DETAILED ACTION
Claims 1-20 are presented for examination. This action is made in response to the communication filed March 22, 2023.
Claims 1-3, 5, 8, 10-13, 15-18, and 20 are rejected as provisional nonstatutory double patenting with US 2023/0385484 A1.
Claims 1-20 are rejected under 35 USC 112(b) as indefinite.
Claims 1-20 are rejected under 35 USC 101 as ineligible.
Claims 1-5 and 9-20 are rejected under 35 USC 102(a)(1) as anticipated by Wein.
Claims 6-7 are rejected under 35 USC 103 as obvious over Wein in view of Yang.
Claim 8 is rejected under 35 USC 103 as obvious over Wein in view of Duysinx.
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 5, 8, 10-13, 15-18, and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 6-7, 9, and 11-12 of copending Application No. 18/324,815 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the reference application claims a specific species, “a sheet part having beads,” and the instant application claims the genus of that species, “ a manufacturing product.”
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
The following is a claim-to-claim mapping of the features. This is relatively simple, as the claims are largely redundant, differing in only a few places that specify in the co-pending application that the design is of a bead. There are also a few formal substitutions in phrasing, such as method for approach or having for comprising.
Claims
18/188,331
Claims
18/324,815
1,11,16
1. A computer-implemented method for designing a manufacturing product, the method comprising:
1,11
1. A computer-implemented method for designing a sheet part having beads, the method comprising:
obtaining a CAD model representing the manufacturing product, the CAD model including a feature tree having one or more CAD parameters each having an initial value;
obtaining a CAD model representing the part, the CAD model including a feature tree having one or more CAD parameters each having an initial value;
obtaining an optimization program specified by one or more use and/or manufacturing performance indicators, the one or more indicators comprising one or more objective functions and/or one or more constraints; and
obtaining a bead optimization program specified by one or more use and/or manufacturing performance indicators, the one or more indicators having one or more objective functions and/or one or more constraints; and
modifying the initial values of the one or more CAD parameters by solving the optimization program using a gradient-based optimization approach, the optimization approach having as free variable the one or more CAD parameters, the optimization approach using sensitivities, each sensitivity being an approximation of a respective derivative of a respective performance indicator with respect to a respective CAD parameter.
modifying the initial values of the one or more CAD parameters by solving the optimization program using a gradient-based bead optimization method, the optimization method having as free variable the one or more CAD parameters, the optimization approach using sensitivities, each sensitivity being an approximation of a respective derivative of a respective performance indicator with respect to a respective CAD parameter.
2,12,17
2. The computer-implemented method of claim 1, wherein the sensitivities are compositions of:
2,12
2. The computer-implemented method of claim 1, wherein the sensitivities are compositions of:
approximated respective derivatives each of a respective performance indicator with respect to a scalar field, the scalar field being an implicit representation of the manufacturing product, and
approximated respective derivatives each of a respective performance indicator with respect to a bead pattern nodal positions of a shell mesh of the part, […]
approximated respective derivatives each of the scalar field with respect to a respective CAD parameter.
approximated respective derivatives each of the geodesic signed distance field with respect to a respective CAD parameter of a CAD definition of the bead pattern of the part.
3,13,18
3. The computer-implemented method of claim 2, wherein the sensitivities are compositions of:
2,12
2. The computer-implemented method of claim 1, wherein the sensitivities are compositions of:
approximated respective derivatives each of a respective performance indicator with respect to a density field, the density field representing a distribution of material density of the manufacturing product,
approximated respective derivatives each of a respective performance indicator with respect to a bead pattern nodal positions of a shell mesh of the part, […]
approximated respective derivatives each of the density field with respect to a signed distance field, the signed distance field being a distribution of signed distances with respect to an outer surface representation of the manufacturing product, and
approximated respective derivatives each of the nodal positions with respect to a geodesic signed distance field on the part, the geodesic signed distance field being a distribution of geodesic signed distances of nodes to the bead pattern on the part, and
approximated respective derivatives each of the signed distance field with respect to a respective CAD parameter.
approximated respective derivatives each of the geodesic signed distance field with respect to a respective CAD parameter of a CAD definition of the bead pattern of the part.
5,15,20
5. The computer-implemented method of claim 4, wherein the function is a smooth Heaviside projection.
6
6. The computer-implemented method of claim 5, wherein h is a smooth Heaviside function.
8
The computer-implemented method of claim 3, wherein each respective approximated derivative
7
The computer-implemented method of claim 3, wherein each respective approximated derivative
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of the signed distance field with respect to a respective CAD parameter is of the type:
of the geodesic signed distance field with respect to a respective CAD parameter rm is of the type:
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where the signed distance field is a distribution of signed distances SDFi each from an element i of a discretization ω of a region encompassing a geometric representation of the product to the outer surface representation of the product, where Ωparam is a set of the CAD parameters, where CADm is a respective CAD parameter of the set, and where hm > 0 is a small perturbation.
where GSDFi is the geodesic signed distance field for mesh node position i, where Ωparam is a set of the CAD parameters, and where hm>0 is a small perturbation.
where CADm is a respective CAD parameter of the set
CAD parameter rm (NOTE CADm = rm/2)
10,20
10. The computer-implemented method of claim 1, further comprising, prior to solving the optimization program, computing the sensitivities.
9
9. The computer-implemented method of claim 1, further comprising, prior to solving the optimization program, computing the sensitivities.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The independent claims recite, “each sensitivity being an approximation of a respective derivative of a respective performance indicator with respect to a respective CAD parameter.” The relationship between these elements is unclear. What is with respect to what else? Clarification is required.
Dependent claims that depend from rejected claims are rejected based on their dependency.
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 subject matter is directed to an abstract idea without significantly more. The claims recite mental processes that are capable of being performed in the mind and/or with the aid of pen and paper and are also mathematical concepts, abstract ideas.
Independent Claims
Claims 1, 11 and 16
Claim 16 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite mental processes, which are abstract ideas.
Claim 16 recites:
modify the initial values of the one or more CAD parameters by solving the optimization program using a gradient-based optimization approach, the optimization approach having as free variable the one or more CAD parameters, the optimization approach using sensitivities, each sensitivity being an approximation of a respective derivative of a respective performance indicator with respect to a respective CAD parameter. (Mental Processes, Mathematical Concepts – The modification of values of parameters by conducting gradient-based operations for CAD applications is practically performable in the mind and/or with the aid of pen and paper, so this claim feature is an evaluation, a mental process, an abstract idea. Further, modifying values based on gradient based optimization (regression) is per se a mathematical calculation, a mathematical concept, an abstract idea. See Example 47 for the per se characterization of gradient-based math.)
Claim 16 recites mental processes and mathematical concepts, which are abstract ideas.
Claim 16 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Application?
No.
Claim 16 recites the following additional limitations:
A computer system comprising:
a processor coupled to a memory, the memory having recorded thereon a computer program having instructions for designing a manufacturing product that when executed by the processor causes the processor to be configured to:
The use of a processor and memory with instructions is a generic computing operation recited at a high level, which, under MPEP 2106.05(f), fails to integrate the abstract idea into a practical application. The specific data stored also merely limits the abstract idea to a particular technological environment, which, under MPEP 2106.05(h), fails to integrate the abstract idea into a practical application.
obtain a CAD model representing the manufacturing product, the CAD model including a feature tree having one or more CAD parameters each having an initial value;
obtain an optimization program specified by one or more use and/or manufacturing performance indicators, the one or more indicators comprising one or more objective functions and/or one or more constraints; and
This is mere data gathering akin to the MPEP 2106.05(g) examples: “i. Performing clinical tests on individuals to obtain input for an equation” “v. Consulting and updating an activity log, Ultramercial,” “i. Limiting a database index to XML tags” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.” Accordingly, this is extra-solution activity and fails to integrate the abstract ideas into a practical application. The specific data recited also merely limits the abstract idea to a particular technological environment, which, under MPEP 2106.05(h), fails to integrate the abstract idea into a practical application.
Claim 16 fails to recite any additional limitations that integrate the abstract idea into a practical application.
Claim 16 is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
Claims 16 recites the following additional limitations:
A computer system comprising:
a processor coupled to a memory, the memory having recorded thereon a computer program having instructions for designing a manufacturing product that when executed by the processor causes the processor to be configured to:
The generic use of processor and memory is a generic computing operation, which, under MPEP 2106.05(f), fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would be indicative of an inventive concept at Step 2B. The specific data used also merely limits the abstract idea to a particular technological environment, which, under MPEP 2106.05(h), fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would be indicative of an inventive concept at Step 2B.
obtain a CAD model representing the manufacturing product, the CAD model including a feature tree having one or more CAD parameters each having an initial value;
obtain an optimization program specified by one or more use and/or manufacturing performance indicators, the one or more indicators comprising one or more objective functions and/or one or more constraints; and
This is well-understood, routine, and conventional (WURC) activity akin to the MPEP 2106.05(d) examples: “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “v. Electronically scanning or extracting data from a physical document” “i. Determining the level of a biomarker in blood by any means “ “v. Analyzing DNA to provide sequence information or detect allelic variants” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Because this limitation is WURC, it fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept. The specific data used also merely limits the abstract idea to a particular technological environment, which, under MPEP 2106.05(h), fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would be indicative of an inventive concept at Step 2B.
The additional limitations fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
Claim 16 is ineligible.
Regarding claims 1 and 11, claims 1 and 11 are directed to a method and a CRM, respectively. Claim 1 recites the method executed by the system of claim 16. Claim 11 recites a CRM that is an embodiment of the memory of claim 16. Accordingly, claims 1 and 11 are ineligible for at least the same reasons as claim 16.
Dependent Claims
The dependent claims 2-10, 12-15 and 17-20 are also ineligible for the following reasons.
Claims 2, 12, and 17
wherein the sensitivities are compositions of: approximated respective derivatives each of a respective performance indicator with respect to a scalar field, the scalar field being an implicit representation of the manufacturing product, and approximated respective derivatives each of the scalar field with respect to a respective CAD parameter.
The recitations here do not positively recite the determinations of these. Therefore, these are either elements of the abstract idea of the modifying step or they ae mere data gathering and WURC for the same reasons as the obtaining steps of the independent claims.
Claims 2, 12, and 17 fail to recite any additional limitations that confer eligibility.
Claims 2, 12, and 17 are ineligible.
Claims 3, 13, and 18
wherein the sensitivities are compositions of: approximated respective derivatives each of a respective performance indicator with respect to a density field, the density field representing a distribution of material density of the manufacturing product, approximated respective derivatives each of the density field with respect to a signed distance field, the signed distance field being a distribution of signed distances with respect to an outer surface representation of the manufacturing product, an approximated respective derivatives each of the signed distance field with respect to a respective CAD parameter.
The recitations here do not positively recite the determinations of these. Therefore, these are either elements of the abstract idea of the modifying step or they ae mere data gathering and WURC for the same reasons as the obtaining steps of the independent claims.
Claims 3, 13, and 18 fail to recite any additional limitations that confer eligibility.
Claims 3, 13, and 18 are ineligible.
Claims 4, 14, and 19
wherein the density field corresponds to a projection of the signed distance field by a function that maps ]-∞;+∞[ onto [0;1] and that has well-defined first order derivatives.
These limitations merely describe parameters of the modifying step, so they are elements of the abstract idea.
Should it be found otherwise, the recitations here do not positively recite the determinations of these. Therefore, these are either elements of the abstract idea of the modifying step or they ae mere data gathering and WURC for the same reasons as the obtaining steps of the independent claims.
Claims 4, 14, and 19 fail to recite any additional limitations that confer eligibility.
Claims 4, 14, and 19 are ineligible.
Claims 5, 15, and 20
wherein the function is a smooth Heaviside projection.
This limitation merely describes parameters of the modifying step, so it is an element of the abstract idea.
Claims 5, 15, and 20 fail to recite any additional limitations that confer eligibility.
Claims 5, 15, and 20 are ineligible.
Claim 6
wherein the density field is a distribution of material density values ρi(SDFi), with ρi being a smooth Heaviside projection of SDFi of the type:
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where the signed distance field is a distribution of signed distances SDFi each from an element i of a discretization ω of a region encompassing a geometric representation of the product to the outer surface representation of the product, where α ≥ 0 is a steepness coefficient of the smooth Heaviside projection, and where l is an average size of the elements in the discretization.
This merely describes the manner in which the mental processes and mathematical concepts of claim 1 perform, so these limitations are elements of the abstract idea.
Claim 6 fails to recite any additional limitations that confer eligibility.
Claim 6 is ineligible.
Claim 7
wherein each respective approximated derivative
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of the density field with respect to the signed distance field is of the type:
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.
This merely describes the manner in which the mental processes and mathematical concepts of claim 1 perform, so these limitations are elements of the abstract idea.
Should it be found otherwise, these are characteristics of the data gathered, so they are mere data gathering and WURC, and cannot confer eligibility.
Claim 7 fails to recite any additional limitations that confer eligibility.
Claim 7 is ineligible.
Claim 8
wherein each respective approximated derivative
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of the signed distance field with respect to a respective CAD parameter is of the type:
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where the signed distance field is a distribution of signed distances SDFi each from an element i of a discretization ω of a region encompassing a geometric representation of the product to the outer surface representation of the product, where Ωparam is a set of the CAD parameters, where CADm is a respective CAD parameter of the set, and where hm > 0 is a small perturbation.
This merely describes the manner in which the mental processes and mathematical concepts of claim 1 perform, so these limitations are elements of the abstract idea.
Should it be found otherwise, these are characteristics of the data gathered, so they are mere data gathering and WURC, and cannot confer eligibility.
Claim 8 fails to recite any additional limitations that confer eligibility.
Claim 8 is ineligible.
Claim 9
wherein each sensitivity
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is of the type:
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where the signed distance field is a distribution of signed distances SDFi each from an element i of a discretization ω of a region encompassing a geometric representation of the product to the outer surface representation of the product, where Ωparam is a set of the CAD parameters, where CADm is the respective CAD parameter, where the density field is a distribution of material density values ρiSDFi, where KPIn is the respective performance indicator, and where Ωscore is the set of performance indicators.
This merely describes the manner in which the mental processes and mathematical concepts of claim 1 perform, so these limitations are elements of the abstract idea.
Should it be found otherwise, these are characteristics of the data gathered, so they are mere data gathering and WURC, and cannot confer eligibility.
Claim 9 fails to recite any additional limitations that confer eligibility.
Claim 9 is ineligible.
Claim 10
further comprising, prior to solving the optimization program, computing the sensitivities.
Computing sensitivities was done before the advent of computers, so it is practically performable in the mind or with the aid of pen or paper, so this limitation is an evaluation, a mental process, and abstract idea. Also, the computing of sensitivities is a mathematical calculation in textual form (e.g., using the term computing as a superficial handle for calculating) , which is a mathematical concept, an abstract idea.
Claim 10 fails to recite any additional limitations that confer eligibility.
Claim 10 is ineligible.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 and 9-20: Wein
Claim(s) 1-5 and 9-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by NPL: “A review on feature-mapping methods for structural optimization” by Wein et al. (Wein).
Claims 1, 11, and 16
Regarding claim 16, Wein teaches:
A computer system comprising: a processor coupled to a memory, the memory having recorded thereon a computer program having instructions for designing a manufacturing product that when executed by the processor causes the processor to be configured to: (Wein Page 1, Introduction “These methods have been largely motivated by the need to embed primitive-shaped components in free-form designs, to design structures made of stock material, to control certain dimensions of the structure, and ultimately, to provide a geometric representation that is directly understood by computer aided design (CAD) systems.” – CAD is executed on computers with processors and memory.)
obtain a CAD model representing the manufacturing product, the CAD model including a feature tree having one or more CAD parameters each having an initial value; (Wein page 2, 2.1 High-level geometric features “In this paper, we define a geometric feature as a geometric solid with a high-level parameterization. A geometric solid is here understood as a closed regular set of points, i.e., a set that equals the closure of its interior (cf. Shapiro (2002)). Physically, we consider the feature can either be a solid component or a hole in a solid component. By high-level parameters, we refer to those with a direct spatial dimension associated with the feature’s size, position or orientation. Examples of these parameters are the radius of a fillet, the thickness of a plate, or the location of a primitive (e.g. a bar or circle). Notably, these high-level parameters are the ones often employed to represent solids in CAD systems. The advantage of having these dimensions as direct design variables is that they simplify enforcing the presence of these features and to control their dimensions, as opposed to the indirect and more verbose low-level representations of solids, such as those that are pixel or voxel-based.” – CAD model with parameters that have to have initial values to be manipulated.)
obtain an optimization program specified by one or more use and/or manufacturing performance indicators, the one or more indicators comprising one or more objective functions and/or one or more constraints; and (Wein Page 21, 4.4 Local Minima “The combination of geometric features may lead to unfavorable local minima. To illustrate this, we consider the example shown in Fig. 19. Four bars are modelled with hyperellipses. Three of the bars are fixed, and another one is moved by changing h. For h ∈ {0,L/2,L} the moving bar entirely overlaps with one of the fixed bars. The design region is meshed with square bilinear elements with a relatively fine mesh. A binary pseudo-density mapping is used, where the element pseudo-density is either ρmin or 1 depending on whether the element centroid is outside or inside of a bar, respectively. The combination is performed using a map-then-combine approach with a true maximum of the pseudo-density values. Fig. 19 shows the compliance as a function of h/L. The actual magnitude of the compliance is not important; what is important is the presence of two distinct local minima, one of which (h/L ≈ 0.43) is clearly worse than the other (h/L ≈ 0.79). Therefore, if a gradient-based optimizer is used and the initial design has h < L/2, the optimizer will most likely converge to the poor local minimum.” – The model uses a gradient-based optimizer to converge the the local minimum of an objective function, the objective function representing a difference from a desired value, such as a constraint or design specification.)
modify the initial values of the one or more CAD parameters by solving the optimization program using a gradient-based optimization approach, the (Wein Page 21, 4.4 Local Minima “The combination of geometric features may lead to unfavorable local minima. To illustrate this, we consider the example shown in Fig. 19. Four bars are modelled with hyperellipses. Three of the bars are fixed, and another one is moved by changing h. For h ∈ {0,L/2,L} the moving bar entirely overlaps with one of the fixed bars. The design region is meshed with square bilinear elements with a relatively fine mesh. A binary pseudo-density mapping is used, where the element pseudo-density is either ρmin or 1 depending on whether the element centroid is outside or inside of a bar, respectively. The combination is performed using a map-then-combine approach with a true maximum of the pseudo-density values. Fig. 19 shows the compliance as a function of h/L. The actual magnitude of the compliance is not important; what is important is the presence of two distinct local minima, one of which (h/L ≈ 0.43) is clearly worse than the other (h/L ≈ 0.79). Therefore, if a gradient-based optimizer is used and the initial design has h < L/2, the optimizer will most likely converge to the poor local minimum.” – The model uses a gradient-based optimizer to converge the local minimum of an objective function, the objective function representing a difference from a desired value of a design objective, such as a constraint or design specification.)
optimization approach having as free variable the one or more CAD parameters, (Wein Page 21, 4.4 Local Minima “Three of the bars are fixed, and another one is moved by changing h. For h ∈ {0,L/2,L} the moving bar entirely overlaps with one of the fixed bars. The design region is meshed with square bilinear elements with a relatively fine mesh.” – h is a free variable representing a CAD parameter.)
the optimization approach using sensitivities, each sensitivity being an approximation of a respective derivative of a respective performance indicator with respect to a respective CAD parameter. (Wein Pages 9-10, 3.1.5 Sensitivity Analysis “One of the appealing features of the element pseudo-density approach in feature-mapping methods is that, as in density-based topology optimization, the computation of design sensitivities is much simpler than for approaches that must compute boundary sensitivities (as in some level-set methods). Moreover, as we will show in this section, the computation of sensitivities is closely connected to that of density-based methods. Sensitivity analysis in density-based topology optimization is well established. It can be readily performed on the discretized algebraic system resulting from a finite element analysis for a wide range of functions, and even multiphysics problems fit one of the known generalized derivations. Using adjoint differentiation (e.g. Tr¨ oltzsch (2010)), the sensitivity of a function J(ρ,u(ρ)) with respect to an element pseudo-density” – Derivatives of performance indicators are used to account for sensitivities in the optimization.)
Regarding claims 1 and 11, claims 1 and 11 recite features similar to those of claim 16 and are rejected for at least the same reasons.
Claims 2, 12, and 17
Regarding claim 17, Wein teaches the features of claim 16 and further teaches:
wherein the sensitivities are compositions of: approximated respective derivatives each of a respective performance indicator with respect to a scalar field, the scalar field being an implicit representation of the manufacturing product, and (Wein Pages 12-13, Computing the signed distance “Feature-mappingmethodsoftenemployasigned-distanceimplicitfunctionwhenusingpseudo-densitymapping,asthismain tainsthetransitionzone,asdiscussedinSec.3.1.4. […] To obtain the signed-distance for more complex explicit geometry descriptions, a popular method is to compute an equiv alent implicit function, which is also a signed distance (i.e. a signed-distance level-set function). This can be achieved using schemes popular with level-set methods, such as the fast-marching method (Adalsteinsson and Sethian, 1999), or iteratively solving a Hamilton-Jacobi equation in pseudo-time t
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These methods can also be used to compute the signed distance for other implicit geometry descriptions. However, these methods can be computationally expensive, especially if required each time the design changes. Thus, for implicit geometry representations, Zhou et al. (2016) propose using a first-order Taylor approximation of the signed-distance function in the form of d(x) ≈ φ(x) ||∇φ(x)||” – Approximate derivatives are used to relate the changes to a performance indicator to a signed distance field, which is a scalar field. All adjustments are reflected through the mathematical relationships relating the parameter changes, to the density changes, to how that changes the signed distance field.)
approximated respective derivatives each of the scalar field with respect to a respective CAD parameter. (Wein Page 15, 3.2.3 Sensitivity analysis “The second approach is to discretize, then differentiate, but with a semi-analytical approach. The idea is to compute the derivative of the element matrices with respect to the design variables using the finite difference method. This derivative term is then inserted into the analytically derived sensitivity formula. Thus, sensitivities are consistent with the numerical discretization, but the semi-analytical approach avoids explicitly computing derivatives with respect to changes in the integration sub-domains. The finite difference approach is reasonably efficient, as it is only performed for elements that contain an interface and does not require assembling and solving a system of equations. This approach has proved effective and has been used in several feature-mapping methods. – Approximate derivates are used to relate the changes in the signed distance field back to the original CAD model.)
Regarding claims 2 and 12, claims 2 and 12 recite features similar to those of claim 13 and are rejected for at least the same reasons.
Claims 3, 13, and 18
Regarding claim 18, Wein teaches the features of claim 16 and further teaches:
wherein the sensitivities are compositions of: approximated respective derivatives each of a respective performance indicator with respect to a density field, the density field representing a distribution of material density of the manufacturing product, (Wein 3.1.5, Sensitivity Analysis “Sensitivity analysis in density-based topology optimization is well established. It can be readily performed on the discretized algebraic system resulting from a finite element analysis for a wide range of functions, and even multiphysics problems fit one of the known generalized derivations […] We briefly review sensitivity analysis for standard density-based topology optimization and consider the easy static case, where the finite element system matrix K depends explicitly on the vector of element pseudo-densities ρ, the state solution u depends only implicitly on ρ and the boundary conditions are assumed to be design-independent. […] Once solution is computed, feature-mapping methods with pseudo-densities only need to compute the derivative of the boundary mapping function to obtain derivatives of pseudo-densities with respect to the high-level design parameters, sj. The final derivative is obtained by the chain rule as
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- The reference uses an approximate derivative comprised of a sum of sub-derivatives to model the changes of density in response to the changes in parameter values in the optimization problem.)
approximated respective derivatives each of the density field with respect to a signed distance field, the signed distance field being a distribution of signed distances with respect to an outer surface representation of the manufacturing product, and (Wein Pages 12-13, 3.1.7 Feature-mapping methods often employ a signed-distance implicit function when using pseudo-density mapping, as this maintains the transition zone, as discussed in Sec. 3.1.4. For some explicit geometry descriptions, the signed distance can be easily computed using an analytical expression. For example, the distance to the edge of a circular, or spherical feature can be directly computed from the feature parameters (center coordinates and radius). […] To obtain the signed-distance for more complex explicit geometry descriptions, a popular method is to compute an equivalent implicit function, which is also a signed distance (i.e. a signed-distance level-set function). This can be achieved using schemes popular with level-set methods, such as the fast-marching method (Adalsteinsson and Sethian, 1999), or iteratively solving a Hamilton-Jacobi equation in pseudo-time t
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These methods can also be used to compute the signed distance for other implicit geometry descriptions. However, these methods can be computationally expensive, especially if required each time the design changes. Thus, for implicit geometry representations, Zhou et al. (2016) propose using a first-order Taylor approximation of the signed-distance function in the form of d(x) ≈ φ(x) ||∇φ(x)||” – Approximate derivatives are used to relate the changes to a performance indicator to a signed distance field, which is a scalar field. All adjustments are reflected through the mathematical relationships relating the parameter changes, to the density changes, to how that changes the signed distance field.)
approximated respective derivatives each of the signed distance field with respect to a respective CAD parameter. (Wein Page 15, 3.2.3 Sensitivity analysis “The second approach is to discretize, then differentiate, but with a semi-analytical approach. The idea is to compute the derivative of the element matrices with respect to the design variables using the finite difference method. This derivative term is then inserted into the analytically derived sensitivity formula. Thus, sensitivities are consistent with the numerical discretization, but the semi-analytical approach avoids explicitly computing derivatives with respect to changes in the integration sub-domains. The finite difference approach is reasonably efficient, as it is only performed for elements that contain an interface and does not require assembling and solving a system of equations. This approach has proved effective and has been used in several feature-mapping methods. – Approximate derivates are used to relate the changes in the signed distance field back to the original CAD model.)
Regarding claims 3 and 13, claims 3 and 13 recite features similar to those of claim 18 and are rejected for at least the same reasons.
Claims 4, 14, and 19
Regarding claim 19, Wein teaches the features of claim 18 and further teaches:
wherein the density field corresponds to a projection of the signed distance field by a function that maps ]-∞;+∞[ onto [0;1] and that has well-defined first order derivatives. (Wein Page 10, 3.1.6 Numerical integration of the boundary mapping function “In principle, density-based feature-mapping requires the element-constant pseudo-density to be found by integrating the smoothed Heaviside, or boundary mapping function (8).” – A smoothed Heaviside function is used to project the signed distance field values into the density space. This takes the values from a continuous values that can have any value (e.g., -infinity to infinity) to a 0 to 1 mapping.)
Regarding claims 4 and 14, claims 4 and 14 recite features similar to those of claim 19 and are rejected for at least the same reasons.
Claims 5, 15, and 20
Regarding claim 20, Wein teaches the features of claim 19 and further teaches:
wherein the function is a smooth Heaviside projection. (Wein Page 10, 3.1.6 Numerical integration of the boundary mapping function “In principle, density-based feature-mapping requires the element-constant pseudo-density to be found by integrating the smoothed Heaviside, or boundary mapping function (8).” – A smoothed Heaviside function is used to project the signed distance field values into the density space.)
Regarding claims 5 and 15, claims 5 and 15 recite features similar to those of claim 20 and are rejected for at least the same reasons.
Claim 9
Regarding claim 9, Wein teaches the features of claim 3, and further teaches:
wherein each sensitivity
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is of the type:
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where the signed distance field is a distribution of signed distances SDFi each from an element i of a discretization ω of a region encompassing a geometric representation of the product to the outer surface representation of the product, where Ωparam is a set of the CAD parameters, where CADm is the respective CAD parameter, where the density field is a distribution of material density values ρi(SDFi), where KPIn is the respective performance indicator, and where Ωscore is the set of performance indicators. (Wein 3.1.5, Sensitivity Analysis “Sensitivity analysis in density-based topology optimization is well established. It can be readily performed on the discretized algebraic system resulting from a finite element analysis for a wide range of functions, and even multiphysics problems fit one of the known generalized derivations […] We briefly review sensitivity analysis for standard density-based topology optimization and consider the easy static case, where the finite element system matrix K depends explicitly on the vector of element pseudo-densities ρ, the state solution u depends only implicitly on ρ and the boundary conditions are assumed to be design-independent. […] Once solution is computed, feature-mapping methods with pseudo-densities only need to compute the derivative of the boundary mapping function to obtain derivatives of pseudo-densities with respect to the high-level design parameters, sj. The final derivative is obtained by the chain rule as
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- The reference uses an approximate derivative comprised of a sum of sub-derivatives to model the changes of density in response to the changes in parameter values in the optimization problem. Pages 12-13, 3.1.7 Feature-mapping methods often employ a signed-distance implicit function when using pseudo-density mapping, as this maintains the transition zone, as discussed in Sec. 3.1.4. For some explicit geometry descriptions, the signed distance can be easily computed using an analytical expression. For example, the distance to the edge of a circular, or spherical feature can be directly computed from the feature parameters (center coordinates and radius). […] To obtain the signed-distance for more complex explicit geometry descriptions, a popular method is to compute an equivalent implicit function, which is also a signed distance (i.e. a signed-distance level-set function). This can be achieved using schemes popular with level-set methods, such as the fast-marching method (Adalsteinsson and Sethian, 1999), or iteratively solving a Hamilton-Jacobi equation in pseudo-time t
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These methods can also be used to compute the signed distance for other implicit geometry descriptions. However, these methods can be computationally expensive, especially if required each time the design changes. Thus, for implicit geometry representations, Zhou et al. (2016) propose using a first-order Taylor approximation of the signed-distance function in the form of d(x) ≈ φ(x) ||∇φ(x)||” – Approximate derivatives are used to relate the changes to a performance indicator to a signed distance field, which is a scalar field. All adjustments are reflected through the mathematical relationships relating the parameter changes, to the density changes, to how that changes the signed distance field. Page 15, 3.2.3 Sensitivity analysis “The second approach is to discretize, then differentiate, but with a semi-analytical approach. The idea is to compute the derivative of the element matrices with respect to the design variables using the finite difference method. This derivative term is then inserted into the analytically derived sensitivity formula. Thus, sensitivities are consistent with the numerical discretization, but the semi-analytical approach avoids explicitly computing derivatives with respect to changes in the integration sub-domains. The finite difference approach is reasonably efficient, as it is only performed for elements that contain an interface and does not require assembling and solving a system of equations. This approach has proved effective and has been used in several feature-mapping methods. – Approximate derivates are used to relate the changes in the signed distance field back to the original CAD model. Based on these expressed relationships, it is clear that the chain of approximate partial derivatives expressed in the claim is fully represented in the Wein reference.)
Claim 10
Regarding claim 10, Wien teaches the features of claim 1, and further teaches:
further comprising, prior to solving the optimization program, computing the sensitivities. (Wein Page 167, 3.2 Geometric feature based topology estimation “A conceptual framework of OFM is demonstrated in Fig. 5 . To realize the OFM, feature technology should be involved because both the geometric and semantic information included in the machining feature definition [83] is mandatory to quantitatively evaluate the manufacturing feasibility. Therefore, this sub- section investigates the geometric feature based topology optimization methods: their current status and future research directions.” SEE ALSO Table 5 (shown below) – As illustrated in table 5, sensitivity analysis is performed before and after feature-based shape optimization.)
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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 6-7: Wein and Yang
Claim(s) 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over NPL: “A review on feature-mapping methods for structural optimization” by Wein et al. (Wein) in view of NPL: “An Efficient Topology Description Function Method Based on Modified Sigmoid Function” by Yang et al. (Yang).
Claim 6
Regarding claim 6, Wein teaches the features of claim 5, and further teaches:
wherein the density field is a distribution of material density values ρi(SDFi), with ρi being a smooth Heaviside projection of SDFi (Wein Page 10, 3.1.6 Numerical integration of the boundary mapping function “In principle, density-based feature-mapping requires the element-constant pseudo-density to be found by integrating the smoothed Heaviside, or boundary mapping function (8).” – Wein teaches that the material density field values are mapped to the signed distance field using a smoothed Heaviside function.)
Wein teaches the use of a smoothed Heaviside function to project between density and signed distance fields, but it does not specify the form of the smooth Heaviside function used, however, Wein in view of Yang teaches:
of the type:
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where the signed distance field is a distribution of signed distances SDFi each from an element i of a discretization ω of a region encompassing a geometric representation of the product to the outer surface representation of the product, where α ≥ 0 is a steepness coefficient of the smooth Heaviside projection, and where l is an average size of the elements in the discretization. (Yang Page 2, 2.1 Sigmoid Function "Sigmoid function [39] is a continuous nonlinear activation function. The origin of the name, sigmoid, is from the fact that the function is 𝑆-shaped. This function is called logistic function by the statisticians. Using 𝜒 as input, 𝑔(𝜒) as output, and 𝛼 which is a positive number as a contrast factor term, the sigmoid function can be expressed as
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- The denominator of the exponential function of the claim will be a fixed constant that is no different from the alpha value in the reference. That is, the parameter values will be derived and the result with be a constant within a particular problem set, so there is no distinction between the equation in the reference and the equation in the claims.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the generic recitation of a smoothed Heaviside function in Wein by the specific Heaviside function of Yang because the person of ordinary skill in the art would be motivated based on the aim of Wein to investigate the use of smoothing Heaviside projection methods to look to Yang, which provides a simple and efficient smoothing Heaviside modified sigmoid function. (Wein Abstract “For the former case, which we refer to as the pseudo-density approach, a test problem is formulated to investigate aspects of the material interpolation, boundary smoothing and numerical integration.” Page 5, Second Paragraph “Thus, feature mapping using pseudo-densities requires a choice of several key ingredients: 1) the type of material interpolation function, µ(ρe), 2) the form of the Heaviside (or smooth boundary) function”; Yang Abstract “By employing the modified sigmoid function, a simple yet efficient strategy is presented to tackle the computational difficulties because of the nonsmoothness of Heaviside function in topology optimization problem.”)
Claim 7
Regarding claim 7, Wein in view of Yang teaches the features of claim 6, and further teaches:
wherein each respective approximated derivative
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of the density field with respect to the signed distance field is of the type:
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(Yang Page 2, 2.1 Sigmoid Function "Sigmoid function [39] is a continuous nonlinear activation function. The origin of the name, sigmoid, is from the fact that the function is 𝑆-shaped. This function is called logistic function by the statisticians. Using 𝜒 as input, 𝑔(𝜒) as output, and 𝛼 which is a positive number as a contrast factor term, the sigmoid function can be expressed as
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- The smoothed Heaviside relationship expressed in Yang is the relationship between the Wein density field and the Wein signed distance field. The equation in claim 7 is merely the derivative of the equation in claim 6, and it is known from Wein that a derivative or approximate derivative of the equations relating changes in the density field to the changes in the signed distance field is used to relate changes of one coordinate system (e.g., made during the optimization of the objective function) to the other.)
Claim 8: Wein and Duysinx
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over NPL: “A review on feature-mapping methods for structural optimization” by Wein et al. (Wein) in view of NPL: “SENSITIVITY ANALYSIS” by Duysinx (Duysinx).
Claim 8
Regarding claim 8, Wein teaches the features of claim 3. Wein does not appear to explicitly teach, but Wein in view of Duysinx teaches:
wherein each respective approximated derivative
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of the signed distance field with respect to a respective CAD parameter is of the type:
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where the signed distance field is a distribution of signed distances SDFi each from an element i of a discretization ω of a region encompassing a geometric representation of the product to the outer surface representation of the product, where Ωparam is a set of the CAD parameters, where CADm is a respective CAD parameter of the set, and where hm > 0 is a small perturbation. (DUYSINX Slide 7
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This teaches using the standard central finite difference approximation of the claim as an estimated derivative for sensitivity analysis.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the sensitivity analysis of Wein by the second order central finite difference approximation of Duysinx because the person of ordinary skill in the art would be motivated by the aim of Wein to gain better control over the geometry in the optimization that includes sensitivity analysis to look to the finite difference sensitivity analysis of Duysinx, which provides the advantage of producing accurate derivative estimates in a large range of step-sizes.
(Wein Abstract “The main motivation for using these methods is to gain better control over the geometry to, for example, facilitate imposing direct constraints on geometric features, while avoiding issues with re-meshing.”; Duysinx Slide 13:
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Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11,055,908 B1 to Tewari et al. (Teaches topology optimization including finite difference and approximation of the Heaviside function)
US 2021/0390229 A1 to Mirzendehdel et al. (Teaches topology optimization including finite difference and approximation of the Heaviside function)
US 2021/0365007 A1 to Kim et al. (Teaches topology optimization including finite difference and approximation of the Heaviside function)
US 2021/0365004 A1 to Weinberg et al. (Teaches topology optimization including finite difference and approximation of the Heaviside function)
US 2020/0356638 A1 to Nomura et al. (Teaches topology optimization including finite difference and approximation of the Heaviside function)
US 2011/0270587 A1 to Yamada et al. (Teaches topology optimization including finite difference and approximation of the Heaviside function)
US 2017/0270664 A1 to Hoogi et al. (Teaches the smoothed Heaviside function as a projection function for thresholding)
NPL: “Efficient Algorithm for Level Set Method Preserving Distance Function” by Estellers et al. (Teaches using a level set method for topology optimization)
NPL: “A survey of manufacturing oriented topology optimization methods” by Liu et al. (Teaches topology optimization including finite difference and approximation of the Heaviside function)
NPL: “Irreversibility of recursive Heaviside memory functions: a distributional perspective on structural cognition” by Shin (Teaches the smoothed Heaviside function as a projection function for thresholding)
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/J.M.W./Examiner, Art Unit 2188 /RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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