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 .
Election/Restrictions
Applicant’s election without traverse of Invention II, claims 10-17 in the reply filed on 4/6/2026 is acknowledged. Claims 1-9 and 18-20 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 4/6/2026.
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.
Claim(s) 10-11 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2017/0100601 to Xiao et al. (Xiao) (cited by applicant).
In reference to at least claim 10
Xiao discloses a system for delivering neurostimulation to tissue of a patient using multiple electrodes (e.g. DBS system 100), the system comprising: a stimulation control circuit (e.g. pulse generator 108 includes stimulation engine 640) to deliver the neurostimulation according to a specified stimulation configuration, the stimulation configuration including multiple stimulation parameters (e.g. “pulse generator 108 can provide therapy to brain or other tissue by directing pulses of electrical current to each electrode 104 based on the values stored in stimulation settings 530.”, para. [0033], [0032]); a port to receive a designation of a target region for the neurostimulation (e.g. Figs. 6A, 7, “brain geometry data unique to a specific patient (for example MRI data) can be combined with brain atlas data to generate a discrete brain geometry grid. Data regarding the configuration of the electrodes 104 on the implanted lead 102 or 202 can be used to generate a finite element model of the electrode configuration at 404.”, para. [0029]); a user interface (e.g. user interface of programmer device 502) configured to receive one or more optimization criteria for the neurostimulation of the target region (e.g. “Programmer device 502 can present a user interface adapted to allow a user, such as a clinician, patient, or researcher, to review, monitor, and update device data and settings.”, para. [0031]); a programming control circuit (e.g. Figs. 6A-6B, para. [0011], [0031]-[0032]) configured to: specify multiple stimulation configurations (e.g. para. [0029]), perform multiple optimization algorithms, each algorithm to determine a stimulation configuration solution from the multiple stimulation configurations (e.g. multiple optimization algorithms, each having its own optimization criteria: linear programming LP, quadratic programming OP, maximum deviation MD, para. [0042]); determine a first likelihood of finding a stimulation configuration solution using a first optimization algorithm according to the target region and the one or more optimization criteria (e.g. any of MD method, QP method or LP method can be considered “first optimization algorithm” 410, Fig. 4, “The Max Curve can be used as criteria for convex optimization via one or more optimization methods (such as convex optimization in one embodiment) at 410”, para. [0029], “Optimization engine 610 can use the Max Curve values calculated by discretization engine 608 as a goal to reach when determining optimum stimulation settings 530 (such as at 410 of FIG. 4).”, para. [0041]-[0051], “FIG. 8 depicts example therapy configurations generated via each of the MD method 802, QP method 804, and LP method 806 for a device implanted in the thalamus of a non-human primate with a DBS array having 32 electrodes. Each potential configuration 802, 804 806 is a group of stimulation settings 530 that can be provide to, or programmed into, pulse generator 108”, para. [0055]-[0059]); determine a second likelihood that the stimulation configuration solution found by the first optimization algorithm is a better stimulation configuration solution than a stimulation configuration solution that would be found by a second optimization algorithm (e.g. any of MD method, QP method or LP method can be considered “second optimization algorithm” 410, Fig. 4, “The Max Curve can be used as criteria for convex optimization via one or more optimization methods (such as convex optimization in one embodiment) at 410”, para. [0029], “Optimization engine 610 can use the Max Curve values calculated by discretization engine 608 as a goal to reach when determining optimum stimulation settings 530 (such as at 410 of FIG. 4).”, para. [0041]-[0051], “FIG. 8 depicts example therapy configurations generated via each of the MD method 802, QP method 804, and LP method 806 for a device implanted in the thalamus of a non-human primate with a DBS array having 32 electrodes. Each potential configuration 802, 804, 806 is a group of stimulation settings 530 that can be provide to, or programmed into, pulse generator 108”, para. [0055]-[0059]); select the first optimization algorithm or another optimization algorithm according to the determined first and second likelihoods (e.g. compare solutions, 412, Fig. 4, “The solutions can be compared at 412, and device programming can be set and/or carried out at 414”, para. [0029], fig. 4; “Optimization engine 610 can use the Max Curve values calculated by discretization engine 608 as a goal to reach when determining optimum stimulation settings 530 (such as at 410 of FIG. 4)”, para. [0041]-[0051], “If multiple solutions are generated, they can be compared to determine the actual device settings to use (such as at 412 of FIG. 4). In embodiments, optimization engine 610 can generate solutions for each optimization method and present them to the user of programmer device 502 to choose the ideal therapy configuration for the patient.”, para. [0055]-[0059]); and recurrently change stimulation parameters according to the selected optimization algorithm to determine the stimulation configuration solution based on the one or more optimization criteria (e.g. any of MD method, QP method or LP method 410, Fig. 4, “The Max Curve can be used as criteria for convex optimization via one or more optimization methods (such as convex optimization in one embodiment) at 410”, para. [0029], “Optimization engine 610 can use the Max Curve values calculated by discretization engine 608 as a goal to reach when determining optimum stimulation settings 530 (such as at 410 of FIG. 4).”, para. [0041]-[0051], “FIG. 8 depicts example therapy configurations generated via each of the MD method 802, QP method 804, and LP method 806 for a device implanted in the thalamus of a non-human primate with a DBS array having 32 electrodes. Each potential configuration 802, 804, 806 is a group of stimulation settings 530 that can be provide to, or programmed into, pulse generator 108”, para. [0055]-[0059]) and presenting the stimulation configuration solution to a user (e.g. “In embodiments, optimization engine 610 can generate solutions for each optimization method and present them to the user of programmer device 502 to choose the ideal therapy configuration for the patient.”, para. [0055]-[0059]).
In reference to at least claim 11
Xiao discloses wherein the programming control circuit is configured to: specify electrode configurations that include a selection of one or more electrodes of the multiple electrodes (e.g. “each electrode 1-32 is assigned a color corresponding to the amount of current assigned to it for each pulse.”, para. [0055]); identify, when performing the first optimization algorithm, an initial set of stimulation settings, find an approximate stimulation configuration solution using the initial set of stimulation settings, and identify a next set of stimulation settings based on the approximate stimulation configuration solution (e.g. “At 406, an activation function (AF) value at each of the grid points can be calculated and used to construct a theoretical Max Curve at 408. The Max Curve represents the greatest likelihood for cellular depolarization that can be transferred to any grid point given a fixed amount of current generated by pulse generator 108. The Max Curve can be used as criteria for convex optimization via one or more optimization methods (such as convex optimization in one embodiment) at 410 to determine how much energy to output to each electrode. The solutions can be compared at 412, and device programming can be set and/or carried out at 414.”, para. [0029]); and when performing the other optimization algorithm, test every available electrode configuration of the multiple electrodes when finding the stimulation configuration solution (e.g. Fig. 8, “each electrode 1-32 is assigned a color corresponding to the amount of current assigned to it for each pulse.”, para. [0055]).
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(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2017/0100601 to Xiao et al. (Xiao). In view of US 2019/0184171 to Mustakos et al (Mustakos).
In reference to at least claim 12
Xiao teaches a device according to claim 10 and further discloses wherein the programming control circuit is configured to: receive one or more avoidance regions for the neurostimulation (e.g. “in an embodiment, the stimulation settings can be configured to avoid activation of non-target tissue such that the method further comprises identifying one or more grid points representing non-target tissue to be avoided when stimulating through the one or more electrodes”, para. [0066]).
However, Xiao does not explicitly disclose determining the stimulation configuration solution using a weighted summation including a stimulated target volume of the target region, a stimulated avoidance volume of the one or more avoidance regions, and a total stimulated volume; and determine the stimulation configuration solution according to the weighted summation.
Mustakos discloses programming neurostimulation utilizing a weighted summation (e.g. “weight factors”, para. [0079]-[0081]) including a stimulated target volume of the target region (e.g. “target region”, para. [0079]-[0081]), a stimulated avoidance volume of the one or more avoidance regions (e.g. “avoidance region”, para. [0079]-[0081]), and a total stimulated volume (e.g. “target region” and “avoidance region”, para. [0079]-[0081]); and determine the stimulation configuration solution according to the weighted summation (e.g. The 3D voxelized model, along with the voxel volumes and the voxel values associated with the 3D voxels, may be provided to the stimulation programmer circuit 820 for determining a metric value.”, para. [0081], “with the best metric value MV.sub.opt, among other stimulation parameters, may be displayed on the display screen of the user interface 810.”, para. [0090]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Xiao to include utilizing a weighted summation including a stimulated target volume of the target region, a stimulated avoidance volume of the one or more avoidance regions, and a total stimulated volume and determining the stimulation configuration solution according to the weighted summation, as taught by Mustakos, in order to effectively and efficiently search for a desirable treatment space (‘171, para. [0004]) and to provide a better starting point for treatment space that reduces search time and simplifies the optimization process to allow the patient a higher quality of treatment with desirable side effects (‘171, para. [0097]).
Claim(s) 13-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2017/0100601 to Xiao et al. (Xiao).
In reference to at least claims 13
Xiao teaches a system according to claim 10. Xiao further discloses that it was known in the art to utilize a storage device to store a database of stimulation setting results determined for a patient population and searching the database to identify stored stimulation setting results for the received target region (e.g. “The solutions for each simulation are stored in a large lookup table. Given a new target volume for stimulation, the pre-compiled database can be searched for the setting that gives the most overlap between the solution VTA and the target volume.”, para. [0009]). Therefore it would have been well within the level of ordinary skill in the art to modify the system of Xiao to use pre-compiled databases with the first optimization algorithm to identify stored stimulation setting results for the received target region and determine the first likelihood of finding the stimulation configuration solution according to the identified stored stimulation setting results in order to provide device settings that provide the most overlap between the volume of target tissue and the target volume (e.g. para. [0009]).
In reference to at least claims 14-15
Xiao teaches a system according to claim 10. Xiao further discloses wherein the programming control circuit is configured to: select one or more electrode configurations of the multiple electrodes to activate the target region; and determine the first likelihood of finding the stimulation configuration solution and determine the second likelihood that the stimulation configuration solution is the better stimulation configuration according to matching the one or more electrode configurations of the neurostimulation system to electrode configurations (e.g. compare solutions, 412, Fig. 4, “The solutions can be compared at 412, and device programming can be set and/or carried out at 414”, para. [0029], fig. 4; “Optimization engine 610 can use the Max Curve values calculated by discretization engine 608 as a goal to reach when determining optimum stimulation settings 530 (such as at 410 of FIG. 4)”, para. [0041]-[0051], “If multiple solutions are generated, they can be compared to determine the actual device settings to use (such as at 412 of FIG. 4). Regarding matching the one or more electrode configurations of the neurostimulation system to electrode configurations for the stored stimulation setting results of the database, as stated above, Xiao discloses that it was known in the art to utilize a storage device to store a database of stimulation setting results determined for a patient population and searching the database to identify stored stimulation setting results for the received target region (e.g. “The solutions for each simulation are stored in a large lookup table. Given a new target volume for stimulation, the pre-compiled database can be searched for the setting that gives the most overlap between the solution VTA and the target volume.”, para. [0009]). Therefore it would have been well within the level of ordinary skill in the art to modify the system of Xiao to use pre-complied databases for comparing the one or more electrode configurations of the neurostimulation system to electrode configurations for the stored stimulation setting results of the database in order to provide device settings that provide the most overlap between the volume of target tissue and the target volume (e.g. para. [0009]). Xiao further discloses recurrently changing the stimulation parameters (e.g. any of MD method, QP method or LP method 410, Fig. 4, “The Max Curve can be used as criteria for convex optimization via one or more optimization methods (such as convex optimization in one embodiment) at 410”, para. [0029], “Optimization engine 610 can use the Max Curve values calculated by discretization engine 608 as a goal to reach when determining optimum stimulation settings 530 (such as at 410 of FIG. 4).”, para. [0041]-[0051], “FIG. 8 depicts example therapy configurations generated via each of the MD method 802, QP method 804, and LP method 806 for a device implanted in the thalamus of a non-human primate with a DBS array having 32 electrodes. Each potential configuration 802, 804, 806 is a group of stimulation settings 530 that can be provide to, or programmed into, pulse generator 108”, para. [0055]-[0059]).
In reference to at least claim 16
Xiao further discloses that when utilizing the pre-complied database, a search of the database can be performed to identify stored stimulation setting results that include at least one optimization criterion of the received one or more optimization criteria (e.g. “The solutions for each simulation are stored in a large lookup table. Given a new target volume for stimulation, the pre-compiled database can be searched “, para. [0009]) and using that information to determine the first likelihood of finding the stimulation configuration solution according to the stored stimulation setting results identified according to the at least one optimization criterion (e.g. “The solutions for each simulation are stored in a large lookup table. Given a new target volume for stimulation, the pre-compiled database can be searched for the setting that gives the most overlap between the solution VTA and the target volume.”, para. [0009]).
In reference to at least claim 17
Xiao discloses wherein the at least one optimization criterion includes a weighting of activation of tissue outside the target region (e.g. “non-target tissue such that the method further comprises identifying one or more grid points representing non-target tissue to be avoided when stimulating through the one or more electrodes; determining a set of stimulation settings for at least one of the one or more electrodes such that an actual activation function value for each of the one or more non-target tissue grid points is as close as possible to the minimum activation function value calculated for each of the one or more grid points; “, para. [0066]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2023/0248979 to Bokil et al. which discloses a neurostimulation adapted to intrinsic frequency drift that utilizes a gradient descent algorithm.
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