DETAILED ACTION
This Office Action is a first Office Action on the merits of the application. Claims 1 - 20 are presented for examination. Claims 1 - 5, 11, and 19 are rejected.
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 .
Drawings Objections
The drawings are objected to because the words shown in FIGS. 1, 2, 6, 8, 9, 10, 13, 15, and 16 are blurry and difficult to read. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification Objection
The disclosure is objected to because of the following informalities: Page 34 shows Algorithm 1, however, there are variables show which are blurry and difficult to read. Appropriate correction is required.
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.
Claim 11 is 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.
Claim 11 lacks antecedent basis for “are the points” (Claim 11, line 17).
Suggested language: Amend the claim to recites “are points”.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 5, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (“Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization”), hereinafter “Wang”, and further in view of Dupont De Dinechin (U.S. PG Pub 2021/0004719 A1), hereinafter “Dupont De Dinechin”.
As per claim 1, Wang discloses:
a computer-implemented method of machine-learning comprising obtaining a dataset of 3D modeled objects each representing a mechanical part, each 3D modeled object including a specification of a geometry of the mechanical part (Wang, page 4, left column, lines 12 - 18 discloses generating datasets used to obtain airfoil shape distribution and trained using a type of neural network, and page 4, left column, lines 25 - 34 adds generating three-dimensional samples, three-dimensional surface shapes, and volumetric objects trained using a type of network.)
learning a set of parameterization vectors each respective to a respective 3D modeled object of the dataset (Wang, page 5, right column, lines 23 - 33 discloses the neural network (VAEGAN) model learning regarding airfoil reconstruction and learning latent features for shape parameterization, with the latent features including latent feature vectors.)
Page 1, Abstract, lines 5 - 7 of Wang clarifies that VAEGAN is a type of neural network.
and a neural network configured to take as an input a parameterization vector (Wang, page 4, right column, lines 24 - 30 discloses a neural network that uses a latent vector as input.)
Page 1, Abstract, lines 5 - 7 of Wang clarifies that VAEGAN is a type of neural network.
and to output a representation of a 3D modeled object usable in a differentiable simulation-based shape optimization (Wang, page 6, left column, lines 20 - 23 discloses using learned feature vectors from the VAEGAN model to optimize airfoil shapes, and page 9, right column, lines 19 - 21 adds synthesizing the airfoils to use in simulations.)
While Wang discloses a loss function for VAE to minimize, Wang does not expressly disclose:
the learning including minimizing a loss which penalizes, for each 3D modeled object of the dataset, a disparity between the output of the neural network for an input parameterization vector respective to the 3D modeled object and a representation of the 3D modeled object usable in a differentiable simulation-based shape optimization.
Dupont De Dinechin however discloses:
the learning including minimizing a loss which penalizes, for each 3D modeled object of the dataset, a disparity between the output of the neural network for an input parameterization vector respective to the 3D modeled object and a representation of the 3D modeled object usable in a differentiable simulation-based shape optimization (Dupont De Dinechin, par [0139] discloses using the neural network VAEGAN to minimize losses between an input and output of the neural network, with par [0145] adding small size latent vector regarding input data regarding an image 3D shape, and par [0225] adds small elements for simulation and optimization.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the VAEGAN neural network, object reconstruction and latent vector of Wang with the minimizing losses regarding VAEGAN, latent vectors, simulation and optimization teaching of Dupont De Dinechin. The motivation to do so would have been because Dupont De Dinechin discloses the benefit of including a generative autoencoder, which uses a latent vector of small size input data while being able to generate new functional structures on the fly that generate “realistic” functional structures, compared to non-generative autoencoders which are unable to generate a new data from a random latent vector (Dupont De Dinechin, par [0145]).
As per claim 19, the combination of Wang and Dupont De Dinechin discloses:
the method of machine learning according to claim 1 (See rejection above regarding claim 1).
a non-transitory computer readable medium having stored thereon a program that when executed by a processor causes the processor to implement the method (Dupont De Dinechin, par [0065] discloses a data storage medium, including memory, to store instructions executable by a computer.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the VAEGAN neural network, object reconstruction and latent vector of Wang with the minimizing losses regarding VAEGAN, latent vectors, simulation and optimization teaching of Dupont De Dinechin and the medium storing instructions for a computer to execute, also found in Dupont De Dinechin. The motivation to do so would have been because Dupont De Dinechin discloses the benefit of including a generative autoencoder, which uses a latent vector of small size input data while being able to generate new functional structures on the fly that generate “realistic” functional structures, compared to non-generative autoencoders which are unable to generate a new data from a random latent vector (Dupont De Dinechin, par [0145]).
For claim 4: The combination of Wang and Dupont De Dinechin discloses claim 4: The method of claim 1, wherein the set of parameterization vectors is a set of latent vectors (Wang, page 4, right column, lines 24 - 30 discloses a neural network that uses a latent vector as input.)
For claim 5: The combination of Wang and Dupont De Dinechin discloses claim 5: The method of claim 1, wherein:
each 3D modeled object of the obtained dataset further includes a specification of one or more physical attributes of the mechanical part (Dupont De Dinechin, par [0058] discloses a 3D modeled object with designs associated with the physical properties.)
the representation is a representation of the geometry and of the one or more physical attributes (Dupont De Dinechin, par [0058] discloses a mechanical assembly of rigid parts being represented by a 3D modeled object, and par [0069] adding the geometry of a product represented in a 3D modeled object.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the VAEGAN neural network, object reconstruction and latent vector of Wang with the minimizing losses regarding VAEGAN, latent vectors, simulation and optimization teaching of Dupont De Dinechin, and the additional teaching of a mechanical assembly of rigid parts, geometry, and 3D modeled object representing the assembly of rigid parts, also found in Dupont De Dinechin. The motivation to do so would have been because Dupont De Dinechin discloses the benefit of including a generative autoencoder, which uses a latent vector of small size input data while being able to generate new functional structures on the fly that generate “realistic” functional structures, compared to non-generative autoencoders which are unable to generate a new data from a random latent vector (Dupont De Dinechin, par [0145]).
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (“Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization”), in view of Dupont De Dinechin (U.S. PG Pub 2021/0004719 A1), and further in view of Kehl et al. (U.S. PG Pub 2021/0150231 A1), hereinafter “Kehl”.
As per claim 2, the combination of Wang and Dupont De Dinechin discloses the method of claim 1.
The combination of Wang and Dupont De Dinechin does not expressly disclose:
wherein the representation is an implicit representation.
Kehl however discloses:
wherein the representation is an implicit representation (Kehl, par [0072] discloses using SDF to provide a projection of a surface, including projecting object-surface points that lie on the surface of an object.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the VAEGAN neural network, object reconstruction and latent vector of Wang and the minimizing losses regarding VAEGAN, latent vectors, simulation and optimization teaching of Dupont De Dinechin with the signed distance fields (SDF) regarding the surface of an object teaching of Kehl. The motivation to do so would have been because Kehl discloses the benefit of the differentiable SDF renderer that avoids mesh-related problems, including connectivity or intersections, includes a different way of sampling the representation, and employs an alternative formulation for rendering implicit surfaces that can be used for back-propagation (Kehl, par [0068]).
For claim 3: The combination of Wang, Dupont De Dinechin, and Kehl discloses claim 3: The method of claim 2, wherein:
the implicit representation is a signed distance field representation (Kehl, par [0072] discloses using SDF to provide a projection of a surface, including projecting object-surface points that lie on the surface of an object.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the VAEGAN neural network, object reconstruction and latent vector of Wang and the minimizing losses regarding VAEGAN, latent vectors, simulation and optimization teaching of Dupont De Dinechin with the signed distance fields (SDF) regarding the surface of an object teaching of Kehl and the additional teaching of the signed distance field as the presentation, also found in Kehl. The motivation to do so would have been because Kehl discloses the benefit of the differentiable SDF renderer that avoids mesh-related problems, including connectivity or intersections, includes a different way of sampling the representation, and employs an alternative formulation for rendering implicit surfaces that can be used for back-propagation (Kehl, par [0068]).
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter under 35 U.S.C. 103:
Claims 6 and 12: The prior art of over Wang et al (“Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization”) discloses VAEGAN neural network, object reconstruction and latent vector, Dupont De Dinechin (U.S. PG Pub 2021/0004719 A1) discloses minimizing losses regarding VAEGAN, latent vectors, simulation and optimization, and Kehl et al. (U.S. PG Pub 2021/0150231 A1) discloses signed distance fields (SDF) regarding the surface of an object teaching of Kehl and the additional teaching of the signed distance field as the presentation.
However, none of the references cited, including the prior art of Wang, Dupont De Dinechin, and Kehl, taken either alone or in combination with the prior art of record discloses for claim 6: A computer-implemented method, for claim 12: a device, and for claim 20: A non-transitory computer readable medium for applying a neural network learnable according to machine-learning, comprising the steps of applying the learnable neural network for differentiable simulation-based shape optimization, in which a 3D model of a mechanical part and its physical constraints and the differentiable simulator are all obtained to perform the simulation-based optimization of the 3D modeled object, in which a loss is minimized between the value of the simulator and the output for the neural network, in combination with the remaining elements and features of the claimed invention. It is for these reasons that the applicants’ invention defines over the prior art of record.
Dependent claims 7 - 11 and 13 - 18 are allowable under 35 U.S.C. 103 for depending from claims 6 and 12, an allowable base claim under 35 U.S.C. 103.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CEDRIC D JOHNSON whose telephone number is (571)270-7089. The examiner can normally be reached M-Th 4:30am - 2:00pm, F 4:30am - 11:30am.
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/Cedric Johnson/ Primary Examiner, Art Unit 2186
November 22, 2025