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
This Office Action is in response to applicant’s communication filed 30 October 2025, in response to the Office Action mailed 30 June 2025. The applicant’s remarks and any amendments to the claims or specification have been considered, with the results that follow.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 30 October 2025 has been entered.
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, 8, 11-13, 16, 17, and 19-22 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.
The term “focusing on” in claim 1 is a relative term which renders the claim indefinite. The term “focusing on” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claims 8, 11, 12, and 19-21 depend upon claim 1, and thus include the aforementioned limitation(s).
The term “focusing on” in claim 13 is a relative term which renders the claim indefinite. The term “focusing on” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 16 depends upon claim 13, and thus includes the aforementioned limitation(s).
The term “focusing on” in claim 17 is a relative term which renders the claim indefinite. The term “focusing on” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
The term “focusing on” in claim 22 is a relative term which renders the claim indefinite. The term “focusing on” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1, 8, 11-13, 16, 17, and 19-22 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mathematical concepts. This judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as described below.
Step 1 for all claims:
Under the first part of the analysis, claims 1, 8, 11, 12, and 20-22 recite a method(s) and claims 13, 16, 17, and 19 recite a device(s). Accordingly, these claims fall within the four statutory categories of invention and the analysis proceeds to Step 2A, prongs 1 and 2, and Step 2B, as described below.
As per claim 1:
Under step 2A, prong 1, the claim recites an abstract idea including the following mathematical concept elements:
the training including minimization of a loss – the loss (function) is a mathematical formula, and minimizing the loss is a mathematical calculation.
the loss including a term that penalizes, for each output respective 3D modeled object, one or more functional scores of the respective 3D modeled object – this is describing the terms of the loss (function), which is a mathematical formula.
wherein the loss further includes a second term that penalizes, for each output respective 3D modeled object, a shape inconsistency of the respective 3D modeled object with respect to the dataset – this is describing a term of the loss (function), which is a mathematical formula.
wherein the second term includes: a reconstruction loss between the respective 3D modeled object and a corresponding ground truth 3D modeled object of the dataset, an adversarial loss relative to the dataset, or a mapping distance measuring a shape dissimilarity between the respective 3D modeled object and a corresponding modeled object of the dataset – these are describing terms of the loss (function), which is a mathematical formula.
the reconstruction loss including a connectivity loss focusing on penalizing disconnected elements of the 3D modeled object, the connectivity loss being based on 0d persistent homology – this is describing terms of the loss (function), which is a mathematical formula (see, e.g., pg. 31 of the specification as filed for the connectivity loss formula).
wherein the training comprises computing, for each respective 3D modeled object, a functional score of the 3D modeled object – computing a score for an object is a mathematical calculation.
the computing being performed by applying to the 3D modeled object one or more among: a deterministic function, … or a deep-learning function – this is describing performing the calculation of the functional score, by using a deterministic function or deep-learning function, which are mathematical formulae/calculations.
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a computer-implemented method for training a deep-learning generative model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
obtaining a dataset of 3D modeled objects – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
each 3D modeled object representing a mechanical part or an assembly of mechanical parts – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
training the deep-learning generative model based on the dataset – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
each functional score measuring an extent of non-respect of a respective functional descriptor among one or more functional descriptors, by the mechanical part or the assembly of mechanical parts, a lower functional score signifying a better respect of the respective functional descriptor – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the deep-learning generative model includes a 3D generative neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
the 3D generative neural network being a neural network configured to generate and output one or more 3D objects – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the 3D generative neural network includes a Variational Autoencoder or a Generative Adversarial Network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the one or more functional descriptors include a connectivity descriptor describing a number of connected components of the 3D modeled object – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and wherein the one or more functional descriptors also include: one or more geometrical descriptors, each geometrical descriptor being a variable representing a spatial structure of the 3D object, and/or one or more affordances, each affordance being a variable representing an interaction of the object within an intended context – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
a simulation-based engine – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and associating the computed functional score to the respective 3D modeled object of the dataset, thereby adding a functional annotation to the respective 3D modeled object in the dataset – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
the deep-learning generative model being configured for use in manufacturing – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
a computer-implemented method for training a deep-learning generative model – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
obtaining a dataset of 3D modeled objects – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
each 3D modeled object representing a mechanical part or an assembly of mechanical parts – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
training the deep-learning generative model based on the dataset – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
each functional score measuring an extent of non-respect of a respective functional descriptor among one or more functional descriptors, by the mechanical part or the assembly of mechanical parts, a lower functional score signifying a better respect of the respective functional descriptor – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the deep-learning generative model includes a 3D generative neural network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
the 3D generative neural network being a neural network configured to generate and output one or more 3D objects – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the 3D generative neural network includes a Variational Autoencoder or a Generative Adversarial Network – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
wherein the one or more functional descriptors include a connectivity descriptor describing a number of connected components of the 3D modeled object – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and wherein the one or more functional descriptors also include: one or more geometrical descriptors, each geometrical descriptor being a variable representing a spatial structure of the 3D object, and/or one or more affordances, each affordance being a variable representing an interaction of the object within an intended context – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
a simulation-based engine – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and associating the computed functional score to the respective 3D modeled object of the dataset, thereby adding a functional annotation to the respective 3D modeled object in the dataset – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
the deep-learning generative model being configured for use in manufacturing – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 8:
The claim recites the following additional mathematical concept elements:
the respective functional score having been computed by using one or more among: a deterministic function, … or a deep-learning function – this is describing performing the calculation of the functional score, by using a deterministic function or deep-learning function, which are mathematical formulae.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the computing is performed by the deep-learning function, the deep-learning function having been trained on a basis of another dataset, the other dataset including 3D objects each associated with a respective functional score – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
a simulation-based engine – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the computing is performed by the deep-learning function, the deep-learning function having been trained on a basis of another dataset, the other dataset including 3D objects each associated with a respective functional score – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
a simulation-based engine – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 11:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein: the one or more geometrical descriptors include: a physical stability descriptor, the physical stability descriptor represents, for a mechanical part or an assembly of mechanical parts, a stability of the mechanical part or the assembly of mechanical parts under an application of gravity force only – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and/or a durability descriptor, the durability descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to withstand the application of gravity force and external mechanical forces – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and/or the one or more affordances include: a support affordance descriptor, the support affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to withstand an application of external mechanical forces only – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and/or a drag coefficient descriptor, the drag coefficient descriptor represents, for a mechanical part or an assembly of mechanical parts, an influence of a fluid environment on the mechanical part or the assembly of mechanical parts – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
a contain affordance descriptor, the contain affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to contain another object in an inside volume of the mechanical part or the assembly of mechanical parts – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
a holding affordance descriptor, the holding affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to support another object via a mechanical connection – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and/or a hanging affordance descriptor, the hanging affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to be supported through a mechanical connection – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein: the one or more geometrical descriptors include: a physical stability descriptor, the physical stability descriptor represents, for a mechanical part or an assembly of mechanical parts, a stability of the mechanical part or the assembly of mechanical parts under an application of gravity force only – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and/or a durability descriptor, the durability descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to withstand the application of gravity force and external mechanical forces – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and/or the one or more affordances include: a support affordance descriptor, the support affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to withstand an application of external mechanical forces only – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and/or a drag coefficient descriptor, the drag coefficient descriptor represents, for a mechanical part or an assembly of mechanical parts, an influence of a fluid environment on the mechanical part or the assembly of mechanical parts – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
a contain affordance descriptor, the contain affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to contain another object in an inside volume of the mechanical part or the assembly of mechanical parts – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
a holding affordance descriptor, the holding affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to support another object via a mechanical connection – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and/or a hanging affordance descriptor, the hanging affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to be supported through a mechanical connection – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 12:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein each 3D modeled object of the dataset represents: a piece of furniture, a motorized vehicle, a non-motorized vehicle, or a tool – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein each 3D modeled object of the dataset represents: a piece of furniture, a motorized vehicle, a non-motorized vehicle, or a tool – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 13:
See the rejection of claim 1, above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a device comprising: a processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and a non-transitory data storage medium – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
having recorded thereon a deep-learning generative model that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and that is taught by the processor being configured to [perform the method] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
a device comprising: a processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and a non-transitory data storage medium – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
having recorded thereon a deep-learning generative model that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and that is taught by the processor being configured to [perform the method] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 16, see the rejection of claim 12, above.
As per claim 17:
See the rejection of claim 1, above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a device comprising: a processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and a non-transitory data storage medium – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
having recorded thereon a computer program including instructions for training a deep-learning generative model that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
that when executed by the processor causes the processor to be configured to [perform the method] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
a device comprising: a processor – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and a non-transitory data storage medium – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
having recorded thereon a computer program including instructions for training a deep-learning generative model that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
that when executed by the processor causes the processor to be configured to [perform the method] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 19:
See the rejection of claim 1, above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a non-transitory computer readable medium having stored thereon a program that when executed by a computer causes the computer to implement the method according to claim 1 – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
a non-transitory computer readable medium having stored thereon a program that when executed by a computer causes the computer to implement the method according to claim 1 – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 20:
See the rejection of claim 1, above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A method of manufacturing comprising: – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
obtaining a deep-learning generative model having been trained to output 3D modeled objects each representing a mechanical part or an assembly of mechanical parts – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
according to a computer-implemented method according to claim 1 – see the rejection of claim 1, above.
using the deep-learning generative model to output a 3D modeled object representing a mechanical part or an assembly of mechanical parts – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and manufacturing the mechanical part or assembly of mechanical parts – this amounts to mere instructions to apply the judicial exception, and/or generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A method of manufacturing comprising: – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
obtaining a deep-learning generative model having been trained to output 3D modeled objects each representing a mechanical part or an assembly of mechanical parts – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
according to a computer-implemented method according to claim 1 – see the rejection of claim 1, above.
using the deep-learning generative model to output a 3D modeled object representing a mechanical part or an assembly of mechanical parts – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and manufacturing the mechanical part or assembly of mechanical parts – this amounts to mere instructions to apply the judicial exception, and/or generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 21:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the training evaluates a functional validity of each respective 3D modeled object of the dataset in a respective context – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
the functional validity comprising shape and/or structural soundness of the respective 3D modeled object and/or physical realizability and interaction quality of the respective 3D modeled object in the respective intended context – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the training evaluates a functional validity of each respective 3D modeled object of the dataset in a respective context – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
the functional validity comprising shape and/or structural soundness of the respective 3D modeled object and/or physical realizability and interaction quality of the respective 3D modeled object in the respective intended context – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 22:
See the rejection of claim 1, above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A method comprising: – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
providing an input 3D modeled object – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
to a deep-learning generative model trained by training a deep-learning generative model that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A method comprising: – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
providing an input 3D modeled object – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
to a deep-learning generative model trained by training a deep-learning generative model that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data, generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
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.
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 8, 11-13, 16, 17, and 19-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Balashova et al. (Structure-Aware Shape Synthesis, Sept 2018, pgs. 140-149 – cited in an IDS), in view of Dewaele (US 2010/0189320), further in view of Mehr (US 2018/0314917), and further in view of Brüel-Gabrielsson et al. (A Topology Layer for Machine Learning, Apr 2020, pgs. 1-13 – cited in an IDS).
As per claim 1, Balashova teaches a computer-implemented method for training a deep-learning generative model that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured [a method used in computer graphics/vision of training a generative model to generate 3D shapes (objects), such as a chair (pg. 140, section 1; etc.); where a chair is a mechanical part/assembly of mechanical parts to be manufactured. Examiner’s Note: “to be manufactured” in the preamble merely states, the purpose or intended use of the invention, rather than any distinct definition of any of the claimed invention’s limitations, is not considered a limitation and is of no significance to claim construction. See MPEP § 2111.02(II).], the method comprising: obtaining a dataset of 3D modeled objects, each 3D modeled object representing a mechanical part or an assembly of mechanical parts; and training the deep-learning generative model based on the dataset [a large collection of 3D shape training examples and ground truth data is used to train the generative model(s) (pg. 140, section 1; pg. 142, fig. 2; pg. 143, algorithm 1; etc.)], the training including minimization of a loss [training the model includes minimizing a loss function (pg. 142, section 3.2; etc.)], the loss including a term that penalizes, for each output respective 3D modeled object, one or more functional scores of the respective 3D modeled object, each functional score measuring an extent of non-respect of a respective functional descriptor among one or more functional descriptors, a lower functional score signifying a better respect of the respective functional descriptor, by the mechanical part or the assembly of mechanical parts [the generative model system includes a structure detector that takes a 3D shape (object) and outputs a structure representation, which is used to provide a shape-structure consistency loss (functional score) as part of the combined loss function with the correctness/shape loss used to train the model(s) (pgs. 141-143, sections 3-3.3 and fig. 2; etc.), which penalizes non-respect (i.e., they are inconsistent) of the structural description, and thus a lower score signifies a better respect of the functional descriptor], wherein the loss further includes a second term that penalizes, for each output respective 3D modeled object, a shape inconsistency of the respective 3D modeled object with respect to the dataset [the generative model system includes a structure detector that takes a 3D shape (object) and outputs a structure representation, which is used to provide a shape-structure consistency loss (functional score) as part of the combined loss function with the correctness/shape loss (another term) used to train the model(s) (pgs. 141-143, sections 3-3.3 and fig. 2; etc.), where the correctness/shape loss term penalizes inconsistency of the 3D modeled object to the training dataset], wherein the second term includes: a reconstruction loss between the respective 3D modeled object and a corresponding ground truth 3D modeled object of the dataset, an adversarial loss relative to the dataset, or a mapping distance measuring a shape dissimilarity between the respective 3D modeled object and a corresponding modeled object of the dataset [the shape loss can include a reconstruction loss of a variational autoencoder (pg. 142, section 3.1; etc.)], wherein the deep-learning generative model includes a 3D generative neural network, the 3D generative neural network being a neural network configured to generate and output one or more 3D objects, wherein the 3D generative neural network includes a Variational Autoencoder or a Generative Adversarial Network [a method used in computer graphics/vision of training a generative model to generate 3D shapes (objects), such as a chair (pg. 140, section 1; etc.); where the generative model can include Variational Autoencoders (VAE) or Generative Adversarial Networks (GAN) (pg. 142, section 3.1; etc.)], wherein the one or more functional descriptors include a connectivity descriptor [the structure descriptions (functional descriptors) could also include different numbers of landmarks, importance weighting of landmarks, landmark connectivity, etc. (pg. 147, section 6; etc.)], and wherein the one or more functional descriptors also include: one or more geometrical descriptors, each geometrical descriptor being a variable representing a spatial structure of the 3D object, and/or one or more affordances, each affordance being a variable representing an interaction of the object within an intended context [the structure descriptions (functional descriptors) could also include symmetries, parts, affordances, semantic relationships, economic parts, support and stability, etc. (pg. 141, sections 1-2; etc.), where the loss function includes structural correctness and structural robustness terms (pg. 143, equation (2); etc.); which are geometrical descriptor variables representing a spatial structure of the 3D object], and wherein the training comprises computing, for each respective 3D modeled object, a functional score of the 3D modeled object, the computing being performed by applying to the 3D modeled object one or more among: a deterministic function, a simulation-based engine, or a deep-learning function [the generative model system includes a structure detector that takes a 3D shape (object) and outputs a structure representation, which is used to provide a shape-structure consistency loss (functional score) as part of the combined loss function with the correctness/shape loss used to train the model(s) (pgs. 141-143, sections 3-3.3 and figs. 2-3; etc.); where the shape-structure consistency score thus includes a deterministic function (the scoring) and a deep-learning function/simulation based engine (the structure detector)] and associating the computed functional score to the respective 3D modeled object of the dataset, thereby adding a functional annotation to the respective 3D modeled object in the dataset [the generative model system includes a structure detector that takes a 3D shape (object) and outputs a structure representation, which is used to provide a shape-structure consistency loss (functional score) as part of the combined loss function with the correctness/shape loss used to train the model(s) (pgs. 141-143, sections 3-3.3 and figs. 2-3; etc.) and which includes labelling/annotating the objects in the dataset (pg. 142, section 3.1; pg. 143, section 3.2; etc.)],.
While Balashova teaches that the functional descriptors include a connectivity descriptor (see above), it has not been relied upon for teaching wherein the one or more functional descriptors include a connectivity descriptor describing a number of connected components of the 3D modeled object, the reconstruction loss including a connectivity loss focusing on penalizing disconnected elements of the 3D modeled object, the connectivity loss being based on 0d persistent homology; and the deep-learning generative model being configured for use in manufacturing.
Dewaele teaches wherein the one or more functional descriptors include a connectivity descriptor describing a number of connected components of the 3D modeled object, and the reconstruction loss including a connectivity loss focusing on penalizing disconnected elements of the 3D modeled object, [a shape description is provided from a shape model that includes connection vectors of connections between landmarks (components of the 3D modeled object) (paras. 0070-71; see also: 0034, 0043, 0109-111, 0197-202, fig. 10; etc.) which can be used as part of a cost function (abstract; claims 1, 4; etc.) and/or feature similarity measurements (paras. 0055-61, etc.); for the connectivity descriptor including landmark connectivity, in Balashova above].
Balashova and Dewaele are analogous art, as they are within the same field of endeavor, namely 3D modeling/model generation.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the landmark connection description in the shape descriptor and the connectivity component in the cost function, as taught by Dewaele, in the structure descriptor and loss function used in the system taught by Balashova.
Dewaele provides motivation as [the shape model/description can be used to ensure that plausible results (structures/shapes/etc.) are generated (para. 0014, etc.) and in order to compare profiles, it is necessary to measure the extent by which the values of certain points of the profiles vary from the mean with respect to other points (para. 0057, etc.)]. Balashova also provides motivation as [the structure chosen is coarse, leading the synthesized shapes to sometimes omit finer details (which is an inherent problem with coarse shape representations), which can be mitigated by imposing additional structural constraints, such as different number of landmarks, importance-weighting of landmarks, or landmark connectivity (i.e., that taught by Dewaele, above) and learning shape synthesis models at a higher resolution (pg. 147, section 6; etc.)].
Mehr teaches the deep-learning generative model being configured for use in manufacturing [the VAE or GAN can be trained to produce a 3D model (paras. 0079-83, etc.) reconstruction of a 3D modeled object in a CAD system (paras. 0086-87, fig. 2, etc.), where the 3D modeled object may represent the geometry of a product to be manufactured in the real world subsequent to completion of its virtual design (para. 0050; see also: 0004, 0049, etc.); which model can be trained according to the method of claim 1, as taught by Balashova/Dewaele, above].
Balashova/Dewaele and Mehr are analogous art, as they are within the same field of endeavor, namely training/utilizing ML models for 3D object generation/synthesis.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to train and use the VAE/GAN to generate 3D modeled objects as taught by Balashova/Dewaele, for the training and use of the VAE/GAN to generate 3D modeled objects as parts to be manufactured in the system taught by Mehr.
Mehr provides motivation as [training and using the model to create 3D modeled objects speeds up the design of parts, which speeds up the manufacturing process (para. 0049, etc.)].
Brüel-Gabrielsson teaches the connectivity loss being based on 0d persistent homology [The dimension of Hk(X) counts the number of k-dimensional features of X. For example, dim H0(X) counts the number of connected components, dim H1(X) counts the number of holes, and so on. Persistent homology studies how homology changes over an increasing sequence of complexes. The full information about how homology is born and dies over the filtration can be represented as a multi-set of pairs (b; d) where b is the birth parameter of a homology class, and d is the death parameter of that class. We use loss functions that can be expressed in terms of three parameters (including b and d) (pg. 2, section 2 and equation (1); etc.); which is a connectivity loss based on 0d persistent homology (see also: pg. 31, lines 3-16 of the specification as filed, which shows using the same connectivity loss)].
Balashova/Dewaele and Brüel-Gabrielsson are analogous art, as they are within the same field of endeavor, namely improving deep learning models for 3D object data.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use the connectivity loss based on 0d persistent homology, as taught by Brüel-Gabrielsson, with the connectivity loss used in the system taught by Balashova/Dewaele.
Brüel-Gabrielsson provides motivation as [persistent homology, or simply persistence, can be used in deep learning for preprocessing to provide topological features for learning, where the connectivity loss based on persistent homology allows persistence to be applied more broadly and flexibly, and avoid errors/failures in 3D generative models that are often topological in nature (pg. 1, section 1; etc.)].
As per claim 8, Balashova/Dewaele/Mehr/Brüel-Gabrielsson teaches wherein the computing is performed by the deep-learning function, the deep-learning function having been trained on a basis of another dataset, the other dataset including 3D objects each associated with a respective functional score, the respective functional score having been computed by using one or more among: a deterministic function, a simulation-based engine, or a deep-learning function [the generative model system includes a structure detector that takes a 3D shape (object) and outputs a structure representation, which is used to provide a shape-structure consistency loss (functional score) as part of the combined loss function with the correctness/shape loss used to train the model(s), where the structure detector is a convolutional model trained on a training dataset (the another dataset) comprising clean meshes and synthesized meshes (Balashova: pgs. 141-143, sections 3-3.3 and figs. 2-3; etc.)].
As per claim 11, Balashova/Dewaele/Mehr/Brüel-Gabrielsson teaches wherein: the one or more geometrical descriptors include: a physical stability descriptor, the physical stability descriptor represents, for a mechanical part or an assembly of mechanical parts, a stability of the mechanical part or the assembly of mechanical parts under an application of gravity force only, and/or a durability descriptor, the durability descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to withstand the application of gravity force and external mechanical forces, and/or the one or more affordances include: a support affordance descriptor, the support affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to withstand an application of external mechanical forces only, and/or a drag coefficient descriptor, the drag coefficient descriptor represents, for a mechanical part or an assembly of mechanical parts, an influence of a fluid environment on the mechanical part or the assembly of mechanical parts, a contain affordance descriptor, the contain affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to contain another object in an inside volume of the mechanical part or the assembly of mechanical parts, a holding affordance descriptor, the holding affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to support another object via a mechanical connection, and/or a hanging affordance descriptor, the hanging affordance descriptor represents, for a mechanical part or an assembly of mechanical parts, a capability of the mechanical part or the assembly of mechanical parts to be supported through a mechanical connection [the structure descriptions (functional descriptors) could also include symmetries, parts, affordances, semantic relationships, economic parts, support and stability, etc. (Balashova: pg. 141, sections 1-2; etc.), which includes at least physical stability, affordance, and durability descriptors; and/or a shape description is provided from a shape model that includes connection vectors of connections between landmarks (Dewaele: paras. 0070-71; see also: 0034, 0043, 0109-111, 0197-202, fig. 10; etc.) which can be used as part of a cost function (Dewaele: abstract; claims 1, 4; etc.) and/or feature similarity measurements (Dewaele: paras. 0055-61, etc.)].
As per claim 12, Balashova/Dewaele/Mehr/Brüel-Gabrielsson teaches wherein each 3D modeled object of the dataset represents: a piece of furniture, a motorized vehicle, a non-motorized vehicle, or a tool [a method used in computer graphics/vision of training a generative model to generate 3D shapes (objects), such as a chair (Balashova: pg. 140, section 1; etc.); where a chair is a piece of furniture].
As per claim 13, see the rejection of claim 1 above, wherein Balashova/Dewaele/Mehr/Brüel-Gabrielsson also teaches a device comprising: a processor; and a non-transitory data storage medium having recorded thereon a deep-learning generative model that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured and that is taught by the processor being configured to [perform the method] [the method may be implemented as a computer program product or computer executable program code adapted to carry out the method and stored in a computer readable medium (Dewaele: claims 21-22, etc.)].
As per claim 16, see the rejection of claim 12, above.
As per claim 17, see the rejection of claim 1 above, wherein Balashova/Dewaele/Mehr/Brüel-Gabrielsson also teaches a device comprising: a processor; and a non-transitory data storage medium having recorded thereon a computer program including instructions for training a deep-learning generative model that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured that when executed by the processor causes the processor to be configured to [perform the method] [the method may be implemented as a computer program product or computer executable program code adapted to carry out the method and stored in a computer readable medium (Dewaele: claims 21-22, etc.)].
As per claim 19, see the rejection of claims 13 and/or 17, above.
As per claim 20, Balashova/Dewaele/Mehr/Brüel-Gabrielsson teaches a method of manufacturing comprising: obtaining a deep-learning generative model having been trained to output 3D modeled objects each representing a mechanical part or an assembly of mechanical parts according to a computer-implemented method according to claim 1 [see the rejection of claim 1, above, and the VAE or GAN can be trained to produce a 3D model (Mehr: paras. 0079-83, etc.) reconstruction of a 3D modeled object in a CAD system (Mehr: paras. 0086-87, fig. 2, etc.), where the 3D modeled object may represent the geometry of a product to be manufactured in the real world subsequent to completion of its virtual design (Mehr: para. 0050; see also: 0004, 0049, etc.); which model can be trained according to the method of claim 1, as taught by Balashova/Dewaele, above]; using the deep-learning generative model to output a 3D modeled object representing a mechanical part or an assembly of mechanical parts [a method used in computer graphics/vision of training a generative model to generate 3D shapes (objects), such as a chair (Balashova: pg. 140, section 1; etc.); where a chair is a mechanical part/assembly of mechanical parts to be manufactured; and the VAE or GAN can be trained to produce a 3D model (Mehr: paras. 0079-83, etc.) reconstruction of a 3D modeled object in a CAD system (Mehr: paras. 0086-87, fig. 2, etc.)] and manufacturing the mechanical part or assembly of mechanical parts [the 3D modeled object designed by the method may represent an industrial product which may be any mechanical part, such as a part of a terrestrial vehicle (including e.g. car and light truck equipment, racing cars, motorcycles, truck and motor equipment, trucks and buses, trains), a part of an aerial vehicle (including e.g. airframe equipment, aerospace equipment, propulsion equipment, defense products, airline equipment, space equipment), a part of a naval vehicle (including e.g. navy equipment, commercial ships, offshore equipment, yachts and workboats, marine equipment), a general mechanical part (including e.g. industrial manufacturing machinery, heavy mobile machinery or equipment, installed equipment, industrial equipment product, fabricated metal product, tire manufacturing product), an electro-mechanical or electronic part (including e.g. consumer electronics, security and/or control and/or instrumentation products, computing and communication equipment, semiconductors, medical devices and equipment), a consumer good (including e.g. furniture, home and garden products, leisure goods, fashion products, hard goods retailers' products, soft goods retailers' products), a packaging (including e.g. food and beverage and tobacco, beauty and personal care, household product packaging); and may represent the geometry of a product to be manufactured in the real world subsequent to completion of its virtual design (Mehr: para. 0050; see also: 0004, 0049, etc.)].
Examiner’s Note: the reasoning and motivation for the combination are also provided in the rejection of claim 1, above.
As per claim 21, Balashova/Dewaele/Mehr/Brüel-Gabrielsson teaches wherein the training evaluates a functional validity of each respective 3D modeled object of the dataset in a respective intended context, the functional validity comprising shape and/or structural soundness of the respective 3D modeled object and/or physical realizability and interaction quality of the respective 3D modeled object in the respective intended context [Structure-aware shape synthesis process captures structurally meaningful shape elements, such as legs of a chair (Balashova: pg. 140, fig. 1; etc.) the structure descriptions (functional descriptors) could also include symmetries, parts, affordances, semantic relationships, economic parts, support and stability, etc. (Balashova: pg. 141, sections 1-2; etc.), where the loss function includes structural correctness and structural robustness terms (Balashova: pg. 143, equation (2); etc.); where the structure-aware shape synthesis is the intended context of the shapes].
As per claim 22, see the rejection of claim 1 above, wherein Balashova/Dewaele/Mehr/Brüel-Gabrielsson also teaches a method, comprising: providing an input 3D modeled object to a deep-learning generative model trained by training a deep-learning generative model that outputs 3D modeled objects each representing a mechanical part or an assembly of mechanical parts to be manufactured [a method used in computer graphics/vision of training a generative model to generate 3D shapes (objects), such as a chair (Balashova: pg. 140, section 1; etc.), where the generative model system includes a structure detector that takes a 3D shape (object) and outputs a structure representation, which is used to provide a shape-structure consistency loss (functional score) as part of the combined loss function with the correctness/shape loss used to train the model(s) (Balashova: pgs. 141-143, sections 3-3.3 and fig. 2; etc.); where a chair is a mechanical part/assembly of mechanical parts; and may represent the geometry of a product to be manufactured in the real world subsequent to completion of its virtual design (Mehr: para. 0050; see also: 0004, 0049, etc.)].
Response to Arguments
Applicant's arguments filed 30 October 2025 have been fully considered but they are not persuasive.
Applicant argues that Balashova and Dewaele are not analogous art, because “Dewaele follows specific constraints found on digital images, such [as] following anatomical shapes represented by the medical image for achieving an accurate reconstruction” and that landmarks must delimit curve outlines.
However, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, the constraints provided by Dewaele, including utilizing a shape description and connection vectors (of landmark connections) provides that the shapes generated are plausible results (see, e.g., Dewaele: para. 0014, etc.). Furthermore, Balashova teaches the structure chosen is coarse, leading the synthesized shapes to sometimes omit finer details (which is an inherent problem with coarse shape representations), which can be mitigated by imposing additional structural constraints, such as different number of landmarks, importance-weighting of landmarks, or landmark connectivity (i.e., that taught by Dewaele, above) and learning shape synthesis models at a higher resolution (pg. 147, section 6; etc.). Therefore, one of ordinary skill in the art would have been motivated to combine the teachings of these references, in the manner described.
Additionally, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Applicant also argues that the cited art does not teach the reconstruction loss comprising a connectivity loss focusing on penalizing disconnected elements of the 3D modeled object, the connectivity loss being based on 0d persistent homology.
However, Dewaele teaches a shape description is provided from a shape model that includes connection vectors of connections between landmarks (components of the 3D modeled object) (paras. 0070-71; see also: 0034, 0043, 0109-111, 0197-202, fig. 10; etc.) which can be used as part of a cost function (abstract; claims 1, 4; etc.) and/or feature similarity measurements (paras. 0055-61, etc.); for the connectivity descriptor including landmark connectivity, in Balashova; while the newly cited reference, to Brüel-Gabrielsson, has been relied upon for teaching the connectivity loss being based on 0d persistent homology.
Applicant also argues that the cited art does not teach wherein the training evaluates a functional validity of each respective 3D modeled object of the dataset in a respective intended context, the functional validity comprising shape and/or structural soundness of the respective 3D modeled object and/or physical realizability and interaction quality of the respective 3D modeled object in the respective intended context
However, Balashova teaches that the structure-aware shape synthesis process captures structurally meaningful shape elements, such as legs of a chair (Balashova: pg. 140, fig. 1; etc.) the structure descriptions (functional descriptors) could also include symmetries, parts, affordances, semantic relationships, economic parts, support and stability, etc. (Balashova: pg. 141, sections 1-2; etc.), where the loss function includes structural correctness and structural robustness terms (Balashova: pg. 143, equation (2); etc.); where the structure-aware shape synthesis is the intended context of the shapes, which is evaluating the functional validity of the object in context (e.g., a chair’s ability to stand and hold weight).
Applicant also argues, regarding the rejections under 35 U.S.C. 101, that the claims (including minimization of a loss) do not set forth or describe any mathematical relationships, calculations, formulas or equations using words or mathematical symbols.
However, the claims do recite a mathematical formula and/or calculation, as described in the rejections (e.g., the training including minimization of a loss – where the loss (function) is a mathematical formula, and minimizing the loss is a mathematical calculation). Here, the “loss” is explicitly a mathematical formula, including a number of recited “terms” (see, e.g., the loss formula and terms found on pgs. 28, 31-34, and 36, etc., of the specification as filed). Additionally, it is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea). See MPEP § 2106.04(a)(2).
Applicant argues that the analysis provided in the prior action, regarding the alleged improvement to a computer or technology, are improper, and that the claim must be considered as a whole.
However, the claim elements have been considered both individually and as a whole, as described in the prior action. Further, applicant has not described how the “improvement is integrated into the claim as a whole” beyond what was addressed. The examiner finds that the prior described improvement was, at most, an improvement to the abstract idea (as described in the prior action).
Applicant also argues that the loss “focuses on penalizing disconnected elements of the 3D modeled object” and, thus, provides improved functionality.
However, beyond issues with “focusing on” (see the rejections, above), this is also an improvement to the mathematical function, as addressed above.
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 2-7, 9, 10, 14, 15, and 18 are cancelled; claims 1, 8, 11-13, 16, 17, and 19-22 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Grabner et al. (What Makes a Chair a Chair?, June 2011, pgs. 1529-1536) – discloses an affordance detector model that calculates affordances for 3D objects (chairs).
Laga et al. (Geometry and Context for Semantic Correspondences and Functionality Recognition in Man-Made 3D Shapes, 2013, pgs. 1-16) – discloses a system for automatic recognition of functional parts of 3D shapes.
Hassanin et al. (A New Localization Objective for Accurate Fine-Grained Affordance Segmentation Under High-Scale Variations, Feb 2020, pgs. 28123-28132) – discloses a system for segmentation of 3D object parts for determining functionality and affordance of individual parts.
Ibragimov et al. (Shape Representation for Efficient Landmark-Based Segmentation in 3-D, April 2014, pgs. 861-874) – discloses minimizing a cost function/sum that includes connections between landmark points in landmark-based shape representations of 3-D environments.
Grabner et al. (What Makes a Chair a Chair?, June 2011, pgs. 1529-1536) – discloses object categorization/classification of 3D objects/environments, including affordance detection.
Naruniec (US 2022/0058822) – discloses a method of training a landmark model including connections and distance between landmarks.
Fedyukov (US 2021/0049811) – discloses a system of clothing selection utilizing 3D body modelling and using a loss function that includes penalties for mismatches between landmarks.
Saveliev (US 2007/0036434) – discloses topology-based partitioning and analysis of dynamic (3D) images, including a generator of persistent homology groups.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE GIROUX whose telephone number is (571)272-9769. The examiner can normally be reached M-F 10am-6pm.
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/GEORGE GIROUX/Primary Examiner, Art Unit 2128