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 .
Detailed Action
Claims 1-16 are pending.
Drawings
The drawings filed on 12/16/2022 are accepted.
Oath/Declaration
4. For the record, the Examiner acknowledges that the Oath/Declaration submitted on 12/16/2022 has been received.
Information Disclosure Statement
5. The information disclosure statements (IDS) submitted on 12/16/2022 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, an initialed and dated copy of Applicant's IDS form SB08 filed 12/16/2022 is attached to the instant Office action.
Examiner Notes
6. 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. 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.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
7. Claims 14 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 14 and 15 recite the limitation “a realization or manufacturing system”, which is a disconnected system, because the realization or manufacturing system isn't connected to anything else previously claimed in independent claim 8. The claims 14 and 15 recite elements (e.g., manufacturing system) are disconnected in form and function from any other part of the claim (e.g., claim 8), i.e., a completely separate system implies there are missing elements. Further, neither claims 14 nor 15 actively recite the manufacture step(s).
Therefore, appropriate corrections are required.
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.
8. Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite a mental process; see MPEP 2106.04(a)(2)(III).
Step 1
The claims under Step 1 are directed towards a method (claims 1-7) and a system (8-17).
Claim 1 recites:
A method for automated design generation, (field of use) (See Step 2A Prong 2 and Step 2B)
the method comprising: receiving design domain and boundary conditions for at least one part of an assembly to be generated; (data gathering activity)
evaluating an objective and constraints; computing decision variables based on the objective and the constraints; (Mental Processes using evaluation or judgement)
augmenting or filtering the decision variables based on at least a plurality of inertial considerations; (Mental Processes using evaluation or judgement or using simple math)
determining or updating design variables based on decision variables; (Mental Processes using evaluation or judgement)
generating a design for the at least one part; (insignificant extra solution activity)
and outputting information configured to realize or manufacture the at least one part. (insignificant post solution activity, outputting data)
Step 2A, prong 1:
The limitations of claim 1: “evaluating an objective and constraints; computing decision variables based on the objective and the constraints; determining or updating design variables based on decision variables” are recitations of evaluation or judgement that fall within the Mental Processes enumerated category of abstract ideas because it could be "performed by human without a computer", i.e. mental processes that require human to perform the claim abovementioned limitations. The ‘evaluation of the objective (function) and constraints’ are generic design and does not seem related to specific characteristics or features which would then be incorporated into the manufactured article. Further, the limitation: “augmenting or filtering the decision variables based on at least a plurality of inertial considerations” is recitation of evaluation or judgement that fall within the Mental Processes enumerated category of abstract ideas or this limitation can be performed using simple math. Under BRI and conventional meaning in the art, the claim term “augmenting or filtering the decision variables” refers to how to expand or reduce the search space to find optimal solutions. Accordingly, at step 2A, prong one, claim 1 as a whole is found to recite a judicial exception and is drawn to an abstract idea.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application because the claim language only recites elements that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitations fall within the mental processes grouping. Therefore, the claim 1 recites an abstract idea because it does not impose any meaningful limitations on practicing the abstract idea. Claim 1 has no additional limitations that integrate the abstract idea into a practical application. The preamble recited: “A method for automated design generation”, is recitation of field of use. This limitation, amounts to merely indicating a field of use or technological environment and cannot integrate a judicial exception into a practical application. Additionally, the limitation “receiving design domain and boundary conditions for at least one part of an assembly to be generated” is recitation of data gathering activity, i.e., this limitation recites insignificant extra-solution activity because it involves Mere data gathering (See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iv): Obtaining information about transactions). The limitation “generating a design for the at least one part” is recitation of insignificant extra-solution activity. (please see MPEP 2106.05(g)). Further, the last limitation “outputting information configured to realize or manufacture the at least one part” is recitation of insignificant extra or post solution activity of outputting the result, i.e., intent of the information which is output. Therefore, the abovementioned limitations do not integrate a judicial exception into a practical application.
Step 2B:
The claim 1 as a whole does not include any further additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with in the Step 2A, Prong Two analysis, with respect to integration of the abstract idea into a practical application. The additional element: “A method for automated design generation”, is recitation of field of use or technological environment and does not amount to significantly more than the judicial exception. Additionally, the limitation “receiving design domain and boundary conditions for at least one part of an assembly to be generated” is recitation of data gathering activity and does not amount to significantly more than the judicial exception. The limitation “generating a design for the at least one part” is recitation of insignificant extra-solution activity and does not amount to significantly more than the judicial exception. Further, the last limitation “outputting information configured to realize or manufacture the at least one part” is recitation of insignificant extra or post solution activity of outputting the result and does not amount to significantly more than the judicial exception.
Therefore, the claim 1 is not patent eligible under 35 USC 101.
Independent Claim 8 is substantially similar to claim 1 and therefore is rejected under the same rationale as stated above. The additional elements in claim 8: “A system for automated design generation, the system comprising: at least one processor and at least one memory having instructions stored thereon wherein execution of the instructions by the processor …” is recited at a high-level of generality (i.e., as a generic computer/hardware) such that it amounts no more than mere instructions to apply the exception using a generic computer. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(b) (“Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014).”).
Claims 2-7 are rejected as a Judicial Exception (JE) since they do not add significantly more than the abstract idea or a practical application.
Claims 2-4 are dependent on independent claim 1 and includes all the limitations of claim 1. The limitations of claim 2-4 are recitations of Mathematical concepts. Under BRI and conventional meaning in the art, these limitations refer to mathematical equations/relations because the claim term in claim 2 “Finite element method” (FEM) is a popular method for numerically solving differential equations. Further, the claim terms in claim 4: “center of mass, moment of inertia” refer to linear motion and rotational resistance respectively. Therefore, the abovementioned limitations do not amount to significantly more than the abstract idea.
Claims 5-7 are dependent on independent claim 1 and includes all the limitations of claim 1. The limitation of claim 5: “augmenting or filtering” is recitation of evaluation or judgement that fall within the Mental Processes enumerated category of abstract ideas or this limitation can be performed using simple math. The limitations of claims 6 and 7 are recitations of insignificant extra or post solution activities of outputting the result (please see MPEP 2106.05(g)).
Dependent Claims 9-13 are dependent on independent claim 8 and substantially similar to claims 2-6 and therefore are rejected under the same rationale as stated above.
Claims 14-16 are dependent on independent claim 8. The claim limitations of claims 14 and 15 are recitations of non-functional data descriptions which do not add anything more to overcome the abstract idea. The limitation in claim 16: “perform further analysis or simulation” is recitations of Mathematical concepts. The analysis recited in claim 16 might be FEM analysis, which is a popular method for numerically solving differential equations. Therefore, the abovementioned limitations do not amount to significantly more than the abstract idea.
Therefore, the claims 1-17 are not patent eligible.
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 set forth in Graham, v. John Deere Co., 383 U.S.1.148 USPQ 459 (1966), that are applied 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 non-obviousness.
9. Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Suresh et al. (Pub. No. US20180079149A1) and in view of an Article “Interior structural optimization based on the density-variable shape modeling of 3D printed objects” by Dawei Li et al. (hereinafter Li, article published online on 2015).
Regarding Claim 1, Suresh teaches a method for automated design generation, (Suresh disclosed in page 1 para [0010]: “providing systems and processes for additive manufacturing using a topology optimization (TO) framework that generates designs that have significantly reduced support structure requirements during manufacture.” It has been discussed in page 3 para [0043-0048] about “topographical optimization of the automotive part of FIG. 18A, with an additive manufacturing build direction”).
Suresh teaches the method comprising: receiving boundary conditions (Suresh disclosed in page 4 para [0051]: “The processor 102 receives, as input, an initial object design 110. The initial object design 110 may be input by a user of an interface 108, which may be presented to a user on the computing device 100 … In some embodiments, the interface 108 may be configured to prompt the user to provide the initial object design 110, …”. In para [0054]: “There are several TO methods employed today to solve such TO problems; … we propose to use the level-set based Pareto Topology Optimization (PareTO) method for the following reasons: (1) in level-set methods, the boundary is well-defined at all times, making it easier to impose support structure constraints, …”. Further, in page 5 para [0059]: “To dynamically estimate the support volume, assuming that support structures are vertical, a simple integral of the support length over the boundary may be multiplied by a suitable fill ratio …”).
Suresh teaches evaluating an objective and constraints; (Suresh disclosed in page 4 para [0065]: “In some embodiments, the present TO framework may impose an absolute support volume constraint S<=Smax, where S is the total support volume of the optimized design and Smax is an upper limit of the total support volume, selected by the designer. … Alternatively, or additionally, relative upper bound constraints may be imposed, using the PareTO method of generating multiple topologies for various volume fractions to store reference support volumes S(v). The reference support volumes may be generated according to a relative support volume constraint, …”. In page 6 para [0076]: “the present methods may impose the support structure constraint. The original support-constrained TO problem above may be expressed in the standard form: … A popular method for solving such constrained optimization method is the augmented Lagrangian method, where the constraint and objective are combined to a single field, leading to an augmented topological field: …”).
Suresh teaches computing decision variables based on the objective and the constraints; (Examiner would construe the claim term “decision variables” comprise gradients and sensitivity fields, in light of Specification of current Application para [0004].
Suresh disclosed in page 5 para [0067]: “A gradient based TO framework may then be used for solving the above problem. The framework will rely on both (1) topological sensitivity for performance, and (2) the proposed topological sensitivity for support structure volume. With respect to (1), the PareTO method relies on the concept of topological sensitivity for driving the optimization process. To illustrate, FIG. 4A presents a first object design 400 that represents a structural topology in the design space Ω0 …”. In para [0069]: “Thus the topological sensitivity can be computed as follows: (1) finite element analysis (FEA) is carried over the domain, (2) stresses and strains are computed, and (3) then the topological sensitivity field is computed. FIG. 4C illustrates the resulting field 420 of topological sensitivity.”).
Suresh teaches augmenting or filtering the decision variables based on at least a plurality of inertial considerations; (Suresh disclosed in page 2 para [0013]: “Computing the first augmented topological field may further include combining a first sensitivity field corresponding to the first topological sensitivity with a second sensitivity field corresponding to the second topological sensitivity according to an augmented Lagrangian method to produce the first augmented topological field. The method may further include: receiving a target fractional volume that is less than the first fractional volume; … ; determining a second unconstrained support volume of a third number of support structures required to support the object during the additive manufacturing, in the build direction, of the object from the second unconstrained optimized design; … a second augmented topological field; and performing a fixed-point iteration of the first intermediate design based on the second augmented topological field to produce a final optimized design comprising the target fractional volume of material and having a final optimized support volume less than or equal to the second unconstrained support volume multiplied by the support constraint parameter.” The disclosures “topological field” and “the target fractional volume of material” correspond to claim elements “decision variables” and “inertial considerations” respectively).
Suresh teaches determining or updating design variables based on decision variables; (Suresh disclosed in page 6-7 para [0078]: “Referring to FIGS. 8A-B, to produce the augmented topological field 802, the two topological sensitivity fields … are normalized to unity at an instance when the weight w = 0.5. The resulting field 802 is a combination of the two fields 502, 702, and the relative weight is automatically determined from the Lagrangian formulation.” In para [0080]: “At step 904, the system may compute the augmented (i.e., weighted) topological field T. As described above, one embodiment of computing the augmented topological field T includes: carrying out FEA on the topology Ω; computing each of the normalized sensitivity fields Tj, Ts; computing the weighted field T from Tj, and Ts; and smoothing the field T. In some embodiments, every time the topology Ω changes, FEA must be executed and the topological sensitivities recomputed.”
The disclosure “the system may compute the augmented (i.e., weighted) topological field; every time the topology Ω changes, FEA must be executed and the topological sensitivities recomputed” teaches the limitation “determining or updating design variables based on decision variables”).
However, Suresh does not explicitly teach the limitation “receiving design domain for at least one part of an assembly to be generated; generating a design for the at least one part; and outputting information configured to realize or manufacture the at least one part.
Li teaches receiving design domain for at least one part of an assembly to be generated; (Li disclosed in page 1628 section 1 (left col.): “we focus on the density distribution of objects because the moment of inertia affects the behavior of objects in the real world ... With the goal of achieving a sound structure in every part of the model, especially the cantilever structure, we propose an internal structural optimization technique based on the mechanical modeling method to reinforce the cantilever structural strength while reducing supply consumption. The key idea is to calculate a continuous density distribution that satisfies the detected stress of the object and to generate a hollowed shell structure in this phase. To represent the density distribution with an object, we construct a gradational porous structure using a mathematical 3D implicit function in the shell and optimize its strength according to the density distribution.”).
Li teaches generating a design for the at least one part; (Li disclosed in page 1631 section 4.2: “In this work, we use an interactive option to split the region in accordance with previously estimated results on density distributions or with the desired parts chosen by the designer. Once a selection is made, the selected interior is separated from the base mesh to form a single part. The entire interactive design process and result are shown in Fig. 9. To implement the separation of parts, we must fill the remaining hole. We use a hole filling algorithm to construct a double planar mesh patch and then deform this mesh to fit each hole using mesh deformation techniques. … Finally, the deformed patch and split parts should be stitched up. The entire process is completed automatically through an interactive operation (Fig. 10).” Fig. 10 a-e shown the “Deformation and stitching of planar mesh patches and assembled results”).
and Li teaches outputting information configured to realize or manufacture the at least one part. (Li disclosed in page 1630 section 3.3: “In this work, we choose porous structures (Fig. 7a) as the infill material with a large surface area ratio and high strength to-weight ratio. … With Eq. (3), we find that the stress σ of a cross section is inversely proportional to the moment of inertia with a constant load, … In this way, we can change the stress of the cross section by controlling the infill density and ultimately obtaining a reasonable output. … we can combine this function with Eqs. (12) and (13) to obtain the expected internal structures.” In page 1632 section 5.1: “Porous structure modeling has been extensively explored in tissue engineering and computer-aided design (CAD); porous structures are also called lattice structures. … an implicit function to generate external support for 3D printing with a porous structure; this optimization provides functions for minimizing support structures through both the definition of the built orientation of optimal parts and the definition of optimally graded cellular structures. … In this work, we reformulate the implicit functions combined with local infilling density and create an adaptive internal porous structure.”).
Suresh and Li are analogous because they are related to have structural optimization for 3Dobjects with desired structures can be fabricated with low material consumption. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Suresh and Li, to modify implementing/adding decision variable (e.g., topological field) in Suresh’s teaching, to include generating design and manufacture a design part based on inertial consideration e.g., moment of inertia in Li’s teaching. The suggestion/motivation for doing so would have been obvious by Li because “In this work, we present a density variable shape modeling method to meet the required strength of 3D objects. We propose a physical modeling methodology to improve the structurally weak areas of 3D printed objects. The proposed method consists of two steps. Density distribution is first estimated to satisfy the detected stress of 3D objects. With our method, 3Dobjects with desired sound structures can be fabricated with low material consumption. In addition, the whole design process can be completed easily and interactively.” (Li disclosed in page 1627 heading ‘Abstract’ and page 1634 heading ‘Conclusion’).
Regarding Claim 2, Suresh and Li teach the method as set forth in claim 1, wherein Suresh teaches the evaluating comprises performing a finite element analysis. (Suresh disclosed in page 5 para [0069-0070]: “Thus the topological sensitivity can be computed as follows: (1) finite element analysis (FEA) is carried over the domain, … Referring to FIG. 5A, for an intermediate topology 500 (of the first design 400 of FIG. 4A), (1) FEA is carried over the topology 500, …”. In page 7 para [0080]: “At step 904, the system may compute the augmented (i.e., weighted) topological field T. As described above, one embodiment of computing the augmented topological field T includes: carrying out FEA on the topology Ω; computing each of the normalized sensitivity fields Tj, Ts; … In some embodiments, every time the topology Ω changes, FEA must be executed and the topological sensitivities recomputed.”)
Regarding Claim 3, Suresh and Li teach the method as set forth in claim 1, wherein Suresh teaches the decision variables comprise gradients and sensitivity fields. (Suresh disclosed in page 5 para [0067]: “A gradient based TO framework may then be used for solving the above problem. The framework will rely on both (1) topological sensitivity for performance, and (2) the proposed topological sensitivity for support structure volume. With respect to (1), the PareTO method relies on the concept of topological sensitivity for driving the optimization process. To illustrate, FIG. 4A presents a first object design 400 that represents a structural topology in the design space Ω0 …”. In para [0069]: “Thus the topological sensitivity can be computed as follows: (1) finite element analysis (FEA) is carried over the domain, (2) stresses and strains are computed, and (3) then the topological sensitivity field is computed. FIG. 4C illustrates the resulting field 420 of topological sensitivity.”).
Regarding Claim 4, Suresh and Li teach the method as set forth in claim 1, however, Suresh doesn’t explicitly teach the limitation “the plurality of inertial considerations comprises mass, moment of inertia, and center of mass”.
wherein Li teaches the plurality of inertial considerations comprises mass, moment of inertia, and center of mass. (Under BRI, Examiner would construe the claim term “mass” as “volume”
Li disclosed in page 1627 heading ‘Abstract’: “Physical modeling is a novel theory for 3D printing; this approach involves the use of a single material to control physical properties, such as center of mass, total mass, and moment of inertia. In this work, we present a density variable shape modeling method to meet the required strength of 3D objects.” In page 1628 heading ‘Introduction’ (left col.): “we focus on the density distribution of objects because the moment of inertia affects the behavior of objects in the real world and determines the stress states of objects. With the goal of achieving a sound structure in every part of the model, especially the cantilever structure, we propose an internal structural optimization technique …”. Further, in page 1632-1633 section 5.2: “We use gyroid surfaces, which are found in nature as self-assembled bicontinuous cubic structures with interfaces that separate adjacent regions of different compositions. These interfaces, often called intermaterial dividing surfaces (IMDS), … In bicontinuous structures, one IMDS divides the space into two distinct volumes. Each volume or region forms a continuous network in the system.”).
Suresh and Li are analogous because they are related to have structural optimization for 3Dobjects with desired structures can be fabricated with low material consumption. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Suresh and Li, to modify implementing/adding decision variable (e.g., topological field) in Suresh’s teaching, to include generating design and manufacture a design part based on inertial consideration e.g., moment of inertia in Li’s teaching. The suggestion/motivation for doing so would have been obvious by Li because “In this work, we present a density variable shape modeling method to meet the required strength of 3D objects. We propose a physical modeling methodology to improve the structurally weak areas of 3D printed objects. The proposed method consists of two steps. Density distribution is first estimated to satisfy the detected stress of 3D objects. With our method, 3Dobjects with desired sound structures can be fabricated with low material consumption. In addition, the whole design process can be completed easily and interactively.” (Li disclosed in page 1627 heading ‘Abstract’ and page 1634 heading ‘Conclusion’).
Regarding Claim 5, Suresh and Li teach the method as set forth in claim 1, wherein Suresh teaches the augmenting or filtering is also based on design or manufacturing considerations. (Suresh disclosed in page 6-7 para [0078]: “Referring to FIGS. 8A-B, to produce the augmented topological field 802, ... The resulting field 802 is a combination of the two fields 502, 702, and the relative weight is automatically determined from the Lagrangian formulation.” In para [0080]: “At step 904, the system may compute the augmented (i.e., weighted) topological field T. As described above, one embodiment of computing the augmented topological field T includes: carrying out FEA on the topology Ω; … In some embodiments, every time the topology Ω changes, FEA must be executed and the topological sensitivities recomputed.” Further in page 8 para [0094-0095]: “The framework may include other AM-related constraints, such as surface roughness, volumetric error, inter-layer fusion, and so on. The proposed method may be coupled with methods for finding the optimum build direction to further reduce support volume. The information presented in Table 1 shows that the improvements in object design for AM via the present support volume sensitive TO framework do not impose significant additional computational cost on the system (e.g., the computing device 100 of FIG. 1) generating the optimized design.”).
Regarding Claim 6, Suresh and Li teach the method as set forth in claim 1, even Suresh teaches about “additive manufacturing” in page 3 para [0049]: “systems and computer-implemented methods for generating designs for additive manufacturing (AM) that are topologically optimized according to a topological optimization (TO) process that maximizes performance, …”.
However, Suresh doesn’t explicitly teach the whole limitation “the outputting information configured to realize or manufacture comprises outputting information configured for at least one of: three-dimensional printing, additive manufacturing, subtractive manufacturing, or hybrid manufacturing”.
wherein Li teaches the outputting information configured to realize or manufacture comprises outputting information configured for at least one of: three-dimensional printing, additive manufacturing, subtractive manufacturing, or hybrid manufacturing. (Li disclosed in page 1630 section 3.3: “In this work, we choose porous structures (Fig. 7a) as the infill material with a large surface area ratio and high strength to-weight ratio. … With Eq. (3), we find that the stress σ of a cross section is inversely proportional to the moment of inertia with a constant load, … In this way, we can change the stress of the cross section by controlling the infill density and ultimately obtaining a reasonable output. … we can combine this function with Eqs. (12) and (13) to obtain the expected internal structures.” In page 1632 section 5.1: “Porous structure modeling has been extensively explored in tissue engineering and computer-aided design (CAD); porous structures are also called lattice structures. … an implicit function to generate external support for 3D printing with a porous structure; this optimization provides functions for minimizing support structures through both the definition of the built orientation of optimal parts and the definition of optimally graded cellular structures. … In this work, we reformulate the implicit functions combined with local infilling density and create an adaptive internal porous structure.”).
Suresh and Li are analogous because they are related to have structural optimization for 3Dobjects with desired structures can be fabricated with low material consumption. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Suresh and Li, to modify implementing/adding decision variable (e.g., topological field) in Suresh’s teaching, to include generating design and manufacture a design part based on inertial consideration e.g., moment of inertia in Li’s teaching. The suggestion/motivation for doing so would have been obvious by Li because “In this work, we present a density variable shape modeling method to meet the required strength of 3D objects. We propose a physical modeling methodology to improve the structurally weak areas of 3D printed objects. The proposed method consists of two steps. Density distribution is first estimated to satisfy the detected stress of 3D objects. With our method, 3Dobjects with desired sound structures can be fabricated with low material consumption. In addition, the whole design process can be completed easily and interactively.” (Li disclosed in page 1627 heading ‘Abstract’ and page 1634 heading ‘Conclusion’).
Regarding Claim 7, Suresh and Li teach the method as set forth in claim 1, however, Suresh doesn’t explicitly teach the limitation “outputting information for further simulation or analysis before realization or manufacturing”.
further Li teaches outputting information for further simulation or analysis before realization or manufacturing. (Li disclosed in page 1634 section 6: “To validate the effectiveness of the proposed method, we design a typical cantilever structure model and load a force on the surface. … First, we can derive the local stress distribution (Fig. 13b) that determines the local gradational infilling distribution (Fig. 13c). Then, the infilled model is analyzed with the FEM (Fig. 13d) in terms of maximum stress. … Table 1 shows that the difference between the theoretical ratio and the actual infilling ratio is less than 1%. This finding demonstrates the reliability of our methodology. Figure 14a, b shows that the amount of material can be significantly reduced while maintaining safe levels of stress. We then define the maximum stress-to-infill ratio as the material utilizing rate μ=σ/φ(Ω). Therefore, we can enlarge this parameter to reduce material consumption sufficiently.” In same page section 7: “We propose a physical modeling methodology to improve the structurally weak areas of 3D printed objects. The proposed method consists of two steps. Density distribution is first estimated to satisfy the detected stress of 3D objects. An internal cellular structure is then generated with a pure mathematical 3D implicit function to represent the density distribution. With this method, we optimize the structure of the model for 3D printing to increase its strength and minimize the use of materials.”).
Suresh and Li are analogous because they are related to have structural optimization for 3Dobjects with desired structures can be fabricated with low material consumption. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Suresh and Li, to modify implementing/adding decision variable (e.g., topological field) in Suresh’s teaching, to include generating design and manufacture a design part based on inertial consideration e.g., moment of inertia in Li’s teaching. The suggestion/motivation for doing so would have been obvious by Li because “In this work, we present a density variable shape modeling method to meet the required strength of 3D objects. We propose a physical modeling methodology to improve the structurally weak areas of 3D printed objects. The proposed method consists of two steps. Density distribution is first estimated to satisfy the detected stress of 3D objects. With our method, 3Dobjects with desired sound structures can be fabricated with low material consumption. In addition, the whole design process can be completed easily and interactively.” (Li disclosed in page 1627 heading ‘Abstract’ and page 1634 heading ‘Conclusion’).
Regarding Claim 8, the same ground of rejection is made as discussed in claim 1 for substantially similar rationale, therefore claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Suresh and Li as discussed above for substantially similar rationale. In addition, claim 8 recites following limitations:
Suresh teaches a system for automated design generation, the system comprising: at least one processor and at least one memory having instructions stored thereon wherein execution of the instructions by the processor (Suresh disclosed in page 1 para [0010]: “providing systems and processes for additive manufacturing using a topology optimization (TO) framework that generates designs that have significantly reduced support structure requirements during manufacture.” In page 3 para [0050]: “FIG. 1 illustrates an exemplary system for optimizing a design of an object according to a support volume sensitive TO framework. A computing device 100 includes a processor 102 that executes device logic 104 within the processor 102 or contained in memory 106 of the computing device 100. The device logic 104 configures the processor 102 to perform the processes described herein.”).
Regarding claims 9-13, Suresh and Li teach the system as set forth in claim 8, are incorporating the rejections of claims 2-6, because claims 9-13 have substantially similar claim language as claims 2-6, therefore claims 9-13 are rejected under 35 U.S.C. 103 as being unpatentable over Suresh and Li as discussed above for substantially similar rationale.
Regarding claim 14, Suresh and Li teach the system as set forth in claim 8, further Suresh teaches a realization or manufacturing system. (Suresh disclosed in page 3 para [0049]: “Described here are systems and computer-implemented methods for generating designs for additive manufacturing (AM) that are topologically optimized according to a topological optimization (TO) process that maximizes performance, …”).
Regarding claim 15, Suresh and Li teach the system as set forth in claim 14, wherein Suresh teaches the realization or manufacturing system comprises at least one of: a three-dimensional printing system, an additive manufacturing system, a subtractive manufacturing system, or a hybrid manufacturing system. (Suresh disclosed in page 3 para [0049]: “Described here are systems and computer-implemented methods for generating designs for additive manufacturing (AM) that are topologically optimized according to a topological optimization (TO) process that maximizes performance, …”. In page 4 para [0052]: “The optimized design 130 is topologically optimized for performance, i.e., an object manufactured by AM processes from the optimized design 130 performs substantially the same functions as an object manufactured from the initial object design 110. The optimization is further constrained to minimize the total volume of support structures (e.g., support structures 132) needed during fabrication of the corresponding object by AM processes.” Further, in page 5 para [0069]: “topological sensitivity fields can be computed for various performance metrics, both in 2D and 3D.”).
Regarding claim 16, Suresh and Li teach the system as set forth in claim 8, wherein Suresh teaches the system is further caused to perform further analysis before information is output to realize. (Suresh disclosed in page 7 para [0081]: “the system may extract a new topology 2 using fixed-point iteration. At step 908, the system may compare the new topology to the previous topology to determine whether the topology has converged. ... If the topology has converged, at step 910 the system may determine whether a desired volume for the isosurface has been reached. If so, at step 912 the system may output a final optimized isosurface corresponding to the last - extracted, converged topology.”).
However, Suresh doesn’t explicitly teach the limitation “perform further analysis or simulation before to manufacture the part.”
Li teaches perform further analysis or simulation before to manufacture the part (Li disclosed in page 1634 section 6: “To validate the effectiveness of the proposed method, we design a typical cantilever structure model and load a force on the surface. … First, we can derive the local stress distribution (Fig. 13b) that determines the local gradational infilling distribution (Fig. 13c). Then, the infilled model is analyzed with the FEM (Fig. 13d) in terms of maximum stress. … Table 1 shows that the difference between the theoretical ratio and the actual infilling ratio is less than 1%. This finding demonstrates the reliability of our methodology. Figure 14a, b shows that the amount of material can be significantly reduced while maintaining safe levels of stress. We then define the maximum stress-to-infill ratio as the material utilizing rate μ=σ/φ(Ω). Therefore, we can enlarge this parameter to reduce material consumption sufficiently.” In same page section 7: “We propose a physical modeling methodology to improve the structurally weak areas of 3D printed objects. The proposed method consists of two steps. Density distribution is first estimated to satisfy the detected stress of 3D objects. An internal cellular structure is then generated with a pure mathematical 3D implicit function to represent the density distribution. With this method, we optimize the structure of the model for 3D printing to increase its strength and minimize the use of materials.”).
Suresh and Li are analogous because they are related to have structural optimization for 3Dobjects with desired structures can be fabricated with low material consumption. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Suresh and Li, to modify implementing/adding decision variable (e.g., topological field) in Suresh’s teaching, to include generating design and manufacture a design part based on inertial consideration e.g., moment of inertia in Li’s teaching. The suggestion/motivation for doing so would have been obvious by Li because “In this work, we present a density variable shape modeling method to meet the required strength of 3D objects. We propose a physical modeling methodology to improve the structurally weak areas of 3D printed objects. The proposed method consists of two steps. Density distribution is first estimated to satisfy the detected stress of 3D objects. With our method, 3Dobjects with desired sound structures can be fabricated with low material consumption. In addition, the whole design process can be completed easily and interactively.” (Li disclosed in page 1627 heading ‘Abstract’ and page 1634 heading ‘Conclusion’).
Conclusion
10. The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. An NPL “Density Aware Shape Modeling to Control Mass Properties of 3D Printed Objects” by Daiki Yamanaka et al. presented a density aware shape modelling method to control the mass properties of 3D printed objects. A continuous density distribution is generated that satisfies the given mass proper ties and a 3D printable model is generated that represents this density distribution using a truss structure. The number of nodes and their positions are iteratively optimized so as to minimize error between the target density and the density of the truss structure. With our technique, 3D printed objects that have desired mass properties can be fabricated. The effectiveness of density aware modelling method has been demonstrated with numerous results and have shown various applications of presented method.
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/NUPUR DEBNATH/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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