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 .
Claims Status
Claims 1-24 are pending.
Claims 1-24 are examined.
Priority
The instant application is a national stage application of PCT/US2022/023301, filed 04/04/2022, which claims priority to US provisional application No. 63/170512, filed 04/04/2021. The provisional application No. 63/170512 does not provide support for the claimed limitations. Therefore, the Effective Filing Date (EFD) assigned to each of the claims 1-24 is the filing date of application PCT/US2022/023301, filed 04/04/2022.
Information Disclosure Statement
The Information Disclosure Statements filed 09/20/2023 and 03/23/2025 are in compliance with the provisions of 37 CFR 1.97 and have therefore been considered. Signed copies of the IDS documents are included with this Office Action.
Drawings
The drawings filed 09/20/2023 are accepted.
Specification
The disclosure is objected to because of the following informalities:
In paragraph [0053], “modeled electrode Each contact objects 262 results in one or more resultant virtual area of effect” requires correction.
Appropriate correction is required.
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.
Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to an abstract idea of mental steps, mathematic concepts, or a natural law without significantly more.
The MPEP at MPEP 2106.03 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of
matter?
(2A)(1) Are the claims directed to a judicially recognized exception, i.e. a law of nature,
a natural phenomenon, or an abstract idea?
(2A)(2) If the claims are directed to a judicial exception under Prong One, then is the
judicial exception integrated into a practical application?
(2B) If the claims are directed to a judicial exception and do not integrate the judicial
exception, do the claims provide an inventive concept?
With respect to step (1): Yes, the claims recite systems and a method.
With respect to step (2A)(1): The claims are directed to abstract ideas of mathematical concepts and mental processes.
“Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection” (MPEP 2106.04). Abstract ideas include mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (procedures for observing, evaluating, analyzing/judging and organizing information (MPEP 2106.04(a)(2)). Laws of nature or natural phenomena include naturally occurring principles/relations that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature (MPEP 2106(b)).
Mathematical concepts recited in claims 1 and 11:
generate a virtual space mesh model representative of an anatomical structure based on a set of cross-sectional imaging data, the virtual space mesh model including a volumetric set of electrical properties
Mental processes recited in claims 1 and 11:
determine, based on the virtual space mesh model relative to a region of interest defined within the virtual space mesh model, a set of electric field therapy treatment parameters to be applied to the anatomical structure by an implantable electric field therapy application system such that a resultant area of effect of the implantable electric field therapy application system reaches a sufficient coverage threshold across the region of interest, the set of electric field therapy treatment parameters including at least one of” one or more electrode configuration parameters of the implantable electric field therapy application system; one or more stimulating parameters descriptive of a stimulating waveform to be applied through the implantable electric field therapy application system; and a maximal permissible post-resection residual region of a tumor within the region of interest
Mental processes recited in claim 19:
determine, based on the virtual space mesh model relative to a region of interest defined within the virtual space mesh model, a set of electric field therapy treatment parameters to be applied to the anatomical structure by an implantable electric field therapy such that a resultant area of effect of the implantable electric field therapy application system reaches a sufficient coverage threshold across the region of interest, the set of electric field therapy treatment parameters including at least one of: one or more electrode configuration parameters of the implantable electric field therapy application system; one or more stimulating parameters descriptive of a stimulating waveform to be applied through the implantable electric field therapy application system; and a maximal permissible post-resection residual region of a tumor within the region of interest
Dependent claims 2, 3, 5-10, 12, 15-18, and 20-24 recite additional steps that either are directed to abstract ideas or further limit the judicial exceptions in independent claims 1, 11, and 19, and as such, are further directed to abstract ideas. Hence, the claims explicitly recite numerous elements that individually and in combination constitute abstract ideas. The relevant recitations are:
Claim 2: “identify one or more tissue segments of patient anatomy based on the set of cross-sectional imaging data; and associate one or more electrical properties of the volumetric set of electrical properties with each respective tissue segment of the one or more tissue segments”
Claim 3: “one or more machine learning models operable to identify the one or more tissue segments within the set of cross-sectional imaging data and associate one or more electrical properties with each respective tissue segment of the one or more tissue segments within a set of patient imaging data”
Claims 5, 15, and 20: “conduct a systematic volumetric assessment of the region of interest defined within the virtual space mesh model relative to an expected coverage zone applied by one or more electrodes of the implantable electric field therapy application system with respect to the set of electric field therapy treatment parameters and the virtual space mesh model”
Claims 6, 16, and 21: “simulate application of electric field therapy to the virtual space mesh model according to the set of electric field therapy treatment parameters by one or more modeled electrode objects representative of one or more electrodes of the implantable electric field therapy application system”
Claims 7, 17, and 22: “sweep one or more electric field therapy treatment parameters of the set of electric field therapy treatment parameters across a range during iterative simulation of the application of electric field therapy to the virtual space mesh model; and determine one or more electric field therapy treatment parameters of the set of electric field therapy treatment parameters that result in an area of effect of the one or more electrodes reaching a sufficient coverage threshold across the region of interest”
Claims 8, 18, and 23: “determine, by a machine learning model, one or more electric field therapy treatment parameters of the set of electric field therapy treatment parameters of the set of electric field therapy treatment parameters based on a similarity of the virtual space mesh model to one or more master cases”
Claims 9 and 24: “the one or more electrode configuration parameters includes at least one of: a quantity of one or more electrodes of the implantable electric field therapy application system to be implanted within tissue; one or more electrode design parameters of the one or more electrodes of the implantable electric field therapy application system; and a position of each electrode of the one or more electrodes of the implantable electric field therapy application system relative to the region of interest”
Claim 10: “update one or more parameters of the system based on a comparison between the one or more expected values and one or more measured values following application of electric field therapy to the anatomical structure through the implantable electric field therapy application system”
Claim 12: “identifying, by a first machine learning model in association with the processor, one or more tissue segments of patient anatomy based on the set of cross-sectional imaging data; and associating, by a second machine learning model in association with the processor, one or more electrical properties of the volumetric set of electrical properties with each respective tissue segment of the one or more tissue segments”
The abstract ideas in the claims are evaluated under Broadest Reasonable Interpretation (BRI) and determined herein to each cover mental processes and mathematic concepts because the claims recite no more than using mathematical algorithms to construct a model in order to simulate and electric field therapy treatment. The claims also recite using mental processes to make determinations of the parameters of the treatment and adjusting the mathematical models to optimize the simulation. As disclosed in the Specification in paragraph [0026], the generation of the mesh model is performed using a machine learning based model.
With respect to step (2A)(2): The claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)). The claimed additional elements are analyzed alone or in combination to determine if the judicial exception is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exception, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
Claims 1 and 11 recite the following additional elements that are not abstract ideas:
a system comprising a processor in communication with a memory, the memory including instructions
Claim 19 recites the following additional elements that are not abstract ideas:
a system comprising a processor in communication with a memory, the memory including instructions
receive a virtual space mesh model representative of an anatomical structure based on a set of cross-sectional imaging data, the virtual space mesh model including a set of electrical properties associated with each voxel of a plurality of voxels present within the virtual space mesh model
The step of receiving a virtual mesh model gathers the data on which the judicial exceptions are performed and is thus a data gathering step. Data gathering does not impose any meaningful limitation on the abstract idea, or how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)). The element of a system comprising a processor in communication with a memory, the memory including instructions, is directed to a generic computer. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc. ... are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(f)).
Dependent claims 4, 13, and 14 are directed to further limitations of data gathering or limiting data gathered.
None of these dependent claims recite additional elements, alone or in combination, which would integrate a judicial exception into a practical application.
Lastly, the claims have been evaluated with respect to step (2B): Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims lack a specific inventive concept. Under said analysis, Applicant is reminded that the judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements described above do not rise to the level of significantly more than the judicial exception. As set forth in the MPEP at 2106.05(d).I, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represent well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to claims 1 and 11: The additional elements of a system comprising a processor in communication with a memory, the memory including instructions, do not rise to the level of significantly more than the judicial exception. With respect to the system, as exemplified in the MPEP at 2106.05(f) with reference to Alice Corp. 573 US at 223, 110 USPQ2d at 1983 “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible”. Therefore, the device constitutes no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the abstract idea (see MPEP 2105(b)I-III). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claims 4 and 14: The additional element of a plurality of cross-sectional anatomical images of an anatomical structure obtained through one or more magnetic resonance imaging method does not rise to the level of significantly more than the judicial exception. The prior art McIntyre et al. (US 2019/0247665, IDS reference) discloses that a deep brain stimulation procedure typically involves first obtaining preoperative images of a patient’s brain such as by using a magnetic resonance imaging device (paragraph [0004]). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 13: The additional elements of training a first machine learning model using a set of training data, and training a second machine learning model using a set of training data do not rise to the level of significantly more than the judicial exception. The prior art Wainberg et al. (“Deep learning in biomedicine”, Nature Biotechnology, published September 2018) discloses standard statistical and machine learning methods comprising input features and training cases (page 834, column 2, paragraph 2) and discloses that the most commonly used training method is backpropagation (page 830, column 2, paragraph 2). As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
With respect to claim 19: The additional elements of a system comprising a processor in communication with a memory, the memory including instructions, and receiving a virtual space mesh model do not rise to the level of significantly more than the judicial exception. With respect to the system, as exemplified in the MPEP at 2106.05(f) with reference to Alice Corp. 573 US at 223, 110 USPQ2d at 1983 “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible”. Therefore, the device constitutes no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the abstract idea (see MPEP 2105(b)I-III). With respect to receiving the mesh model, as exemplified in the MPEP at 2106.05(d).II, with reference to buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), a computer receiving data over a network is a routine and conventional activity. Furthermore, with reference to Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, storing and retrieving information in memory is a routine and conventional activity. As such, it is recognized that these additional limitations are routine, well understood, and conventional in the art. These limitations do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide a non-conventional or unconventional step. As such, these limitations fail to rise to the level of significantly more.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. Individually, the limitations of the claims and the claims as a whole have been found lacking.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4-7, 9, 10, 11, 14-17, 19-22, and 24 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by McIntyre et al. (US 2019/0247665, IDS reference).
Regarding claims 1 and 11, McIntyre et al. teaches a system comprising a computer wherein the computer includes imaging data storage, a processor (paragraph [0070]), and a machine-accessible medium carrying instructions for executing the method (paragraph [0050]), to perform the method comprising:
generate a virtual space mesh model representative of an anatomical structure (paragraph [0026]) based on a set of cross-sectional imaging data (paragraphs [0079]; [0028]), the virtual space mesh model including a volumetric set of electrical properties (paragraph [0026]); and
determine, based on the virtual space mesh model relative to a region of interest defined within the virtual space mesh model, a set of electric field therapy treatment parameters to be applied to the anatomical structure by an implantable electric field therapy application system such that a resultant area of effect of the implantable electric field therapy application system reaches a sufficient coverage threshold across the region of interest (paragraphs [0080]; [0059]), the set of electric field therapy treatment parameters including at least one of:
one or more electrode configuration parameters of the implantable electric field therapy application system (paragraph [0089]); and one or more stimulating parameters descriptive of a stimulating waveform to be applied through the implantable electric field therapy application system (paragraphs [0040]; [0080]).
Regarding claim 19, McIntyre et al. teaches a system comprising a computer wherein the computer includes imaging data storage, a processor (paragraph [0070]), and a machine-accessible medium carrying instructions for executing the method (paragraph [0050]), to perform the method comprising:
receiving a virtual space mesh model representative of an anatomical structure based on a set of cross-sectional imaging data, the virtual space mesh model including a set of electrical properties associated with each voxel of a plurality of voxels present within the virtual space mesh model (paragraphs [0082]-[0085]); and
determine, based on the virtual space mesh model relative to a region of interest defined within the virtual space mesh model, a set of electric field therapy treatment parameters to be applied to the anatomical structure by an implantable electric field therapy application system such that a resultant area of effect of the implantable electric field therapy application system reaches a sufficient coverage threshold across the region of interest (paragraphs [0080]; [0059]), the set of electric field therapy treatment parameters including at least one of:
one or more electrode configuration parameters of the implantable electric field therapy application system (paragraph [0089]); and one or more stimulating parameters descriptive of a stimulating waveform to be applied through the implantable electric field therapy application system (paragraphs [0040]; [0080]).
Regarding claim 2, the claim is directed to identifying one or more tissue segments of patient anatomy based on the set of cross-sectional imaging data; and associating one or more electrical properties of the volumetric set of electrical properties with each respective tissue segment of the one or more tissue segments. McIntyre et al. teaches the system of claim 1. McIntyre et al. also teaches identifying tissue segments of patient anatomy based on the DTI imaging data (paragraphs [0027]; [0053]) and teaches associating a volumetric set of electrical properties with the tissue segments (paragraphs [0025]; [0027]).
Regarding claims 4 and 14, the claims are directed to the set of cross-sectional imaging data including a plurality of cross-sectional anatomical images of an anatomical structure obtained through one or more magnetic resonance imaging methods. McIntyre et al. teaches the system of claim 1 and the method of claim 11. McIntyre also teaches the set of cross-sectional imaging data being obtained through diffusion tensor imaging (DTI) magnetic resonance imaging modality (paragraph [0051]) and teaches alternative methods of designating white matter and grey matter in a conductivity tensor and applying the tensors to the nodes of the mesh model using co-registration with anatomical MRI data (paragraph [0053]).
Regarding claims 5, 15, and 20, the claims are directed to conducting a systematic volumetric assessment of the region of interest defined within the virtual space mesh model relative to an expected coverage zone applied by one or more electrodes of the implantable electric field therapy application system with respect to the set of electric field therapy treatment parameters and the virtual space mesh model. McIntyre et al. teaches the system of claim 1, the method of claim 11, and the system of claim 19. McIntyre et al. teaches conducting a systematic volumetric assessment of the region of interest defined in the mesh model relative to an expected coverage zone applied by the electrodes (paragraphs [0056], [0063]).
Regarding claims 6, 16, and 21, the claims are directed to simulating application of electric field therapy to the virtual space mesh model according to the set of electric field therapy treatment parameters by one or more modeled electrode objects representative of the one or more electrodes of the implantable electric field therapy application system. McIntyre teaches the system of claim 5, the method of claim 15, and the system of claim 20. McIntyre et al. also teaches simulating application of the electric field therapy to the neuron model (paragraphs [0059]; [0080]).
Regarding claims 7, 17, and 22, the claims are directed to sweeping one or more electric field therapy treatment parameters of the set of electric field therapy treatment parameters across a range during iterative simulation of the application of electric field therapy to the virtual space mesh model; and determining one or more electric field therapy treatment parameters of the set of electric field therapy treatment parameters that result in an area of effect of the one or more electrodes reaching a sufficient coverage threshold across the region of interest. McIntyre et al. teaches the system of claim 6, the method of claim 16, and the system of claim 21. McIntyre et al. also teaches sweeping the electric field therapy treatment parameters across a range of the neuron model to optimize and calculate the parameters that result in reaching the target threshold, and teaches calculating several models (paragraph [0080]), and teaches performing iterative calculations (paragraph [0026]).
Regarding claims 9 and 24, the claims are directed to the one or more electrode configuration parameters including at least one of: a quantity of one or more electrodes of the implantable electric field therapy application system to be implanted within tissue; one or more electrode design parameters of the one or more electrodes of the implantable electric field therapy application system; and a position of each electrode of the one or more electrodes of the implantable electric field therapy application system relative to the region of interest. McIntyre teaches the system of claim 1 and the system of claim 19. McIntyre et al. also teaches electrode configuration parameters including electrode position and electrode design (paragraph [0072]).
Regarding claim 10, the claim is directed to updating one or more parameters of the system based on a comparison between one or more expected values and one or more measured values following application of electric field therapy to the anatomical structure through the implantable electric field therapy application system. McIntyre et al. teaches the method of claim 1. McIntyre et al. also teaches updating the parameters of the system based on a comparison between comparing the target volume of influence to the measured volume of influence on the neuron or axon model (paragraph [0080]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3, 8, 12, 13, 18, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over McIntyre et al., as applied to claims 1, 2, 4-7, 9, 10, 11, 14-17, 19-22, and 24 in the 102 rejection above, in view of Erturk et al. (“Predicting in vivo MRI Gradient-Field Induced Voltage Levels on Implanted Deep Brain Stimulation Systems Using Neural Networks”, Frontiers in Human Neuroscience, published February 2020).
Regarding claim 3, the claim is directed to one or more machine learning models operable to identify the one or more tissue segments within the set of cross-sectional imaging data and associate one or more electrical properties with each respective tissue segment of the one or more tissue segments within a set of patient imaging data. McIntyre et al. teaches the system of claim 2.
McIntyre et al. does not teach the claim elements of one or more machine learning models.
However, Erturk et al. predicting MRI gradient-field induced voltage levels on implanted deep brain stimulation systems using neural networks (Abstract). Erturk et al. teaches an in-house fully automated software to identify locations in a human body scan model that are clinically relevant DBS implant lead/extension routing trajectories and extracting tangential e-field distributions along DBS implant trajectories and calculating MRI gradient-filed induced voltage values at maximum gradient slew rates (page 2, column 2, Section Methods – page 3, column 1, paragraph 1).
Regarding claims 8, 18, and 23, the claims are directed to determining, by a machine learning model, one or more electric field therapy treatment parameters of the set of electric field therapy treatment parameters based on a similarity of the virtual space mesh model to one or more master cases. McIntyre et al. teaches the system of claim 5, the method of claim 15, and the system of claim 20.
McIntyre et al. does not teach the claim elements of determining by a machine learning model.
However, Erturk et al. teaches using the machine learning models to determine electric field therapy treatment parameters (page 3, column 1, Section Feature 1), and training the model using the Condensed dataset and Extended dataset (page 3, column 2, Section Neural Network Predictive Model Performance Evaluation).
Regarding claim 12, the claim is directed to identifying, by a first machine learning model in association with the processor, one or more tissue segments of patient anatomy based on the set of cross-sectional imaging data; and associating, by a second machine learning model in association with the processor, one or more electrical properties of the volumetric set of electrical properties with each respective tissue segment of the one or more tissue segments. McIntyre et al. teaches the system of claim 11. McIntyre et al. also teaches identifying tissue segments of patient anatomy based on the DTI imaging data (paragraphs [0027]; [0053]) and teaches associating a volumetric set of electrical properties with the tissue segments (paragraphs [0025]; [0027]).
McIntyre et al. does not teach the claim elements of identifying by a first machine learning model, and associating by a second machine learning model.
However, Erturk et al. teaches an in-house fully automated software to identify locations in a human body scan model that are clinically relevant DBS implant lead/extension routing trajectories and extracting tangential e-field distributions along DBS implant trajectories and calculating MRI gradient-filed induced voltage values at maximum gradient slew rates (page 2, column 2, Section Methods – page 3, column 1, paragraph 1). Erturk et al. also teaches using a neural network model to determine electric field therapy treatment parameters (page 3, column 1, Section Feature 1).
Regarding claim 13, the claim is directed to training, by a processor, the first machine learning model to identify the one or more tissue segments within the set of cross-sectional imaging data using a set of training data that demonstrates tissue segmentation for a plurality of cross-sectional images across a plurality of training cases; and training, by a processor the second machine learning model to associate set of the one or more tissue segments within the set of cross-sectional imaging data using a set of training data that demonstrates estimation of a set of electrical properties for a plurality of cross-sectional images across a plurality of training cases. McIntyre et al. teaches the method of claim 12 in view of Erturk et al.
McIntyre et al. does not teach the claim elements of training machine learning models.
However, Erturk et al. teaches training the neural network using the Condensed-Dataset and training a final neural network using the Extended-Dataset of the body-models (page 3, column 2, Section Neural Network Predictive Model Performance), the body-models comprising tissue distributions and associated electrical properties of the model data (page 2, column 2, Section EM Simulation Methodology and Dataset Generation).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the machine learning models of Erturk et al. to the method and system of McIntyre et al. because both McIntyre et al. ad Erturk et al. are directed to simulating deep brain stimulation systems (see Abstract of both). Erturk et al. teaches electromagnetic simulations to calculate clinically relevant induced voltage levels (Abstract), potential hazards of deep brain stimulation systems due to MRI gradient-induced extrinsic voltage causing unintended tissue stimulation and device malfunction, and thus a need to calculate clinically relevant gradient-field induced voltage levels on deep brain stimulation systems (page 2, column 1, paragraph 2). Erturk et al. teaches that using machine learning with computational modeling and simulations develops an accurate predictive model to determine MRI gradient-field induced voltage levels on implanted deep brain stimulation systems. Thus, one of ordinary skill in the art would have a reasonable expectation of success of using machine learning models to simulate a deep brain stimulation system and the MRI gradient-field induced voltage levels and would be motivated to do so in order to accurately predict unintended consequences.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emilie A Smith whose telephone number is (571)272-7543. The examiner can normally be reached 9am - 5pm.
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/E.A.S./Examiner, Art Unit 1686
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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