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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/26/2026 has been entered.
This Office Action is in response to claims filed on 06/26/2026.
Claims 1, 2 and 4-5 are pending.
Claim 3 was cancelled.
Claims 1 and 5 are amended.
Claim Rejections - 35 USC § 112
Applicant’s arguments and amendments, see pages 5-6 of the remarks, filed 06/26/2026, with respect to claims 1,2 and 4-5 have been fully considered and are persuasive. The rejections under 35 USC 112 of claims 1-5 have been withdrawn.
Claim Rejections - 35 USC § 102
Applicant’s arguments and amendments, see pages 6-7 of the remarks, filed 06/26/2026, with respect to claims 1,2 and 4-5 have been fully considered and are persuasive. The rejections under 35 USC 112 of claims 1,2 and 4-5 have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of William A. P. Smith, NPL, “3D Data Representation, Storage and Processing”, Published: 2020.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Dmytro Derkach, NPL “Tensor Decomposition and Non-linear Manifold Modeling for 3D Head Pose Estimation”, Published: 13 August 2019, (hereafter Derkach), in views of William A. P. Smith, NPL, “3D Data Representation, Storage and Processing”, Published: 2020 (hereafter Smith).
Regarding claim 1. Derkach teaches a method for pre-processing geometric model data of a three-dimensional (3D) modeling software for deep learning (Page 1578, col 2, compared to deep neural network)(Page 1582, col 1, methods based on DNN, Deep learning methods),
the geometric model data being related to presenting a 3D model that is created using the 3D modeling software and containing data related to at least one geometric object in the 3D model (Page 1575, fig 7, 3D model)(Page 1572. Fig 4, geometric models dataset),
the 3D model having a predetermined spatial coordinate system that has a first axis, a second axis and a third axis which are mutually perpendicular to each other (Page 1576, col 1, facial landmarks are available, their coordinates are used as input features)(Page 1568, fig 1, I1, I2, I3 ),
each of the at least one geometric object being assigned a property (Page 1570, fig 2, geometric object, having different properties of pose, rotation on the vertical axis), the method to be implemented by a processor and comprising steps of:
determining the smallest value of dimension of the at least one geometric object (Page 1572, col 1, Decompose using HOSVD, Compute W=, );
determining a size of a virtual grid that is visualized as a cube having a side length equal to the smallest value of dimension (Page 1568, fig 1, cube)(Page 1569, col 1, Dy, Dp, Dr, bins)(Page 1572, col 2, 3-D tensor of size 20x72x1024, based on the number of objects within the database (20 geometric objects))(Page 1576, col 2, 5D tensor, 28 subjects, 40 bins yaw and pitch, 30 bins roll, and 36 dimensional features, 12 landmarks, 3 coordinates);
generating an empty tensor that is visualized as a cuboid consisting of a plurality of the virtual grids, the cuboid having a size that is determined based on a largest value of dimension among the values of dimension of the at least one geometric object along the first, the second and the third axes (Page 1576, col 2, perform dimensionality reduction, U(y), U(p), U(r))(Page 1572, col 2, 3-D tensor of size 20x72x1024, based on the number down sampling of the objects within the database (32x32 pixels=1024));
assigning an initial value to each of the virtual grids of the empty tensor (Page 1572, col 1, D(*), number of bins to discretize);
for those of the virtual grids of the empty tensor that each correspond to one of the at least one geometric object (Page 1576, col 2, 5D tensor, 28 subjects),
replacing the initial value of each of those of the virtual grids with a pre- determined identification attribute value that corresponds uniquely to the property of the corresponding one of the at least one geometric object (Page 1572, Algorithm 2, initialize w, and u with zeros, in the for loop the values are replaced with attributes of the object);
generating a 3D geometric model tensor from the empty tensor (Page 1572, Algorithm 2, estimate angles from the initialized variables);
using the 3D geometric model tensor as an input for deep learning (Page 1572, col 1, Decompose using HOSVD, Compute W=, ); and
saving the 3D geometric model tensor in a database in a predefined format (Page 1579, store the data tensor, implies storing the data of 28 subjects).
Derkach does not teach determining a smallest value of dimension among values of dimension of the at least one geometric object along the first, the second and the third axes.
Smith teaches determining a smallest value of dimension among values of dimension of the at least one geometric object along the first, the second and the third axes (Page 281, sec 6.2.3.1, representation for voxel data is the octree, the key idea, fully occupied are not divided, partially occupied are divided, until either all voxels are occupied or empty, smallest allowable voxel size) (Page 282, fig 6.8, octree adaptive representation, where smallest voxels are used) (Page 297, fig 6.17, different voxels size, being used with different colors for properties, extraordinary points are shown in blue).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Derkach to incorporate the teachings of Smith to determine the smallest value dimension for the geometric object along the axes because it represents 3D data having different subdivisions and assigning properties for different surfaces (Smith, Page 265, abstract).
Regarding claim 2. Derkach and Smith teach the method of claim 1, the at least one geometric object as a whole having a first maximum value of dimension along the first axis, a second maximum value of dimension along the second axis, and a third maximum value of dimension along the third axis (Page 1574, max, 255 for 8 bit images)(Page 1580, Number of bins),
wherein the step of generating an empty tensor includes using the first maximum value of dimension to serve as a length of the cuboid, using the second maximum value of dimension to serve as a height of the cuboid, and using the third maximum value of dimension to serve as a width of the cuboid (Page 1580, Table 4 and table 5, num of bins, 40X40X30).
Regarding claim 3. Cancelled.
Regarding claim 4. Derkach and Smith teach the method of claim 2, wherein the step of generating an empty tensor includes, with respect to each of the values of dimension of the at least one geometric object, dividing the value of dimension by the side length of the virtual grid (Page 1573, fig 5, each object is divided for different angles, thus the bins for the attribute)(Page 1570, fig 2, dividing the object into different angles, around the vertical axis), and,
in the case that the value of dimension of the at least one geometric object is not divisible by the side length of the virtual grid (Page 1573, fig 5, 360 degrees divided by 17 images, having a rounded interval of 20 degrees),
rounding a quotient up to an integer unconditionally (Page 1573, fig 5, divided for every 20 degrees).
Regarding claim 5. Derkach teaches a system for pre-processing geometric model data of a 3D modeling software for deep learning (Page 1578, col 2, compared to deep neural network)(Page 1582, col 1, methods based on DNN, Deep learning methods), the geometric model data being related to presenting a 3D model that is created using the 3D modeling software and containing data related to at least one geometric object in the 3D model (Page 1575, fig 7, 3D model)(Page 1572. Fig 4, geometric models dataset), the 3D model having a predetermined spatial coordinate system that has a first axis, a second axis and a third axis which are mutually perpendicular to each other (Page 1576, col 1, facial landmarks are available, their coordinates are used as input features)(Page 1568, fig 1, I1, I2, I3 ),
each of the at least one geometric object being assigned a property (Page 1570, fig 2, geometric object, having different properties of pose, rotation on the vertical axis), the system comprising:
a storage device having stored therein the 3D modeling software, the geometric model data, and a pre-processing module; and a processor electrically connected to said storage device (Page 1579, col 2, compute, implies storing the data, thus a computer comprising storage for computing), and when executing the pre- processing module being configured to
determining the smallest value of dimension of the at least one geometric object (Page 1572, col 1, Decompose using HOSVD, Compute W=, );
determine a size of a virtual grid that is visualized as a cube having a side length equal to the smallest value of dimension (Page 1568, fig 1, cube)(Page 1569, col 1, Dy, Dp, Dr, bins)(Page 1572, col 2, 3-D tensor of size 20x72x1024, based on the number of objects within the database (20 geometric objects))(Page 1576, col 2, 5D tensor, 28 subjects, 40 bins yaw and pitch, 30 bins roll, and 36 dimensional features, 12 landmarks, 3 coordinates),
generate an empty tensor that is visualized as a cuboid consisting of a plurality of the virtual grids, the cuboid having a size that is determined based on a largest value of dimension among the values of dimension of the at least one geometric object along the first, the second and the third axes (Page 1576, col 2, perform dimensionality reduction, U(y), U(p), U(r))(Page 1572, col 2, 3-D tensor of size 20x72x1024, based on the number down sampling of the objects within the database (32x32 pixels=1024));
assign an initial value to each of the virtual grids of the empty tensor (Page 1572, col 1, D(*), number of bins to discretize);
for those of the virtual grids of the empty tensor that each correspond to one of the at least one geometric object (Page 1576, col 2, 5D tensor, 28 subjects),
replace the initial value of each of those of the virtual grids with a pre- determined identification attribute value that corresponds uniquely to the property of the corresponding one of the at least one geometric object (Page 1572, Algorithm 2, initialize w, and u with zeros, in the for loop the values are replaced with attributes of the object);
generate a 3D geometric model tensor from the empty tensor(Page 1572, Algorithm 2, estimate angles from the initialized variables);
use the 3D geometric model tensor as an input for deep learning (Page 1572, col 1, Decompose using HOSVD, Compute W=, ); and
save the 3D geometric model tensor in a database in a predefined format (Page 1579, store the data tensor, implies storing the data of 28 subjects).
Derkach does not teach determining a smallest value of dimension among values of dimension of the at least one geometric object along the first, the second and the third axes.
Smith teaches determining a smallest value of dimension among values of dimension of the at least one geometric object along the first, the second and the third axes (Page 281, sec 6.2.3.1, representation for voxel data is the octree, the key idea, fully occupied are not divided, partially occupied are divided, until either all voxels are occupied or empty, smallest allowable voxel size) (Page 282, fig 6.8, octree adaptive representation, where smallest voxels are used) (Page 297, fig 6.17, different voxels size, being used with different colors for properties, extraordinary points are shown in blue).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Derkach to incorporate the teachings of Smith to determine the smallest value dimension for the geometric object along the axes because it represents 3D data having different subdivisions and assigning properties for different surfaces (Smith, Page 265, abstract).
Conclusion
The prior art made of record, listed on PTO-892, and not relied upon is considered pertinent to applicant's disclosure.
Shaobo Xia, NPL, “Geometric Primitives in LiDAR Point Clouds: A Review”, discloses categorizing geometric primitives into classes, geometric primitive extraction having real world constraints.
Ran Cheng, NPL, “A Sparse Semantic Scene Completion Network for LiDAR Point Clouds”, discloses a sparse convolution based neural network that predicts the semantically completed scene from a single, unified LiDAR point cloud. Further discloses efficiently learns features from sparse 3D data, segmenting cuboids with semantic labels.
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/A.C./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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