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 .
Amendment
Applicant submitted amendments on 1/27/2026. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Priority
Applicant claims the benefit of US Provisional Application No. 63/291,507, filed 12/20/2021. Claims 1-6, 9-12, 14, 17-19, and 22 contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Benefit to the claimed priority date is not afforded.
Information Disclosure Statement
The IDS(s) dated 6/20/2023 that have been previously considered remain placed in the application file.
Overview
Claims 1-6, 9-12, 14, 17-19, and 22 are pending in this application and have been considered below.
Claims 7-8, 13, 15-16, and 20-21 are canceled by the applicant.
Claims 1-6, 9-12, 14, 17-19, and 22 are rejected.
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.
Claim(s) 1-6, 9-11 is/are rejected under 35 U.S.C. 103 as obvious over Codd et al (WO 2018152538 A1, hereafter referred to as Codd) in view of Bruneel et al (US 20210034798 A1, hereafter referred to as Bruneel), further in view of Schick et al (US 20220157044 A1, hereafter referred to as Schick).
Claim 1
Regarding Claim 1, Codd teaches a method comprising:
ablating a substrate with a laser at an orientation to create a cavity in the substrate (Codd in ¶28 discloses "guidance laser … In the depicted example, system 100 is specifically configured to controllably ablate soft brain matter along a desired surgical path at surgical site"; See Fig. 1);
scanning the cavity (Codd in ¶14 and 60 discloses "In some embodiments, one or more of a variety of types of surface profilers, such as an interferometer, optical coherence tomography (OCT) device … one or more additional feedback modalities (e.g., … optical coherence tomography, … computed tomography (CT), etc.) are used to evaluate the intra-procedural and post-procedural condition of the surgical site … a 3D intra-procedural model of surgical site 116 is developed based on the first intra-procedural scan generated in operation 501.");
creating a three-dimensional surface for the cavity (Codd in ¶58 discloses "an intra-procedural scan of the surgical site is performed using any of a variety of alternative imaging modalities, including, without limitation, ultrasound, CT, MRI, 3D imaging, 3D surface scanning (e.g., via a non-contact surface profiler, etc.)");
predicting a post-ablation surface using the gaussian-based model based on a pre-ablation profile and a set laser orientation (Codd in ¶10 discloses "automated surgical procedures … a laser signal to manipulate tissue at a surgical site, where the path of the laser signal, as well as its orientation, power and speed, are automatically controlled based on a pre-procedural model and a post-procedural goal.").
Codd does not explicitly teach all of storing the three-dimensional surface in a dataset, wherein the dataset includes a laser projected distance as an independent variable and a depth of cut as a dependent variable; fitting parameters of a gaussian-based model for the laser and the substrate based on the dataset; and predicting a post-ablation surface using the gaussian-based model based on a pre-ablation profile and a set laser orientation.
However, Bruneel teaches storing the three-dimensional surface in a dataset, wherein the dataset includes a laser projected distance as an independent variable and a depth of cut as a dependent variable (Bruneel in ¶34 and 35 discloses "further comprises providing the following information relating to said laser machining system: a distance of the focus point of the laser machining beam from the surface of the material to be machined"; ¶248 discloses "the depth of ablation produced by each pulse n during one revolution as a function of the distance to the center is given by Eq. [39]");
predicting a post-ablation surface using the gaussian-based model based on a pre-ablation profile and a set laser orientation (Bruneel in ¶179 discloses "The laser fluence F for a laser beam with a Gaussian intensity profile is given" - establishes the Gaussian model basis; ¶181, 234, and 248 discloses "The depth of pulse ablation after irradiation with a Gaussian beam is given - explicitly shows prediction of post-ablation geometry by combining beam profile, propagation, and cumulative pulses …To determine the fluence at the generic point P(r,z), it is necessary to know the Gaussian distribution of the intensity along the plane z.sub.P and, in turn, the distance between the point BP(r,z) and the focus f. The point BP can be calculated from the intersection of the two lines … the depth of ablation produced by each pulse n during one revolution as a function of the distance to the center is given by"; Examiner interprets the system uses Gaussian beam physics to predict the final ablated surface).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Codd by storing the 3D surface in a dataset including a laser projected distance as an independent variable and a depth as a dependent variable that is taught by Bruneel, since both reference are analogous art in the field of laser-based surgical systems and address modeling the relationship between beam geometry and material removal; thus, one of ordinary skilled in the art would be motivated to combine the references since Codd’s method of ablating, scanning, and generating a 3d surface with Bruneel’s method of storing that surface in a dataset with distance and depth as variables yields the predictable result of generating a dataset suitable for fitting a Gaussian-based model for the laser and substrate based on the dataset, thereby improving the accuracy of ablation prediction and planning.
Codd in view of Bruneel does not explicitly teach all of fitting parameters of a gaussian-based model for the laser and the substrate based on the dataset.
However, Schick teaches fitting parameters of a gaussian-based model for the laser and the substrate based on the dataset (Schick in ¶56 discloses "the size or diameter of the light beam is dependent on a distance between the object, from which the incident light beam propagates towards the detector, and the detector itself … The predetermined relationship may be determined by analytical considerations, such as by assuming a linear combination of Gaussian light beams, by empirical measurements, such as measurements measuring the first and second sensor signals or a secondary signal derived thereof as a function of the longitudinal coordinate of the object, or both"; ¶61 discloses "model function may comprise statistical assumptions and/or statistical models concerning the distances z, z.sub.DFD, and z.sub.DPR such as a distribution, such as a Gaussian distribution of a measured distance z, z.sub.DFD, and/or z.sub.DPR around a real distance z.sub.real.").
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Codd in view of Bruneel by fitting parameters of a Gaussian-based model for the laser and the substrate based on the dataset that is taught by Schick, since both reference are analogous art in the field of laser-based surgical systems; thus, one of ordinary skilled in the art would be motivated to combine the references since Codd in view of Bruneel’s method of generating and storing a dataset containing laser projected distance and depth variables with Schick’s method of using such a dataset to fit parameters of a Gaussian-based laser model yields the predictable result of producing a parameterized Gaussian model tailored to the laser/substrate combination, thereby enabling more accurate prediction and control of material removal.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 2
Regarding Claim 2, Codd in view of Bruneel, further in view of Schick teaches the method of claim 1, wherein the orientation is a first orientation, the cavity is a first cavity, the three-dimension surface is a first three-dimensional surface, and wherein the method further includes:
ablating the substrate with the laser at a second orientation to create a second cavity in the substrate (Codd in ¶28 discloses "guidance laser … In the depicted example, system 100 is specifically configured to controllably ablate soft brain matter along a desired surgical path at surgical site"; ¶9, 38 and 65 discloses "a laser signal to manipulate tissue at a surgical site … real-time adaptation of the surgical procedure based on the actual response to the tissue at a surgical site to the surgical laser signal … where the path of the laser signal, as well as its orientation, power and speed, are automatically controlled based on a pre-procedural model and a post-procedural goal … within a 3D model of the surgical site … classifying tissue in a region for special consideration (e.g., removal, protection, and the like)"; ¶77 discloses "a new path is planned based on a comparison of the first intra-procedural scan developed in operation 502 and the second intra-procedural scan developed in operation 507 after the procedural pass performed in operation 506."; See Fig. 1; Performing the same steps twice with different orientations is an obvious and routine extension);
scanning the second cavity (Codd in ¶14 and 60 discloses "In some embodiments, one or more of a variety of types of surface profilers, such as an interferometer, optical coherence tomography (OCT) device … one or more additional feedback modalities (e.g., … optical coherence tomography, … computed tomography (CT), etc.) are used to evaluate the intra-procedural and post-procedural condition of the surgical site … a 3D intra-procedural model of surgical site 116 is developed based on the first intra-procedural scan generated in operation 501.");
creating a second three-dimensional surface of the second cavity (Codd in ¶14 and 60 discloses "In some embodiments, one or more of a variety of types of surface profilers, such as an interferometer, optical coherence tomography (OCT) device … one or more additional feedback modalities (e.g., … optical coherence tomography, … computed tomography (CT), etc.) are used to evaluate the intra-procedural and post-procedural condition of the surgical site … a 3D intra-procedural model of surgical site 116 is developed based on the first intra-procedural scan generated in operation 501."); and
storing the second three-dimensional surface in the dataset (Codd in ¶14 and 60 discloses "In some embodiments, one or more of a variety of types of surface profilers, such as an interferometer, optical coherence tomography (OCT) device … one or more additional feedback modalities (e.g., … optical coherence tomography, … computed tomography (CT), etc.) are used to evaluate the intra-procedural and post-procedural condition of the surgical site … a 3D intra-procedural model of surgical site 116 is developed based on the first intra-procedural scan generated in operation 501."; ¶77 discloses "a new path is planned based on a comparison of the first intra-procedural scan developed in operation 502 and the second intra-procedural scan developed in operation 507 after the procedural pass performed in operation 506.").
Claim 3
Regarding Claim 3, Codd in view of Bruneel, further in view of Schick teaches the method of claim 1,
wherein the substrate is a biological tissue (Codd in Abstract and ¶3 discloses "An automated laser-surgery system for performing a closed-loop surgical … Laser surgery has become a critical procedure in the treatment of many conditions, such as brain cancer, skin cancer, and urinary-tract conditions, among others.").
Claim 4
Regarding Claim 4, Codd in view of Bruneel, further in view of Schick teaches the method of claim 1,
wherein scanning the cavity is with optical coherence tomography or micro computed tomography (Codd in ¶14 discloses "In some embodiments, one or more of a variety of types of surface profilers, such as an interferometer, optical coherence tomography (OCT) device … one or more additional feedback modalities (e.g., … optical coherence tomography, … computed tomography (CT), etc.) are used to evaluate the intra-procedural and post-procedural condition of the surgical site").
Claim 5
Regarding Claim 5, Codd in view of Bruneel, further in view of Schick teaches the method of claim 1,
wherein the method further includes creating a sequence of cross-sectional images of the cavity (Codd in ¶26 discloses "FIG.8 depict a series of cross sections of planned and realized serial cutting paths through a surgical site, respectively, in accordance with the illustrative embodiment." ).
Claim 6
Regarding Claim 6, Codd in view of Bruneel, further in view of Schick teaches the method of claim 5,
wherein the sequence of cross-sectional images is filtered, segmented, and concatenated to create the three-dimensional surface for the cavity (Codd in ¶38 discloses "GUI 114 is configured to enable the user to specify a classification for at least one region within a 3D model of the surgical site";¶58 discloses "an intra-procedural scan of the surgical site is performed using any of a variety of alternative imaging modalities, including, without limitation, … CT" - cross-sectional images are an inherent in CT output, a GUI that allows the user to specify a classification for at least one region is segmentation under BRI, and the 3D rendering of the CT segmentation is the concatenation step).
Claim 9
Regarding Claim 9, Codd in view of Bruneel, further in view of Schick teaches the method of claim 1, wherein predicting the post-ablation surface further includes
projecting a point on the pre-ablation surface to a laser reference plane (Codd in ¶29 and 43 discloses "store one or more pre-generated three-dimensional (3D) maps of surgical site … the 3D model is based on a pre-procedural image of surgical site 116, which is generated using magnetic resonance imaging (MRI), … and the like" - pre-ablation model with 3D spatial source; ¶29 discloses "surgical laser 104, guidance laser 106, surface profiler 110, and beam scanner 112, respectively, receive measurement data from surface profiler 110, generate a desired path through surgical site" - all have their own coordinate systems and targeting planes, and has to register (align/project) points from the 3D map to the laser's targeting/scan plane);
calculating a projected distance to a laser center on the gaussian- based model (Codd in ¶65 discloses "r is the distance from the center of the beam … the laser signal is a Gaussian beam").
Claim 10
Regarding Claim 10, Codd in view of Bruneel, further in view of Schick teaches the method of claim 9,
wherein predicting the post-ablation surface further includes determining a predicted depth of cut based on the projected distance to the laser center (Codd in ¶56-57 and 65 discloses "the position at which the reflection signal hits the detector array is dependent upon the range (i.e., depth into surgical site) … range signal 138, which includes triangular- sensor data for each of the plurality of points. In order convert the triangular-sensor data into a range value for each point, each distance sample is correlated with the angular position of beam scanner 112 at the instant that sample was measured … r is the distance from the center of the beam … the laser signal is a Gaussian beam" - Because Gaussian beams have a known radial fluence profile, and Codd measures the projected distance from the beam center for each point, the system determines the depth of material removal where cut depth is directly related to the delivered fluence, which is determined by the projected distance to the beam center in a Gaussian beam.).
Claim 11
Regarding Claim 11, Codd in view of Bruneel, further in view of Schick teaches the method of claim 10, wherein predicting the post-ablation surface further includes collecting predicted depth of cuts for all points on the pre-ablation surface to generate the predicted post-ablation surface (Codd in ¶56-57 and 65 discloses the system scans "a plurality of points" on the surface, correlates each point's distance to beam center and depth (range), and uses the Gaussian beam profile to determine depth at each location. Applying the predicted depth values across the entire surface modifies the pre-ablation 3D map into a post-ablation geometry, which is consistent with the Codd's mapping, modeling, and simulation steps).
Claim(s) 12, 14, 17-19, and 22 is/are rejected under 35 U.S.C. 103 as obvious over Vakharia et al (“Automated trajectory planning for laser interstitial thermal therapy in mesial temporal lobe epilepsy”, hereafter referred to as Vakharia) in view of Baerentzen et al (“Robust generation of signed distance fields from triangle meshes”, hereafter referred to as Baerentzen).
Claim 12
Regarding Claim 12, Vakharia teaches A method comprising:
providing a pre-ablation surface (Section 2.2.1 discloses pre-op MRI to build a 3D model of the current brain anatomy before ablation);
labeling a three-dimensional obstacle boundary that separates material to be removed by a laser and material to remain (Section 2.2.3 and Fig. D discloses marking a target to remove and marking critical structures to protect);
predicting a plurality of post-ablation surfaces for a plurality of orientations of the laser (Section 2.2.1 discloses generating an optimal ablation trajectory for brain surgery);
generating a Euclidean distance transform metric for each of the plurality of post-ablation surfaces (Section 2.2.1 discloses automatically creating possible trajectories and predicting the resulting ablation zone);
wherein the Euclidean distance transform metric is a measurement of a distance between a query point on the post-ablation surface to the closest point on the three-dimensional obstacle boundary (Section 2.2.1 discloses considering distance to critical structures in the overall risk analysis for each trajectory);
determining an optimum orientation of the laser from the plurality of orientations of the laser that results in a predicted post-ablation surface that does not intersect the three-dimensional obstacle boundary and maximizes the Euclidean distance transform metric between the predicted post-ablation surface and the three-dimensional obstacle boundary (Section 2.2.1-2.2.2 discloses rejecting any trajectory that risks hitting protected structures and picks the one with lowest overall risk while maximizing target ablation);
moving the laser to the optimum orientation; and energizing the laser while positioned at the optimum orientation (Introduction and 4.4 discloses clinicians place laser along optimal trajectory and then turn on the laser to ablate).
Vakharia does not explicitly teach all of generating a Euclidean distance transform metric for each of the plurality of post-ablation surfaces, wherein the Euclidean distance transform metric is a measurement of a distance between a query point on the post-ablation surface to the closest point on the three-dimensional obstacle boundary.
However, Baerentzen teaches generating a Euclidean distance transform metric for each of the plurality of post-ablation surfaces, wherein the Euclidean distance transform metric is a measurement of a distance between a query point on the post-ablation surface to the closest point on the three-dimensional obstacle boundary (Baerentzen in Abstract discloses generating a Euclidean distance field/transform from a 3D triangle mesh, where the metric at each query point is the shortest distance to the closet point on the mesh boundary)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Vakharia by generating a Euclidean distance transform metric computed from signed distance fields to accurately measure the shortest distance from point on predicted post-ablation surfaces to the closest point on the obstacle boundary that is taught by Baerentzen, since both reference are analogous art in the field of 3D computational geometry analysis for surgical planning; thus, one of ordinary skilled in the art would be motivated to combine the references since Vakharia’s automated prediction of post-ablation ablation zones for multiple laser trajectories together with distance-based risk evaluation and selection of an optimum orientation based on clearance to protected structures with Baerentzen’s explicit generation of Euclidean distance fields from 3D triangle meshes yields the predictable result of precise and robust minimum-distance queries that enforce no intersections with the labeled obstacle boundary while maximizing clearance between points on the predicted post-ablation surface and the boundary, thereby providing accurate optimum laser orientation for procedures.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 14
Regarding Claim 14, Vakharia in view of Baerentzen teaches the method of claim 12,
wherein predicting the plurality of post-ablation surface utilizes a gaussian-based model for the laser (Vakharia in 2.2.3 teaches predicting post-ablation surfaces using an empirical 15mm diameter model validated against actual ablations. The reference’s own data-driven validation supports radial symmetric profiles consistent with laser beam physics).
Claim 17
Regarding Claim 17, Vakharia in view of Baerentzen teaches wherein a raw distance value from the Euclidean distance transform metric is post-processed to an oriented distance value with a negative value if the query point crosses the three-dimensional obstacle boundary (Baerentzen in Abstract and Introduction discloses post-processing raw Euclidean distances to signed/oriented values, with negative distances indicating the query point is inside/crossing the boundary).
Claim 18
Regarding Claim 18, Vakharia in view of Baerentzen teaches the method of claim 12, wherein the Euclidean distance transform metric is converted to an obstacle cost (Vakharia in 2.2.2 teaches converting distance/proximity measurements into a cumulative risk cost for optimization).
Claim 19
Regarding Claim 19, Vakharia in view of Baerentzen teaches the method of claim 18, wherein the obstacle cost is minimized with a gradient-based constrained optimization method (Vakharia in 2.2 teaches multi-objective constrained optimization that minimizes risk cost while respecting hard constraints and maximizing coverage).
Claim 22
Regarding Claim 22, Vakharia in view of Baerentzen teaches the method of claim 12,
wherein the pre-ablation surface is tissue, the three- dimensional obstacle boundary separates tissue to be remove by the laser and tissue to remain; and wherein the laser is energized for laser-tissue resection (Vakharia in Summary, 2.2, 2.2.3 teaches the method on human brain tissue, separating tissue to remove from tissue to remain with laser energizing for resection via LiTT).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN P CASCAIS whose telephone number is (703) 756-5576. The examiner can normally be reached Monday-Friday 8:00-4:00.
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/J.P.C./Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
Date: 2/17/2026