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 .
This Office Action is in response to claims filed on 02/06/2026
Claims 1-5 are pending.
Claim Rejections - 35 USC § 112
Applicant’s arguments, see page 5 of the remarks, filed 02/06/2026, with respect to claims 1-5 have been fully considered and are persuasive. The rejections under 35 USC 112 of claims 1-5 has been withdrawn.
Claim Rejections - 35 USC § 102
Applicant’s arguments, see pages 5-6 of the remarks, filed 02/06/2026, with respect to claims 1-5 have been fully considered and are not persuasive.
Applicant argues, Claim 1 recites “a method comprising: determining a size of a virtual grid based on the smallest value of the geometric object’s dimensions along three axes; and generating an empty tensor having a size that is determined based on the largest value of the geometric object’s dimensions. That is, the size of the virtual grid and the size of the empty tensor are both dynamically determined based on the dimensions of the geometric object.” Examiner notes, the claim does not recite “the size of the virtual grid and the size of the empty tensor are both dynamically determined based on the dimensions of the geometric object”, it only recites “determining the size of a virtual grid based on the smallest value of the geometric object’s dimensions along three axes” and “generating an empty tensor having a size that is determined based on the largest value of the geometric object’s dimensions”, thus determining the size of the virtual grid based on the smallest value (a constant value given by the dataset of 20 objects) and generating an empty tensor having a size that is determined based on the largest value (a constant value given by the down sampling). As applicant notes Derkach “utilizes a predefined and fixed tensor size”, Examiner notes the predefined and fixed tensor was determined based on the smallest value and the largest value of the objects dataset dimensions. Therefore, Derkach determines the size of the virtual grid based on the smallest values of the geometric object’s dimensions along the three axes and generates an empty tensor having a size based on the largest value of the geometric object’s dimensions.
Applicant further argues “assigning an initial value of each virtual grid of the empty tensor and replacing the initial value of each virtual grid that corresponds to the geometric object with a pre-determined identification attribute value that corresponds uniquely to the property of the geometric object”. Applicant notes, Derkach “focuses on mathematical tensor decomposition of depth images into subspaces”. Examiner notes, Derkach on page 1572, algorithm 2, initializes and replaces each value within the loop with pre-defined functions attributes that correspond to the geometric object. Examiner further notes, Derkach discloses on page 1571, equation 14, shows the values of these coefficients separately against the rotation angle and models as vectors of real functions based on cosines, defining a spiral-like structure that analytically represents the underlying manifold. Thus, Derkach initializes the variable attributes and within the loops replaces the values with value coefficients as indicated in equation 14. Claims 5 recites a system having the same technical features as the method claim 1, claim 5 is also taught by Derkach by the same arguments as above. The rejections under 35 USC 102 of claims 1-5 are maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 5 recite the limitation “generating a 3D geometric model tensor from the empty tensor by, for those of the virtual grids”. It is unclear from the claim language whether “a 3D geometric model tensor is generated” or “by” which steps its generated. For compact prosecution, Examiner is interpreting the claim as follows:
“generating a 3D geometric model tensor from the empty tensor by, the virtual grids of the empty tensor that each correspond to one of the at least one geometric object, replacing the initial value of each of those 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;” .
Correction is therefore required.
Claims 2-4 are dependent claims and do not resolve the indefinite issue in the independent claim, and thus, are also rejected under 112(b) by virtue of their dependence on the rejected independent claim.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dmytro Derkach, NPL “Tensor Decomposition and Non-linear Manifold Modeling for 3D Head Pose Estimation”, Published: 13 August 2019, (hereafter Derkach).
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 a size of a virtual grid that is visualized as a cube based on 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 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);
generating a 3D geometric model tensor from the empty tensor by(Page 1572, Algorithm 2, estimate angles from the initialized variables),
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, 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).
Regarding claim 2. Derkach teaches 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. Derkach teaches the method of claim 2, wherein the step of determining a virtual grid includes determining the smallest value of dimension of the at least one geometric object (Page 1572, col 1, Decompose using HOSVD, Compute W=, ), and
making a side length of the virtual grid equal to the smallest value of dimension (Page 1580, Table 4 and table 5, num of bins, 3X3X2).
Regarding claim 4. Derkach teaches 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
determine a size of a virtual grid that is visualized as a cube based on 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 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);
generate a 3D geometric model tensor from the empty tensor by(Page 1572, Algorithm 2, estimate angles from the initialized variables),
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, and 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).
Conclusion
THIS ACTION IS MADE FINAL. 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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/A.C./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189