The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Response to Arguments
The cancellation of claims 6 and 13 obviate the claim objections and the claim rejections under 35 USC 112(b) and 112(d). The amendment to claim 14 overcomes the previous claim objection to that claim.
Applicant's arguments filed June 12, 2026 have been fully considered but they are not persuasive.
On page 11 of the remarks, the application argues that “Bikson mainly aims at DC stimulations and only mentions in a single paragraph the possibility of using AC voltages for achieving AC stimulation.” It is noted that the applicant has therefore agreed that Bikson teaches the possibility of AC stimulation, which was mentioned in the previous rejection. Therefore, this is not a persuasive argument that Bikson fails to teach alternating current stimulation.
On page 11 of the remarks, the application argues that “Bikson does not propose to make use of a stimulation device based on a helmet with patient specific design.” The examiner respectfully disagrees. Firstly, it is noted that the claims are directed to “a computer-implemented method for computing a patient-specific set of configuration parameters suitable for configuring and/or choosing an electrical stimulation device…”. Claim 1 has been amended to state within the preamble that the device “comprises a number of N non-invasive stimulation electrodes arranged in a 3D-geometry on a shell of a helmet or bonnet with a patient-specific design”. This is ambiguous as to whether the helmet/bonnet has a patient-specific design, or if the “N non-invasive stimulation electrodes [are] arranged in a 3D-geometry on a shell of a helmet”/bonnet with a patient-specific design. In the latter, the helmet/bonnet is not at all required to be anything other than generic. Next, it is noted that step (A) of claim 1 is amended to state “and deriving a geometrical head model allowing accurate modeling of the electrical properties of the head of said patient for whom the helmet/bonnet is designed from the patient-specific data”. This statement, along with the preamble stating “wherein the design is based on the anatomy of the brain of an individual patient” imparts that the geometrical model is a model of the electrical properties of the brain.
Based on the above, it does not appear to be relevant whether or not Bikson proposes to make use of a stimulation device based on a helmet with patient specific
design, to which the applicant argues on page 11 that Bikson lacks therein. Additionally, the applicant makes the statement that “In the customized helmet approach according to the claims, the positions of the electrodes are comprised in a digital personalized head model which takes into account the specific anatomy of the patient's skull and brain (as visible in the "medical imaging technique" cited in the claims)” (emphasis added). It is noted that based on the discussion of the preceding paragraph, the claim does not require a “customized helmet approach”, and more specifically it fails to claim a “customized helmet approach… which takes into account the specific anatomy of the patient’s skull”. At most, it claims that it takes into account the electrical properties of a patient’s brain when determining the arrangement of N non-invasive stimulation electrodes in a 3D geometry on a shell”, where the shell is “of a helmet or a bonnet”, and this is done specific to the patient.
As described above, this claimed subject matter is taught by Bikson. Specifically, Bikson teaches "Electrode configuration or placement of individual electrodes in the electrode configuration can facilitate: control the volume of (brain) tissue activated by neurocranial electrostimulation; ... accommodation of individual factors, for example to optimize treatment based on patient specific anatomical features" (see paragraph 9). Additionally, paragraph 52 teaches the patient-specific DTI imaging data is used to “describe the inhomogeneous and anisotropic tissue properties near the electrode. In this example, such DTI imaging data is used to explicitly define one or more axonal trajectories, if needed, or to help define nuclear boundaries specified in the anatomical MRI”, so that neuronal elements positioned relative to electrodes may be modeled. Additionally paragraph 86 teaches an “image model includes a 2-D or 3-D image of the target tissue along with certain electrical characteristics of the target tissue. For example, the image model can be of transcranial tissue and can include tissue conductance values. … The image model can be … a patient specific image model.” Also see, in addition to the above, paragraphs 40, 80 and 101-102, which each describe different ways in which the data obtained and used may be patient-specific in some form or another. As such, Bikson explicitly teaches throughout its disclosure that the models generated and the electrode configurations being simulated by a computer-implemented method are made specific to a patient via images of the patient. As such, Bikson reads on the claims, in light of the arguments, and the rejections below are applicable to the claims.
Claim Rejections - 35 USC § 112
Second Paragraph
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5, 7-12, 14-15 and 21 are 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.
Claim 1 is rejected because “allowing accurate modeling” in line 10 is unclear and indefinite. The terms “accurate modeling” is not clear, since it cannot be determined what is consider accurate versus inaccurate modeling. The term “accurate” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Secondly, the use of the word “allowing” imparts the meaning that the model is capable of being made accurately, but not that the model is actually created with accuracy.
Claims 2-5, 7-12, 14-15 and 21 are rejected based on their dependency from claim 1, which therefore includes the issue identified above in claim 1.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-5, 8-12 and 21 are rejected under 35 U.S.C. 102(a)(1) as anticipated by Bikson et al. (US Patent Pub. No. 2012/0245653).
Bikson discloses a computer-implemented method for computing a patient-specific set of configuration parameters suitable for configuring and/or choosing an electrical stimulation device (“methods, systems, and apparatus, including computer programs … for developing transcranial electrical stimulation protocols” - see Abstract), which is designed for electrical stimulation of neuronal tissue (), and which comprises a number of N non-invasive stimulation electrodes arranged in a 3D-geometry on a shell of a helmet or a bonnet (see Figures 3-4) with a patient-specific design (see paragraph 24 where it teaches optimization may include “electrode number, position of each electrode”, which may be determined via the forward model solved solution series, and also paragraph 26 which teaches to obtain images of the target tissue and ascertaining tissue properties, and it states that “Tissue properties can be … approximated from image data, as described in U.S. Patent Application Pub. No. US 2007/0043268 A1, incorporated herein by reference in its entirety, which is therefore patient-specific; also see paragraphs 9, 40, 52, 80, 86 and 101-102 as described above in the Response to Arguments section describing teachings of patient-specific computation), where the design is based on the anatomy of the brain of an individual patient (see the previous parenthetical remark), and wherein the shell is table in shape (see skull cap 430 in Figure 4, which is stable in shape while being worn, at the least). More specifically, Bikson discloses:
extracting patient-specific data including at least one region of interest from at least one pre-existing image previously acquired using a medical imaging technique (see paragraph 86, also see paragraphs 25-35 for in-depth discussion of the use of MRI to acquire brain images, also see paragraph 41) and deriving a geometrical head model allowing accurate modeling of the electrical properties of the head of said patient for whom the helmet/bonnet is designed from the patient-specific data (see paragraph 52 for teaching that patient-specific DTI imaging data is used to “describe the inhomogeneous and anisotropic tissue properties near the electrode. In this example, such DTI imaging data is used to explicitly define one or more axonal trajectories, if needed, or to help define nuclear boundaries specified in the anatomical MRI”, so that neuronal elements positioned relative to electrodes may be modeled. Additionally, paragraph 86 teaches an “image model includes a 2-D or 3-D image of the target tissue along with certain electrical characteristics of the target tissue. For example, the image model can be of transcranial tissue and can include tissue conductance values. … The image model can be … a patient specific image model.”);
simulating via computer simulation an electrical field distribution that can be generated using the N non-invasive stimulation electrodes of the electrical stimulation device when these N electrodes are arranged in a particular 3D-gemoetry on the shell on positions defined by the patient specific design and when these N electrodes are configured with a specific set of configuration parameters (see paragraph 9, "Electrode configuration or placement of individual electrodes in the electrode configuration can facilitate: control the volume of (brain) tissue activated by neurocranial electrostimulation; ... accommodation of individual factors, for example to optimize treatment based on patient specific anatomical features"; paragraph 75 teaches that “The forward model M can be computed from an 3D distribution of electrical conductances and the locations of the electrodes and locations for which the field is to be computed”) defining a specific transcranial alternating current stimulation (tACS) scheme, taking into consideration the extracted patient specific data (Bikson teaches that one option for the stimulation is that “The electrical stimulation applied is an alternating current of 0-10 mA and 0 H-1 kHz” (see paragraph 7 and claim 24 of Bikson)) by making use of the head model such that the optimization performed in step B) is based on said geometrical head model (see Figure 7, for which the first step is obtaining image data of the target tissue, and the final step is optimizing electrode configuration and stimulation parameters; this with all the teachings above and in the Response to Arguments section illustrating patient-specific optimization and simulations/modeling teaches these claim limitations);
varying a set of simulation parameters during iterative application of step B) to compute and optimize the patient-specific set of configuration parameters, which are thus optimized for the patient (also see paragraphs 22-23, where it is noted that the specific electrode being used is iterated throughout the stimulation, which constitutes a change in location of the stimulation, which is a “set of simulation parameters”; also see Figure 7, for which the first step is obtaining image data of the target tissue, and the final step is optimizing electrode configuration and stimulation parameters); and
configuring the stimulation device with the optimized set of configuration parameters that are derived such that the stimulation device is configured to focus the electrostimulation onto the at least one region of interest (Figures 3-4 illustrate electrodes placed on the surface of the skin via a wearable device, such as skull cap 430 (see paragraphs 36-37); Paragraph 37 additionally discusses how not all electrodes need to be activated, such as when the optimized configuration does not call for all electrodes in each particular location to be utilized to treat a target; additionally, Bikson states in paragraph 10 that “Electrical stimulation systems for patient treatment can be programmed in accordance with electrode configurations and stimulation parameters identified using the optimized stimulation model”).
Regarding claim 3, it is noted that Bikson teaches that “This model is repeated for each electrode position in the plurality of electrodes in the electrode configuration surrounding the target tissue, (e.g., 100 electrode positions=100 simulations)” (see paragraph 22).
Regarding claim 4, it is noted that paragraph 23 states that “predictions can be made as to what electrical fields will develop in the target tissue when any combination of electrodes is activates at any intensity.” Additionally, “At 670, the forward model is optimized, as described above, to predict the electrode configuration and current or voltage intensities required to produce the desired tissue response” (see paragraph 84). Additionally, it is noted that paragraph 7 of Bikson teaches that the parameters which may be optimized include “The parameters altered include changing the voltage, current, activation time, location, sequence or number of electrodes. The desired response is optimized with a minimum number of electrodes.”
Regarding claim 5, Bikson teaches that “Optimization in accordance with aspects of the present invention can also allow determination of the optimal electrode configuration (electrode number, position of each electrode, current at each electrode) based on an outcome specified by the clinician, such as production of a desired electrical field at a desired tissue location” (see paragraph 24, emphasis added). This teaches that the determined protocol would include determining amperage on a per electrode basis (i.e., “current at each electrode”). Also, Bikson teaches in paragraph 7 that “The desired response is optimized with a minimum number of electrodes”, thereby teaching that the protocol comprising “driving a number of N stimulation electrodes”.
Regarding claim 8, it is noted that Bikson teaches that the patient-specific data extracted in step A) can include “3D-distribition of brain tissue”, because in paragraph 46 it is taught that an alternative to diffusion tensor imaging (DTI) could be “a simple designation of a white matter and a grey matter conductivity tensor, as discussed above. These two universal conductivity tensors could then be applied to the nodes of the FEM mesh using co-registration with the anatomical MRI. In this manner, the individual voxels of the MRI data are designated as either white matter or grey matter using post-processing image analysis. Then, each such voxel is assigned a conductivity dependent on whether it was classified as white matter or grey matter, which white matter voxels having a different conductivity value than grey matter voxels”, and then further teaches that in another, second technique “anatomical MRI is sub-divided into different designated brain regions on a voxel-by-voxel basis using post-processing image analysis.” In paragraph 47, Bikson then teaches that “a finite element model (FEM) is created using the conductivity data obtained at 304”, which thereby teaches that “the optimization performed in step B) … takes into account… is based on a geometrical head model derived from the patient-specific data extracted in step A).
Regarding claim 9, Bikson teaches “wherein the stimulation parameters varied in step C) comprise… amperage… of driving currents to be applied to individual ones of the stimulation electrodes of the device, because Bikson teaches that “Optimization in accordance with aspects of the present invention can also allow determination of the optimal electrode configuration (electrode number, position of each electrode, current at each electrode) based on an outcome specified by the clinician, such as production of a desired electrical field at a desired tissue location” (see paragraph 24, emphasis added). This teaches that the determined protocol would include determining amperage on a per electrode basis (i.e., “current at each electrode”).
Regarding claim 10, Bikson teaches that “the computer algorithm can evaluate various VOA's against either or both of the following input criteria: (a) one or more regions in which activation is desired; or (b) one or more regions in which activation should be avoided” (see paragraph 59).
Regarding claim 11, Bikson teaches that additional practical constraints may include minimizing “the number of electrodes” (see paragraph 10), which reads on “maximum number Nmax of available ones of the stimulation electrodes”.
Regarding claim 12, Bikson teaches that “The 3D current distribution can be obtained, for instance, from a segmentation of a 3D image of the tissue such as MRI, CT, DTI, etc.” (see paragraph 77).
Regarding claim 21, Bikson teaches that Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer… a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA)…” (see paragraph 115).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Bikson as applied to claim 1 above, and further in view of Santarnecchi et al. (US Patent Pub. No. 2021/0031034).
Bikson is described above with respect to claim 1. While Bikson teaches that “The 3D current distribution can be obtained, for instance, from a segmentation of a 3D image of the tissue such as MRI, CT, DTI, etc.” (see paragraph 77). However, Bikson does not explicitly state that PET is used.
Santarnecchi teaches systems and methods for treating brain disease using targeted neurostimulation (see Title and Abstract). In paragraph 7, Santarnecchi discusses that non-invasive brain stimulation (NIBS) is used to engage a target map, and that this includes transcranial current stimulation (tCS) which also includes tACS. As stated in paragraph 6, “a target map for the subject can be developed from positron emission tomography (PET) data and from magnetic resonance imaging (MRI) data collected from the subject.” “Embodiments of the present invention may include PET with partial volume correction, based on cerebral and cerebellar individual grey/white matter masks, which helps produce more accurate maps and which show protein deposit variations at the sulcal/gyral level” (see paragraph 50). “A protein target map for the human subject was developed by identifying amyloid protein targets based on PET and MRI imaging data” (see paragraph 86). Paragraph 87 then teaches, among other relevant aspects, “Shown in Fig. 2 is the personalized NIBS stimulation parameters, using eight stimulating electrodes, to activate the target map of the human subject.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize PET imaging and targeting of proteins, such as amyloids in the brain to generate a target map, as taught by Santarnecchi, within the system and methods of Bikson in order to provide electrical stimulation therapy to these areas of the brain because Bikson teaches in paragraph 9 that its invention could be used for the “treatment of a neurological or psychiatric disease” without explaining how any specific neurological disease could benefit from the optimization techniques taught therein. However, Santarnecchi fills this gap by providing key insight into how the images would be used at the beginning of the process in order to provide a specific treatment for a specific neurological disease – Alzheimer’s. As such, this increases the utility of Bikson by teaching exactly how to manipulate the image data to get a desired outcome.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Bikson as applied to claim 1 above, and further in view of Michael et al. (CN 109069828).
Bikson is described above with respect to claim 1. It is noted that Figure 6 illustrate a flowchart in which the end product is “manufacture selected electrode” (see paragraph 64, “At 510, at least one electrode is manufactured using the selected at least one electrode morphology”). Paragraph 41 discusses a method of determining the volume of interest. Additionally, “Optimization in accordance with aspects of the present invention can also allow determination of the optimal electrode configuration” (see paragraph 24, which paragraph then states that the optimal electrode configuration may include the number and/or position of the electrodes. However, Bikson does not explicitly teach a pre-defined set of different ones of the stimulation devices.
Michael teaches “a system and method of non-invasive management of headache” in which the “a system and method of non-invasive management of headache” (see Abstract), which includes TENS and transcranial stimulation. Michael teaches that “each treatment plan may include a predefined set of one or more treatment devices with their corresponding configuration and treatment parameters and treatment delivery schedule (e.g., time and order). the second controller 721 can configure the user interface 722 to present on the display the stored treatment plan and via the input device receives user selection or modification of one or more treatment plans” (see page 14, paragraph beginning with “input device of the user interface 722…”, see near end of that paragraph).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize a set of predefined treatment devices, as taught by Michael, that relate to specific treatment plans within the system and methods of Bikson, in order to allow a user to choose a treatment as necessary and have the necessary equipment to deliver the treatment without a clinician present, thereby allowing for personal, home use.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Bikson as applied to claim 1 above, and further in view of Gliner et al. (US Patent Pub. No. 2004/0158298).
Bikson is described above with respect to claim 1. However, Bikson fails to teach a sensing/recording aspect, as claim in claim 14.
Gliner teaches systems and methods for automatically optimizing the stimulus parameters and/or the configuration of electrodes to provide neural stimulation to a patient (see Abstract). “The pulse system 140 can generate and send energy pulses to the electrode array, and the converter 150 can receive signals from the sensing device 180” (see paragraph 24). “The pulse system 140 generates and sends a single pulse or pulse train to the active therapy electrodes in accordance with the command signals, and the sensing device 180 senses the neural responses, motor responses, or other types of responses to the stimulus” (see paragraph 27; also see paragraph 26).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include a sensing device, as taught by Gliner, into the transcranial stimulation system of Bikson because this allows for automatic and instantaneous feedback that allows the clinician “to optimize the therapy for the particular patient” (see paragraph 27 of Gliner).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Bikson as applied to claim 1 above, and further in view of Zhao et al. (WO 2021/203719).
Bikson is described above with respect to claim 1. However, Bikson fails to teach generating multiple sets of treatment options over multiple sessions, as claim in claim 15.
Zhao teaches a “method and apparatus, a tracking, objective evaluation and prediction method for the effects of neuromodulation therapy over multiple courses or multiple sessions can be formed, and according to the expected therapeutic effects, a subsequent method for therapeutic schedule implementation and scheme parameter selection is qualitatively and quantitatively modulated and optimized, and a neurological function state can be objectively evaluated and predicted (see Abstract). More specifically, Zhao teaches “According to the expected treatment effect and the actual treatment effect, objectively predict the number of subsequent treatments or the number of treatment courses, and even generate multiple combinations and options of the parameters of the electrical stimulation treatment plan and the number of treatment courses according to the patient’s acceptance of the electrical stimulation treatment. Patients can choose freely according to their feelings and life rules” (see paragraph beginning “Obviously, the parameters…”, middle of page 12).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to provide for a system that generates multiple optimized options for electrical stimulation, as taught by Zhao, and to include this feature into the system and methods of Bikson in order to allow for “the patient’s acceptance of the electrical stimulation treatment. Patients can choose freely according to their feelings and life rules” (see quote above from Zhao), which would provide the patient with added comfort with regard to their care/treatment.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES KISH whose telephone number is (571)272-5554. The examiner can normally be reached M-F 10:00a - 6p EST.
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, Unsu Jung can be reached at (571) 272-8506. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES KISH/ Primary Examiner, Art Unit 3792
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