal when the pulse ends (paragraphs [0142]-[0143], [0147], note that the “dynamic computation”/dynamic adjustment in real time of parameters that specify a pulses start/end phases/duration and amplitude corresponds to the stimulation (i.e. ultrasound emissions in the above combined references) being adaptively modulated in one or more of timing, amplitude, duration, etc., in response to changes in the detected phase/dominant frequency). The method may be performed iteratively to calculate, in each iteration, the instantaneous phase and instantaneous amplitude for the most recent sample in the sample window and to control stimulation based on the instantaneous phase and instantaneous amplitude (paragraph [0132]; Figure 1A, wherein steps 101, 102, 103, 100 are iteratively/continuously performed).
At the time of the invention, it would have been obvious to one of ordinary skill in the art to have the invention of the Patent further comprise transforming the acquired real-time data into a frequency domain and using the frequency domain, determine a dominant frequency within a slow-wave frequency band of the acquired real-time data and that the detected phase is associated with the determined dominant frequency and further that the ultrasound emissions are adaptively modulated in one or more of timing, amplitude, duration, and spatial focus in response to changes in the detected phase or dominant frequency, as taught by Grossman et al., in order to cause a user to fall asleep faster or modify a sleep state or behavior associated with a sleep state or hinder a transition from a waking state to a sleep state or from a sleep state to another sleep state (Abstract; paragraphs [0014]-[0015]).
With regards to instant claim 21, claim 5 of the Patent sets forth the same limitations.
With regards to instant claim 22, claim 6 of the Patent sets forth the same limitations.
With regards to instant claim 23, claim 7 of the Patent sets forth the same limitations.
With regards to instant claim 24, claim 8 of the Patent sets forth the same limitations.
With regards to instant claim 27, claim 9 of the Patent sets forth the same limitations.
With regards to instant claim 28, claim 10 of the Patent sets forth the same limitations.
With regards to instant claim 29, claim 11 of the Patent sets forth the same limitations.
With regards to instant claim 30, claim 12 of the Patent sets forth the same limitations.
With regards to instant claim 33, claim 14 of the Patent sets forth the same limitations.
With regards to instant claim 34, claim 15 of the Patent sets forth the same limitations.
With regards to claim 37, Onarheim et al. disclose that the at least one biometric feature of the user is selected from one or more eyes of the user, one or more ears of the user, an eyebrow ridge of the user, a nose of the user, a mouth of the user, a jawline of the user, and combinations thereof (paragraph [0292], referring to the 10-20 coordinate system relying upon identification of the earlobes (i.e. ears), nasopharyngeal (i.e. nose) sites and nasion [which is associated with the eyes and nose (i.e. area between eyes, just above the bridge of the nose)]; Figures 9-10).
With regards to claim 38, Onarheim et al. meet the limitations (see double patenting rejection above of claim 16 under Onarheim et al.).
With regards to claim 39, Grossman et al. meet the limitations (see double patenting rejection above of claims 16 and 31).
With regards to claim 40, Grossman et al. meet the limitations (see double patenting rejection above of claims 16 and 31).
Response to Arguments
Applicant's arguments filed October 1, 2025 have been fully considered but they are not persuasive.
With regards to Shin, Applicant argues that Shin describes detecting slow oscillations and applying stimulation, but it does not disclose real-time, continuously updated phase detection.
Examiner respectfully disagrees and points to paragraph [0135] of Shin, referring to in-phase mice receiving stimulation during NREM sleep, which occurred in synchrony with “up-states of online (i.e., in real time from the brain)-detected slow oscillations”, and therefore phase detection (i.e. detected slow oscillations) is performed in real time and is dynamically updated based on the real-time data. Additionally, paragraphs [0002] and [0055] of Shin refers to the EEG assessing cerebral function in a “continuous” manner and refers to slow oscillation being applied “continuously” to mice and mice receiving the stimulation with the slow oscillation during slow wave sleep, and thus EEG provides the real-time data.
With regards to Grossman, Applicant argues that Grossman discusses phase-locked stimulation, but does not teach dynamic, real-time updating of phase detection or adaptive modulation of ultrasound parameters in response to EEG changes.
Examiner respectfully disagrees and points to paragraphs [0142]-[0143 and [0147] of Grossman, which discloses that the neuromodulator may dynamically compute parameters that specify a pulse’s start phase, end phase, minimum duration and maximum duration, as well as dynamically compute other parameters, such as the type(s) of stimulation, location(s) of stimulation, maximum and minimum amplitude of stimulation and duration of stimulation session, such as dynamically computing the start phase as corresponding to the phase of an endogenous neural signal when the pulse ends. The “dynamic computation”/dynamic adjustment in real time of parameters that specify a pulses start/end phases/duration and amplitude corresponds to the stimulation (i.e. ultrasound emissions in the above combined references) being adaptively modulated in one or more of timing, amplitude, duration, etc., in response to changes in the detected phase/dominant frequency. Grossman further discloses in paragraph [0132] that the method may be performed iteratively to calculate, in each iteration, the instantaneous phase and instantaneous amplitude for the most recent sample in the sample window and to control stimulation based on the instantaneous phase and instantaneous amplitude. Further, Figure 1A of Grossman depicts that the steps 101, 102, 103, 100 are iteratively/continuously performed. Therefore, Grossman does disclose that the stimulation/ultrasound emissions are adaptively modulated in one or more of timing, amplitude, duration, and spatial focus in response to changes in the detected phase or dominant frequency.
The claims therefore remain rejected under the previously applied prior art.
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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/KATHERINE L FERNANDEZ/Primary Examiner, Art Unit 3798
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