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 .
Response to Amendment
Claims 1-25 remain pending. Amendments made to claims overcome all previously held 101 rejections.
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.
Claims 1 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn et al. (Hahn, Oliver, and Tom Abel. "Multi-scale initial conditions for cosmological simulations." Monthly Notices of the Royal Astronomical Society 415.3 (2011): 2101-2121.) in view of Zheng (CN111028335A, published 04/17/2020), and Liu (CN109983507A, published 07/05/2019).
With respect to claim 1, Hahn et al. teaches selecting elements of a multi-scale kernel according to resolutions in an adaptive grid (see page 5 section 2.3.2 paragraphs 1 and 2: The kernel is defined by refinement level (resolution) according to refinement grid ( adaptive grid). ), conduct convolutions on the adaptive grid with the selected elements of the multi- scale kernel (see page 2 paragraph 2: “…multi-scale algorithm combined with an adaptive multi-grid algorithm…”),
Hahn et al. does not explicitly teach generating a signed distance field based on convolutions, a computer system comprising a network controller, a processor coupled to a network controller, nor a memory coupled to the processor, the memory including a set of instructions.
Zheng teaches generating a signed distance field based on convolutions (“the voxel information deep convolutional neural network of the invention uses need to cloud as an input, and outputs the corresponding SDF”, page 3 lines 29-30).
Zheng is analogous art to the claimed invention reasonably pertinent to the problem of describing the surface of a scanned object using a distance field. Zheng is directed to a patch reconstruction method that uses 3D point cloud data and a deep convolutional neural network model (“The purpose of the invention is aiming at the disadvantage of the current technology and provides a high efficient and high quality using point cloud data of patch reconstruction method based on deep learning.” Page 2 lines 12-13) to generate a signed field distance (“the voxel information deep convolutional neural network of the invention uses need to cloud as an input, and outputs the corresponding SDF”, page 3 lines 29-30). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to utilize the teachings of Zheng’s SDF in combination with the teachings of Hahn et al’s galaxy clusters and density profiles, with the expectation that doing so would increase the greater efficiency of the system (“The beneficial effects of the invention are as follows: the invention uses strong fitting capability of deep learning, fusion according to the corresponding to the input voxel point cloud to generate SDF, which avoids complex geometry operations, and SDF calculated by the sub-block of the SDF of integrated complete to improve the calculating reliability of SDF is obtained. method at the same time, the text block may be processed in parallel for each individual block, so the invention has high efficiency.”, page 3 lines 19-23).
Liu teaches a computer system comprising a network controller (“network controller”, page 6 paragraph 4 line 6), a processor coupled to a network controller (“In some embodiments, ICH 130 such that peripheral component connected to the memory device 120 and the processor” page 6 paragraph 4 lines 1-2 and “network controller 134 also may be coupled to the ICH 130.” Page 6 paragraph 4 lines6 -7), and a memory coupled to the processor, the memory including a set of instructions (“may include stored thereon one or more machine readable medium embodying machine executable instructions, the machine executable instructions... machine readable medium may include, but are not limited to: floppy diskettes, optical disks, CD-ROMs (compact disk-read only memory), and magnetooptical disks, ROMs, RAMs, EPROM (erasable programmable read only memory), EEPROM (electrically erasable programmable read-only memory), magnet or optical cards, flash memory, or adapted to store machine-readable medium executing other types of non-transitory machine instruction” page 4, lines 1-2 and 4-8).
Liu is analogous art in the same field of endeavor as the claimed invention. Liu is directed to solving 3D image based problems using CNNs, more specifically it uses CNN regression logic allowing the relocation of large structures owing to the use of 3D point clouds (“CNN regression logic for the large structure to execute relocation” page 20 paragraph 4 lines 1-2 and “generate a 3D point cloud (PCi). 3D point cloud is to realize volume in its location and/or spatial relocation of representation. from may be used based on six-dimensional pose estimation and associated visual data movement technology of shape (e.g., using camera pose estimation and analysis of spatial change and time change associated with the capture of the image sequence) to generate a 3D point cloud” page 21 lines 18-22). Although, the structure of a network controller and memory is not explicitly mentioned in Hahn et al. or Zheng, a person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the teachings of Hahn et al and Zheng, with Liu, with the expectation that employing such structure could lead to the effective execution of the process disclosed by Hanh et al. and Zheng, since Liu details a reasonably pertinent and analogous process, itself (“the memory device 120 can operate as a system memory of the system 100, to store data 122 and instructions 121 for when the one or more processors 102 executing application or process” page 6 paragraph 3 lines 3-5).
With respect to claim 7, Hahn et al. teaches selecting elements of a multi-scale kernel according to resolutions in an adaptive grid (see page 5 section 2.3.2 paragraphs 1 and 2: The kernel is defined by refinement level (resolution) according to refinement grid ( adaptive grid).), conduct convolutions on the adaptive grid with the selected elements of the multi- scale kernel (see page 2 paragraph 2: “…multi-scale algorithm combined with an adaptive multi-grid algorithm…”),
Hahn et al. does not explicitly teach generating a signed distance field based on convolutions, nor at least one non-transitory computer readable storage medium comprising a set of instructions.
Zheng teaches generating a signed distance field based on convolutions (“the voxel information deep convolutional neural network of the invention uses need to cloud as an input, and outputs the corresponding SDF”, page 3 lines 29-30).
Zheng is analogous art to the claimed invention reasonably pertinent to the problem of describing the surface of a scanned object using a distance field. Zheng is directed to a patch reconstruction method that uses 3D point cloud data and a deep convolutional neural network model (“The purpose of the invention is aiming at the disadvantage of the current technology and provides a high efficient and high quality using point cloud data of patch reconstruction method based on deep learning.” Page 2 lines 12-13) to generate a signed field distance (“the voxel information deep convolutional neural network of the invention uses need to cloud as an input, and outputs the corresponding SDF”, page 3 lines 29-30). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to utilize the teachings of Zheng’s SDF in combination with the teachings of Hahn et al’s galaxy clusters and density profiles, with the expectation that doing so would increase the greater efficiency of the system (“The beneficial effects of the invention are as follows: the invention uses strong fitting capability of deep learning, fusion according to the corresponding to the input voxel point cloud to generate SDF, which avoids complex geometry operations, and SDF calculated by the sub-block of the SDF of integrated complete to improve the calculating reliability of SDF is obtained. method at the same time, the text block may be processed in parallel for each individual block, so the invention has high efficiency.”, page 3 lines 19-23).
Liu teaches at least one non-transitory computer readable storage medium comprising a set of instructions (“may include stored thereon one or more machine readable medium embodying machine executable instructions, the machine executable instructions... machine readable medium may include, but are not limited to: floppy diskettes, optical disks, CD-ROMs (compact disk-read only memory), and magnetooptical disks, ROMs, RAMs, EPROM (erasable programmable read only memory), EEPROM (electrically erasable programmable read-only memory), magnet or optical cards, flash memory, or adapted to store machine-readable medium executing other types of non-transitory machine instruction” page 4, lines 1-2 and 4-8).
Liu is analogous art in the same field of endeavor as the claimed invention. Liu is directed to solving 3D image based problems using CNNs, more specifically it uses CNN regression logic allowing the relocation of large structures owing to the use of 3D point clouds (“CNN regression logic for the large structure to execute relocation” page 20 paragraph 4 lines 1-2 and “generate a 3D point cloud (PCi). 3D point cloud is to realize volume in its location and/or spatial relocation of representation. from may be used based on six-dimensional pose estimation and associated visual data movement technology of shape (e.g., using camera pose estimation and analysis of spatial change and time change associated with the capture of the image sequence) to generate a 3D point cloud” page 21 lines 18-22). Although, the structure of memory is not explicitly mentioned in Hahn et al. or Zheng, a person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the teachings of Hahn et al and Zheng, with Liu, with the expectation that employing such structure could lead to the effective execution of the process disclosed by Hanh et al. and Zheng, since Liu details a reasonably pertinent and analogous process, itself (“the memory device 120 can operate as a system memory of the system 100, to store data 122 and instructions 121 for when the one or more processors 102 executing application or process” page 6 paragraph 3 lines 3-5).
Claims 2, 4-5, 8, and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn et. al, Zheng, and Liu as applied to claim 1 above, and further in view of Kim et al (Kim, Theodore, and Ming C. Lin. "Fast animation of lightning using an adaptive mesh." IEEE Transactions on Visualization and Computer Graphics 13.2 (2007): 390-402.).
With respect to claim 2, Hahn et al., Zheng, and Liu teach the computing system of claim 1. Hahn et al. teaches wherein to select the elements of the multi- scale kernel, when executed, cause the :determining of relative positions of a center cell in the adaptive grid and face-adjacent neighbor cells of the center cell in the adaptive grid (see page 5 section 2.3.2 paragraphs 1 and 2: The kernel is defined by refinement level (resolution) according to refinement grid ( adaptive grid). and Figures 3 and 4, which show the shape of the grid), and wherein two or more of the relative positions are to be different from one another (See Figure 3 and Figure 4, for the shape of the grid), determining relative sizes of the center cell and the face-adjacent neighbor cells (See Figure 3 and Figure 4 for the shape of the grid), and mapping the center cell and the face-adjacent neighbor cells to the elements of the multi-scale kernel based on the relative positions and the relative sizes (see page 5 section 2.3.2 paragraphs 1 and 2: The kernel is defined by refinement level (resolution) according to refinement grid ( adaptive grid) and Figures 3 and 4, which show the shape of the grid).
Hahn et al. does not teach wherein the center cell is to correspond to a center element in the multi-scale kernel.
Kim et al. teaches wherein the center cell is to correspond to a center element in the multi-scale kernel (“In order to compute the value I(T, µ) at each point on the kernel, we need to determine a value µ at each sample. If we assume the point light source projects onto the center of the kernel, the µ value at kernel sample (x, y) follows by trigonometry (Eqns. 10 - 12)”).
Kim et al. is analogous art in the same field of endeavor as the current invention. Kim et al. is directed to applying convolution to an adaptive grid (“The DBM can be very expensive to compute, so we introduce a fast version of DBM using an adaptive mesh.” Page 1 paragraph 2). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the system of Hahn et al., Zheng, and Liu with the teachings of Kim et al., by utilizing Kim et al’s teaching of center importance and mesh shape, with the expectation that doing so would lead to increases in performance and memory usage (“While the adaptive grid solver can greatly decrease the memory footprint and running time of the simulation, we still encounter problems in the presence of complex boundaries. In this case, the adaptive solver can perform on par with or slightly worse than the regular grid case. Alternate particle-based methods might be able to alleviate some of these problems” page 11 section 10 paragraph 2).
With respect to claim 4, Hahn et al, Zheng, Liu and Kim et al. teach the computing system of claim 2. Hahn et al. further teaches the computing system of claim 2, wherein two or more of the relative sizes are to be different from one another (See Figure 3 and Figure 4, for the shape of the grid).
With respect to claim 5, Hahn et al, Zheng, Liu and Kim et al. teach the computing system of claim 2. Kim et al. further teaches the computing system of claim 2, wherein the relative sizes are to be equal to one another (“However, as the grid cell is homogeneous,…” page 10 paragraph 1 ).
With respect to claim 8, Hahn et al, Zheng, and Liu teach the at least one non-transitory computer readable storage medium of claim 7 and render obvious all additional limitations in view of Kim et al., in consideration of claim 2, because of their substantial similarity.
With respect to claim 10, Hahn et al, Zheng, Liu and Kim et al. teach the at least one non-transitory computer readable storage medium of claim 8 and render obvious all additional limitations in in consideration of claim 4, because of their substantial similarity.
With respect to claim 11 Hahn et al, Zheng, Liu and Kim et al. teach the at least one non-transitory computer readable storage medium of claim 8 and render obvious all additional limitations in in consideration of claim 5, because of their substantial similarity.
Claims 3 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn et al. , Zheng, Liu and Kim et al. as applied to claims 2 and 8, respectfully, and further in view of Pfister (DE69924230T2, published 03/30/2006).
With respect to claim 3, Hahn et al., Zheng, Liu, and Kim et al. teach the computing system of claim 2, but do not teach the computing system of claim 2, wherein the center cell and the face- adjacent neighbor cells are mapped to the elements of the multi-scale kernel via a hash table, and wherein the hash table is to include a plurality of spatial configuration keys.
Pfister teaches the center cell and the face- adjacent neighbor cells mapped to the elements of the multi-scale kernel via a hash table (”if a surface element is added to the sequential list, its position in a hash table stored according to his position” page 10 lines 21-22 and “If this surface element is not limited due to the camera position, then the position of the surface element corresponding to a viewing matrix M” page 18 lines 19-20 Viewing matrix as Kernel), and wherein the hash table is to include a plurality of spatial configuration keys (“if a surface element is added to the sequential list, its position in a hash table stored according to his position” page 10 lines 21-22).
Pfister is analogous art in the same field of endeavor as the claimed invention. Pfister is directed towards a graphic system specifically the modeling of graphical objects (“It is a method for modeling a representation of a graphical Object” page 4 line 16). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the system described by Hahn et al., Zheng, Liu and Kim et al., with the use of Pfister’s hash table and spatial configuration keys with the expectation that doing so would lead to a simplification of the process (“…their Simplicity. The method can be performed with integer arithmetic, to calculate new positions along a line”, page 12 lines 8-9).
With respect to claim 9, Hahn et al, Zheng, Liu and Kim et al. teach the at least one non-transitory computer readable storage medium of claim 8 and render obvious all additional limitations in view of Pfister, in consideration of claim 3, because of their substantial similarity.
Claims 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn et al., Zheng, and Liu as applied to claims 1 and 7, respectfully, and further in view of Bressler (WO2019012539A1, published 01/17/2019).
With respect to claim 6, Hahn et al., Zheng, and Liu teach the computing system of claim 1 but do not teach the computing system of claim 1, wherein the instructions, when executed, further cause the computing system to generate the adaptive grid based on one or more point clouds, wherein the adaptive grid is to contain data at multiple resolutions, and wherein the signed distance field is to describe a surface of a scanned object.
Bressler teaches generating an adaptive grid based on one or more point clouds, wherein the adaptive grid is to contain data at multiple resolutions (“Methods and systems are provided, which convert points in a point cloud into a model for 3D printing in a computationally efficient manner and while maintaining and possibly adjusting shape, volume and color information. Methods include deriving, from the points, a crude mesh which is a watertight alpha shape with respect to the points”, page 4 paragraph 0026 lines 1-4), and wherein the signed distance field is to describe a surface of a scanned object (“Similarly, an embodiment may determine that a point is on, or included in a mesh, e.g., if the signed distance field is zero around the point”, page 10 paragraph 0063 lines 8-9).
Bressler is analogous art in the same field of endeavor as the claimed invention. It is directed towards 3D modeling, more specifically printing a 3D model from point cloud data (“The present invention relates generally to three-dimensional (3D) printing. More specifically, the present invention relates to generating a printable file based on point cloud input” page 2 paragraph 0001). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the teachings of Hahn et al., Zheng, and Liu with Bressler, by utilizing Bressler’s point cloud and surface teachings with the expectation that doing so would lead to better quality and more accurate 3D information gathered from objects (“Methods and systems are provided, which convert points in a point cloud into a model for 3D printing in a computationally efficient manner” page 4 paragraph 0026 lines 1-2 and “Combining the computational geometry approach with the field approach is synergetic and results in better information content of the resulting model for 3D printing while consuming less computational resources” page 4 paragraph 0026 lines 6-8).
With respect to claim 12, Hahn et al, Zheng, and Liu. teach the at least one non-transitory computer readable storage medium of claim 7 and render obvious all additional limitations in view of Bressler, in consideration of claim 6, because of their substantial similarity.
Claims 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn et al. in view of Zheng, and Schmidt (US20190134915A1, published 05/09/2019).
With respect to claim 13, Hahn et al. teaches selecting elements of a multi-scale kernel according to resolutions in an adaptive grid (see page 5 section 2.3.2 paragraphs 1 and 2: The kernel is defined by refinement level (resolution) according to refinement grid ( adaptive grid).),conduct convolutions on the adaptive grid with the selected elements of the multi- scale kernel (see page 2 paragraph 2: “…multi-scale algorithm combined with an adaptive multi-grid algorithm…”)
Hahn et al. does not explicitly teach generating a signed distance field based on convolutions, a computer system comprising a network controller, a processor coupled to a network controller, nor a memory coupled to the processor, the memory including a set of instructions.
Zheng teaches generating a signed distance field based on convolutions (“the voxel information deep convolutional neural network of the invention uses need to cloud as an input, and outputs the corresponding SDF”, page 3 lines 29-30).
Zheng is analogous art to the claimed invention reasonably pertinent to the problem of describing the surface of a scanned object using a distance field. Zheng is directed to a patch reconstruction method that uses 3D point cloud data and a deep convolutional neural network model (“The purpose of the invention is aiming at the disadvantage of the current technology and provides a high efficient and high quality using point cloud data of patch reconstruction method based on deep learning.” Page 2 lines 12-13) to generate a signed field distance (“the voxel information deep convolutional neural network of the invention uses need to cloud as an input, and outputs the corresponding SDF”, page 3 lines 29-30). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to utilize the teachings of Zheng’s SDF in combination with the teachings of Hahn et al’s galaxy clusters and density profiles, with the expectation that doing so would increase the greater efficiency of the system (“The beneficial effects of the invention are as follows: the invention uses strong fitting capability of deep learning, fusion according to the corresponding to the input voxel point cloud to generate SDF, which avoids complex geometry operations, and SDF calculated by the sub-block of the SDF of integrated complete to improve the calculating reliability of SDF is obtained. method at the same time, the text block may be processed in parallel for each individual block, so the invention has high efficiency.”, page 3 lines 19-23).
Schmidt teaches a semiconductor apparatus comprising: one or more substrates (“semiconductor memory devices, such as EPROM, EEPROM” page 14 col. 1 paragraph 0052 lines 13-14 silicon as substrate); and logic coupled to the one or more substrates (“semiconductor memory devices, such as EPROM, EEPROM” page 14 col. 1 paragraph 0052 lines 13-14 silicon as substrate), wherein the logic is implemented at least partly in one or more of configurable or fixed-functionality hardware (“The computer program may comprise instructions executable by a computer, the instructions comprising means for causing the above system to perform the method. The program may be recordable on any data storage medium, including the memory of the system. The program may for example be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them.” Page 14 col 1 paragraph 0053 lines 1-8 and “semiconductor memory devices, such as EPROM, EEPROM” page 14 col. 1 paragraph 0052 lines 13-14 silicon as substrate)
Schmidt is analogous art in the same field of endeavor as the claimed invention. It is directed towards computer programs and systems that specifically involve 3D objects (“The invention relates to the field of computer programs and systems, and more specifically to a method, system and program for of additive manufacturing of a three-dimensional (3D) part.” Page 10 col. 1 paragraph 0002). Although, the structure of a semiconductor apparatus with one or more substrates, coupled with logic is not explicitly stated in Hahn et al. or Zheng, a person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious that employing such structure would lead to the effective execution of the process disclosed by Hahn et al. and Zheng, since Schmidt details a reasonably pertinent and analogous process, itself (“the memory having recorded thereon a computer program comprising instructions for performing the method” page 12 col.2 paragraph 0039 lines 4-6 and “The method comprises providing a surface representation of a 3D part in a 3D scene… a signed distance field is obtained. An iso-surface of the 3D part at iso-value zero is computed, the computation being based on the signed distance field. Then a 3D part is additive manufactured” page 11 col.2 paragraph 0034 lines 3-5 and 17-18 and page 12 col.1 lines 1-3).
With respect to claim 19, Hahn et al., Zheng, Schmidt and Kim et al. teach the semiconductor apparatus of claim 13. Schmidt further teaches the semiconductor apparatus of claim 13, wherein the logic coupled to the one or more substrates includes transistor channel regions that are positioned within the one or more substrates (“semiconductor memory devices, such as EPROM, EEPROM” page 14 col. 1 paragraph 0052 lines 13-14 silicon as substrate, architecture as transistor channel).
Claims 14, and 16-17 is rejected under 35 U.S.C. 103 as being unpatentable over Hahn et al., Zheng, and Schmidt as applied to claim 13 above, and further in view of Kim et al.
With respect to claim 14, Hahn et al., Zheng, and Schmidt teach the semiconductor apparatus of claim 13. Hahn et al. further teaches the semiconductor apparatus of claim 13, wherein to select the elements of the multi-scale kernel, the logic is to: determine of relative positions of a center cell in the adaptive grid and face-adjacent neighbor cells of the center cell in the adaptive grid (see page 5 section 2.3.2 paragraphs 1 and 2: The kernel is defined by refinement level (resolution) according to refinement grid ( adaptive grid) and see Figure 3 and Figure 4 for the shape of the grid), and wherein two or more of the relative positions are to be different from one another (See Figure 3 and Figure 4, for the shape of the grid), determinine relative sizes of the center cell and the face-adjacent neighbor cells (See Figure 3 and Figure 4, for the shape of the grid), and map the center cell and the face-adjacent neighbor cells to the elements of the multi-scale kernel based on the relative positions and the relative sizes (see page 5 section 2.3.2 paragraphs 1 and 2: The kernel is defined by refinement level (resolution) according to refinement grid ( adaptive grid). and see Figure 3 and Figure 4, for the shape of the grid).
Hahn et al. does not teach wherein the center cell is to correspond to a center element in the multi-scale kernel.
Kim et al. teaches wherein the center cell is to correspond to a center element in the multi-scale kernel (“In order to compute the value I(T, µ) at each point on the kernel, we need to determine a value µ at each sample. If we assume the point light source projects onto the center of the kernel, the µ value at kernel sample (x, y) follows by trigonometry (Eqns. 10 - 12)”).
Kim et al. is analogous art in the same field of endeavor as the current invention. Kim et al. is directed to applying convolution to an adaptive grid (“The DBM can be very expensive to compute, so we introduce a fast version of DBM using an adaptive mesh.” Page 1 paragraph 2). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the system of Hahn et al., Zheng, and Schmidt with the teachings of Kim et al., by utilizing Kim et al’s teaching of center importance and mesh shape, with the expectation that doing so would lead to increases in performance and memory usage (“While the adaptive grid solver can greatly decrease the memory footprint and running time of the simulation, we still encounter problems in the presence of complex boundaries. In this case, the adaptive solver can perform on par with or slightly worse than the regular grid case. Alternate particle-based methods might be able to alleviate some of these problems” page 11 section 10 paragraph 2).
With respect to claim 16, Hahn et al, Zheng, Schmidt and Kim et al. teach the semiconductor apparatus of claim 14. Hahn et al. further teaches the computing system of claim 2, wherein two or more of the relative sizes are to be different from one another (See Figure 3 and Figure 4, for the shape of the grid).
With respect to claim 17, Hahn et al, Zheng, Schmidt and Kim et al. teach the semiconductor apparatus of claim 14. Kim et al. further teaches the computing system of claim 2, wherein the relative sizes are to be equal to one another (“However, as the grid cell is homogeneous,…” page 10 paragraph 1 ).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Hahn et al., Zheng, Schmidt and Kim et al. as applied to claim 14, and further in view of Pfister.
With respect to claim 15, Hahn et al., Zheng, Schmidt, and Kim et al. teach the semiconductor apparatus of claim 14, but do not teach the semiconductor apparatus of claim 14, wherein the center cell and the face- adjacent neighbor cells are mapped to the elements of the multi-scale kernel via a hash table, and wherein the hash table is to include a plurality of spatial configuration keys.
Pfister teaches the center cell and the face- adjacent neighbor cells mapped to the elements of the multi-scale kernel via a hash table (”if a surface element is added to the sequential list, its position in a hash table stored according to his position” page 10 lines 21-22 and “If this surface element is not limited due to the camera position, then the position of the surface element corresponding to a viewing matrix M” page 18 lines 19-20 Viewing matrix as Kernel), and wherein the hash table is to include a plurality of spatial configuration keys (“if a surface element is added to the sequential list, its position in a hash table stored according to his position” page 10 lines 21-22).
Pfister is analogous art in the same field of endeavor as the claimed invention. Pfister is directed towards a graphic system specifically the modeling of graphical objects (“It is a method for modeling a representation of a graphical Object” page 4 line 16). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the system described by Hahn et al., Zheng, Schmidt and Kim et al., with the use of Pfister’s hash table and spatial configuration keys with the expectation that doing so would lead to a simplification of the process (“…their Simplicity. The method can be performed with integer arithmetic, to calculate new positions along a line”, page 12 lines 8-9).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Hahn et al., Zheng, and Schmidt as applied to claim 13 and further in view of Bressler.
With respect to claim 18, Hahn et al., Zheng, and Schmidt teach the semiconductor apparatus of claim 13, but do not teach the semiconductor apparatus of claim 13, wherein the logic is to generate the adaptive grid based on one or more point clouds, wherein the adaptive grid is to contain data at multiple resolutions, and wherein the signed distance field is to describe a surface of a scanned object.
Bressler teaches generating an adaptive grid based on one or more point clouds, wherein the adaptive grid is to contain data at multiple resolutions (“Methods and systems are provided, which convert points in a point cloud into a model for 3D printing in a computationally efficient manner and while maintaining and possibly adjusting shape, volume and color information. Methods include deriving, from the points, a crude mesh which is a watertight alpha shape with respect to the points”, page 4 paragraph 0026 lines 1-4), and wherein the signed distance field is to describe a surface of a scanned object (“Similarly, an embodiment may determine that a point is on, or included in a mesh, e.g., if the signed distance field is zero around the point”, page 10 paragraph 0063 lines 8-9).
Bressler is analogous art in the same field of endeavor as the claimed invention. It is directed towards 3D modeling, more specifically printing a 3D model from point cloud data (“The present invention relates generally to three-dimensional (3D) printing. More specifically, the present invention relates to generating a printable file based on point cloud input” page 2 paragraph 0001). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the teachings of Hahn et al., Zheng, and Schmidt with Bressler, by utilizing Bressler’s point cloud and surface teachings with the expectation that doing so would lead to better quality and more accurate 3D information gathered from objects (“Methods and systems are provided, which convert points in a point cloud into a model for 3D printing in a computationally efficient manner” page 4 paragraph 0026 lines 1-2 and “Combining the computational geometry approach with the field approach is synergetic and results in better information content of the resulting model for 3D printing while consuming less computational resources” page 4 paragraph 0026 lines 6-8).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Hahn et al. in view of Zheng.
With respect to claim 20, Hahn et al. teaches selecting elements of a multi-scale kernel according to resolutions in an adaptive grid (see page 5 section 2.3.2 paragraphs 1 and 2: The kernel is defined by refinement level (resolution) according to refinement grid ( adaptive grid).),conduct convolutions on the adaptive grid with the selected elements of the multi- scale kernel (see page 2 paragraph 2: “…multi-scale algorithm combined with an adaptive multi-grid algorithm…”).
Hahn et al. does not explicitly teach generating a signed distance field based on convolutions, a computer system comprising a network controller, a processor coupled to a network controller, nor a memory coupled to the processor, the memory including a set of instructions.
Zheng teaches generating a signed distance field based on convolutions (“the voxel information deep convolutional neural network of the invention uses need to cloud as an input, and outputs the corresponding SDF”, page 3 lines 29-30).
Zheng is analogous art to the claimed invention reasonably pertinent to the problem of describing the surface of a scanned object using a distance field. Zheng is directed to a patch reconstruction method that uses 3D point cloud data and a deep convolutional neural network model (“The purpose of the invention is aiming at the disadvantage of the current technology and provides a high efficient and high quality using point cloud data of patch reconstruction method based on deep learning.” Page 2 lines 12-13) to generate a signed field distance (“the voxel information deep convolutional neural network of the invention uses need to cloud as an input, and outputs the corresponding SDF”, page 3 lines 29-30). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to utilize the teachings of Zheng’s SDF in combination with the teachings of Hahn et al’s galaxy clusters and density profiles, with the expectation that doing so would increase the greater efficiency of the system (“The beneficial effects of the invention are as follows: the invention uses strong fitting capability of deep learning, fusion according to the corresponding to the input voxel point cloud to generate SDF, which avoids complex geometry operations, and SDF calculated by the sub-block of the SDF of integrated complete to improve the calculating reliability of SDF is obtained. method at the same time, the text block may be processed in parallel for each individual block, so the invention has high efficiency.”, page 3 lines 19-23).
Claims 21 and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Hahn and Zheng as applied to claim 20 above, and further in view of Kim et al.
With respect to claim 21 Hahn and Zheng teach the method of claim 20. Hahn et al. teaches wherein to select the elements of the multi- scale kernel, when executed, cause the :determining of relative positions of a center cell in the adaptive grid and face-adjacent neighbor cells of the center cell in the adaptive grid (see page 5 section 2.3.2 paragraphs 1 and 2: The kernel is defined by refinement level (resolution) according to refinement grid ( adaptive grid) and see Figure 3 and Figure 4, for the shape of the grid), and wherein two or more of the relative positions are to be different from one another (See Figure 3 and Figure 4, for the shape of the grid), determining relative sizes of the center cell and the face-adjacent neighbor cells (See Figure 3 and Figure 4, for the shape of the grid), and mapping the center cell and the face-adjacent neighbor cells to the elements of the multi-scale kernel based on the relative positions and the relative sizes (see page 5 section 2.3.2 paragraphs 1 and 2: The kernel is defined by refinement level (resolution) according to refinement grid ( adaptive grid) and see Figure 3 and Figure 4, for the shape of the grid).
Hahn et al. does not teach wherein the center cell is to correspond to a center element in the multi-scale kernel.
Kim et al. teaches wherein the center cell is to correspond to a center element in the multi-scale kernel (“In order to compute the value I(T, µ) at each point on the kernel, we need to determine a value µ at each sample. If we assume the point light source projects onto the center of the kernel, the µ value at kernel sample (x, y) follows by trigonometry (Eqns. 10 - 12)”).
Kim et al. is analogous art in the same field of endeavor as the current invention. Kim et al. is directed to applying convolution to an adaptive grid (“The DBM can be very expensive to compute, so we introduce a fast version of DBM using an adaptive mesh.” Page 1 paragraph 2). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the system of Hahn et al., and Zheng with the teachings of Kim et al., by utilizing Kim et al’s teaching of center importance and mesh shape, with the expectation that doing so would lead to increases in performance and memory usage (“While the adaptive grid solver can greatly decrease the memory footprint and running time of the simulation, we still encounter problems in the presence of complex boundaries. In this case, the adaptive solver can perform on par with or slightly worse than the regular grid case. Alternate particle-based methods might be able to alleviate some of these problems” page 11 section 10 paragraph 2).
With respect to claim 23, Hahn et al, Zheng and Kim et al. teach the method of claim 21. Hahn et al. further teaches the computing system of claim 2, wherein two or more of the relative sizes are to be different from one another (See Figure 3 and Figure 4, for the shape of the grid).
With respect to claim 24, Hahn et al, Zheng and Kim et al. teach the method of claim 21.. Kim et al. further teaches the computing system of claim 2, wherein the relative sizes are to be equal to one another (“However, as the grid cell is homogeneous,…” page 10 paragraph 1 ).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Hahn et al., Zheng and Kim et al. as applied to claim 21, respectfully, and further in view of Pfister.
With respect to claim 22, Hahn et al., Zheng, and Kim et al. teach the method of claim 21, but do not teach the method of claim 21, wherein the center cell and the face- adjacent neighbor cells are mapped to the elements of the multi-scale kernel via a hash table, and wherein the hash table is to include a plurality of spatial configuration keys.
Pfister teaches the center cell and the face- adjacent neighbor cells mapped to the elements of the multi-scale kernel via a hash table (”if a surface element is added to the sequential list, its position in a hash table stored according to his position” page 10 lines 21-22 and “If this surface element is not limited due to the camera position, then the position of the surface element corresponding to a viewing matrix M” page 18 lines 19-20 Viewing matrix as Kernel), and wherein the hash table is to include a plurality of spatial configuration keys (“if a surface element is added to the sequential list, its position in a hash table stored according to his position” page 10 lines 21-22).
Pfister is analogous art in the same field of endeavor as the claimed invention. Pfister is directed towards a graphic system specifically the modeling of graphical objects (“It is a method for modeling a representation of a graphical Object” page 4 line 16). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the system described by Hahn et al., Zheng, and Kim et al., with the use of Pfister’s hash table and spatial configuration keys with the expectation that doing so would lead to a simplification of the process (“…their Simplicity. The method can be performed with integer arithmetic, to calculate new positions along a line”, page 12 lines 8-9).
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Hahn et al. and Zheng as applied to claim 20, respectfully, and further in view of Bressler.
With respect to claim 25, Hahn et al., and Zheng, teach the method of claim 20 but do not teach the method of claim 20, wherein the instructions, when executed, further cause the computing system to generate the adaptive grid based on one or more point clouds, wherein the adaptive grid is to contain data at multiple resolutions, and wherein the signed distance field is to describe a surface of a scanned object.
Bressler teaches generating an adaptive grid based on one or more point clouds, wherein the adaptive grid is to contain data at multiple resolutions (“Methods and systems are provided, which convert points in a point cloud into a model for 3D printing in a computationally efficient manner and while maintaining and possibly adjusting shape, volume and color information. Methods include deriving, from the points, a crude mesh which is a watertight alpha shape with respect to the points”, page 4 paragraph 0026 lines 1-4), and wherein the signed distance field is to describe a surface of a scanned object (“Similarly, an embodiment may determine that a point is on, or included in a mesh, e.g., if the signed distance field is zero around the point”, page 10 paragraph 0063 lines 8-9).
Bressler is analogous art in the same field of endeavor as the claimed invention. It is directed towards 3D modeling, more specifically printing a 3D model from point cloud data (“The present invention relates generally to three-dimensional (3D) printing. More specifically, the present invention relates to generating a printable file based on point cloud input” page 2 paragraph 0001). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the teachings of Hahn et al., Zheng, with Bressler, by utilizing Bressler’s point cloud and surface teachings with the expectation that doing so would lead to better quality and more accurate 3D information gathered from objects (“Methods and systems are provided, which convert points in a point cloud into a model for 3D printing in a computationally efficient manner” page 4 paragraph 0026 lines 1-2 and “Combining the computational geometry approach with the field approach is synergetic and results in better information content of the resulting model for 3D printing while consuming less computational resources” page 4 paragraph 0026 lines 6-8).
Response to Arguments
Applicant’s arguments filed 10/06/2025 have been fully considered.
On pages 7 and 8, applicant argues the 103 rejection of claims 1-2, 4-5, 7-8, and 10-11. Applicant asserts that Wang does not teach, a multi-scale kernel, conducting convolutions on the adaptive grid, selecting elements of the single convolution layer, nor applying the selected elements while conducting convolutions. The examiner agrees, but upon further search has updated the rejection.
On pages 8-9, applicant argues the 103 rejection of claims 13-14, 16-17, and 19. Applicant asserts that Wang does not teach, a multi-scale kernel, conducting convolutions on the adaptive grid, selecting elements of the single convolution layer, nor applying the selected elements while conducting convolutions. The examiner agrees, but upon further search has updated the rejection.
On page 9, applicant argues the 103 rejection of claims 20-21 and 23-24. Applicant asserts that Wang does not teach, a multi-scale kernel, conducting convolutions on the adaptive grid, selecting elements of the single convolution layer, nor applying the selected elements while conducting convolutions. The examiner agrees, but upon further search has updated the rejection.
Because the above claims are rejected the examiner, the examiner finds the applicants argument about independent claim 7 (page 8), its dependents, and the dependents of claim 3 (page 9), and 20 (page 9) are considered moot.
Due to the substantial changes made to the rejections necessitating a new non-final rejection, the applicant’s request for interview has been denied.
Conclusion
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/REBECCA COLETTE WILLIAMS/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677