DETAILED ACTION
Applicant's response, filed 03 December 2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Acknowledgment is made of applicant’s claim for priority. Application claims benefit of Korean Application No. 10-2021-0089052 and a certified copy has been attached. As such, the effective filing date of claims 1, 3-5, 7-8, and 10is 7/7/2021.
Claim Status
Claims 1, 3-5, 7-8, and 10 are pending.
Claims 2, 6, and 9 are cancelled.
Claims 1, 3-5, 7-8, and 10 are rejected.
Drawings
Response to Amendment
In view of applicant’s amendments to the drawings, previous objections to the drawings are withdrawn.
Claim Objections
Response to Amendment
In view applicant’s amendments to the claims, previous objections to the claims are withdrawn.
Claim Rejections - 35 USC § 101
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 101 have been reviewed, updated, and provided below.
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, 3-5, 7-8, and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method and system for creating a brain stimulation simulation via MRI image data and linear equations. The judicial exception is not integrated into a practical application because while claims 1, 3-5, 7-8, and 10 attempt to integrated the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea or it is insignificant extra solution activity and merely implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of stator subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a method (Claims 1, 3-5, 7-8), and a system (Claim 10)
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas, specifically mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claim 1: Generating a global matrix by using multiple brain models, and performing a simulation are processes of generating, manipulating and interpreting data that can be done via pen and paper and/or in the human mind and are therefore abstract ideas, specifically mental processes. Additionally, according the specification, pages 28 and 31 these are verbal articulations of a mathematical process and therefore an abstract idea, specifically a mathematical concept. Segmenting an MIR image, and generating a global matrix from the map, are processes of generating, manipulating and interpreting data that can be done via pen and paper and/or in the human mind and are therefore abstract ideas, specifically mental processes. Allocating a physical property for each of the plurality of areas is a process of classification/choosing that can be done via pen and paper and/or in the human mind and are therefore abstract ideas, specifically mental processes. Deriving a linear equation using the global matrix, and calculating a solution of the derived linear equation, are verbal articulations of mathematical processes and are therefore abstract ideas, specifically mathematical concepts.
Claim 3: Deriving a mathematical formula is a verbal articulation of a mathematical process and therefore an abstract idea, specifically a mathematical concept. Grouping a plurality of nodes using the formula, and generating a global matrix are processes of generating, manipulating and interpreting data that can be done via pen and paper and/or in the human mind and are therefore abstract ideas, specifically mental processes.
Claim 4: Deriving a mathematical formula is a verbal articulation of a mathematical process and therefore an abstract idea, specifical a mathematical concept.
Claim 5: Each of the groups in claim 3 including 4 nodes forming a tetrahedral shape is directed to the structure of the information itself which is an abstract idea, specifically a mental process. Generating a stiffness matrix is a process of generating data that can be done via pen and paper and/or in the human mind and are therefore abstract ideas, specifically mental processes.
Claim 7: Setting a brain stimulation condition according to a preset guide system is a process of classification that can be done via pen and paper and/or in the human mind and are therefore abstract ideas, specifically mental processes.
Claim 10: Generating a global matrix by using multiple brain models, and performing a simulation are processes of generating, manipulating and interpreting data that can be done via pen and paper and/or in the human mind and are therefore abstract ideas, specifically mental processes.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial exception integrated into a practical application? [see MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claim 1: A system and an external server are all generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)]. Acquiring MRI image data, generating a 3D image using the segmented image, and generating a 3D map from the 3D image are insignificant extra solution activities, specifically mere data gathering (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Claim 8: Providing a brain stimulation simulation result, and outputting a result are insignificant extra solution activities, specifically an insignificant application or necessary data outputting (See Cutting hair after first determining the hair style, In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016) (non-precedential) and Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55) [See MPEP § 2106.05(g)].
Claim 10: A system and an external server are all generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional or nonspecific. These additional elements include:
The additional elements of a system and an external server are generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See § MPEP 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of acquiring MRI image data (Conventional: Katti et al. 2011), providing a brain stimulation simulation result (Conventional: Drawings Figure 3), generating a 3D image using the segmented image (Conventional: Katti et al. 2011), and generating a 3D map from the 3D image (Conventional: Katti et al. 2011) and outputting a result (Conventional: Drawings Figure 3), are insignificant extra solutional activities, specifically mere data gathering, that are recognized as well understood, routine and conventional by the courts (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1, 3-5, 7-8, and 10, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 12/3/2025 have been fully considered but they are not persuasive.
Applicant asserts on page 9 of the Remarks filed 12/3/2025 that the claims are not directed to a mental process for three reasons; 1) the claims are necessarily rooted in computer technology, 2) the claimed invention is directed to an improvement, and 3) the claims amount to significantly more than the judicial exception.
In regards the claims being rooted in computer technology, applicant asserts on page 10 of the Remarks that the present claims cannot practically be performed in the human mind, specifically citing “a user interface for three-dimensional modeling” and the questioning the capability of a person for generating a “a three-dimensional brain map”, i.e. “a human, even with paper, can at most draw a two-dimensional representation of a three-dimensional model”. However, examiner reminds applicant that the three-dimensional brain map that is being generated is itself a two-dimensional representation of a three-dimensional model, not a three-dimensional model itself, and the mere inclusion of computer technology to perform judicial exceptions does not necessitate its use, “…examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept”, See Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360 – MPEP 2106.04(III)(C).
In regards to the claims being directed to an improvement applicant asserts on page 13 of the Remarks, the method uses non-identified information guaranteeing anonymity of the subject. However, examiner reminds applicant that the judicial exception must be directed to either a) the additional elements of the claim, or b) the additional elements in conjunction with the judicial exception, but cannot come from the judicial exception alone, i.e. “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements”, See Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) – MPEP 2106.05. Here the use of a specific dataset is directed to the information or data itself, not the system or method, which is an abstract idea and therefore cannot serve as the basis for a technological improvement.
In regards the claims being significantly more, as known as a particular transformation, applicant asserts on page 14 of the Remarks that under the Federal Circuit McRo was stated to “the computer here is employed to perform a distinct process to automate a task previously performed by humans” and that an ordered combination of specific rules amounts to using the three-dimensional interface to enable anonymity and processing of large datasets. However, examiner reminds applicant that in McRO they were applying streams to provide the lip synchronization which was producing the animation, which was an additional element and was an improvement to technology. Here however, the claimed improvement was to the data that was being used, which cannot be an improvement as stated before, and therefore the method cannot be a particular transformation as there is no improved elements.
Claim Rejections - 35 USC § 103
Response to Amendment
In view of applicant’s amendments to the claims, previous rejections under 35 U.S.C. 103 have been reviewed, updated, and provided below.
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 1, 3-5, 7-8, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Astrom et al. (Medical & biological engineering & computing (2009) 21-28; previously cited), Tanenbaum et al. (Distributed Systems: Principles and Paradigms (2007) 1-705; previously cited), Katti et al. (International Journal of Dental Clinics (2011) 65-70; previously cited), and Bischoff-Grethe et al. (Human Brain Mapping (2007) 892-903; newly cited) as evidenced by Santiago (Stack Exchange: Computational Science (2014); previously cited).
Claim 1 is directed to a method for brain stimulation simulation using a computing device to generate a matrix for performing the simulation and then performing the simulation.
Claim 10 is directed to a system for brain stimulation simulation using a computing device to generate a matrix for performing the simulation and then performing the simulation.
Astrom et al. teaches in the abstract “The aim of this study was to develop a general method for setting up patient-specific 3D computer models of DBS, based on magnetic resonance images, and to demonstrate the use of such models for assessing the position of the electrode contacts and the distribution of the electric field in relation to individual patient anatomy”, on page 22, column 2, paragraph 2 “A software tool was developed in MatLab 7.0 to set up an anatomical property matrix for the finite element brain model”, and on page 25, column 1, paragraph 1 “A general method for creating patient-specific finite element models and simulations of DBS, based on MRI, was presented”, reading on a brain stimulation simulation method according to a preset guide system using an external server, the method performed by a computing device, the method comprising: a first server generating a global matrix for performing brain stimulation simulation on a plurality of objects by using a plurality of brain models for each of the plurality of objects; and a second server being provided with the generated global matrix from the first server and performing the brain stimulation simulation on the plurality of objects by using the provided global matrix. Astrom et al. teaches on page 22, column 1, paragraph 1 “The overall aim of the present study was to develop a general method for setting up patient-specific 3D finite element models of DBS based on pre- and postoperative magnetic resonance images (MRI) acquired during DBS surgery”, reading on acquiring an MRI image of the plurality of objects. Astrom et al. teaches on page 22, column 2, paragraph 2 “Intensity-based segmentation was used to identify anatomical areas with gray matter, white matter, cerebrospinal fluid and large blood vessels in several different MR-images”, reading on segmenting the acquired MRI image into a plurality of areas. Astrom et al. teaches on page 24, column 1, paragraph 2 “the electric potential, the electric field and the second difference of the electric potential were visualized in 3D”. Astrom et al. teaches on page 23, column 2, paragraph 2 “The finite element brain model was set up in a multiphysic modeling program. The domains, including the brain model anatomy and the electrode models, were divided into *2,000,000 tetrahedral mesh elements”, and on page 22, column 2, paragraph 2 “The property matrix was based on preoperative MRI voxel intensity data, which were classified into different tissues”, reading on generating a three-dimensional brain map configured with a plurality of meshes based on properties of each of the plurality of areas included in the generated three-dimensional brain image; and generating the global matrix by using the generated three- dimensional brain map. Astrom et al. teaches on page 24, columns 1-2, paragraph 3 and 1 respectively “A common way to visualize all degrees of freedom of second-order tensors is to use glyphs, where each tensor is represented with a geometric object [32, 37]. Two elliptic glyphs were rendered in every sampled point: one black glyph for visualization of depolarization of a straight axon and one white glyph for visualization of hyperpolarization of a straight axon. The eigenvectors were used as the principal axes, and the eigenvalues were used as the radii of the ellipsoids. The activation function tensor D was calculated”, reading on deriving a linear equation for the provided global matrix by using the provided global matrix; and calculating a solution of the derived linear equation as the result of the brain stimulation simulation. Astrom et al. teaches on page 22, column 2, paragraph 2 “A software tool was developed in MatLab 7.0 to set up an anatomical property matrix for the finite element brain model. The property matrix was based on preoperative MRI voxel intensity data, which were classified into different tissues. Intensity-based segmentation was used to identify anatomical areas with gray matter, white matter, cerebrospinal fluid and large blood vessels in several different MR-images”, and on page 23, column 2, paragraph 2 “The finite element brain model was set up in a multiphysic modeling program. The domains, including the brain model anatomy and the electrode models, were divided into *2,000,000 tetrahedral mesh elements with the highest mesh density close to the electrodes”, reading on wherein the generating the global matrix further includes setting a brain stimulation condition for performing the brain stimulation simulation, the set brain stimulation condition including at least one of a plurality of stimulation positions according to the preset guide system, a number of electrodes attachable to the plurality of stimulation positions, and an intensity of brain stimulation.
Astrom et al. does not teach the use of a first and second server.
Tanenbaum et al. teaches on page 18, paragraph 4-paragraph 2 of page 20, characteristics of grid computing systems, specifically in paragraph 4 of page 18 “grid computing systems have a high degree of heterogeneity: no assumptions are made concerning hardware, operating systems, networks, administrative domains, security policies”, further on page 19, paragraphs 22-paragraph 1 of page 20 the layered architecture of grid computing systems is provided, in paragraph 5 of page 18 the structure is provided “resources consist of compute servers (including supercomputers, possibly implemented as cluster computers), storage facilities, and databases. In addition, special networked devices such as telescopes, sensors, etc., can be provided as well”, with the use of plural “servers”, reading on a first server and a second server.
Astrom et al. and Tanenbaum et al. do not teach generating a full 3D representation of the brain image but merely the electrical potentials.
Katti et al. teaches on page 67, column 2, paragraph 2 “T1 Weighted images are more commonly used to demonstrate anatomy. In practice, images often must be acquired with both T1 and T2 weighting to separate the several tissues by contrast resolution. Localization of MRI to specific part of the body (selecting a slice) and the ability to create a 3 dimensional image depends on the fact that the larmor frequency of a nucleus is governed in part by the strength of the external magnetic field”, on page 68, column 2, paragraph 2 “T1 and T2 Weighted images are obtained for examinations of oral and maxillofacial regions. T1-Weighted images are used for anatomical evaluation and T2- weighted images are for the detection of pathological processes. Both T1 and T2 - Weighted images are studied for disease detection, extent and character. Images in the Coronal and Axial planes are routinely obtained for three-dimensional evaluation of disease in MR examinations”, and on page 69, column 2, paragraph 2 the concept of volume imaging or 3D imaging from MRI data slices, which in view of Astrom et al. reads on generating a three-dimensional brain image by using the MRI image segmented into the plurality of areas, and allocating a physical property for each of the plurality of areas to each of the plurality of areas generated by segmenting the acquired MRI image, a type of the physical property allocated to each ofthe plurality of areas being determined according to a type of brain stimulation to be simulated.
Bischoff-Grethe et al. teaches in the abstract “program was developed that uses models of nonbrain structures for removing potentially identifying facial features. When a novel image is presented, the optimal linear transform is computed for the input volume. A brain mask is constructed by forming the union of all voxels with nonzero probability of being brain and then morphologically dilated. All voxels outside the mask with a nonzero probability of being a facial feature are set to 0. The algorithm was applied to 342 datasets that included two different T1-weighted pulse sequences and four different diagnoses”, which reads on the global matrix includes non-identified information that does not infer an object of the plurality of objects, and wherein the non-identified information includes a brain stimulation condition list and boundary conditions.
It would have been obvious at the time of invention to a person skilled in the art to modify the teachings of Astrom et al. for the method of claim 1 in terms of simulation of brain stimulation, with the teachings of Tanenbaum et al. for the use of distributed systems as the latter teaches on page 30, paragraph 3, and page 31, paragraph 1 “One important advantage is that they make it easier to integrate different applications running on different computers into a single system. Another advantage is that when properly designed, distributed systems scale well with respect to the size of the underlying network”. One would have had a reasonable expectation of success given that the latter teaches on page 31, paragraph 4 “Distributed computing systems are typically deployed for high-performance applications often originating from the field of parallel computing. A huge class of distributed can be found in traditional office environments where we see databases playing an important role” and on the whole is a review and how to guide for implementing distributed systems. Additionally, it would have been obvious at the time of invention to modify the teachings of Astrom et al. and Tanenbaum et al. for the previously described method with the teachings of Katti et al. for volume imaging from MRI slice data as Katti et al. explains in the abstract “Magnetic Resonance Imaging (MRI) has progressed over 30 years from being a technique with great potential to one that has become the primary diagnostic investigation for many clinical problems” and in the introduction paragraph “MRI is a non-invasive method of mapping the internal structure and certain aspects of function within the body”. Finally, it would have been obvious at the time of first filing to have modified the previous teachings with those from Bischoff-Grethe et al. as the latter teaches in the abstract “Due to the increasing need for subject privacy, the ability to deidentify structural MR images so that they do not provide full facial detail is desirable… Results suggested that the automatic defacing algorithm is robust, efficiently removes nonbrain tissue, and does not unduly influence the outcome of the processing methods utilized”. One would have had a reasonable expectation of success given that Katti et al. is a review paper on the applications and methods associated MRI data including 3D visualization, Astrom et al. is focused on the use of MRI data for 3D modeling, and Bischoff-Grethe et al. is taking the information used in the latter two and stripping it of any identifying information. Therefore, it would have been obvious at the time of invention to a person skilled in the art to modify the teachings of each and to be successful.
Claim 3 is directed to the method of claim 2 and thus claim 1, but further specifies deriving a mathematical formula for the simulation, creating groups of nodes, and generating a global matrix.
Astrom et al. and Tanenbaum et al. teach the method of claims 1, and 9-10 as previously described.
Astrom et al. teaches on page 23, column 2, paragraph 3 “The electrical conductivity properties at the integration points were calculated by linear interpolation of the mapped electrical conductivity values. The equation for steady currents was used to calculate the electric potential distribution in the vicinity of the electrodes”, and on the same page, column 1, paragraph 2 “A transformation matrix describing the transformation from the postoperative to the preoperative coordinate system was then calculated (Fig. 2b). The transformation matrix was applied to the electrodes in the postoperative model, which were transferred to the preoperative brain model”, reading on deriving a mathematical formula for performing the brain stimulation simulation; grouping a plurality of nodes included in the generated three- dimensional brain map into a plurality of groups and generating a unit matrix for each of the plurality of groups by using the derived mathematical formula; and generating one global matrix by combining the generated unit matrix.
Claim 4 is directed to the method of claim 3 and thus claim 1, but further specifies the formula being derived as one of those specified in the group provided.
Astrom et al. and Tanenbaum et al. teach the method of claims 1, and 9-10 as previously described.
Astrom et al. teaches on page 23, column 2, paragraph 3 “The equation for steady currents was used to calculate the electric potential distribution” with the equation of steady current being the same general function as an equation of constant current, i.e. the terms are often used interchangeably to describe a DC current that does not vary with time, thereby reading on wherein the deriving the mathematical formula includes deriving the mathematical formula for performing the brain stimulation simulation, a form of the derived mathematical formula being determined according to a purpose of performing the brain stimulation simulation, wherein the purpose includes at least one among time-series current prediction, constant current and low-frequency current prediction, and vibration prediction for ultrasound stimulation.
Claim 5 is directed to the method of claim 3 and thus claim 1 but further specifies the nodes forming a tetrahedral shape, and generating a matrix using a finite element method.
Astrom et al. and Tanenbaum et al. teach the method of claims 1, and 9-10 as previously described.
Astrom et al. teaches on page 23, column 2, paragraph 2 “The finite element brain model was set up in a multiphysic modeling program. The domains, including the brain model anatomy and the electrode models, were divided into *2,000,000 tetrahedral mesh elements”, and it is inherent that any FEM model would contain a stiffness matrix (Santiago et al. 2014), reading on wherein each of the plurality of groups includes four nodes forming a tetrahedral shape, and wherein the generating the unit matrix for each of the plurality of groups includes generating a stiffness matrix for the four nodes forming the tetrahedral shape by using a finite element method (FEM).
Claim 7 is directed to the method of claim 2 and thus claim 1, but further specifies including a brain stimulation condition and it being one of those specified in the group provided.
Astrom et al. and Tanenbaum et al. teach the method of claims 1, and 9-10 as previously described.
Astrom et al. teaches on page 24, column 1, paragraph 1 “A surgical planning system, the FrameLink Planning Station TM was used by an experienced user as a reference system to evaluate the accuracy of the position of the modeled electrode positions… The position of the electrodes in the finite element model was compared with the positions of the electrodes in the preoperative MRI”, reading on wherein the generating the global matrix further includes setting a brain stimulation condition for performing the brain stimulation simulation, the set brain stimulation condition including at least one of a plurality of stimulation positions according to the preset guide system, a number of electrodes attachable to the plurality of stimulation positions, and an intensity of brain stimulation.
Claim 8 is directed to the method of claim 2 and thus claim 1, but further specifies the outputting of a combination of the result of the simulation and a generated 3D map.
Astrom et al. and Tanenbaum et al. teach the method of claims 1, and 9-10 as previously described.
Astrom et al. teaches on page 24, column 2, paragraph 3 A patient-specific finite element model of the brain, based on pre- and postoperative MRI, was set up and the electric potential generated by the DBS electrodes during clinically effective parameter settings was simulated. The electric potential, the electric field and the activation function were visualized in 3D (Fig. 3a–c, e, f). An axial MRI was also visualized for comparison of the spatial resolution of the model anatomy with the MRI (Fig. 3d). The position of the active electrode contacts in relation to the anatomy could clearly be identified in the model”, reading on further comprising the first server being provided with a result of brain stimulation simulation for the plurality of objects from the second server and outputting a combination of the provided result of the brain stimulation simulation and the generated three-dimensional brain map.
Response to Arguments
Applicant's arguments filed 12/3/2025 have been fully considered but they are not persuasive. Applicant asserts on page 16-18 of the Remarks that in view of the present amendments claim 1 is novel and non-obvious over the cited prior art.
Firstly, applicant asserts that Astrom et al. is generally related to a simulation approach and argues that the present invention is not directed to a simulation and that the simulation of electrical brain stimulation is electrical conductivity per tissue, yet the cited passage on page 16 of the Remarks from Anstrom et al. states “Intensity-based segmentation was used to identify anatomical areas with grey matter, white matter, cerebrospinal fluid and large blood vessels…”, which exactly reads on electrical conductivity per tissue. Furthermore, applicant attempts to assert that the invention may use physical properties such as density, stiffness modulus, viscous modulus, viscosity coefficient, etc., but also uses the operative phrase “may allocate, as the physical properties…”, which indicates the possible use of such metrics but not the necessitation.
Second applicant asserts that the global matrix from Anstrom et al. is directed to the visualization and is not related to a linear equation which is derived from said global matrix. However, Anstrom et al. specifically states in the citation on page 17 of the Remarks “The eigenvectors were used as the principal axes, and the eigenvalues were used as the radii of the ellipsoids”, here eigenvectors are solutions to a linear equation derived from the matrix presented in equation 2, also cited on page 17 of the Remarks.
Finally, applicant asserts that the newly amended claims contain new limitations not addressed in the previously cited references. Examiner agrees but has provided newly cited references that do teach said limitations as provided in the above rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.N.A./Examiner, Art Unit 1687
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685