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
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.
Claim 18 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.
Claim 18 is rejected because it states “The device of claim 1…”, where claim 1 is a method.
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.
Claims 1, 3-6, 15, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over McIntyre et al. (US Patent Pub. No. 2006/0017749) in view of Soin (US Patent Pub. No. 2021/0346701).
Regarding claims 1 and 20, McIntyre discloses “brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS)” (see Abstract). The system and methods of McIntyre create a patient-specific neural stimulation modeling system (PSNSMS) (see paragraph 76, where it states that “The PSNSMS allows interactive manipulation of patient-specific electrical models of the brain for analysis of brain stimulation methods. This provides a virtual laboratory for surgeons, technicians, or engineers to optimize or otherwise adjust neural stimulation treatment, such as by varying electrode position, stimulation protocol, or electrode design”). “[T]he PSNSMS includes the following components: … (3) integration of functional or anatomical imaging data into a visualization platform that can be combined with the electric field modeling results” (see paragraph 83). Therefore, this requires “receiving a scan image of a treatment region” prior to integrating the functional or anatomical imaging data into the PSNSMS (see also paragraph 79-81 where diffusion tensor imaging and MR imaging data is discussed). Paragraph 85 provides details of an example method in which “The example of FIG. 6 also includes stored volumetric imaging data 610 and volumetric anatomic atlas data 612. Using a computer FEM solver to solve the electric field model 602, together with the neuron or axon model 608 … a volume of influence 614 is calculated. …a correlation between the two is computed at 618. In a further example, several model-computed volumes of influence (e.g., using different electrode locations or parameter settings) are computed and correlated to the target volume of influence, such as to optimize or otherwise select a desirable electrode location or stimulation parameter settings.” This reads on “determining a purported location of a stimulation probe inserted within the scan image; determining a position of a target region within the scan image relative to the purported location”. Additionally, paragraph 101 discusses that “One purpose of the PSNSMS is to determine optimal or desirable preoperative electrode locations… This typically involves determining a target volume of tissue that should be activated by the stimulation… For example, in the case of STN DBS for Parkinson's disease, current anatomical and physiological knowledge indicate that the target volume of tissue is the dorsal half of the STN. Therefore, in this example, for each patient-specific 3D brain atlas we determine a target VOA defined by the dorsal half of the STN. We then determine test VOAs generated by a range of electrode positions within the STN and/or a range of stimulation parameter settings for each of those electrode locations. These test VOAs are then compared to the target VOA. The electrode position and/or stimulation parameter setting that generates a test VOA that most closely matches the target VOA is provided as the model-selected electrode position and/or stimulation parameter setting.” This also reads on “determining a position of a target region within the scan image relative to the purported location” as it is explicitly stated that this is done and that the subthalamic nucleus is the target region. Additionally, this also teaches “determining a purported location of a stimulation probe inserted within the scan image” in that the entire purpose of this method in paragraph 101 is to determine an optimal electrode location by iterating through multiple positions within the model (i.e., simulation). Also of note, this teaches “concluding an efficacy resulting from activation of an electrode delivering the electrical energy resulting from the stimulation probe at the purposed location” since it teaches “These test VOAs are then compared to the target VOA. The electrode position … that generates a test VOA that most closely matches the target VOA is provided as the model-selected electrode position.”
It is noted that McIntyre states in paragraph 102 that “In one variant of this selection process, engineering optimization is used to assist the selection process. Examples of possible constraints on the selection process include one or more of … limiting the stimulus amplitude to being greater than -10 V and less then 10V.” However, there is not an explicit teaching of “computing a strength of the electrical energy at the position based on the electric field at the position”.
Soin teaches a neuromodulation system and method with feedback optimized electrical field generation for stimulating target tissue of a patient to treat neurological and non-neurological conditions (see Abstract). Soin teaches that “The shapes, sizes, and arrangements of the electrodes 30 and the spacing between electrodes 30 can be selected in order to generate one or more electric fields in and around the target tissue 12 having desired properties, depending at least in part on the nature and location of the target tissue, the condition being treated, and treatment being provided” (see paragraph 90). Paragraph 90 also states that “a specific electrode 30 and/or one or more pairs or other combinations of electrodes 30 can be selected to optimize and maximize the strength or intensity of the electric field in the target tissue 12 to provide optimal therapeutic results and/or to control the strength of the electric field in the target tissue 12 to prevent discomfort to the patient 14 and damage to the target tissue 12. For example, specific electrodes 30 or pairs or other combinations of electrodes 30 having different distances from and/or different orientations, e.g., angles, with respect to the target tissue 12 can be selected”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to consider not only whether or not the test VOAs match the target VOAs (see paragraph 103 of McIntyre), but also to consider if the strength or intensity of the electric field in the target tissue is at a desired strength, as taught by Soin, in order “to provide optimal therapeutic results” and/or “to prevent discomfort to the patient 14 and damage to the target tissue 12” (see paragraph 90 of Soin). Doing so would further improve the simulations of McIntyre to provide further optimized electrode locations and stimulation parameters pre-operatively.
Regarding claims 3 and 17, McIntyre states that “Diffusion tensor imaging (DTI) characterizes the diffusional behavior of water in tissue on a voxel-by-voxel basis” (see paragraph 28). It is noted that voxels are simply three-dimensional pixels.
Regarding claim 4, McIntyre states in paragraph 101 that, with emphasis added, “One purpose of the PSNSMS is to determine optimal or desirable preoperative electrode locations… This typically involves determining a target volume of tissue that should be activated by the stimulation… For example, in the case of STN DBS for Parkinson's disease, current anatomical and physiological knowledge indicate that the target volume of tissue is the dorsal half of the STN. Therefore, in this example, for each patient-specific 3D brain atlas we determine a target VOA defined by the dorsal half of the STN. We then determine test VOAs generated by a range of electrode positions within the STN and/or a range of stimulation parameter settings for each of those electrode locations. These test VOAs are then compared to the target VOA. The electrode position and/or stimulation parameter setting that generates a test VOA that most closely matches the target VOA is provided as the model-selected electrode position and/or stimulation parameter setting.”
Regarding claim 5, it is re-iterated that the above rejection of claim 4 relies on the teaching of McIntyre that states “We then determine test VOAs generated by a range of electrode positions within the STN”. This teaches “adjusting the purported location of the stimulation probe to an alternate purposed location”. Additionally, this same quote in the rejection of claim 4 states “These test VOAs are then compared to the target VOA. The electrode position … that generates a test VOA that most closely matches the target VOA is provided as the model-selected electrode position”, which reads on re-evaluating the efficacy based on the stimulation probe being disposed in the alternate purported location.
Regarding claim 6, it is noted that paragraph 95 of McIntyre teaches that a 5.7 µm diameter double cable myelinated axon model was incorporated into their STN DBS FEM “to quantify the neural response to stimulation. By positioning the axon in different locations relative to the electrode and modulating the stimulation parameters one can determine the threshold stimulus necessary to activate the neuron.” Therefore, this teaches that the electrode is simulated to stimulate an axon within the subthalamic nucleus.
Regarding claim 15, it is noted that the “modeling application” as claimed is rejected on the basis of the rejection of claim 1. Additionally, McIntyre teaches an “imaging data storage 404” (see Figure 4) which is part of the computer and reads on “an interface to a scan medium for receiving a scan image of a treatment region”. McIntyre teaches “a stimulation probe operable for insertion to a purported location within the scan image” as taught in the rejection of claim 1. Additionally, McIntyre teaches “a DBS stimulator configured to receive voltage instructions based on activation of the electrode at a predetermined efficacy and energize the probe according to the voltage instructions following insertion into the treatment region” via “DBS controller circuit 434” (see Figure 4), since in paragraph 73 it states “the computer 402 includes a telemetry circuit 432 for programming or otherwise communicating with an implantable DBS controller circuit 434, such as to adjust electrical stimulation parameters using the VOA or scoring information discussed above.”
Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over McIntyre in view of Soin as applied to claims 1 above, and further in view of Goetz et al. (US Patent Pub. No. 2011/0093030).
McIntyre in combination with Soin is described above with regard to claim 1. Although claim Figure 3 of Soin illustrates multiple electrodes per DBS probe, neither reference clearly teaches determination of strengths per electrode.
Goetz teaches managing electrical stimulation therapy based on variable electrode combinations (see Title). Within the system and methods of Goetz, the system is used in “determining the variable electrical stimulation contributions of each electrode to the stimulation or shielding zone” (see paragraph 35). “A stimulation zone is an area of stimulation defined by a collection of electrodes, their contributions, and an intensity” (see paragraph 96). “A zone shape, or indication of zone extent, is a graphical indication used to show which electrodes are recruited by a stimulation zone and their relative contributions to that zone” (see paragraph 98). Paragraph 122 teaches that field strength may also be displayed.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to calculation the contribution of each electrode, as taught by Goetz, and to use this in determining field strength within the activation zone (i.e,. the volume of activation) in McIntyre as combined with Soin in order to no only optimize lead placement but to optimize the volume of activation of the multi-electrode lead, thereby improving beyond the methods of MyIntyre.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over McIntyre in view of Soin as applied to claim 1 above, and further in view of Makarov et al. (US Patent Pub. No. 2022/0088404).
McIntyre in combination with Soin is described above with regard to claim 1. While Soin teaches to optimize and maximize the strength or intensity of the electric field in the target tissue 12 to provide optimal therapeutic results, there is no explicit teaching that the strength is determined by surface charge density, as claimed.
Makarov teaches methods and system for modeling EM brain stimulation and brain recordings with boundary element approach with fast multipole acceleration (see Title). Specifically regarding claim 12, Makarov teaches that “This problem is equivalent to finding the electric field at target points rm generated by the point charges located at source points rn. The accuracy of the FMM (the number of levels) is conventionally estimated for arbitrary volumetric charge distributions. However, for surface-based charge distributions, a much better relative accuracy is observed. For example, with the intrinsic method accuracy set as ε=0.1, the mean error for the pial cortical surface (GM shell) may be as low as 0.1% with respect to the electric field amplitude and 0.08 deg with respect to the field angle deviation as compared to the most accurate solution (i.e., the solution where FMM precision is set to maximum)” (see paragraph 59, after equation 6; also see paragraphs 11-12).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize surface-based charge distributions in finding the electric field at target points, as taught by Makarov, with the system and methods of the combination of McIntyre with Soin in order to optimize the field strength at the location by calculating it. In other words, although Soin does not expressly teach calculating the strength via surface-based charge distributions, the reference does generally teach optimizing field strength at a target location, and determination of electric field strength is well known in the art with the use of fast multipole methods; accordingly, thus the use this relationship to perform the method taught by McIntyre as combined with Soin would amount to choosing from a finite number of electric field strength computational methods available in the art at the time of the invention, which has previously been held as unpatentable (KSR v. Teleflex).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over McIntyre in view of Soin as applied to claim 1 above, and further in view of Arnholt et al. (US Patent Pub. No. 2023/0414161, effectively filed June 27, 2022, therefore being prior art under 102(a)(2)).
McIntyre in combination with Soin is described above with regard to claim 1. While Soin teaches to optimize and maximize the strength or intensity of the electric field in the target tissue 12 to provide optimal therapeutic results, there is no explicit teaching that the strength is determined by faceted volumetric representations, as claimed.
Arnholt teaches an interactive medical visualization system and methods for visualizing stimulation lead placement (see Title). Specifically with regard to claim 13, Arnholt teaches that “The medical visualization system herein can be configured to provide optimal placement of one or more cancer therapy stimulation leads and can provide a graphic representation of an electric field zone overlaid at the site of lead placement on the three-dimensional model. FIG. 2 includes a graphic representation of electric field strength zone 208 as associated with cancer therapy stimulation leads 204 and 206. The graphic representation of the electric field strength zone 208 can include one or more gradients of electric field strengths as represented by electric field strengths 210, 212, 214, 216, 218, and 220 that decrease in intensity in a radial direction away from the center of the cancer therapy stimulation lead 204” (see paragraph 155).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to represent the electric field strength graphically and volumetrically in a lead placement tool, as taught by Arnholt, and to include this feature into the system and methods of McIntyre with Soin because it can “provide a user with interactive capabilities to identify how different possible placements of the virtual stimulation leads can focus a therapy on a desired target area while sparing tissue damage to healthy tissues” and can improve “safety of the subject” (see paragraph 141 for both quotes).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over McIntyre in view of Soin as applied to claim 1 above, and further in view of Tol et al. (EP 2656876).
McIntyre in combination with Soin is described above with regard to claim 1. While Soin teaches to optimize and maximize the strength or intensity of the electric field in the target tissue 12 to provide optimal therapeutic results, there is no explicit teaching that the strength be determined by a derivative of a value of the electric field.
Tol teaches systems and methods related to DBS (see Figure 1). “Figure 6 shows the field distribution near a standard distal end of lead with electrodes… The equipotential lines cannot enter such a material and thus the region 410 and are bent around its shape. Therefore, the density of the equipotential lines at the left-hand side of the bar 400 around region 410 is relatively high. This results in high electric field strength there because the electric field strength is proportional to the density of the equipotential lines or in other words the electric field equals the space derivative of the electric potential field” (emphasis added).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize the relationship between the strength of the electric field and the space derivative of the electric potential field, as taught by Tol, with the system and methods of the combination of McIntyre with Soin in order to optimize the field strength at the location by calculating it. In other words, although Soin does not expressly teach calculating the strength via a derivative, the reference does generally teach optimizing field strength at a target location, and determination of electric field strength is well known in the art as it being the space derivative of the electric potential field is a known natural phenomenon; accordingly, thus the use this relationship to perform the method taught by McIntyre as combined with Soin would amount to choosing from a finite number of electric field strength computational methods available in the art at the time of the invention, which has previously been held as unpatentable (KSR v. Teleflex).
Allowable Subject Matter
Claims 2, 8-11, 16 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/JAMES KISH/ Primary Examiner, Art Unit 3792