DETAILED CORRESPONDENCE
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This non-final office action is in response to the Request for Continuing Examination communication filed by the Applicant on 3 December 2025. Claims 4, 7, and 11-13 are cancelled. Claims 1, 2, 9 and 10 have been amended and are considered below. Claims 1-3, 5, 6, and 8-10 are pending and considered below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1-3, 5, 6, 9, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Alyagon et al. (20190247654) in view of Etkin et al. (20210353224) and in further view of McIntyre et al (20200001071).
Claims 1, 9, and 10: Alyagon discloses a method, system and computer readable medium to assess major depressive disorder (MDD) disease state in a subject during the course of therapy ([160-166]), the method comprising:
receiving electrophysiological measurements for assessment ([103 “indicates that an electrophysiological signal of a subject recorded within a given time period after applying TMS to the subject may serve as an indicator of the responsiveness of the subject to treating the subject for a given neuropsychiatric condition using a given therapy,”]);
generating, using a neural network ([33-36, 37 “computer processor is configured to construct the brain network activity pattern by constructing a brain network activity pattern in which each node represents a cluster of vectors of data characteristics, and the connectivity weights of each one of the respective nodes represents at least one cluster property describing a pair of clusters represented by said the respective pair of nodes,” 38]), a prediction of a disease state based on the electrophysiological measurements ([103 “the computer processor predicts an outcome of treating the subject for a neuropsychiatric condition, using a given therapy. For some applications, the computer processor generates an output on an output device (such as monitor 18) in response to the predicted outcome,” 114 “in response to the brain network activity, the computer processor predicts an outcome of treating the subject for a neuropsychiatric condition, using a given therapy. For some applications, the computer processor generates an output on an output device (such as a display) in response to the predicted outcome,” 160-165, 166 “major depressive disorder,”]);
generating a set of features using the electrophysiological measurements and the predicted disease state ([97 “correlation between the T-scores of both the ADHD patients and the healthy subjects and a predicted ADHD symptoms score, the predicted score being based upon (a) the P300 amplitudes recorded during unsuccessful stop signal tasks performed by the patients/subjects,” 103 “predicts an outcome of treating the subject for a neuropsychiatric condition, using a given therapy. For some applications, the computer processor generates an output on an output device (such as monitor 18) in response to the predicted outcome,” 112 “predicts an outcome of treating the subject for a neuropsychiatric condition, using a given therapy. For some applications, the computer processor generates an output on an output device (such as monitor 18) in response to the predicted outcome. For example, the EEG signal of a patient suffering from ADHD may be recorded (e.g., after applying dTMS to the subject). The power of a given frequency band (e.g., the alpha band, or the low gamma band) is calculated, and in response thereto, the responsiveness of the patient to using dTMS to treat the patient for ADHD is predicted,” 113 “indicate that the responsiveness of an ADHD patient to treatment using dTMS may be predicted based upon recordings from the FC4 electrode of an EEG recording on the first day of treatment,” 114]);
Alyagon does not explicitly disclose, however McIntyre discloses:
wherein the electrophysical measurements are local field potential measurements of a deep brain stimulation site ([32 “Clinical deep brain stimulation (DBS) technology is evolving to enable chronic recording of local field potentials (LFPs) that represent electrophysiological biomarkers of the underlying disease state. However, little is known about the biophysical basis of LFPs, or how the patient's unique brain anatomy and electrode placement impact the recordings. Therefore, a patient-specific computational framework to analyze LFP recordings within a clinical DBS context was developed,” 35 “ability to obtain chronic recordings facilitates the possible use of LFPs as clinical biomarkers in closed-loop control systems, which have become a driving force in the development of commercial DBS technology. Specifically, beta band (12-30 Hz) activity has received the greatest attention in PD research because of its correlative association with PD symptoms. As such, LFP recordings of STN beta activity represent viable control signals for adaptive subthalamic DBS systems in humans,” 53 “example integrated STN LFP model, according to various aspects discussed herein. As shown at image 510, each STN neuron received either synchronous or asynchronous synaptic inputs. The model of the example use case used a radius of 3 mm, centered on contact 1, to define the synchronous neurons. In diagrams 520, the voltage recorded on each DBS electrode contact was defined from the sum of all transmembrane currents generated by each compartment of each neuron using a reciprocity based solution. Bipolar recording pairs were calculated as the difference between the appropriate monopolar recordings. Graphs 530 show a comparison of the time-domain simulated LFP to the clinical LFP recorded from the 0-1 contact pair. Normalized amplitudes are expressed in arbitrary units. Graphs 540 show power spectra of the model and clinical LFPs, showing beta activity in both cases,” 79 “an example of an optimized STN LFP model, according to various aspects discussed herein. Image 810 shows a search of the parameter space to find an optimal synchronous radius and normalized position. The fitness metric incorporated coherence between the clinical and model power spectra, as well as the relative normalized beta power across the contact pairs. Image 820 shows a visual schematic of the optimized STN LFP model, indicating optimal radius and position values for the example use case,” 80 “patient-specific LFP model was used to estimate the location and radius of synchronous beta activity within the STN using clinically-recorded LFPs from each of the 3 bipolar pairs,”]) Examiner Note: Examiner under a broadest reasonable interpretation understands the disclosures of McIntyre to disclose a wide range of mechanisms and processes to detect and process deep brain related field potentials and the inclusion of the measurements with respect to determining and processing stimulated and measured information as related to brain neurological measurements.
Therefore it would be obvious for Alyagon wherein the electrophysical measurements are local field potential measurements of a deep brain stimulation site as per the steps of McIntyre in order to robustly process collected brain functionality data with respect to mental disease states and implement the collected and processed data to optimize treatments for patients with major depressive disorder conditions and result in better outcomes.
Alyagon does not explicitly disclose, however Etkin discloses:
executing a feature compression network using the set of features to define a representation of the electrophysiological measurements ([42 “brain activity level” refers to measurable (e.g., quantifiable) neural activity. Measurable neural activity includes, but is not limited to, a magnitude of activity, a frequency of activity, a delay of activity, or a duration of activity. Brain activity levels may be measured (e.g., quantified) during periods in which no stimulus is presented. In embodiments, the brain activity level measured in the absence of a stimulus is referred to as a baseline brain activity level,” 43-45]) within a low-dimensional latent space ([102 “Since model fitting is done under a sparse constraint on the number of spatial filters, this serves as well to reduce dimensions of the underlying latent signals and thus the chance of overfitting,” 108 “result of the algorithm-enforced low-dimensionality constraint on the latent signals in SELSER, only a few latent signals were obtained in each model (FIG. 10). For the sertraline alpha REO model, the scalp and cortical spatial maps of the two latent signals with the most positive and negative regression weights are shown in FIG. 2C and FIG. 2D, respectively,” 135 “static dimensionality reduction and dynamic dimensionality reduction have been used in predicting motor and olfactory states.sup.49,50. Leveraging EEG dynamics information by incorporating frequency filter optimization into SELSER may further enhance its performance,”]);
defining spectral discriminative components from the set of features ([196 “spectrally filtered EEG data were then re-referenced to the common average; 5) Bad epochs were rejected by thresholding the magnitude of each epoch….of the 266 patients with pretreatment EEG recordings, 228 had usable EEG data for analyses,” 226 “spectrally filtered EEG data was then re-referenced to the common average and epoched with respect to the TMS pulse (−500˜1500 ms),” 275 “weighted phase-lag index (e.g., measuring a coherence between the EEG phases of two regions of the brain), imaginary coherence (e.g., an imaginary part of the spectral coherence between the EEG data of two regions of the brain), cordance (e.g., a combination of absolute power and relative power of different EEG frequencies),”]), wherein the spectral discriminative components are low-dimensional latent representations of a subset of the set of features that are predictive of the disease state ([129 “critical element in attaining individual level-robust outcome prediction may be the use of a latent space computational model (i.e. spatial filters optimized by leveraging the treatment outcome information),” 131-134, 276 “high-dimensionality of the unprocessed EEG data may cause overfitting at the regression model 116. As such, the filter 112 may be configured to reduce the dimensionality of the EEG data including by merging two or more signals to generate a single latent signal. Two or more signals may be merged based at least on the two or more signals exhibiting an above threshold positive covariance, for example, by changing (e.g., increasing and/or decreasing in value) in tandem,” 285 “machine learning controller 110 may train the machine learning model 115 by solving a convex optimization problem that includes a penalty term to limit the dimensionality of the latent signals. The global minimum solution to the convex optimization problem may therefore minimize the error in the prediction generated by the regression model 116 as well as minimize the quantity of latent signals generated by the filter,” 382 “filter is configured to reduce a dimensionality of the first data of the patient including by merging, into a single latent signal, two or more signals in the first data, and wherein the two or more signals are merged based at least on a covariance between the two or more signals.]); and
adjusting a treatment associated with MDD based on the spectral discriminative components ([197 “spectrally filtered EEG data were then re-referenced to the common average,” 226, 275 “regression model 116 may be trained to generate a treatment prediction outcome based on one or more features extracted by the feature extractor….weighted phase-lag index (e.g., measuring a coherence between the EEG phases of two regions of the brain), imaginary coherence (e.g., an imaginary part of the spectral coherence between the EEG data of two regions of the brain), cordance (e.g., a combination of absolute power and relative power of different EEG frequencies), approximate entropy (e.g., a measure for quantifying the amount of regularity and unpredictability of fluctuations in the EEG data),”]).
Therefore it would be obvious for Alyagon to defining spectral discriminative components from the set of features wherein the spectral discriminative components are low-dimensional latent representations of a subset of the set of features that are predictive of the disease state, defining spectral discriminative components from the set of features wherein the spectral discriminative components are low-dimensional latent representations of a subset of the set of features that are predictive of the disease state, and adjusting a treatment associated with MDD based on the spectral discriminative components as per the steps of Etkin in order to robustly process collected brain functionality data with respect to mental disease states and implement the collected and processed data to optimize treatments for patients with major depressive disorder conditions and result in better outcomes.
Claim 2: Alyagon discloses a method characterizing a depression state of a subject during the course of therapy, the method comprising:
receiving electrophysiological measurements from a sensor associated with a brain of the subject, wherein the electrophysiological measurements characterizes a depression state progression ([24 “computer processor is configured to predict the outcome of treating the subject for the neuropsychiatric condition using the given therapy by predicting a rate of improvement in the subject's neuropsychiatric condition, in response to being treated with the given therapy,” 102 “degree of correlation between (a) improvements to patients' T-scores, and (b) the power of respective frequency components of the two-second interval EEG samples as recorded at the FC4 EEG electrode at the first treatment session. As shown, there is a correlation between many frequency components of the two-second interval EEG samples as recorded at the first treatment session and the improvements to the patients' T-scores,” 105-110]); Examiner Note: Examiner interprets the reception of EEG samples (i.e., sensors) and the processing of the data to disclose the reception of electrophysiological measurements from sensors and determination of depression related information.
generating the depression state based on the spectral discriminative components ([165 “sections of the inter-treatment EEG signals were sampled, and the samples were spectrally analyzed, such that the powers of respective frequency components within the samples were calculated,” 166, 168-171, 172 “graphs showing the relationship between (a) the time after initiating dTMS treatment of major depressive disorder patients to respective percentage improvements from pre-treatment baseline in the patients' HDRS, and (b) the power of respective frequency components of the thirteen-second interval EEG samples as recorded at respective EEG electrodes prior to treatment commencing, in accordance with some applications of the present invention,” 186]).
Alyagon does not explicitly disclose however McIntyre discloses:
wherein the electrophysical measurements are local field potential measurements of a deep brain stimulation site ([32 “Clinical deep brain stimulation (DBS) technology is evolving to enable chronic recording of local field potentials (LFPs) that represent electrophysiological biomarkers of the underlying disease state. However, little is known about the biophysical basis of LFPs, or how the patient's unique brain anatomy and electrode placement impact the recordings. Therefore, a patient-specific computational framework to analyze LFP recordings within a clinical DBS context was developed,” 35 “ability to obtain chronic recordings facilitates the possible use of LFPs as clinical biomarkers in closed-loop control systems, which have become a driving force in the development of commercial DBS technology. Specifically, beta band (12-30 Hz) activity has received the greatest attention in PD research because of its correlative association with PD symptoms. As such, LFP recordings of STN beta activity represent viable control signals for adaptive subthalamic DBS systems in humans,” 53 “example integrated STN LFP model, according to various aspects discussed herein. As shown at image 510, each STN neuron received either synchronous or asynchronous synaptic inputs. The model of the example use case used a radius of 3 mm, centered on contact 1, to define the synchronous neurons. In diagrams 520, the voltage recorded on each DBS electrode contact was defined from the sum of all transmembrane currents generated by each compartment of each neuron using a reciprocity based solution. Bipolar recording pairs were calculated as the difference between the appropriate monopolar recordings. Graphs 530 show a comparison of the time-domain simulated LFP to the clinical LFP recorded from the 0-1 contact pair. Normalized amplitudes are expressed in arbitrary units. Graphs 540 show power spectra of the model and clinical LFPs, showing beta activity in both cases,” 79 “an example of an optimized STN LFP model, according to various aspects discussed herein. Image 810 shows a search of the parameter space to find an optimal synchronous radius and normalized position. The fitness metric incorporated coherence between the clinical and model power spectra, as well as the relative normalized beta power across the contact pairs. Image 820 shows a visual schematic of the optimized STN LFP model, indicating optimal radius and position values for the example use case,” 80 “patient-specific LFP model was used to estimate the location and radius of synchronous beta activity within the STN using clinically-recorded LFPs from each of the 3 bipolar pairs,”]) Examiner Note: Examiner under a broadest reasonable interpretation understands the disclosures of McIntyre to disclose a wide range of mechanisms and processes to detect and process deep brain related field potentials and the inclusion of the measurements with respect to determining and processing stimulated and measured information as related to brain neurological measurements.
Therefore it would be obvious for Alyagon wherein the electrophysical measurements are local field potential measurements of a deep brain stimulation site as per the steps of McIntyre in order to robustly process collected brain functionality data with respect to mental disease states and implement the collected and processed data to optimize treatments for patients with major depressive disorder conditions and result in better outcomes
Alyagon does not explicitly disclose, however Etkin discloses:
generating spectral discriminative components from a set of features derived from the electrophysiological measurements ([46 “detected by electroencephalography (EEG), magnetoencephalography (MEG), or other electrophysiological or neurophysiological recording methods,” 197, 226 “spectrally filtered EEG data was then re-referenced to the common average and epoched with respect to the TMS pulse (−500˜1500 ms),”]), wherein the spectral discriminative components are low- dimensional latent representations of a subset of the set of features that are predictive of the depression state ([129 “critical element in attaining individual level-robust outcome prediction may be the use of a latent space computational model (i.e. spatial filters optimized by leveraging the treatment outcome information),” 131-134, 276 “high-dimensionality of the unprocessed EEG data may cause overfitting at the regression model 116. As such, the filter 112 may be configured to reduce the dimensionality of the EEG data including by merging two or more signals to generate a single latent signal. Two or more signals may be merged based at least on the two or more signals exhibiting an above threshold positive covariance, for example, by changing (e.g., increasing and/or decreasing in value) in tandem,” 285 “machine learning controller 110 may train the machine learning model 115 by solving a convex optimization problem that includes a penalty term to limit the dimensionality of the latent signals. The global minimum solution to the convex optimization problem may therefore minimize the error in the prediction generated by the regression model 116 as well as minimize the quantity of latent signals generated by the filter,” 382 “filter is configured to reduce a dimensionality of the first data of the patient including by merging, into a single latent signal, two or more signals in the first data, and wherein the two or more signals are merged based at least on a covariance between the two or more signals.]);
adjusting a treatment associated with MDD based on the spectral discriminative components. ([197 “spectrally filtered EEG data were then re-referenced to the common average,” 226, 275 “regression model 116 may be trained to generate a treatment prediction outcome based on one or more features extracted by the feature extractor….weighted phase-lag index (e.g., measuring a coherence between the EEG phases of two regions of the brain), imaginary coherence (e.g., an imaginary part of the spectral coherence between the EEG data of two regions of the brain), cordance (e.g., a combination of absolute power and relative power of different EEG frequencies), approximate entropy (e.g., a measure for quantifying the amount of regularity and unpredictability of fluctuations in the EEG data),”]).
Therefore it would be obvious for Alyagon to generate spectral discriminative components from a set of features derived from the electrophysiological measurements, wherein the spectral discriminative components are low- dimensional latent representations of a subset of the set of features that are predictive of the depression state and adjusting a treatment associated with MDD based on the spectral discriminative components as per the steps of Etkin in order to robustly process collected brain functionality data with respect to mental disease states and implement the collected and processed data to optimize treatments for patients with major depressive disorder conditions and result in better outcomes.
Claim 3: Alyagon in view of Etkin discloses the method of claim 2 above, and Alyagon further discloses wherein the characterization of the depression state progression comprises the identification of at least one discrete disease state or the disease trajectory within at least one disease state ([72 “degree of correlation between (a) improvements to patients' Hamilton depression rating scale (“HDRS”) of major depressive disorder patients after four weeks of dTMS treatment versus (b) Long Interval Cortical Inhibition TMS-evoked,” 73 “major depressive disorder patients to whom dTMS was applied, the degree of correlation between (a) improvements to patients' HDRS after four weeks of dTMS treatment versus (b) LICI-TEP deflection values generated by a single pulse that was recorded on the first day of a treatment prior to initiation of treatment, in accordance with some applications of the present invention, 74-77, 161-170, Figs 11A, 11B, 12A, 12B, 13A, 13B]).
Claim 5: Alyagon in view of Etkin the method of claim 1 above, and Alyagon further discloses wherein the electrophysiological measurements are associated with neural stimulation ([79, 80 “one or more pulses of transcranial magnetic stimulation (e.g., a train of pulses that includes a plurality of pulses) are applied to a subject. For example, the subject may be a subject suffering from attention deficit hyperactivity disorder (ADHD). Within a given time period of having applied one of the one or more pulses of transcranial magnetic stimulation to the subject, an electrophysiological signal (typically, an electroencephalography (EEG) signal) of the subject is detected,” 81-89]).
Claim 6: Alyagon the method or system of claim 5, and Alyagon further discloses wherein the neural stimulation is acute ([125 “vectors of waveform characteristics are extracted separately for time-separated TMS stimuli, to define clusters of points where each point within the cluster corresponds to a response to a stimulus applied at a different time,” 126, 128, 129 “first brain network activity pattern can be useful, for example, for monitoring changes in the brain function of the subject over time (e.g., monitoring brain plasticity or the like) since it allows comparing the brain network activity pattern to a previously constructed unassociated subject-specific brain network activity pattern,” 131]). Examiner Note: Examiner’s interpretation of acute neural stimulation is guided by Applicant’s written description at least at paragraph [119] which recites “Beta band power in SCC has been shown to reflect emotional processing as well as depression severity in acute recordings. Changes in beta power in SCC induced by acute stimulation have been shown to correlate with short-term changes in symptoms,” and paragraphs [157] and [158] which recites, “acute and chronic effects observed here with chronic DBS are similar to the effects observed with both rapid-acting and slow-acting antidepressants.” Examiner interprets the disclosures of the instant invention to relate to the collection of brain activity patterns and the comparison of the collected patterns to previously collected data, and resulting in a measure of the neural stimulation.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Alyagon et al. (20190247654) in view of Etkin et al. (20210353224) and in further view of McIntyre et al (20200001071) and Howard (20210138249).
Claim 8: Alyagon in view of Etkin and McIntyre does not explicitly disclose the elements of Claim 1, however Howard discloses the method as for Claim 1, wherein a brain map ([166 “techniques for brain interfacing, mapping neuronal structure (Google earth for brains), manipulating cellular structure, cognitive, and brain augmentation via implants, and curing, not just managing, neurological disorders,” 167-170]) is configured to predict transitions in brain states of a subject ([839 “Mapping the temporality of thought requires the connection of several such mind states over time, which are themselves composed of FCU units. In order to develop the relationship between the FCU and temporality, FCU/MCP uses the Markov Decision Process model to build mind state transitions through reasoning and decision-making. This analytical process forms the foundation for the two linked goals of FCU/MCP: the empirical and predictive analysis of cognitive information, as well as the modification of brain processes to alter that information,” 840, 841, 858 “Maxent model is predictive analysis. Given a mind state correlated with a series of spoken concepts, future behavior (depressive vs. non-depressive) and linguistics (attributable to cognitive state) can be discerned to a reasonable measure of certainty using Maxent,”]).
Therefore it would be obvious for Alyagon wherein a brain map is configured to predict transitions in brain states of a subject as per the disclosures of Howard in order to precisely predict and determine brain state transitions in accordance with the collection and interpretation of brain maps and thereby resulting in more accurate determination of treatment methodologies.
Response to Arguments/Amendments
After careful review of Applicant’s remarks/arguments filed on 3 December 2025, Applicant's arguments with respect to claims 1-3, 5, 6, and 8-10 have been fully considered and are discussed below.
Claim Rejections - 35 USC § 103
Applicant’s arguments and amendments, see Remarks/Amendments, filed 3 December 2025, with respect to the rejection(s) of claim(s) 1-3, 5, 6, and 8-10 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of the combination of previously cited to references Alyagon in view of Etkin and Howard and in further view of newly identified reference McIntyre. Applicants amended the independent claims to include specific reference to the interpretation of local field potential measurements as related to deep brain functioning and as explained above in the rejection newly identified reference McIntyre discloses the newly included functionality of the invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Please see attached References Cited form 892.
See Shanechi (12,097,029) for disclosures related to the reception and decoding of large brain activity signals by the collection and tracking of detected brain activity as collected from local field potential leads. See at least columns 1-3.
See DeBarros et al. (20220143415) for disclosure related to the collection of electrophysical signals from humans as related to the detection of responses to local field potential signals. See at least paras. [51]-[81].
See Arlotti et al. (20220016415) for disclosures related to implementing a system for deep brain stimulation including the collection of neurological responses to a variety of stimulus methods and the analysis of the collected signals for diagnostic purposes. See at least paras. [45]-[70].
See Molnar et al. (20210038897) for disclosures related to the provision of deep brain stimulation and the collection and monitoring of a wide variety of symptoms and the development of treatment therapies with respect to the collected data. See at least paras. [38]-[58].
See Howard (20200222010) for disclosures related to the collection of brain related neurological functioning, mapping, manipulating cellular structure, and providing implants ad a variety of treatment methods. See at least paras. [178]-[229].
See Kumari et al., Phase-Dependent Deep Brain Stimulation: A Review, Brain Science 2021, 11, 414. https://doi.org/10.3390/brainsci11040414, for disclosures related to reviews recent trends in deep brain stimulation systems and their non-invasive counterparts, in the use of phase specific stimulation to manipulate individual neural oscillations. In particular, the paper reviews the methods adopted in different brain stimulation systems and devices for stimulating at a definite phase to further optimize closed loop brain stimulation strategies
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID STOLTENBERG whose telephone number is (571)270-3472. The examiner can normally be reached on generally 8am to 8pm EST M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached on 571 272 6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DAVID J STOLTENBERG/ Primary Examiner, Art Unit 3685