DETAILED ACTION
Claims 1-20 are presented for examination.
This Office Action is in response to submission of documents on January 23, 2026.
Rejection of claims 1-20 under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Rejection of claims 1, 3-8, 10-15, and 17-20 under 35 U.S.C. 102(a)(1) as being anticipated by Chang.
Rejection of claims 2, 9, and 16 under 35 U.S.C. 103 as being obvious over Chang in view of Bandara.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on February 21, 2023; July 18, 2024; November 14, 2024; June 2, 2025; and January 23, 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
a design space access engine configured to…; and
a structural design engine configured to…:
The term “engine” is a non-structural term that is recited as a generic placeholder with no specific structural meaning. The “engines” are modified by “configured to,” which is functional language that acts as a linking term between the generic placeholder and its function. Finally, in both instances, the “engine” is not modified by sufficient structure, material, or acts for performing the function. Accordingly, claims 8-14 are interpreted under 35 U.S.C. 112(f) as reciting functional claim language.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 is directed to judicial exceptions without significantly more. The claims recite mental processes. This judicial exception is not integrated into a practical application because the additional elements that are recited in the claims are extra-solution activities that do not integrate the judicial exceptions into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because courts have found that the steps of accessing and/or transmitting data and recitations of generic computer components are not significantly more than a judicial exception.
Claim 1
Step 1: The claim is directed to a process, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: The claim 1 limitations include (bolded for abstract idea identification):
Claim 1
Mapping Under Step 2A Prong 1
A method comprising: by a computing system:
accessing a design space of a physical structure;
encoding the design space into a set of 3-dimensional (3D) rectangles, wherein each 3D rectangle defines candidate beam locations in the physical structure and wherein the candidate beam locations of the 3D rectangles are defined by lines between vertex pairs of each 3D rectangle;
providing the encoded design space as an input to a machine-learning (ML) model;
generating, through the ML model, a design of the physical structure based on the encoded design space, wherein the design of the physical structure comprises beams at beam locations determined by the ML model from the candidate beam locations; and
providing the design of the physical structure in support of manufacture of the physical structure.
Abstract Idea: Mental Process
As described in the Specification, “In the example in Figure 2, the structural design engine 110 encodes the design space 210 into 3D rectangles to generate the encoded design space 220. The encoded design space 220 as illustrated in Figure 2 includes eight (8) different 3D rectangles, including 3D rectangles of differing height (and thus differing volume). As also shown in Figure 2, an illustrative example of a given 3D rectangle in the encoded design space 220 is shown as the 3D rectangle 222.” Spec. at [0026]. A person having ordinary skill in the can, with pencil and paper, us observation, evaluations, judgment, and opinion to generate an “encoded” design space. See e.g., MPEP 2106.04(a)(2), Subsection III. Further, the Specification discloses that the encoded design space may be limited in the amount of information that is included: “As such, providing all of the 3D points in a design space 210 as an input into the ML model 120 may overwhelm the ML learning processes applied by the ML model 120. By encoding the design space 210 into a sparser representation, the structural design engine 110 may reduce the number of input points or input values used to represent the design space 210 for a beam-based physical structure and ML-based processing of such a design.” Spec. at [0023]. Thus, the limitation is a mental process.
Abstract Idea: Mental Process
The limitation recites a mental process that can be performed by a person having ordinary skill in the art by observing the candidate beam locations included in the encoded design space, evaluating the potential impact of one or more beams that could be included in the final design (this may additionally involve mathematical calculations, also an abstract idea), and select, through judgment and/or opinion, where to place beams in the physical structure. See e.g., MPEP 2106.04(a)(2), Subsection III.
Step 2A, Prong 2: The claim 1 limitations recite (bolded for additional element identification):
Claim 1
Mapping Under Step 2A Prong 2
A method comprising: by a computing system:
accessing a design space of a physical structure;
encoding the design space into a set of 3-dimensional (3D) rectangles, wherein each 3D rectangle defines candidate beam locations in the physical structure and wherein the candidate beam locations of the 3D rectangles are defined by lines between vertex pairs of each 3D rectangle;
providing the encoded design space as an input to a machine-learning (ML) model;
generating, through the ML model, a design of the physical structure based on the encoded design space, wherein the design of the physical structure comprises beams at beam locations determined by the ML model from the candidate beam locations; and
providing the design of the physical structure in support of manufacture of the physical structure.
Reciting generic computer components is the additional element of instructions to apply the recited judicial exception, which courts have found does not integrate the judicial exception into a practical application. See MPEP 2106.05(f).
The limitation is directed to transmitting data from a storage medium to one or more other components. Providing data (i.e., transmitting data) is an extra-solution activity that does not integrate the judicial exception into a practical application. The limitation does not recite, with specificity, how the data is provided and therefore does not improve the functioning of a computer. See MPEP 2106.05(d)(II).
Providing data (i.e., transmitting data) is an extra-solution activity that does not integrate the judicial exception into a practical application. The limitation does not recite, with specificity, how the data is provided and therefore does not improve the functioning of a computer. See MPEP 2106.05(d)(II).
The machine learning model is not recited with specificity such that it is more than mere instructions to apply a judicial exception via generic computer components. Reciting generic computer components is the additional element of instructions to apply the recited judicial exception, which courts have found does not integrate the judicial exception into a practical application. See MPEP 2106.05(f).
Providing data (i.e., transmitting data) is an extra-solution activity that does not integrate the judicial exception into a practical application. The limitation does not recite, with specificity, how the data is provided and therefore does not improve the functioning of a computer. See MPEP 2106.05(d)(II).
Step 2B: Regarding Step 2B, the inquiry is whether any of the additional elements (i.e., the elements that are not the judicial exception) amount to significantly more than the recited judicial exception.
Transmitting data is an extra-solution activity that courts have found does not amount to significantly more than the recited judicial exception. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Reciting generic computer components is the additional element of instructions to apply the recited judicial exception, which courts have found does not integrate the judicial exception into a practical application. See MPEP 2106.05(f), Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016).
Accordingly, claim 1 is rejected for being directed to unpatentable subject matter.
Claim 2
Claim 2 recites wherein the 3D rectangle defines a fully connected structure wherein candidate beam locations are defined between all vertex pairs of each 3D rectangle. The claim does not recite any judicial exceptions nor additional elements but instead merely specifies data that is recited in claim 1. Accordingly, claim 2 is rejected for being directed to unpatentable subject matter.
Claim 3
Claim 3 recites wherein providing the input to the ML model further comprises: providing design parameters for the physical structure, wherein the design parameters comprise force values applicable to the physical structure, a structure type, or a combination of both. Providing data is an additional element of transmitting data, which is an extra-solution activity that courts have found does not integrate a judicial exception into a practical application nor amount to significantly more than the judicial exception(s). See MPEP 2106.05(d)(II); Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Accordingly, claim 3 is rejected for being directed to unpatentable subject matter.
Claim 4
Claim 4 recites wherein generating the design of the physical structure though the ML model further comprises determining a beam classification for each of the beams at the determined beam locations. The claim recites the judicial exception of a mental process that can be performed by a human using observation, evaluation, judgment, and opinion, and further can be performed in the human mind and/or with pencil and paper. See MPEP 2106.04(a)(2), Subsection III. For example, generating a classification can include reviewing beam placement in a structure and, through knowledge and expertise, select a classification to assign to each beam. Further, as previously indicated, using a machine learning model to perform the judicial exception is mere instructions to apply. See MPEP 2106.05(f), Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016). Accordingly, claim 4 is rejected for being directed to unpatentable subject matter.
Claim 5
Claim 5 recites wherein generating the design of the physical structure though the ML model further comprises determining a beam classification for each of the beams at the determined beam locations, offset or rotation values to interconnect the beams at the determined beam locations, end-cut classifications to apply at connection points between the beams at the determined beam locations, or any combination thereof. The claim recites the judicial exception of a mental process that can be performed by a human using observation, evaluation, judgment, and opinion, and further can be performed in the human mind and/or with pencil and paper. See MPEP 2106.04(a)(2), Subsection III. For example, generating a classification can include reviewing beam placement in a structure and, through knowledge and expertise, select a classification to assign to each beam. Further, as previously indicated, using a machine learning model to perform the judicial exception is mere instructions to apply. See MPEP 2106.05(f), Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014), Gottschalk v. Benson, 409 U.S. 63, 70, 175 USPQ 673, 676 (1972), Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 112 USPQ2d 1750 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016). Accordingly, claim 5 is rejected for being directed to unpatentable subject matter.
Claim 6
Claim 6 recites training the ML model, including by:
accessing a set of physical structure designs;
The limitation is directed to the additional element of data transmission from a storage medium to one or more other components. Transmitting data is an extra-solution activity that courts have found does not integrate the judicial exception into a practical application and further does not amount to significantly more than the recited judicial exception. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
for a given physical structure design in the set, generating an encoded design space for the given physical structure design, wherein: the encoded design space for the given physical structure design comprises encoded 3D rectangles mapped to different portions of the given physical structure design; each encoded 3D rectangle defines possible beam locations in a design space of the given physical structure design; and each encoded 3D rectangle encodes which of the possible beam locations map to beams in the given physical structure design and which of the possible beam locations do not map to any beams in the given physical structure design; and
The limitation is a mental process that can be performed by a human in the human mind using pencil and paper and/or a generic computer to perform the step. See MPEP 2106.04(a)(2), Subsection III. Besides “generating an encoded design space,” as previously rejected with regards to claim 1, the claim recites additional specific details of the “encoded design space” and thus does not include additional elements that would integrate the judicial exception into a practical application and further do not amount to significantly more than the judicial exception.
providing the encoded design space for the given physical structure design as training data for the ML model.
The limitation is directed to the additional element of data transmission (i.e., “providing” data). Transmitting data is an extra-solution activity that courts have found does not integrate the judicial exception into a practical application and further does not amount to significantly more than the recited judicial exception. See Intellectual Ventures I v. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Accordingly, claim 6 is rejected for being directed to unpatentable subject matter.
Claim 7
Claim 7 recites wherein generating the encoded design space for the given physical structure design further comprises:
extracting structure data from the given physical structure design, including a beam classification for each of the beams in the given physical structure design, offset or rotation values to interconnect the beams in the given physical structure design, end-cut classifications to apply at connection points between the beams in the given physical structure design, or any combination thereof, and
The limitation is a mental process that can be performed by a human using pencil and paper. For example, the “extracting” can include reviewing the structure data and selecting one or more variables and/or portions of the data. See MPEP 2106.04(a)(2), Subsection III.
encoding the extracted structure data in the encoded design space for the given physical structure design.
The limitation is a mental process that can include, for example, selecting a portion of the extracted data and formatting the data to include with the design space. Thus, in additional to or alternatively, the step can include data transmission from the structured data (and/or the memory space storing the structure data) to the design space (or memory storing the design space), which is an additional element that is not recited with specificity to integrate the judicial exception into a practical application.
Accordingly, claim 7 is rejected for being directed to unpatentable subject matter.
Claims 8-14
Claim 8-14 recites a system with components that perform steps substantially the same as those in claims 1-7. According, for at least the same reasons, claims 8-14 are rejected for being directed to unpatentable subject matter.
Claims 15-20
Claims 15-20 recite “non-transitory readable media” that stores a method with substantially the same imitations as claims 1-2 and 4-7. Accordingly, for at least the same reasons as claims 1-2 and 4-7, claims 15-20 are rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-8, 10-15, and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chang, et al., (“Learning to Simulate and Design for Structural Engineering,” hereafter “Chang”).
Claim 1
Chang discloses:
A method comprising: by a computing system:
However, if NeuralSim is used for evaluation(GA + NeuralSim), it only takes around 30 minutes to complete 1000 iterations. Chang at §5.4.2.
accessing a design space of a physical structure;
Various loads are considered in the simulation: 1. Self-weight load of the building structure, 2. Surface loads on floor panels which are distributed to the underlying beams, 3. Surface loads on the roof story, and 4. Linear loads at the boundary beams for external walls. NeuralSim is then trained with the building skeletons with paired cross-sections and simulated drift ratio. Cheng at §3.2.
See also Fig. 1, illustrating the pipeline of analysis from Building to Cross-section design.
Although the test simulations were performed using synthetic data, the data represents a physical structure and is generated with reference to the design space.
encoding the design space into a set of 3-dimensional (3D) rectangles
As visualized in Figure 1, a typical structural design process starts from a given building design, and then the structural engineer will propose a skeleton design… . Cheng at §1.
The “Structural Skeleton Design” is analogous to the “set of 3-dimensional (3D) rectangles” that represent (i.e., “encoded”) a physical structure in a design space.
wherein each 3D rectangle defines candidate beam locations in the physical structure and
Building skeleton are created by a fixed sampling algorithm due to the deficiency of real-world data. Each building is erected on a rectangular base which edges are sampled between 60ft to 400ft. A grid is created on the base and the intervals are sampled from the set of beam spans, ranging from28ft to 40ft. Chang at §7.1.
The “rectangular base” is analogous to rectangular sections of the building. The vertices are candidate beam locations to be provided to an optimize to adjust the structure into an improved version. See also Figure 1.
wherein the candidate beam locations of the 3D rectangles are defined by lines between vertex pairs of each 3D rectangle;
A grid is created on the base and the intervals are sampled from the set of beam spans, ranging from28ft to 40ft. Chang at §7.1.
Each voxel contains four columns on four vertical side sand four beams which form a rectangle frame on the top to support the floor panel. The story height is fixed at 16ft. Chang at §7.1.
providing the encoded design space as an input to a machine-learning (ML) model;
We represent building geometries as structural graphs. Every bar (column or beam) is represented as a graph node. An edge connects two nodes if the two corresponding bars are joined together. Information of bar i is stored as node feature vi = [p1; p2;B; T;L], where p1 and p2 locates the two endpoints of the bar, B indicates if the bar is a beam or a column, T is a one-hot vector representing the cross-section, and L provides auxiliary loading condition information… Chang at §3.3.
generating, through the ML model, a design of the physical structure based on the encoded design space, wherein the design of the physical structure comprises beams at beam locations determined by the ML model from the candidate beam locations; and
Figure 5 visualizes the design outputs of NeuralSizer (high safety factor + objective weight 10) for various buildings. A cross-section with stronger structural properties is visualized in a darker color and a thicker stick. Chang at §5.3.
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Each of the beams in the visualization are associated with a type of beam that is to be located between vertices of the 3-D rectangular sections of the design.
providing the design of the physical structure in support of manufacture of the physical structure.
Variety Constraint: This constraint comes from the constructability requirement which sets a maximum number for different cross-section types used. Using too many different cross-section types leads to higher manufacturing and transportation cost. Chang at §3.1.
As illustrated in Figure 5, the final design is provided to the user and at last one of the optimization constraints is related to manufacturing considerations.
Claim 3
Chang discloses:
wherein providing the input to the ML model further comprises providing design parameters for the physical structure,
Various loads are considered in the simulation: 1. Self-weight load of the building structure, 2. Surface loads on floor panels which are distributed to the underlying beams, 3. Surface loads on the roof story, and 4. Linear loads at the boundary beams for external walls. Chang at §3.2.
Weight of the structure, loads on floors, and loads on beams are “design parameters.”
wherein the design parameters comprise force values applicable to the physical structure, a structure type, or a combination of both.
Various loads are considered in the simulation: 1. Self-weight load of the building structure, 2. Surface loads on floor panels which are distributed to the underlying beams, 3. Surface loads on the roof story, and 4. Linear loads at the boundary beams for external walls. Chang at §3.2.
The “loads” are “force values.”
Claim 4
Chang discloses:
wherein generating the design of the physical structure though the ML model further comprises determining a beam classification for each of the beams at the determined beam locations.
See Figure 5, illustrating the beam requirements for various beams in the structure. The “beam types” are analogous to “beam classification.”
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Claim 5
Chang discloses:
wherein generating the design of the physical structure though the ML model further comprises determining a beam classification for each of the beams at the determined beam locations, offset or rotation values to interconnect the beams at the determined beam locations, end-cut classifications to apply at connection points between the beams at the determined beam locations, or any combination thereof.
See Figure 5, illustrating the beam requirements for various beams at determined beam locations. The “beam types” are analogous to “beam classification.”
Claim 6
Chang discloses:
training the ML model, including by: accessing a set of physical structure designs;
Due to the lack of real structural design data, we synthesize a dataset that contains building skeletons with randomly sampled cross-sections in real-world scale. We also use Autodesk Robot Structural Simulator (RSA), a simulation software widely used in the industry, to compute the structural simulation results for the synthetic dataset. Chang at §3.2.
for a given physical structure design in the set, generating an encoded design space for the given physical structure design,
NeuralSim is then trained with the building skeletons with paired cross-sections and simulated drift ratio. Chang at §3.2.
wherein: the encoded design space for the given physical structure design comprises encoded 3D rectangles mapped to different portions of the given physical structure design;
As visualized in Figure 1, a typical structural design process starts from a given building design, and then the structural engineer will propose a skeleton design… . Cheng at §1.
The “Structural Skeleton Design” is analogous to the “set of 3-dimensional (3D) rectangles” that represent (i.e., “encoded”) a physical structure in a design space.
each encoded 3D rectangle defines possible beam locations in a design space of the given physical structure design; and
Building skeleton are created by a fixed sampling algorithm due to the deficiency of real-world data. Each building is erected on a rectangular base which edges are sampled between 60ft to 400ft. A grid is created on the base and the intervals are sampled from the set of beam spans, ranging from28ft to 40ft. Chang at §7.1.
The “rectangular base” is analogous to rectangular sections of the building. The vertices are candidate beam locations to be provided to an optimize to adjust the structure into an improved version. See also Figure 1.
each encoded 3D rectangle encodes which of the possible beam locations map to beams in the given physical structure design and which of the possible beam locations do not map to any beams in the given physical structure design; and
We represent building geometries as structural graphs. Every bar (column or beam) is represented as a graph node....A pseudo ground node is connected to all first-story columns and the values of its feature vector are all-1. The structural graph of a simple example structure is illustrated in Figure 4. Story level indices of each bar are also saved. Chang at §3.3.
Locations where the graph has connection between vertices are locations where beams are present and locations where no lines are present indicate where beams are not located. See Figure 4:
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providing the encoded design space for the given physical structure design as training data for the ML model.
We represent building geometries as structural graphs. Every bar (column or beam) is represented as a graph node. An edge connects two nodes if the two corresponding bars are joined together. Information of bar i is stored as node feature vi = [p1; p2;B; T;L], where p1 and p2 locates the two endpoints of the bar, B indicates if the bar is a beam or a column, T is a one-hot vector representing the cross-section, and L provides auxiliary loading condition information… Chang at §3.3.
Claim 7
Chang discloses:
wherein generating the encoded design space for the given physical structure design further comprises: extracting structure data from the given physical structure design, including a beam classification for each of the beams in the given physical structure design, offset or rotation values to interconnect the beams in the given physical structure design, end-cut classifications to apply at connection points between the beams in the given physical structure design, or any combination thereof, and
Information of bar i is stored as node feature vi = [p1,p2,B,T,L], where p1 and p2 locates the two endpoints of the bar, B indicates if the bar is a beam or a column, T is a one-hot vector representing the cross-section, and L provides auxiliary loading condition information, including 1. if the bar is on the roof story, 2. if the bar is on the boundary, and 3. the surrounding floor penal areas which are multiplied by the per-area loads when computing the surface loads. Chang at §3.3.
The “information of bar i” includes information for the beam, such as its cross-section (analogous to a classification).
encoding the extracted structure data in the encoded design space for the given physical structure design.
Information of bar i is stored as node feature vi = [p1,p2,B,T,L], where p1 and p2 locates the two endpoints of the bar, B indicates if the bar is a beam or a column, T is a one-hot vector representing the cross-section, and L provides auxiliary loading condition information… Chang at §3.3.
Storing the information with the graph of the structure is analogous to “encoding” the information.
Claim 8 and 10-14
Claims 8-14 recite a system that performs a method that is substantially the same as the method disclosed in claims 1 and 3-7. Because the “system” does not include hardware and/or other physical components, the “system” is interpreted the same as the “computing system” of claim 1. Accordingly, for at least the same reasons and based on the same prior art as claims 1-7, claims 8-14 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chang.
Claim 15 and 17-20
Claims 15-20 recite:
A non-transitory machine-readable medium comprising instructions that, when executed by a processor, cause a computing system to:
The main contribution of this paper is proposing an end-to end solution to automate the structural design process. As visualized in Figure 1, a typical structural design process starts from a given building design, and then the structural engineer will propose a skeleton design, where the locations and connectivities of columns and beams are defined. Chang at §1.
“Automating the structural design process” requires the implementation of the method using one or more storage devices.
perform a method that is substantially the same as the method disclosed in claims 1 and 4-7.
Accordingly, for at least the same reasons and based on the same prior art as claims 1-2 and 4-7, claims 15-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chang.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being obvious over Chang in view Bandara, et al. (U.S. Pat. No. 11,704,456, hereinafter “Bandara”).
Claim 2
Chang does not appear to disclose:
wherein the 3D rectangle defines a fully connected structure wherein candidate beam locations are defined between all vertex pairs of each 3D rectangle.
Bandara, which is analogous art, discloses:
wherein the 3D rectangle defines a fully connected structure wherein candidate beam locations are defined between all vertex pairs of each 3D rectangle.
At 204, a numerical simulation model is generated using predefined pieces of geometry, where the numerical simulation models the physical properties and behavior of an object. In some implementations, this numerical simulation model is a Finite Element Analysis (FEA) model that uses six pieces of geometry. For example, FIG. 2B shows a numerical simulation model setup 220 involving numerical models usable to compute rows in a linear elastic constitutive matrix to compute material properties of a given lattice. The setup 220 includes six models 222 a, 222 b, 224 a, 224 b, 226 a, 226 b, three of which can be used to measure the direct stress, and the other three can be used to measure shear components for a full numerical test. A beam-shell FE model can be used with constant shell element thickness (e.g., 1 mm thickness) and variable beam thickness depending on volume fraction. Bandara at col. 7, line 61-col. 8, line 11.
The figures, particularly 224a, includes a structure divided into 3D rectangles with the vertices fully connected to each other. As described, the analysis determines the thickness of the beams that comprise the lattice (i.e., connected vertices of the object).
Bandara is analogous art to the claimed invention because both are related to simulations of an object and determining supports for the object based on known or predicted stresses. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine Chang with Bandara to result in a system that determines the beam locations and classifications for potential beam locations other than the edges of a rectangle by additional analyzing other connections between vertices of the rectangles. Motivation to combine includes an improved final design that takes into account all potential locations for beams, therein allowing for greater flexibility when determining the final structure. Accordingly, the final design is optimized for a greater number of potential beams.
Claims 9 and 16
Claims 9 and 16 recite substantially the same limitations as claim 2. Accordingly, for at least the same reasons and based on the same prior art as claim 2, claims 9 and 16 are rejected under 35 U.S.C. 103 as being obvious over Chang in view of Bandara.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Pat. No. 11,373,015: Discloses a method for using CAD to design a mechanical object, including steps related to determining vertices of the object and optimizing connection of beams.
Zhang, et al., “3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders”: discloses “a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs.”
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JOSEPH MORRIS
Examiner
Art Unit 2188
/JOSEPH P MORRIS/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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