DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/02/2026 has been entered.
This Office Action is in response to the claims filed on 03/02/2026.
Claims 1, 8, 10, and 16 have been amended.
Claims 1-20 are pending.
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 .
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the
references as applied to the claims below for the convenience of the applicant. Although
the specified citations are representative of the teachings in the art and are applied to
the specific limitations within the individual claim, other passages and figures may apply
as well. Examiner may also include cited interpretations encompassed within parenthesis, e.g. (Examiner' s interpretation), for clarity. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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 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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/21/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Claim Rejections under 35 U.S.C. § 112(a):
Applicant’s arguments have been fully considered, but were not persuasive. Rejections to claims 3, 13, and 19 are maintained.
Applicant argues Specification disclosure Figure 5 Flowchart 500, step 522 describes “manufacturing a vehicle corresponding to a design created by the disclosed design process.” The examiner respectfully disagrees. FIG.5 Step 522 states “Create Physical Designs and/or Test For Aerodynamic Performance”. This does not comply with the written description requirement regarding claims 3, 13, and 19 which all recite “manufacturing a vehicle”, not “create physical designs”. A person of ordinary skill in the art would conclude Step 522 is referring to the creation of a physical model for real world aerodynamic performance testing (e.g. wind tunnel testing, etc.), not manufacturing an entire vehicle as claims 3, 13, and 19 recite. Thus, Applicant’s argument not persuasive.
Claim Rejections under 35 U.S.C. § 112(b):
Acknowledgement is made of amended claims 8, 10 and 16 to correct antecedent basis issues. Previous rejections to claims 8, 10 and 16 (including dependent claims 9-15 and 17-20) are withdrawn.
Claim Rejections under 35 U.S.C. § 101:
Acknowledgement is made of amended independent claims 1, 8 and 16. Applicants arguments have been fully considered, but are not persuasive. Rejections to claims 1-20 are maintained.
Applicant argues the claims “integrate the supposed judicial exception into a practical application in providing a multi-disciplinary approach for vehicle design, which implements a combination of computational fluid dynamics (CFD) and general adversarial network (GAN) techniques for vehicle design optimization in satisfying multiple competing objectives, including aerodynamic and aesthetic considerations.” The examiner respectfully disagrees.
Per MPEP 2106.04(d) and 2106.05, the courts have identified the following limitations that do not integrate a judicial exception into a practical application and found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception:
• Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
As shown in Claim Rejections - 35 USC §101 section below, the additional claim limitations outside the abstract idea only present Insignificant Extra Solution Activity and/or Mere Instructions To Apply An Exception and/or Field Of Use And Technological Environment. Thus, Applicant’s argument not persuasive.
Claim Rejections under 35 U.S.C. § 103:
Acknowledgement is made of amended independent claims 1, 8 and 16. Applicants arguments have been fully considered, but are not persuasive. Rejections to claims are maintained.
Applicant argues the combination of Fukushige and Nie do not appear to teach or suggest the claimed features of "determine a plurality of candidate vehicle exterior silhouettes by modifying a first subset of vehicle attributes that affect aerodynamic performance, wherein each of the plurality of candidate vehicle exterior silhouettes is a two-dimensional side-view vehicle exterior silhouette", and the “base design STL data” of Fukushige cannot reasonably be considered to correspond to the claimed “plurality of candidate vehicle exterior silhouettes [ ] each of the plurality of candidate vehicle exterior silhouettes is a two-dimensional side-view vehicle exterior silhouette." The examiner respectfully disagrees. As shown in Claim Rejections – 35 USC §103 section below, Fukushige discloses the following (claim limitation is bolded):
determine a plurality of candidate vehicle exterior silhouettes by modifying a first subset of vehicle attributes that affect aerodynamic performance, wherein each of the plurality of candidate vehicle exterior silhouettes is a two-dimensional side-view vehicle exterior silhouette; (“The base design data creating unit 21 creates base design STL data (i.e. exterior silhouettes) from CAD (Computer Aided Design) data representing the vehicle shape of a base vehicle and enters the created base design STL data into the design data storage unit 11. The morphing unit 22 deforms the vehicle shape (i.e. modifies a first subset of vehicle attributes) of the base vehicle represented by the base design STL data based on determined conditions through a morphing technique [...] to create the vehicle shape of a learning vehicle, and enters the learning design STL data representing the created vehicle shape into the design data storage unit 11 [...] The learning data creating unit 30 reads the base design STL data and the learning design STL data [...] The learning data creating unit 30 calculates the aerodynamic performance values” [P.0101-0102]. STL data is interpreted to include a plurality of candidate vehicle exterior silhouettes because “the morphing unit 22 [...] creates learning design STL data (i.e. vehicle exterior silhouettes) representing the vehicle shape of each learning vehicle created through the morphing process and enters the created learning design STL data into the design data storage unit 11.” [P.0110]. Also, STL data is interpreted to include vehicle exterior silhouettes because “a silhouette image may be created from the STL data and the feature points may be determined using the profile line of the silhouette image.” [P.0222]. A person of ordinary skill in the art would conclude that a “silhouette image” used in this context (i.e. aerodynamic performance determination) is a 2D side-view silhouette. Additionally, Fukushige [P.0072] discloses “ FIG. 11 is a diagram illustrating profile shape data according to the first embodiment.” See Fig.11(b) below.)
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Thus, Applicant’s argument not persuasive.
For reasons similar to those provided above with respect to claim 1, rejections to independent claims 8 and 16 are maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
IN GENERAL.—The specification shall contain a written description of the
invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 3, 13 and 19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 3, 13, and 19 all recite “manufacturing a vehicle”. The subject matter of this limitation is not properly described in the application as filed. The manner and process of manufacturing a vehicle is not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor(s), at the time the application was filed, had possession of the claimed 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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception (an abstract idea), as it has not been integrated into a practical application and the claim(s) further do/does not recite significantly more than the judicial exception. Examiner has evaluated the claim(s) under the framework provided in MPEP 2106 and has provided such analysis below.
To determine if a claim is directed to patent ineligible subject matter, the Court
has guided the Office to apply the Alice/Mayo test, which requires:
Step 1. Determining if the claim falls within a statutory category of a Process, Machine, Manufacture, or a Composition of Matter (see MPEP 2106.03);
Step 2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea (MPEP 2106.04);
Step 2A is a two-prong inquiry. MPEP 2106.04(II)(A).
Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2106.04(a)(2).
The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Step 1:
Claims 1-7 are directed to a system, as such these claims fall within the statutory category of a machine.
Claims 8-20 are directed to a method, as such these claims fall within the statutory category of a process.
Step 2A, Prong 1:
The examiner submits that the foregoing claim limitations constitute abstract ideas, as the claims cover mental processes performed on a generic computer, given the broadest reasonable interpretation.
In order to apply Step 2A, a recitation of claims is copied below. The limitations of those claims which describe an abstract idea are bolded.
As per claim 1, the claim recites the limitations of:
determine a plurality of candidate vehicle exterior silhouettes by modifying a first subset of vehicle attributes that affect aerodynamic performance, wherein each of the plurality of candidate vehicle exterior silhouettes is a two-dimensional side-view vehicle exterior silhouette; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) which are defined as concepts that can practically be performed in the human mind (e.g. observations, evaluations, judgments, opinions), or by a human using pen and paper as a physical aid. This limitation is directed towards performing a mental process on a generic computer, since a person can reasonably perform the limitation with or without the aid of pen and paper. For instance, a person can reasonably determine (i.e. evaluate, judge) a plurality of 2D side-view vehicle exterior silhouettes by modifying a subset of vehicle attributes that affect aerodynamic performance with/without the aid of pen and paper.)
select, from the plurality of candidate vehicle exterior silhouettes, a first candidate vehicle exterior silhouette based at least in part on the aerodynamic properties (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)). This limitation is directed towards performing a mental process on a generic computer, since a person can reasonably perform the limitation with or without the aid of pen and paper. For instance, a person can evaluate vehicle aerodynamic properties and then decide which candidate vehicle exterior silhouette to select, based on those properties.)
determine, and based on the candidate vehicle exterior silhouettes, a second subset of vehicle attributes different from the first subset of vehicle attributes, wherein the second subset of vehicle attributes do not affect the aerodynamic properties of the candidate vehicle exterior silhouette and do not alter an outline of the first candidate vehicle exterior silhouette; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)). This limitation is directed towards performing a mental process on a generic computer, since a person can reasonably perform the limitation with or without the aid of pen and paper. For instance, a person can evaluate the candidate vehicle exterior silhouettes and then determine (i.e. evaluate, judge) a different, second subset of vehicle attributes which do not affect aerodynamic attributes nor alter the outline of the first candidate vehicle exterior silhouette.)
and generate,an image of a vehicle, wherein the image depicts three-dimensional features of the vehicle, and wherein the vehicle depicted in the image has the outline that corresponds to the first candidate vehicle exterior silhouette and has both the first subset of vehicle attributes and the second subset of vehicle attributes (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)). This limitation is directed towards performing a mental process on a generic computer, since a person can reasonably perform the limitation with or without the aid of pen and paper. For instance, a person can reasonably evaluate a first and second subset of vehicle attributes and then draw, with the aid of pen/paper, an image depicting 3D features of a vehicle.)
Step 2A, Prong 2:
As per claim 1, this judicial exception is not integrated into a practical application because the additional claim limitations outside the abstract idea only present Insignificant Extra Solution Activity and/or Mere Instructions To Apply An Exception and/or Field Of Use And Technological Environment. In particular, the claim recites the additional limitations:
perform computational fluid dynamics (CFD) simulations to determine aerodynamic properties for the plurality of candidate vehicle exterior silhouettes; (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, this limitation is directed towards mere instructions to implement an abstract idea (i.e. mental process) on a computer. When determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. The limitation as drafted falls within all 3 “apply it” rationales – see MPEP 2106.05(f).)
provide the first candidate vehicle exterior silhouette as an input to a generative adversarial network (GAN) that is trained to learn aesthetic preferences for vehicle attributes; (The additional element amounts to Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process.)
by the GAN (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f) and/or Filed of Use and Technological Environment per MPEP 2106.05(h). Specifically, this limitation is directed towards mere instructions to implement an abstract idea (i.e. mental process) on a computer. For instance, a person can reasonably consider various vehicle exterior silhouettes and then determine (i.e. evaluate, judge) a second subset of vehicle attributes. Also, the limitation amounts to generally linking the use of a judicial exception (i.e. mental process) to a particular technological environment (i.e. GAN) or field of use.)
, by the GAN and based at least in part on the input, (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f) and/or Filed of Use and Technological Environment per MPEP 2106.05(h). Specifically, this limitation is directed towards mere instructions to implement an abstract idea (i.e. mental process) on a computer. For instance, a person can reasonably consider various criteria (i.e. the input) and then draw (i.e. generate) an image of a vehicle based on that criteria. Also, the limitation amounts to generally linking the use of a judicial exception (i.e. mental process) to a particular technological environment (i.e. GAN) or field of use.)
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B:
For step 2B of the analysis, the Examiner must consider whether each claim limitation individually or as an ordered combination amounts to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The additional elements as described in Step 2A Prong 2 are not sufficient to amount to significantly more than the judicial exception because the additional limitations are considered directed towards insignificant extra-solution activity and/or mere instructions to apply an exception and/or field of use and technological environment. Thus, for example, claims that amount to nothing more than insignificant extra-solution activity and instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. See MPEP 2106.05(d)/(f)/(g)/(h).
Per MPEP 2106.05(d)(II), “courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).”
For the foregoing reasons, claim 1 is directed to an abstract idea without significantly more and is rejected as not patent eligible under 35 U.S.C. 101.
Step 2A, Prong 1 (Claim 8):
The examiner submits that the foregoing claim limitations constitute abstract ideas, as the claims cover mental processes performed on a generic computer, given the broadest reasonable interpretation. In order to apply Step 2A, a recitation of claims is copied below. The limitations of those claims which describe an abstract idea are bolded.
Per independent claim 8, the claim recites the limitations of:
determining a plurality of candidate vehicle exterior silhouettes by modifying a first subset of vehicle attributes that affect aerodynamic performance, wherein each of the plurality of candidate vehicle exterior silhouettes is a two-dimensional side-view vehicle exterior silhouette; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) which are defined as concepts that can practically be performed in the human mind (e.g. observations, evaluations, judgments, opinions), or by a human using pen and paper as a physical aid. This limitation is directed towards performing a mental process on a generic computer, since a person can reasonably perform the limitation with or without the aid of pen and paper. For instance, a person can reasonably determine (i.e. evaluate, judge) a plurality of 2D side-view vehicle silhouettes by modifying a subset of vehicle attributes that affect aerodynamic performance with/without the aid of pen and paper.)
selecting, from the plurality of candidate vehicle exterior silhouettes, a first candidate vehicle exterior silhouette based at least in part on the aerodynamic properties (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)). This limitation is directed towards performing a mental process on a generic computer, since a person can reasonably perform the limitation with or without the aid of pen and paper. For instance, a person can evaluate vehicle aerodynamic properties and then decide which candidate vehicle exterior silhouette to select, based on those properties.)
determining, and based on the candidate vehicle exterior silhouettes, a second subset of vehicle attributes different from the first subset of vehicle attributes, wherein the second subset of vehicle attributes do not affect the aerodynamic properties of the candidate vehicle exterior silhouette and do not alter an outline of the first candidate vehicle exterior silhouette; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)). This limitation is directed towards performing a mental process on a generic computer, since a person can reasonably perform the limitation with or without the aid of pen and paper. For instance, a person can evaluate the candidate vehicle exterior silhouettes and then determine (i.e. evaluate, judge) a different, second subset of vehicle attributes which do not affect aerodynamic attributes nor alter the outline of the first candidate vehicle exterior silhouette.)
and generating, by the GAN and based at least in part on the input, an image of a vehicle, wherein the image depicts three-dimensional features of the vehicle, and wherein the vehicle depicted in the image has the outline that corresponds to the first candidate vehicle exterior silhouette and has both the first subset of vehicle attributes and the second subset of vehicle attributes (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)). This limitation is directed towards performing a mental process on a generic computer, since a person can reasonably perform the limitation with or without the aid of pen and paper. For instance, a person can reasonably evaluate a first and second subset of vehicle attributes and then draw, with the aid of pen/paper, an image depicting 3D features of a vehicle.)
Step 2A, Prong 2 (Claim 8):
As per claim 8, this judicial exception is not integrated into a practical
application because the additional claim limitations outside the abstract idea only
present Insignificant Extra Solution Activity and/or Mere Instructions To Apply An Exception and/or Field Of Use And Technological Environment. In particular, the claim recites the additional limitations:
performing computational fluid dynamics (CFD) simulations to determine aerodynamic properties for the plurality of candidate vehicle exterior silhouettes; (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, this limitation is directed towards mere instructions to implement an abstract idea (i.e. mental process) on a computer. When determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. The limitation as drafted falls within all 3 “apply it” rationales – see MPEP 2106.05(f).)
providing the first candidate vehicle exterior silhouette as an input to a generative adversarial network (GAN) that is trained to learn aesthetic preferences for vehicle attributes; (The additional element amounts to Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process.)
by the GAN (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f) and/or Filed of Use and Technological Environment per MPEP 2106.05(h). Specifically, this limitation is directed towards mere instructions to implement an abstract idea (i.e. mental process) on a computer. For instance, a person can reasonably consider various vehicle exterior silhouettes and then determine (i.e. evaluate, judge) a second subset of vehicle attributes. Also, the limitation amounts to generally linking the use of a judicial exception (i.e. mental process) to a particular technological environment (i.e. GAN) or field of use.)
, by the GAN and based at least in part on the input, (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f) and/or Filed of Use and Technological Environment per MPEP 2106.05(h). Specifically, this limitation is directed towards mere instructions to implement an abstract idea (i.e. mental process) on a computer. For instance, a person can reasonably consider various criteria (i.e. the input) and then draw (i.e. generate) an image of a vehicle based on that criteria. Also, the limitation amounts to generally linking the use of a judicial exception (i.e. mental process) to a particular technological environment (i.e. GAN) or field of use.)
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B (Claim 8):
For step 2B of the analysis, the Examiner must consider whether each claim limitation individually or as an ordered combination amounts to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The additional elements as described in Step 2A Prong 2 are not sufficient to amount to significantly more than the judicial exception because the additional limitations are considered directed towards insignificant extra-solution activity and/or mere instructions to apply an exception and/or field of use and technological environment. Thus, for example, claims that amount to nothing more than insignificant extra-solution activity and instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. See MPEP 2106.05(d)/(f)/(g)/(h).
Per MPEP 2106.05(d)(II), “courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking).”
For the foregoing reasons, claim 8 is directed to an abstract idea without significantly more and is rejected as not patent eligible under 35 U.S.C. 101.
Step 2A, Prong 1 (Claim 16):
The examiner submits that the foregoing claim limitations constitute abstract ideas, as the claims cover mental processes, given the broadest reasonable interpretation. In order to apply Step 2A, a recitation of claims is copied below. The limitations of those claims which describe an abstract idea are bolded.
Per independent claim 16, the claim recites the limitations of:
determining, , a second subset of vehicle attributes for a plurality of vehicle images inferred to have at least a threshold level of aesthetic preference; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) which are defined as concepts that can practically be performed in the human mind (e.g. observations, evaluations, judgments, opinions), or by a human using pen and paper as a physical aid. This limitation is directed towards performing a mental process on a generic computer, since a person can reasonably perform the limitation with or without the aid of pen and paper. For instance, a person can reasonably determine (i.e. evaluate, judge) a second subset of vehicle attributes for a plurality of vehicle images inferred (i.e. evaluate, opinion) to have at least a threshold level of aesthetic preference.)
determining, based on the plurality of vehicle images, a corresponding plurality of vehicle exterior silhouettes and first subset of vehicle attributes that affect aerodynamic properties of the plurality of vehicle exterior silhouettes, wherein each of the plurality of vehicle exterior silhouettes is a two-dimensional side-view vehicle exterior silhouette, and wherein the second subset of vehicle attributes do not affect the aerodynamic properties of the plurality of vehicle exterior silhouettes; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)). This limitation is directed towards performing a mental process on a generic computer, since a person can reasonably perform the limitation with or without the aid of pen and paper. For instance, a person can reasonably determine (i.e. evaluate, judge) a plurality of 2D side-view vehicle exterior silhouettes and vehicle attributes that affect and don’t affect the vehicle aerodynamic properties.)
Step 2A, Prong 2 (Claim 16):
As per claim 16, this judicial exception is not integrated into a practical
application because the additional claim limitations outside the abstract idea only
present Insignificant Extra Solution Activity and/or Mere Instructions To Apply An Exception and/or Filed of Use and Technological Environment. In particular, the claim recites the additional limitations:
obtaining a generative adversarial network (GAN) that is trained to learn aesthetic preferences for vehicle attributes; (The additional limitation amounts to Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process.)
, by the GAN, (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f) and/or Filed of Use and Technological Environment per MPEP 2106.05(h). Specifically, this limitation is directed towards mere instructions to implement an abstract idea (i.e. mental process) on a computer. For instance, a person can reasonably consider various vehicle exterior silhouettes and then determine (i.e. evaluate, judge) a second subset of vehicle attributes. Also, the limitation amounts to generally linking the use of a judicial exception (i.e. mental process) to a particular technological environment (i.e. GAN) or field of use.)
and performing computational fluid dynamics (CFD) simulations to determine corresponding aerodynamic properties for the plurality of vehicle exterior silhouettes. (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, this limitation is directed towards mere instructions to implement an abstract idea (i.e. mental process) on a computer. When determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. The limitation as drafted falls within all 3 “apply it” rationales – see MPEP 2106.05(f).)
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B (Claim 16):
For step 2B of the analysis, the Examiner must consider whether each claim limitation individually or as an ordered combination amounts to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The additional elements as described in Step 2A Prong 2 are not sufficient to amount to significantly more than the judicial exception because the additional limitations are considered directed towards Insignificant Extra-Solution Activity and/or Mere Instructions To Apply An Exception and/or Filed of Use and Technological Environment. Thus, for example, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. See MPEP 2106.05(d)/(f)/(g)/(h).
Per MPEP 2106.05(d), The courts have recognized the following computer functions as well‐understood, routine, and conventional functions: i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory.
For the foregoing reasons, claim 16 is directed to an abstract idea without significantly more and is rejected as not patent eligible under 35 U.S.C. 101.
Claim 2 further recites, wherein the second subset of vehicle attributes comprises at least: color, shape, and wheel design. The additional limitation elaborates on the attributes previously established to have been determined by mental processes, and as such is considered to further recite a Mental Process per MPEP 2106.04(a)(2)(III). Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 3 further recites, manufacture the vehicle according to the first vehicle exterior silhouette and having both the first subset of vehicle attributes and the second subset of vehicle attributes The additional limitation amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. For instance, how does a system comprising one or more processors manufacture a vehicle? Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 4 further recites, receive a latent code as the input, in a first latent space; (The additional limitation amounts to Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process.)
determine, based on a non-linear mapping network, an intermediate vector, in a second latent space; (The additional limitations further amount to Mere Instructions to Apply an Exception per MPEP 2106.05(f). The limitation as drafted falls within all 3 “apply it” rationales – see MPEP 2106.05(f).)
and provide the intermediate vector as an input to one or more affine transformations of a synthesis network that synthesizes an image based on the aesthetic preferences. (The additional limitation amounts to Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process.)
Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 5, the system of claim 4, further recites, the non-linear mapping network comprises 5-10 fully connected layers; and the synthesis network comprises 15-20 layers. The additional limitations elaborate on the GAN, previously determined as Mere Instructions to Apply an Exception per MPEP 2106.05(f). Thus, the claim is further directed towards Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 6, the system of claim 5, further recites, wherein each layer of the synthesis network comprises a Gaussian noise input. The additional limitations elaborate on the GAN, previously determined as Mere Instructions to Apply an Exception per MPEP 2106.05(f). Thus, the claim is further directed towards Mere Instructions to Apply an Exception per MPEP 2106.05(f). Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 7 further recites, determine, for each respective candidate vehicle exterior silhouette of the plurality of vehicle exterior silhouettes, a respective first score associated with one or more functional aspects of a candidate vehicle and a respective second score associated with one or more non-functional aspects of the candidate vehicle; and determine a Pareto front based on the first scores and the second scores of the plurality of candidate vehicle exterior silhouettes. The additional limitations are further directed towards mental processes (performing a mental process on a generic computer) per MPEP 2106.04(a)(2)(III), since the limitations can reasonably be performed by a person with or without the aid of pen and paper. For instance, a person can reasonably evaluate each respective candidate vehicle exterior silhouettes and then determine a first/second score (i.e. evaluate, opinion). Also, a person can reasonably determine/calculate a Pareto front based on previously determined scores with the aid of pen/paper. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 9, the method of claim 8, recites substantially the same subject matter as claim 2 and is rejected under similar rationale. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 10, the method of claim 8, further recites wherein the first candidate vehicle exterior silhouette is selected based on aerodynamic drag coefficient determined by the CFD simulations for the plurality of candidate vehicle exterior silhouette. The additional limitations elaborate on the selection of the first candidate vehicle exterior silhouette in claim 8, from which this claim depends, thus is further directed towards mental processes (performing a mental process on a generic computer) per MPEP 2106.04(a)(2)(III). Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 11, the method of claim 8, recites substantially the same subject matter as claim 4 and is rejected under similar rationale. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 12, the method of claim 11, recites substantially the same subject matter as claim 5 and is rejected under similar rationale. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 13, the method of claim 8, recites substantially the same subject matter within claim 3 and is rejected under similar rationale. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 14, the method of claim 11, recites substantially the same subject matter as claim 6 and is rejected under similar rationale. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 15, the method of claim 8, recites substantially the same subject matter as claim 7 and is rejected under similar rationale. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 17, the method of claim 16, recites substantially the same subject matter as claim 4 and is rejected under similar rationale. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 18, the method of claim 17, recites substantially the same subject matter as claim 5 and is rejected under similar rationale. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 19, the method of claim 16, recites substantially the same subject matter within claim 3 and is rejected under similar rationale. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
Claim 20, the method of claim 17, recites substantially the same subject matter as claim 6 and is rejected under similar rationale. Therefore, the claim is not patent eligible under 35 U.S.C. 101.
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.
Claims 1-2, 8-10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Fukushige et al. US Pub. No. 2013/0346047 A1 (hereinafter referred to as “Fukushige”) in view of Nie et al. US Pub. No. 2022/0012596 A1 (hereinafter referred to as “Nie”).
Regarding claim 1, Fukushige discloses, A system, comprising: one or more processors; and memory storing executable instructions that, as a result of execution by the one or more processors, cause the one or more processors to: (“The performance predicting apparatus 1 is embodied by one or more computers and includes a storage unit 10” [P.0097])
determine a plurality of candidate vehicle exterior silhouettes by modifying a first subset of vehicle attributes that affect aerodynamic performance, wherein each of the plurality of candidate vehicle exterior silhouettes is a two-dimensional side-view vehicle exterior silhouette; (“The base design data creating unit 21 creates base design STL data (i.e. exterior silhouettes) from CAD (Computer Aided Design) data representing the vehicle shape of a base vehicle and enters the created base design STL data into the design data storage unit 11. The morphing unit 22 deforms the vehicle shape (i.e. modifies a first subset of vehicle attributes) of the base vehicle represented by the base design STL data based on determined conditions through a morphing technique [...] to create the vehicle shape of a learning vehicle, and enters the learning design STL data representing the created vehicle shape into the design data storage unit 11 [...] The learning data creating unit 30 reads the base design STL data and the learning design STL data [...] The learning data creating unit 30 calculates the aerodynamic performance values” [P.0101-0102]. STL data is interpreted to include a plurality of candidate vehicle exterior silhouettes because “the morphing unit 22 [...] creates learning design STL data (i.e. vehicle exterior silhouettes) representing the vehicle shape of each learning vehicle created through the morphing process and enters the created learning design STL data into the design data storage unit 11.” [P.0110]. Also, STL data is interpreted to include vehicle exterior silhouettes because “a silhouette image may be created from the STL data and the feature points may be determined using the profile line of the silhouette image.” [P.0222]. A person of ordinary skill in the art would conclude that a “silhouette image” used in this context (i.e. aerodynamic performance determination) is a 2D side-view silhouette. Additionally, Fukushige [P.0072] discloses “ FIG. 11 is a diagram illustrating profile shape data according to the first embodiment.” See Fig.11(b) below.)
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perform computational fluid dynamics (CFD) simulations to determine aerodynamic properties for the plurality of candidate vehicle exterior silhouettes; (“The learning data creating unit 30 calculates the aerodynamic performance values and calculates the feature values using CFD (Computational Fluid Dynamics) and the STL data (i.e. plurality of candidate vehicle exterior silhouettes) used to create the learning data.” [P.0102])
select, from the plurality of candidate vehicle exterior silhouettes, a first candidate vehicle exterior silhouette based at least in part on the aerodynamic properties (“The approximate model applying unit 70 selects an approximate model (includes candidate vehicle exterior silhouettes – see FIG.3) to be used based on the feature values (includes aerodynamic properties – see FIG.3) of the design vehicle extracted by the feature value extracting unit 60.” [P.0103])
provide the first candidate vehicle exterior silhouette as an input that is trained to learn aesthetic preferences for vehicle attributes; (”Therefore, the learning data creating unit 30 acquires the exterior feature values representing the exterior features of the vehicle from the (i.e. input) STL data (i.e. includes vehicle outline shape) used to create the learning data and the test design STL data, and creates exterior feature data (i.e. aesthetic preferences for vehicle attributes) which is multidimensional data having the acquired exterior feature values as elements” [P.0113]. STL data is interpreted to include vehicle outline shapes because “a silhouette image may be created from the STL data” [P.0222] and exterior feature data is interpreted as aesthetic preferences for vehicle attributes because “The exterior of a vehicle is multi-dimensionally expressed, for example, using values such as a vehicle height, a vehicle width, a vehicle length, an A-pillar angle, and a wheel base as elements (i.e. aesthetic vehicle attributes).” [P.0113])
determine, and based on the candidate vehicle exterior silhouette, a second subset of vehicle attributes different from the first subset of vehicle attributes; (“The approximate model applying unit 70 creates sensitivity display screen data in which parts of a vehicle body (body) of the design vehicle are displayed in colors (i.e. second subset of vehicle attributes different from the first subset) corresponding to the sensitivity of the Cd value (i.e. calculated aerodynamic performance value [P.0120]) of the corresponding parts, and outputs the created sensitivity display screen data to the designer terminal [...] the colors of the parts determined based on the approximate models (i.e. includes candidate vehicle exterior silhouette)” [P.0121]. Design vehicle body colors are interpreted as a second subset of vehicle attributes due to Applicant’s disclosure “The output 206 of the GAN may be a set of output parameters produced by the GAN that correspond to different aesthetic styles for the given shape, such as different colors, lighting, visual appearance, wheel design, aspect ratio, etc., all of which are constructed from the input exterior shape.” Spec [P.0019])
wherein the second subset of vehicle attributes do not affect the aerodynamic properties of the candidate vehicle exterior silhouette and do not alter an outline of the first candidate vehicle exterior silhouette (As shown above, the second subset of vehicle attributes are vehicle body part colors, which do not affect aerodynamic properties nor alter an outline of the vehicle exterior silhouette.)
and generate, an image of a vehicle, wherein the image depicts three-dimensional features of the vehicle, (“The input unit 91 includes a keyboard or a mouse, and receives an input from a designer. The display unit 93 is a display and displays an image. The design creating unit 92 [...] creates design data representing a vehicle shape of a design vehicle based on information input through the input unit 91. The design creating unit 92 receives the aerodynamic performance values of a design vehicle [...] and displays the received aerodynamic performance values on the display unit 93.” [P.0105]. The output of the display unit is interpreted as a 3D image because “ FIG. 1 (see below) is a diagram illustrating schematic processes in a performance predicting system” [P.0094].)
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and wherein the vehicle depicted in the image has the outline that corresponds to the first candidate vehicle exterior silhouette and has both the first subset of vehicle attributes and the second subset of vehicle attributes. (Reference FIG.2 process below. The image displayed within display unit 93 is an output from the disclosed process that includes the outline that corresponds to the first candidate vehicle exterior silhouette and has both the first subset of vehicle attributes and the second subset of vehicle attributes.)
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Fukushige fails to specifically disclose a generative adversarial network (GAN), determine by the GAN a set of vehicle attributes, and generate by the GAN and based at least in part on the input, an image.
However, the analogous art of Nie discloses, a generative adversarial network (GAN) (“training data 104 is used as input by a training framework 106 to train 102 one or more untrained neural networks 108 using a generative adversarial network (GAN)... training data 104 is a set of images or image data as well as optional labels or classifications in order to provide a set of examples on which one or more untrained neural networks 108 learns to perform a function, such as generation of an image 118 containing a feature specified in a factor code 114” Nie [P.0058]),
determine by the GAN a set of vehicle attributes (“at least one embodiment pertains to processors or computing systems used to train a neural network (i.e. GAN) to determine one or more styles for an input image and then generate features (i.e. attributes) associated with said one or more styles in an output image, where output images comprising features are generated by one or more neural networks“ Nie [P.0001]),
generate by the GAN, and based at least in part on the input, an image (“FIG. 1 is a block diagram illustrating an architecture for training a neural network to generate an output image comprising one or more features specified in a factor code based on an input image,” Nie [P.0003])
Fukushige and Nie are analogous art as they’re both from the same field of endeavor as the claimed invention. Fukushige discloses an apparatus, method, and program which predicts the “functional performance of an object from its shape” [P.0003], specifically vehicle shape aerodynamic performance (computational fluid dynamics (CFD)), as does the claimed invention. Nie discloses apparatuses, systems, and techniques “used to train a neural network to determine one or more styles for an input image and then generate features associated with said one or more styles in an output image, where output images comprising features are generated by one or more neural networks” [P.0001], specifically utilizing a StyleGAN architecture, as does the claimed invention.
Therefore, it would have been obvious to one of ordinary skill in the art before the Applicant' s effective filling date of the claimed invention to have combined the vehicle shape CFD simulation (i.e. aerodynamic simulations), as Fukushige discloses, with a generative adversarial network (GAN), as Nie discloses, in order “to predict functional performance with high accuracy when the amount of design information of an object is large.” Fukushige [P.0006].
Regarding claim 2, Fukushige in view of Nie disclose the system of claim 1, Fukushige further discloses, wherein the second subset of vehicle attributes comprises at least: color, shape, and wheel design. (“The exterior of a vehicle is multi-dimensionally expressed, for example, using values such as a vehicle height, a vehicle width, a vehicle length, an A-pillar angle, and a wheel base (i.e. wheel design) as elements (i.e. vehicle attributes).” Fukushige [P.0113], “Elements other than the length d and the angle Θ may be used as the elements (i.e. vehicle attributes) of the profile shape data. For example, values (such as brightness value, RGB value (i.e. color), and spatial frequency of brightness) relevant to brightness of the points on the profile line may be introduced as the elements.” Fukushige [P.0221])
Independent claim 8 recites substantially the same subject matter as claim 1 and is rejected under similar rationale.
Claim 9, the method of claim 8, recites substantially the same subject matter as claim 2 and is rejected under similar rationale.
Regarding Claim 10, Fukushige in view of Nie disclose the method of claim 8,
Fukushige further discloses, wherein the first candidate vehicle exterior silhouette is selected based on aerodynamic drag coefficient determined by the CFD simulations for the plurality of candidate vehicle exterior silhouette. (“The approximate model applying unit 70 selects an approximate model (includes candidate vehicle exterior silhouettes – see FIG.3) to be used based on the feature values (includes aerodynamic performance value i.e. drag coefficient – see FIG.3) of the design vehicle extracted by the feature value extracting unit 60.” [P.0103]. The aerodynamic drag coefficient is interpreted as determined for the plurality of candidate vehicle exterior silhouette shapes because “The approximate models are used to calculate an aerodynamic performance value (that is, a coefficient of drag which will be, hereinafter, referred to as "Cd value") as functional performance of a design vehicle using the feature values from shape data of the vehicle (hereinafter, referred to as "design vehicle") [P.0291])
Regarding independent Claim 16, Fukushige discloses that is trained to learn aesthetic preferences for vehicle attributes; (”Therefore, the learning data creating unit 30 acquires the exterior feature values representing the exterior features of the vehicle from the STL data used to create the learning data and the test design STL data, and creates exterior feature data (i.e. aesthetic preferences for vehicle attributes) which is multidimensional data having the acquired exterior feature values as elements” [P.0113].)
determining, , a second subset of vehicle attributes for a plurality of vehicle images of aesthetic preference; (“The approximate model applying unit 70 creates sensitivity display screen data in which parts of a vehicle body (i.e. images) of the design vehicle are displayed in colors (i.e. second subset of vehicle attributes) corresponding to the sensitivity of the Cd value of the corresponding parts, and outputs the created sensitivity display screen data to the designer terminal (display unit 93 – see FIG.2) [...] the colors of the parts determined based on the approximate models (i.e. plurality of vehicles)” [P.0121]. Design vehicle body colors are interpreted as a second subset of vehicle attributes of aesthetic preference due to Applicant’s disclosure “The output 206 of the GAN may be a set of output parameters produced by the GAN that correspond to different aesthetic styles for the given shape, such as different colors, lighting, visual appearance, wheel design, aspect ratio, etc., all of which are constructed from the input exterior shape.” Spec [P.0019])
determining, based on the plurality of vehicle images, a corresponding plurality of vehicle exterior silhouettes and first subset of vehicle attributes that affect aerodynamic properties of the plurality of vehicle exterior silhouettes, wherein each of the plurality of vehicle exterior silhouettes is a two-dimensional side-view vehicle exterior silhouette, (”The feature point determining unit 50 matches a profile shape (i.e. first subset of vehicle attributes) of a cross section of a vehicle represented by the STL data (i.e. vehicle exterior silhouettes) of the design vehicle created in step S210 with a profile shape of a cross section of the vehicle represented by the base design STL data of the base vehicle most similar to the design vehicle” [P.0117]. Profile shape is understood to affect aerodynamic properties and is interpreted as a vehicle attribute due to Applicant’s disclosure “vehicle attributes comprises at least: color, shape, and wheel design” Spec. [P.0038]. STL data is interpreted to include vehicle exterior silhouettes because “a silhouette image may be created from the STL data” Fukushige [P.0222]. A person of ordinary skill in the art would conclude that a “silhouette image” used in this context (i.e. aerodynamic performance determination) is a 2D side-view silhouette. Additionally, Fukushige [P.0072] discloses “ FIG. 11 is a diagram illustrating profile shape data according to the first embodiment.” See Fig.11(b) below.)
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and wherein the second subset of vehicle attributes do not affect the aerodynamic properties of the plurality of vehicle exterior silhouettes (the second subset of vehicle attributes are vehicle body part colors, which do not affect aerodynamic properties nor the vehicle exterior silhouette.); and
performing computational fluid dynamics (CFD) simulations to determine corresponding aerodynamic properties for the plurality of vehicle exterior silhouettes. (“The learning data creating unit 30 creates spatial lattices for the vehicle shape represented by the STL data (i.e. vehicle exterior silhouettes) used to create the learning data, calculates the pressure and the velocity distribution for each lattice through the CFD, and acquires the aerodynamic performance values therefrom” [P.0112])
Fukushige fails to specifically disclose obtaining a generative adversarial network (GAN) and determining, by the GAN, a plurality of vehicle images inferred to have at least a threshold level of aesthetic preference.
However, the analogous art Nie discloses, obtaining a generative adversarial network (GAN) (“training data 104 is used as input by a training framework 106 to train 102 one or more untrained neural networks 108 using a generative adversarial network (GAN)... training data 104 is a set of images or image data as well as optional labels or classifications in order to provide a set of examples on which one or more untrained neural networks 108 learns to perform a function, such as generation of an image 118 containing a feature specified in a factor code 114” Nie [P.0058]),
determining, by the GAN, a plurality of vehicle images inferred to have at least a threshold level of aesthetic preference. (“Inference and/or training logic 715 are used to perform inferencing and/or training operations associated with one or more embodiments” Nie [P.0169], “In at least one embodiment, a discriminator 212 discriminates (i.e. to have at least a threshold level) between different data instances, such as categorizing an input data item or image as true or false, real or generated.” Nie [P.0073]. The discriminator is interpreted to disclose at least a threshold level of preference since to discriminate requires recognition of a distinction (i.e. threshold level) from which to discriminate against.)
Fukushige and Nie are analogous art as they’re both from the same field of endeavor as the claimed invention. Fukushige discloses an apparatus, method, and program which predicts the “functional performance of an object from its shape” [P.0003], specifically vehicle shape aerodynamic performance (computational fluid dynamics (CFD)), as does the claimed invention. Nie discloses apparatuses, systems, and techniques “used to train a neural network to determine one or more styles for an input image and then generate features associated with said one or more styles in an output image, where output images comprising features are generated by one or more neural networks” [P.0001], specifically utilizing a StyleGAN architecture, as does the claimed invention.
Therefore, it would have been obvious to one of ordinary skill in the art before the Applicant' s effective filling date of the claimed invention to have combined the vehicle shape CFD simulation (i.e. aerodynamic simulations), as Fukushige discloses, with a generative adversarial network (GAN) and inferred threshold level, as Nie discloses, “to determine one or more styles for an input image and then generate features associated with said one or more styles in an output image, where output images comprising features are generated by one or more neural networks” Nie [P.0001].
Claims 3, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fukushige et al. US Pub. No. 2013/0346047 A1 (hereinafter referred to as “Fukushige”) in view of Nie et al. US Pub. No. 2022/0012596 A1 (hereinafter referred to as “Nie”), in further view of Swallow et al. US Pub. No. 2020/0307102 A1 (hereinafter referred to as “Swallow”).
Regarding claim 3, Fukushige in view of Nie disclose the system of claim 1, but fail to specifically disclose the limitations of claim 3.
However, Swallow discloses wherein the system is further configured to: manufacture the vehicle (“the present disclosure is directed to a system for fabricating a custom vehicle. The system may include an additive manufacturing machine, and at least one computing device operatively connected to the additive manufacturing machine. The at least one computing device includes a memory having computer-executable instructions stored thereon and a processor configured to execute the computer-executable instructions to receive from a user a selection of available components that can be built into the custom vehicle, and to generate a unique model of the custom vehicle based on the selection. The processor is further configured to execute the computer-executable instructions to generate code associated with the unique model, and to direct the code to the additive manufacturing machine, causing the additive manufacturing machine to additively manufacture at least a portion of the custom vehicle.” [P.0008]) (see Claim 1 Fukushige)
Swallow is analogous art as it relates to systems and methods for additive manufacturing of custom vehicles. Its intended use is to enable users to design, customize, and fabricate custom vehicles.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the Fukushige-Nie combination to include vehicle model manufacturing technology, as that of Swallow, in order to physically fabricate/manufacture an established vehicle design.
Claim 13, the method of claim 8, recites substantially the same subject matter as claim 3 and is rejected under similar rationale.
Claim 19, the method of claim 16, recites substantially the same subject matter as claim 3 and is rejected under similar rationale.
Claims 4-6, 11-12, 14, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fukushige et al. US Pub. No. 2013/0346047 A1 (hereinafter referred to as “Fukushige”) in view of Nie et al. US Pub. No. 2022/0012596 A1 (hereinafter referred to as “Nie”), in further view of Karras, Tero, Samuli Laine, and Timo Aila. "A style-based generator architecture for generative adversarial networks." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4401-4410. 2019 (hereinafter referred to as “Karras”).
Regarding claim 4, Fukushige in view of Nie disclose the system of claim 1, although Fukushige fails to specifically disclose wherein the GAN is further configured to: receive a latent code as the input, in a first latent space, determine, based on a non-linear mapping network, an intermediate vector, in a second latent space; and provide the intermediate vector as an input to one or more affine transformations of a synthesis network that synthesizes an image based on the aesthetic preferences. However, Nie further discloses, wherein the GAN is further configured to: receive a latent code as the input, in a first latent space; (“In at least one embodiment, an input 302 is latent code. In at least one embodiment, latent code is a set of data elements wherein similar data elements are closer together in space. If input 302 is an image, in an embodiment, said input image is converted to latent space” Nie [P.0080])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fukushige’s method to include a latent code as an input to the GAN, as Nie discloses, since it “has a profound effect on how
the factors of variation are represented in the network” Nie [Pg.4401 Col.2 P.2].
However, the Fukushige-Nie combination fails to specifically disclose, determine, based on a non-linear mapping network, an intermediate vector, in a second latent space; and provide the intermediate vector as an input to one or more affine transformations of a synthesis network that synthesizes an image based on the aesthetic preferences.
On the other hand, the analogous art Karras discloses determine, based on a non-linear mapping network, an intermediate vector, in a second latent space; (“Figure 1. While a traditional generator [28] feeds the latent code though the input layer only, we first map the input to an intermediate latent space W, which then controls the generator through adaptive instance normalization (AdaIN) at each convolution layer.” Karras [Pg.4402 Fig.1]. From Fig.1(b) (see Fig.1 below), “w” is interpreted as the intermediate vector.) and provide the intermediate vector as an input to one or more affine transformations of a synthesis network that synthesizes an image (As seen in Fig.1(b) below, w (i.e. intermediate vector) is an input to one or more affine transformations (i.e. A) of a synthesis network. “A” is interpreted as affine transformations because “Here “A” stands for a learned affine transform“ Karras [Pg.4402 P.1]) based on the aesthetic preferences (See Fukushige Claim 1).
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Karras is analogous art as it relates to a style based GAN, as does the claimed invention. Karras teaches “an alternative generator architecture for generative adversarial networks” [Abstract], specifically a style-based generator.
Therefore, it would have been obvious to one of ordinary skill in the art before the Applicant' s effective filling date of the claimed invention to have modified the Fukushige - Nie combination to include an intermediate vector as an input to one or more affine transformations of a synthesis network, as Karras discloses, in order to “control the synthesis process [ ] therefore directly controlling the strength of image features at different scales” Karras [Pg.4401 Sec.1 P.2].
Regarding claim 5, Fukushige in view of Nie in further view of Karras disclose the system of claim 4, although Fukushige fails to specifically disclose the non-linear mapping network comprises 5-10 fully connected layers; and the synthesis network comprises 15-20 layers. However, Karras further discloses the non-linear mapping network comprises 5-10 fully connected layers; and the synthesis network comprises 15-20 layers. (“Given a latent code z in the input latent space Z, a non-linear mapping network f : Z → W first produces w ∈ W (Figure 1b, left)” Karras [Pg.4401 Sec.2], “The mapping network f consists of 8 layers and the synthesis network g consists of 18 layers” Karras [Pg.4402 Figure 1])
Karras teaches the additional limitations of claim 5, and maintains the same rationale for combination with Fukushige and Nie as claim 4.
Regarding claim 6, Fukushige in view of Nie in further view of Karas disclose the system of claim 5, however Fukushige fails to specifically disclose wherein each layer of the synthesis network comprises a Gaussian noise input. Karras further discloses wherein each layer of the synthesis network comprises a Gaussian noise input. (“These are single-channel images consisting of uncorrelated Gaussian noise, and we feed a dedicated noise image to each layer of the synthesis network.” Karras [Pg.4402 Col.2 P.1])
Karras teaches the additional limitations of claim 6, and maintains the same rationale for combination with Fukushige and Nie as claim 4.
Claim 11, the method of claim 8, recites substantially the same subject matter as claim 4 and is rejected under similar rationale.
Claim 12, the method of claim 11, recites substantially the same subject matter as claim 5 and is rejected under similar rationale.
Claim 14, the method of claim 11, recites substantially the same subject matter as claim 6 and is rejected under similar rationale.
Claim 17, the method of claim 16, recites substantially the same subject matter as claim 4 and is rejected under similar rationale.
Claim 18, the method of claim 17, recites substantially the same subject matter as claim 5 and is rejected under similar rationale.
Claim 20, the method of claim 17, recites substantially the same subject matter as claim 6 and is rejected under similar rationale.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Fukushige et al. US Pub. No. 2013/0346047 A1 (hereinafter referred to as “Fukushige”)
in view of Nie et al. US Pub. No. 2022/0012596 A1 (hereinafter referred to as “Nie”), in further view of Morrison et al. US Pat. No. 10692242 B1 (hereinafter referred to as “Morrison”) and in further view of Holden et al. US Pub. No. 2010/0318327 A1 (hereinafter referred to as “Holden”).
Regarding claim 7, Fukushige in view of Nie disclose the system of claim 1, but fail to specifically disclose the limitations of claim 7.
However, analogous art of Morrison discloses determine, (see Claim 1 Fukushige), a respective first score associated with one or more functional aspects of a candidate vehicle and a respective second score associated with one or more non-functional aspects of the candidate vehicle; (“using a first machine learning model and based on the detected variances, a first score associated with the vehicle, wherein the first score is representative of a visual condition (i.e. non-functional aspect) of the vehicle [...] a second score associated with the vehicle, wherein the second score is representative of an operational condition (i.e. functional aspect) of the vehicle” Morrison [Col.1 Ln.37-47]).
Morrison is analogous art as it relates to the technical field of vehicle condition assessment and analysis using machine learning. It is intended for real-time, objective evaluation of a vehicle's physical and operational condition by analyzing images and sensor data captured in an inspection bay. The system applies multiple machine learning models to generate scores representing visual and diagnostic conditions, and combines these into an overall vehicle condition score. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to score (as taught by Morrison) candidate vehicle silhouettes (as taught by Fukushige-Nie) in order to aid in decisions regarding a plurality of vehicle exterior sillouettes.
The Fukushige-Nie-Morrison combination fails to specifically disclose
determine a Pareto front based on the first scores and the second scores of the plurality of candidate vehicle exterior silhouettes.
However, the analogous art Holden discloses, determine a Pareto front (“pressure contours with surface restricted streamlines and a detailed view of the flow lines in the wing-fillet region respectively for the designs on the Pareto front of FIG.16” [P.0067]) .
Holden is analogous art as it relates to design optimization. Specifically, Holden discloses a design method for aerodynamic structures (e.g. aircraft) which includes CFD calculations and an optimal Pareto solution set, as does the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art before the Applicant' s effective filling date of the claimed invention to have modified the Fukushige-Nie-Morrison combination to incorporate a Pareto front, as Holden discloses, in order to visualize the trade-offs between conflicting design goals, such as functional vs non-functional aspects of vehicle design.
Claim 15, the method of claim 8, recites substantially the same subject matter as claim 7 and is rejected under similar rationale.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon is
considered pertinent to applicant's disclosure:
Gunpinar, Erkan, Salih Ertug Ovur, and Serkan Gunpinar. "A user-centered side silhouette generation system for sedan cars based on shape templates." Optimization and Engineering 20.3 (2019): 683-723. “A tool for the generation of side silhouettes for sedan cars is developed” [Abstract]
Nozawa, Naoki, et al. "3D car shape reconstruction from a contour sketch using GAN and lazy learning." The Visual Computer 38.4 (2022): 1317-1330. “a novel system to reconstruct a 3D car using a single sketch image” [Abstract]
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/ANTHONY CHAVEZ/ Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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