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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/07/2024 and 12/03/2024 are being considered by the examiner.
Election/Restriction
Applicant’s election without traverse of Group I (Claims 1-15) in the reply filed on 05/27/2026 is acknowledged. Claims 16-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected invention.
Status of Claims
Claims 1-20 are currently pending. Claims 16-20 are withdrawn as being drawn to a non-elected invention. Claims 1-15 are under examination.
Priority
The instant application (filed on 08/14/2024) is a non-provisional application filed under 35 USC 111(a). Acknowledgment is made of Applicant's claim for domestic priority based on provisional application 63/533,038, filed on 08/16/2023. Claims 1-15 are adequately supported in the provisional application to receive an effective filing date of 08/16/2023.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Section 33(a) of the America Invents Act reads as follows:
Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mathematical concepts and mental processes) without significantly more.
Step 1
The invention in claims 1-15 is to a statutory subject matter as the claims recite an apparatus for configuring delivery of neuromodulation, which would belong to one of the four statutory categories.
Step 2A, Prong One
Independent claim 1 recites abstract ideas in the form of mathematical concepts "defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations" (see MPEP 2106.04(a)(2) subsection (I)).
Regarding Claim 1, the limitation “the voxel definition module is configured to determine voxel values for each voxel in the voxel data structure using the probability shells of the plurality of nested probability shells” references an explicit formula (which is further defined in dependent claims) for calculating the voxel values.
Step 2A, Prong Two
For the Claim 1 limitations from Step 2A Prong One, the claim does not recite additional elements that integrate the judicial exception into a practical application. Additional limitations include
a “configuration system,” a “receiver module,” a “structure selection module,” a “voxel definition module,” “nested probability shells,” a “voxel data structure,” and “lead position data.” The data structures (such as nested probability shells, voxel data structure, and lead position data) are part of data gathering (insignificant extra-solution activity), the modules are composed of generic computer structures for performing generic computer functions, and the user interface for the structure selection module is the insignificant extra-solution activity of data display and data entry/gathering. The “configuration system for configuring delivery of neuromodulation to specific tissue of a patient” generally links the exception to a particular technological environment or field of use.
Claims 2, 8, and 10: Further define the formulas used by the voxel definition module to determine patient voxel values.
Claims 3, 7, 9, and 11: The determination of optimal steering and amplitude settings is a mental process (a human could determine which settings are optimal when looking at the data). The claim also makes reference to the formulas in claims 2, 8, and 10.
Claims 4-6 and 12: Further define the formula used in the optimization block calculator.
Note claims 13-15 involve an implementation of the determined settings (determined based on the abstract ideas presented above) into an applied stimulation and are therefore integrated into a practical application.
Step 2B
For the Claim 1 limitations from Step 2A Prong One, the claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception. Additional limitations include a “configuration system,” a “receiver module,” a “structure selection module,” a “voxel definition module,” “nested probability shells,” a “voxel data structure,” and “lead position data.” The data structures (such as nested probability shells, voxel data structure, and lead position data) are part of data gathering (insignificant extra-solution activity), the modules are composed of generic computer structures for performing generic computer functions, and the user interface for the structure selection module is the insignificant extra-solution activity of data display and data entry/gathering. The “configuration system for configuring delivery of neuromodulation to specific tissue of a patient” generally links the exception to a particular technological environment or field of use.
Claims 2, 8, and 10: Further define the formulas used by the voxel definition module to determine patient voxel values.
Claims 3, 7, 9, and 11: The determination of optimal steering and amplitude settings is a mental process (a human could determine which settings are optimal when looking at the data). The claims also make reference to the formulas in claims 2, 8, and 10.
Claims 4-6 and 12: Further define the formula used in the optimization block calculator.
The additional elements are recited at a high level of generality and are invoked only to receive data, perform the stipulated calculations, store data, and present results. These elements perform their ordinary function and do not amount to more than an implementation of the mathematical concepts on a generic computing device. The “structure selection module” is described in the specification (page 23, lines 1-9) as a user interface to enter data into the data structure, which is a component of the calculations.
Therefore, Claims 1-12 are directed to a judicial exception, as abstract ideas (mathematical concepts and mental processes), without significantly more. Claims 13-15 are integrated into a practical application.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-6 and 10-11 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 3: It is unclear how the (a), (b), and (c) steps generate the target and avoid shell structures because there is no mention of target and avoid structures or shells in (a), (b), and (c) as defined in claim 2 (which only consider probability shells).
Claims 3 and 6: It is unclear if there is a substantive difference between the “target and avoid shell structures” in claims 3 and 6 and the “target and avoid structures” first defined in claim 1. Does the “shell” provide some modification to the data or are the terms functionally interchangeable?
Claim 6: It is unclear if the “partial voxel scores” are the same as the “partial voxel values” used throughout the rest of the claims.
Claim 10: The term “structure selection block” lacks an antecedent basis unless this element is supposed to be the same as the “structure selection module” in claim 1. If this is the case, consistent terminology would enhance the clarity of the claim.
Claims 4-5 and 11 are rejected for being dependent on rejected claims.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C.
103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-3, 6-9, and 12-15 are rejected under U.S.C 103 as being unpatentable over Roothans (US 2016/0144194 A1) in view of Butson (NPL, “Probabilistic Analysis of Activation Volumes Generated During Deep Brain Stimulation”) and Patriarche (US 2019/0259197 A1).
Regarding Claim 1, Roothans discloses a configuration system for configuring delivery of neuromodulation to specific tissue of a patient (Fig. 1, [0004]), the system comprising:
• a receiver module configured to receive at least brain anatomy data for a patient ([0023], [0131-0132] – memory stores tissue regions 412 and patient anatomy data 414) and lead position data for a lead forming part of a neuromodulation system ([0022-0026] – lead positioning data), the lead position data indicating a location of the lead in the brain of the patient ([0017] – position of probe relative to brain structures, [0022-0026] – spatial data of lead relative to stimulation site);
• a structure selection module coupled to a user interface providing a graphical output ([0130] – user interface 408 with graphical display for selecting regions) allowing a user to identify and select brain structures in the patient’s brain as target structures and as avoid structures ([0132-0134] – efficacy and adverse effects anatomical regions can be selected by the clinician or preselected based on condition being treated);
• a voxel definition module configured to define portions of the patient’s brain in voxel form as a voxel data structure ([0132] – patient’s brain imaging data is stored as voxels); wherein:
the receiver module receives in the brain anatomy data for a neural structure indicating a probability of a therapeutic outcome resulting from stimulation of volumes ([0135-0137], [0147] - efficacy map from atlas based on database values for a population of patients).
Roothans discloses a clinical efficacy map where “efficacy map information 418 may be substantially associated with atlas information 420, where efficacy map information 418 may indicate areas of the atlases that can be stimulated electrically via electrodes to yield a desired outcome” ([0135-0136]). The processor geometrically warps the patient data to allow the efficacy map to be superimposed on the patient’s brain image ([0136]). The efficacy map can be based on a clinical rating scale score which is used to assign efficacy values to areas of the brain and identify utility for producing a therapeutic effect when stimulated ([0147]).
However, Roothans does not disclose (1) nested probability shells for a neural structure indicating a probability of a therapeutic outcome or (2) the voxel definition module is configured to determine voxel values for each voxel in the voxel data structure using the probability shells of the plurality of nested probability shells.
Butson, in the same field of endeavor of providing deep brain stimulation to treat a brain condition (Abstract), teaches an anatomical atlas with a warping process so that the atlas and patient image match anatomically (pages 4-5, “Patient-Specific Computer Model”). The anatomical atlas voxels are assigned a treatment improvement score as a probability of improvement when stimulated (pages 6-7, “Clinically Scored VTAs”), forming an efficacy map. Butson further teaches: “isosurfaces were then constructed to encompass regions where stimulation-induced activation resulted in clinical improvement (regions associated with >50% or >75% improvement are shown in the Results). The end product of this approach was a probabilistic atlas of clinical outcome scores on a per-symptom basis, demonstrated with illustrative data in Figure 3” (pages. 6-7). Figure 3(c) visually demonstrates an example of nested probability shells where the shells are based on the probability of improvement. In this manner, the effect of the stimulation as it spreads to the surrounding tissue from targeted stimulation epicenter can be tracked (page 2, “Introduction”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Roothans’ clinical effects map (as part of a probabilistic atlas) by incorporating the isosurface-based probabilistic atlas in Butson. This would have been obvious because both Roothans and Butson discuss deep brain stimulation and a warping process (so the atlas and patient image anatomically match) and Butson provides a solution/improvement of categorically sorting tissue into discrete probability tiers to identify and group tissue of similar effectiveness after stimulation (rather than an assignment of probability by individual voxel). Therefore, a person of ordinary skill in the art would be motivated to improve the system of Roothans by incorporating the isosurface-based probabilistic atlas in Butson as a modification of the clinical effect map in Roothans.
The sub-voxel refinement of an anatomical model in Patriarche would be considered “reasonably pertinent” (see MPEP 2141.01(a)1) to the claimed invention because Patriarche teaches a warping process to match up anatomical features in an atlas and patient image ([0004], [0020]). Patriarche teaches tissue transition surfaces in the atlas have a resolution higher than the patient images and the surface boundaries would be placed within a voxel of the patient image after warping (Fig. 3, [0022]). Multiple tissue types, with each tissue type having a characteristic average intensity (Fig. 7A, [0024]), in combination produce the overall voxel intensity in the patient image ([0017], [0030]). Therefore, the sub-voxel volumes of each tissue type are computed ([0022]) as a fraction of the total voxel volume ([0025]). Figure 7B presents an example formula for two tissue types present in a voxel:
A
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B
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y
=
m
e
a
s
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r
e
d
i
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t
e
n
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i
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A = average intensity characteristic of tissue 1
B = average intensity characteristic of tissue 2
x = partial volume fraction of tissue 1
y = partial volume fraction of tissue 2
Therefore, the total intensity of the voxel is a combination of the two tissue types, delineated by the surface boundaries, where the intensities are combined using a weighted average based on fractional volumes of each tissue type ([0025]). Therefore, Patriarche teaches a process to compute a total voxel score in the patient image by using a weighted average of a number of regions separated by boundary surfaces with the patient voxel.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Roothans’ warping of an atlas image onto a patient image (improved with the isosurface efficacy map in Butson) by incorporating the technique for calculating a patient voxel value as the weighted average based on volume occupancy of regions defined by sub-voxel boundaries from the atlas in Patriarche. This would have been obvious because both Roothans and Patriarche discuss a warping process so an atlas and patient’s images anatomically match and Patriarche provides a solution/improvement of a formula for calculating a patient voxel value, where multiple boundaries from the atlas are present within patient voxels, in order to arrive at a more accurate value when the atlas resolution is higher than the patient image resolution. Therefore, a person of ordinary skill in the art would be motivated to improve the combined system of Roothans and Butson by incorporating the technique for calculating a patient voxel value as the weighted average based on volume occupancy of regions defined by patient sub-voxel boundaries from the atlas in Patriarche.
Regarding Claim 2, the configuration system according to Claim 1 is obvious over Roothans in view of Butson and Patriarche, as indicated hereinabove. Roothans does not disclose wherein:
• each probability shell has an outer border and defines an increase in probability relative to volumes outside the probability shell; and
• the voxel definition module is configured to determine voxel values for each voxel in the voxel data structure using the probability shells of the nested probability shells for the neural structure by:
a) selecting a first probability shell;
b) calculating, for the selected probability shell, a fill quantity for each voxel having at least a portion therein, the fill quantity representing a percentage of the voxel that is within the selected probability shell;
c) multiplying, for each voxel in the selected probability shell, the fill quantity by the increase in probability of the selected probability shell, to yield a partial voxel value;
• repeating a), b), and c) for each probability shell of the nested probability shells for the neural structure.
As stated in claim 1, the proposed combination with Butson yields a warping procedure so that an atlas matches the patient image anatomically (pages 4-5, “Patient-Specific Computer Model”). The atlas voxels are assigned a treatment improvement score as a probability of improvement when stimulated (pages 6-7, “Clinically Scored VTAs”). Butson further teaches isosurfaces defining levels of improvement for treating a particular condition (pages. 6-7). Figure 3(c) visually demonstrates an example of nested probability shells where the shells are based on the probability of improvement, with the probability decreasing with successive shells radiating out from the center.
As stated in claim 1, the proposed combination with Patriarche yields multiple tissue types combined to produce the overall voxel intensity in the patient image ([0017], [0030]). Therefore, the sub-voxel volumes of each tissue type are computed ([0022], [0025]) where each tissue has a characteristic average intensity (Fig. 7A, [0024]). The total intensity of the voxel is a combination of the tissue types delineated by the surface boundaries where the intensities are combined using a weighted average based on fractional volumes of each tissue type ([0025], Fig. 7B – example with two tissue types). Therefore, Patriarche teaches a process to compute a total voxel value in the patient image by using a weighted average of a number of regions within the patient voxel separated by boundary surfaces. In this case, the isosurfaces in the combination of Roothans and Butson would similarly produce regions within a patient voxel with the isosurface boundaries after warping.
Note this weighted average calculation described in Patriarche is more intuitively associated with the summation of the products of partial fills and probabilities in claim 8. However, claims 2 and 8 recite mathematically equivalent formulations (see figure below). Claim 2 expresses the calculation in terms of accumulating partial voxel contributions while claim 8 expresses the result as a weighted average based on partial volume occupancy. The specification likewise does not attribute any distinct technical function, computational advantage, or improved accuracy to one formulation over the other. Both equations are therefore seen as alternative mathematical expressions for determining composite voxel probabilities.
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Example voxel with three isosurfaces (displayed in one face of the voxel) where the total probability is calculated via the formulas described in claims 2 and 8. The formula in claim 8 can be reorganized to arrive at the formula in claim 2.
Regarding Claim 3, the configuration system according to Claim 2 is obvious over Roothans in view of Butson and Patriarche, as indicated hereinabove. Roothans further discloses comprising an optimization block configured to determine optimal steering and amplitude settings for use by the neuromodulation system using data passed from the voxel definition module ([0041-0042] – computing optimal stimulation settings based on the projected probabilities of stimulation on a target tissue, [0120-0122] – amplitude and electrode combinations are adjusted to provide optimal therapy for a selected condition).
wherein the voxel definition module is configured to pass a plurality of target shell and avoid shell structures to the optimization block ([0132-0134] – efficacy and adverse regions for anatomical regions can be selected by the clinician or preselected based on the condition being treated);
However, Roothans does not disclose including one or more target shell or avoid shell structures generated by performing steps a), b) and c) for the selected probability shell of the nested probability shell of the neural structure.
As stated in claims 1 and 2, the proposed combination with Butson and Patriarche yields the weighted average based on partial volume values, defined as the volumes between isosurfaces denoting treatment efficacy probability, to arrive at composite voxel values in the patient image. Butson considers these isosurfaces as delineating the boundaries between structures which should be stimulated (those producing an improvement) and those which should be avoided (page 9, “Limitations and Sources of Error”).
Regarding Claim 6, the configuration system according to Claim 3 is obvious over Roothans in view of Butson and Patriarche, as indicated hereinabove. Roothans further discloses wherein each target shell or avoid shell structure comprises a plurality of voxels ([0132-0134] – organ structures assigned as target or avoid structures as regions of voxels). However, Roothans does not disclose partial voxel scores.
As stated in claims 1 and 2, the proposed combination with Butson and Patriarche yields the weighted average based on partial volume values, defined as the volumes between isosurfaces denoting treatment efficacy probability, to arrive at composite voxel values in the patient image. Butson considers these isosurfaces as delineating the boundaries between structures which should be stimulated (those producing an improvement) and those which should be avoided (page 9, “Limitations and Sources of Error”). Patriarche teaches tissue transition surfaces in the atlas have a resolution higher than the patient images and the surface boundaries would be placed within a voxel of the patient image after warping (Fig. 3, [0022]). In light of the stimulation and avoid boundaries in the combination of Roothans and Butson, these boundaries would be placed within voxels after warping as partial values as taught by Patriarche.
Regarding Claim 7, the configuration system according to Claim 2 is obvious over Roothans in view of Butson and Patriarche, as indicated hereinabove. Roothans further discloses the system further comprising an optimization block configured to determine optimal steering and amplitude settings for use by the neuromodulation system using data passed from the voxel definition module; and the voxel definition module is configured to pass the voxel values for the neural structure ([0041-0042] – computing optimal stimulation settings based on the projected probabilities of stimulation on a target tissue where information is contained in voxels as discussed in [0132-0133]; [0120-0122] – amplitude and electrode combinations are adjusted to provide optimal therapy for a selected condition).
However, Roothans does not disclose wherein the voxel definition block is configured to sum the partial voxel values of each voxel to yield summed voxel values for each voxel relative to the neural structure.
As stated in claims 1 and 2, the proposed combination with Butson and Patriarche yields the weighted average of partial volume values, defined as the volumes between isosurfaces denoting treatment efficacy probability, to arrive at composite voxel values in the patient image.
Regarding Claim 8, the configuration system according to Claim 1 is obvious over Roothans in view of Butson and Patriarche, as indicated hereinabove. Roothans does not disclose wherein:
• each probability shell has an outer border and defines a probability applicable to volume within the probability shell that lies outside any further nested probability shell therein; and
• the voxel definition module is configured to determine voxel values for each voxel in the voxel data structure using the probability shells of the plurality of nested probability shells by, for each respective probability shell within which a voxel is at least partly located:
a) calculating a partial fill representing a percentage of the voxel that is in the respective probability shell;
b) calculating a partial voxel value by multiplying the partial fill by the probability for the respective probability shell;
• after completing a) and b) for each respective probability shell, summing all partial voxel values for each voxel, such that each voxel has a single summed voxel value relative to the nested probability shell for the neural structure.
As stated in claim 1, the proposed combination with Butson yields a warping procedure so that an atlas matches the patient image anatomically (pages 4-5, “Patient-Specific Computer Model”). The atlas voxels are assigned a treatment improvement score as a probability of improvement when stimulated (pages 6-7, “Clinically Scored VTAs”). Butson further teaches isosurfaces defining levels of improvement for treating a particular condition (pages. 6-7). Figure 3(c) visually demonstrates an example of nested probability shells where the shells are based on the probability of improvement, with the probability decreasing with successive shells radiating out from the center.
As stated in claim 1, the proposed combination with Patriarche yields multiple tissue types combined to produce the overall voxel intensity in the patient image ([0017], [0030]). Therefore, the sub-voxel volumes of each tissue type are computed ([0022], [0025]) where each tissue has a characteristic average intensity (Fig. 7A, [0024]). The total intensity of the voxel is a combination of the tissue types delineated by the surface boundaries where the intensities are combined using a weighted average based on fractional volumes of each tissue type ([0025], Fig. 7B – example with two tissue types). Therefore, Patriarche teaches a process to compute a total voxel value in the patient image by using a weighted average of a number of regions within the patient voxel separated by boundary surfaces. In this case, the isosurfaces in the combination of Roothans and Butson would similarly produce regions within a patient voxel with the isosurface boundaries after warping.
Regarding Claim 9, the configuration system according to Claim 8 is obvious over Roothans in view of Butson and Patriarche, as indicated hereinabove. Roothans further discloses an optimization block configured to determine optimal steering and amplitude settings for use by the neuromodulation system using data passed from the voxel definition module; wherein the voxel definition module is configured to pass the voxel values to the optimization block ([0041-0042] – computing optimal stimulation settings based on the projected probabilities of stimulation on a target tissue where information is contained in voxels as discussed in [0132-0133]; [0120-0122] – amplitude and electrode combinations are adjusted to provide optimal therapy for a selected condition). However, Roothans does not disclose the summed voxel values.
As stated in claims 1 and 8, the proposed combination with Butson and Patriarche yields the weighted average of partial volume values, defined as the volumes between isosurfaces denoting treatment efficacy probability, to arrive at composite voxel values in the patient image.
Regarding Claim 12, the configuration system according to Claim 1 is obvious over Roothans in view of Butson and Patriarche, as indicated hereinabove. Roothans further discloses wherein the target structures correspond to beneficial therapeutic outcomes, and the avoid structures correspond to adverse therapeutic outcomes ([0132-0134] – efficacy and adverse effects anatomical regions can be selected by the clinician or preselected based on condition being treated).
Regarding Claim 13, the configuration system according to Claim 1 is obvious over Roothans in view of Butson and Patriarche, as indicated hereinabove. Roothans further discloses a therapy selection module ([0135] – clinician may select from among stored efficacy maps, [0139-0143] – combinations of electrodes are presented to the clinician) and a communications module ([0135] – “For example, the clinician may interact with user interface 408 to retrieve the stored efficacy map information 418”), the therapy selection module adapted to:
• present to a user at least one proposed therapy configuration for selection by the user ([0135] – clinician may select from among stored efficacy maps, [0139-0143] – combinations of electrodes are presented to the clinician); and
• in response to the user selecting a proposed therapy configuration for use, commanding the communications module to issue instructions to a pulse generator of the neuromodulation system to implement the selected proposed therapy configuration ([0138] – the display features in [0139-0143] are meant to guide the clinician in selecting electrodes and settings which are subsequently implemented as a stimulation pattern).
Regarding Claim 14, the configuration system according to Claim 13 is obvious over Roothans in view of Butson and Patriarche, as indicated hereinabove. Roothans further discloses wherein the therapy selection module is configured to present to the user a graphic of a stimulation field model for the proposed therapy configuration (Fig. 4-5, [0100-0111]).
Regarding Claim 15, Roothans discloses a neuromodulation system comprising:
• a pulse generator (Fig. 1, [0074-0075] – pulse generator 110);
• a lead configured for coupling to the pulse generator and adapted for positioning in a patient’s brain (Fig. 1, [0074-0075] – lead components 120 and 130, where lead 130 is placed in the brain, are connected to the pulse generator); and
• a configuration system as in claim 13 (see claim 13, which was rejected over Roothans in view of Butson and Patriarche), wherein the pulse generator is adapted to receive the instructions from the therapy selection module and apply the selected proposed therapy configuration to the patient via the lead ([0138] – the display features in [0139-0143] are meant to guide the clinician in selecting electrodes and settings which are subsequently implemented as a stimulation pattern; [0069] – the pulse generator, acting as a program controller, is programmed by the clinician).
Claims 4-5 and 10-11 are rejected under U.S.C 103 as being unpatentable over Roothans (US 2016/0144194 A1) in view of Butson (NPL, “Probabilistic Analysis of Activation Volumes Generated During Deep Brain Stimulation”), Patriarche (US 2019/0259197 A1), and Mustakos (US 2019/0184171 A1).
Regarding Claim 4, the configuration system according to Claim 3 is obvious over Roothans in view of Butson and Patriarche, as indicated hereinabove. Roothans further discloses wherein the optimization block includes a metric calculator configured (Fig. 4, [0089-0090] – calculation unit 336 and preselecting unit 338) to determine each of:
• a target value calculated by determining a volume of activation of target structures for a selected steering configuration and amplitude ([0041-0042] – computing optimal stimulation settings based on the projected probabilities of stimulation on a target tissue where information is contained in voxels as discussed in [0132-0133]; [0120-0122] – amplitude and electrode combinations are adjusted to provide optimal therapy for a selected condition).
Roothans discusses the identification of efficacy and adverse effects for anatomical regions selected by the clinician or preselected based on the condition being treated ([0132-0134]).
However, Roothans does not disclose:
• an avoid structure penalty calculated by determining a volume of activation of avoid structures for the selected steering configuration and amplitude
• a background penalty calculated using a total volume of activation for the selected steering configuration and amplitude; and
• a metric as the target value less the avoid structure penalty and the background penalty.
Mustakos, in the same field of endeavor of providing deep brain stimulation to treat a brain condition (Fig. 5, [0005]), teaches the application of target and avoidance structure weight factors (target structures have positive weight values while avoidance structures have negative weight values) where absolute value of the weighting factor signals the impact of the structure on clinical outcomes ([0079-0080]). The total voxel effect is the product of voxel volume, the efficacy probability, and the target/avoidance weighting with an example of the target/avoidance weighting provided as being normalized between -1 and 1 where 0 is considered to have no effect to stimulation and would be considered a background region ([0084]). The purpose of this weighting is to bias stimulation toward the target regions while minimally stimulating the non-positively weighted values ([0079]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Roothans’ clinical effects map (as part of a probabilistic atlas) by incorporating the target, background, and avoidance weighting term when determining voxel effectiveness in Mustakos. This would have been obvious because both Roothans and Mustakos discuss deep brain stimulation and designation of target/avoidance regions and Mustakos provides a solution/improvement to quantitatively define stimulation effectiveness in terms of a numerical combination, namely the probability of a stimulation effect and whether the structure should be targeted or avoided, to further bias calculations toward prioritizing target regions. Therefore, a person of ordinary skill in the art would be motivated to improve the system of Roothans by incorporating the target, background, and avoidance weighting terms when determining voxel effectiveness in Mustakos.
Regarding Claim 5, the configuration system according to Claim 4 is obvious over Roothans in view of Butson, Patriarche, and Mustakos, as indicated hereinabove. Roothans discloses efficacy and adverse effects for anatomical regions in the patient data can be selected by the clinician or preselected based on the condition being treated ([0132-0134]). However, Roothans does not disclose wherein the optimization block calculates the avoid structure penalty with a user-defined avoid structure weight, and the background penalty with a user-defined background ratio weight.
As stated in claim 4, the proposed combination with Mustakos yields a user-specified weighting and weighting range ([0079] –“In various examples, a user may use the user input device of the user interface 810 to assign or adjust weight factors for various regions based on known clinical effects of the electrostimulation on the respective regions”) of the avoidance, background, and target regions defined in [0084].
Regarding Claim 10, the configuration system according to Claim 1 is obvious over Roothans in view of Butson and Patriarche, as indicated hereinabove. Roothans does not disclose wherein:
• each probability shell has an outer border and defines an increase in probability relative to tissue outside the probability shell; and
• the voxel definition module is configured to determine voxel values for each voxel in the voxel data structure using the probability shells of the plurality of nested probability shells by:
selecting a first probability shell;
calculating, for the selected probability shell, a fill quantity for each voxel having at least a portion therein, the fill quantity representing a percentage of the voxel that is within the selected probability shell; and
calculating, for the selected probability shell, a shell weight by multiplying the increasing probability for the selected probability shell by a target or avoid structure weight received from a user via the structure selection block.
As stated in claim 1, the proposed combination with Butson yields a warping procedure so that an atlas matches the patient image anatomically (pages 4-5, “Patient-Specific Computer Model”). The atlas voxels are assigned a treatment improvement score as a probability of improvement when stimulated (pages 6-7, “Clinically Scored VTAs”). Butson further teaches isosurfaces defining levels of improvement for treating a particular condition (pages. 6-7). Figure 3(c) visually demonstrates an example of nested probability shells where the shells are based on the probability of improvement, with the probability decreasing with successive shells radiating out from the center.
As stated in claim 1, the proposed combination with Patriarche yields multiple tissue types combined to produce the overall voxel intensity in the patient image ([0017], [0030]). Therefore, the sub-voxel volumes of each tissue type are computed ([0022], [0025]) where each tissue has a characteristic average intensity (Fig. 7A, [0024]). The total intensity of the voxel is a combination of the tissue types delineated by the surface boundaries where the intensities are combined using a weighted average based on fractional volumes of each tissue type ([0025], Fig. 7B – example with two tissue types). Therefore, Patriarche teaches a process to compute a total voxel value in the patient image by using a weighted average of a number of regions within the patient voxel separated by boundary surfaces. In this case, the isosurfaces in the combination of Roothans and Butson would similarly produce regions within a patient voxel with the isosurface boundaries after warping.
Note this weighted average calculation described in Patriarche is more intuitively associated with the summation of the products of partial fills and probabilities in claim 8. However, claims 2 and 8 recite mathematically equivalent formulations (see figure below). Claim 2 expresses the calculation in terms of accumulating partial voxel contributions while claim 8 expresses the result as a weighted average based on partial volume occupancy. The specification likewise does not attribute any distinct technical function, computational advantage, or improved accuracy to one formulation over the other. Both equations are therefore seen as alternative mathematical expressions for determining composite voxel probabilities. Note the claim 10 formula is the same as the claim 2 formula except claim 10 introduces a target and avoid structure weight multiplier.
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Example voxel with three isosurfaces (displayed in one face of the voxel) where the total probability is calculated via the formulas described in claims 2 and 8. The formula in claim 8 can be reorganized to arrive at the formula in claim 2.
Mustakos, in the same field of endeavor of providing deep brain stimulation to treat a brain condition (Fig. 5, [0005]), teaches the application of target and avoidance structure weight factors (target structures have positive weight values while avoidance structures have negative weight values) where absolute value of the weighting factor signals the impact of the structure on clinical outcomes ([0079-0080]). The total voxel effect is the product of voxel volume, the efficacy probability, and the target/avoidance weighting with an example of the target/avoidance weighting provided as being normalized between -1 and 1 where 0 is considered to have no effect to stimulation and would be considered a background region ([0084]). The purpose of this weighting is to bias stimulation toward the target regions while minimally stimulating the non-positively weighted values ([0079]). Mustakos yields a user-specified weighting and weighting range ([0079] –“In various examples, a user may use the user input device of the user interface 810 to assign or adjust weight factors for various regions based on known clinical effects of the electrostimulation on the respective regions”) of the avoidance, background, and target regions defined in [0084].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to alter Roothans’ clinical effects map (as part of a probabilistic atlas) by incorporating the target, background, and avoidance weighting term when determining voxel effectiveness in Mustakos. This would have been obvious because both Roothans and Mustakos discuss deep brain stimulation and designation of target/avoidance regions and Mustakos provides a solution/improvement to quantitatively define stimulation effectiveness in terms of a numerical combination, namely the probability of a stimulation effect and whether the structure should be targeted or avoided, to further bias calculations toward prioritizing target regions. Therefore, a person of ordinary skill in the art would be motivated to improve the system of Roothans by incorporating the target, background, and avoidance weighting terms when determining voxel effectiveness in Mustakos.
Regarding Claim 11, the configuration system according to Claim 10 is obvious over Roothans in view of Butson, Patriarche, and Mustakos, as indicated hereinabove. Roothans further discloses an optimization block configured to determine optimal steering and amplitude settings for use by the neuromodulation system using data passed from the voxel definition module ([0041-0042] – computing optimal stimulation settings based on the projected probabilities of stimulation on a target tissue, [0120-0122] – amplitude and electrode combinations are adjusted to provide optimal therapy for a selected condition).
However, Roothans does not disclose wherein the voxel definition module is configured to pass, for each nested probability shell, a set of voxel fill values and a shell weight.
As stated in claims 1 and 10, the proposed combination with Butson and Patriarche yields the weighted average of partial volume values, defined as the volumes between isosurfaces denoting treatment efficacy probability, to arrive at a composite voxel value in the patient image.
As stated in claim 10, the proposed combination with Mustakos yields the total voxel effect is the product of voxel volume, the efficacy probability, and the target/avoidance weighting with an example of the target/avoidance weighting provided as being normalized between -1 and 1 where 0 is considered to have no effect to stimulation and is considered a background region ([0084]). The purpose of this weighting is to bias stimulation toward the target regions while minimally stimulating the non-positively weighted values ([0079]).
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/Benjamin A. Schmitt/
Examiner
Art Unit 3796
/Jennifer Pitrak McDonald/Supervisory Patent Examiner, Art Unit 3796
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