DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The amendment filed on 03/30/2026 has been received and fully considered.
3. Claims 1-20 are presented for examination.
Response to Arguments
4. Applicant's arguments filed 03/30/2026 have been fully considered but they are not persuasive with reference to the art rejection; the rejection under 35 USC 101 has been withdrawn in view of the amendment. Regarding applicant’s assertions that: “the cited sections of the applied references, whether taken alone or in any reasonable combination, do not disclose or suggest at least, "providing, via a user interface, at least one of a visual output or an audio output indicating (a) the first values and the second values or (b) the first selected composite sandwich structure and the second selected composite sandwich structure; and forming, by installing the first selected composite sandwich structure at the first location and the second selected composite sandwich structure at the second location, the structural element,", the Examiner respectfully disagrees and asserts that Irisarri et al., used as the primary reference in the rejection, clearly provides for at least of a visual output (see fig.1 “optimization out provided herein, Output FE model – optimized design”, further at page 3037-3039, The method is performed on a computer, column 1, paragraph 2: "The optimization strategy described in Fig. 1 has been implemented as an interactive optimization tool using Python. The Python tool. can call either MSC Nastran or Altair OptiStruct for step 1 and step 3 optimizations, through text files." The first values and the second values are shown for an example in figures 7 to 9 and selected composite sandwich structure of an example is shown in figure 11. Step 2 output corresponds to the results of the FE analysis of the initial design of step 3. The shift in mass between the outputs of steps 1 and 2 is due to the rounding of the skin thickness to the nearest discrete ply number. Between steps 2 and 3, the mass variation can be explained by variations of the numbers of plies of the skins, but also by continuous variations of the core thickness. Step 3 output corresponds to the results of the final FE analysis performed using a layer-by-layer description of the sandwich composites, so that all the stiffness matrices A, B, D, and H are computed using OptiStruct homogenization module and are fully consistent); Mallapragada et al., cited for further supports in the rejection, provides for forming the composite structure by installing one or more panels at different locations of the model (see para [0069], For example, the composite panels may be installed as shells for electronic devices such as cellular phones and laptop computers. As another example, the composite panels may be installed as door panels and bumpers on automobile vehicles, which includes at least a first “front/rear end bumpers” and a second location “left/right side doors” to form the structural element, and that the combination of the cited references clearly renders obvious the limitations contrary to applicant’s assertions.
Claim Rejections - 35 USC § 103
5. 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.
5.0 Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Irisarri et al. (A general optimization strategy for composite sandwich structures, 18 pages (2021), in view of Mallapragada et al. (USPG_PUB No. 2017/0371980).
5.1 In considering claims 1, 19-20, Irisarri et al. teaches a method for selecting composite sandwich structures for forming a structural element, the method comprising:
calculating a gradient vector of a property of the structural element with respect to first components of a first vector and second components of a second vector (see page 3029, abstract: "gradient based optimization that handles the large number of variables with a continuous representation of the composite mechanical behavior." The continuous representation of the mechanical behavior represents the property. figure 1 and page 3029, column 2, paragraph 1: "As a result, gradient based optimization algorithms shall be used." and equation (2). The optimization variables v, ts, tc represent the first components of a vector and correspond to design variables, skin thickness and core thickness, respectively, see page 3029, column 1 and 2. Note that v is itself a vector, see "v is a vector of continuous design variables used to parametrize the homogenized stiffness of the skin material", at page 3029, column 2, paragraph 2. The optimization variables (v,ts,tc) are defined per-zone, see page 3029, column 1, paragraph 3: "The design variables[...] can be defined for the, whole composite structure, or for each of its sub-regions, so as to define a constant stiffness optimization or a variable stiffness optimization respectively. n2 is the number of sub-regions.". The design variable vector defined for the first sub-region represents the first vector (x) and the design variable vector defined for the second sub-region represents the second vector (y).) , wherein: the first components correspond to first composite sandwich structures available for use at a first location of the structural element and the first vector represents a first combination of the first composite sandwich structures according to the first components (see page 3029, left column, The design variables (v,ts,tc) defined for each sub-region/zone corresponds to a first location and describe the composite sandwich structures available for use at those zones. The design variable vector defined for the first sub-region represents the first vector (x) and the design variable vector defined for the second sub-region represents the second vector (y).) The skin thickness and the core thickness define the combination of the sandwich structures according to their values), and the second components correspond to second composite sandwich structures available for use at a second location of the structural element and the second vector represents a second combination of the second composite sandwich structures according to the second components (see page 3029, left column, The design variables (v,ts,tc) defined for each sub-region/zone corresponds to a second location and describe the composite sandwich structures available for use at those zones. The design variable vector defined for the first sub-region represents the first vector (x) and the design variable vector defined for the second sub-region represents the second vector (y).) The skin thickness and the core thickness define the combination of the sandwich structures according to their values); identifying, based on the gradient vector, first values for the first components and second values for the second components that yield a third value of the property that satisfies a criterion (Using continuous optimization (see figure 1) the target stiffness v* and thickness distribution (t/,t/) are identified based on the gradient vector. The optimal values, i.e. target values (v*,t/,t/) yield a third value of the objective function, i.e. the property, see equation (2). The constraints encode the criterion); selecting, based on the first values and the second values, a first selected composite sandwich structure of the first composite sandwich structures and a second selected composite sandwich structure of the second composite sandwich structures (see fig.1, page 3029-3031, The target values (v*,t/,t/) obtained from Step 1 are used in Step 2 and Step 3 to obtain a layer-by-layer description of the sandwich composites (see figure 1). The first values and the second values are shown for an example in figures 7 to 8 and selected composite sandwich structure of an example is shown in figure 11. The sub-regions of said sandwich composites correspond to the first selected composite sandwich structure and the second selected composite sandwich structure.); and providing, via a user interface, at least one of a visual output or an audio output indicating (a) the first values and the second values or (b) the first selected composite sandwich structure and the second selected composite sandwich structure (see fig.1 “optimization out provided herein”, further at page 3037, The method is performed on a computer, column 1, paragraph 2: "The optimization strategy described in Fig. 1 has been implemented as an interactive optimization tool using Python. The Python tool. can call either MSC Nastran or Altair OptiStruct for step 1 and step 3 optimizations, through text files." The first values and the second values are shown for an example in figures 7 to 8 and selected composite sandwich structure of an example is shown in figure 11. Step 2 output corresponds to the results of the FE analysis of the initial design of step 3. The shift in mass between the outputs of steps 1 and 2 is due to the rounding of the skin thickness to the nearest discrete ply number. Between steps 2 and 3, the mass variation can be explained by variations of the numbers of plies of the skins, but also by continuous variations of the core thickness. Step 3 output corresponds to the results of the final FE analysis performed using a layer-by-layer description of the sandwich composites, so that all the stiffness matrices A, B, D, and H are computed using OptiStruct homogenization module and are fully consistent). While Irisarri et al. does not specifically state that the components are weighted accordingly, he provides for formulating an optimization problem (see section 5.1) which an objective of the overall design problem is to minimize the weight of the structure which would clearly be understood by one of ordinary skilled in the art; however, he does not specifically show forming, by installing the first selected composite sandwich structure at the first location and the second selected composite sandwich structure at the second location, the structural element. Nonetheless, Mallapragada et al. teaches global optimization tool may be used to predict characteristics of a multiple ply layered composite as a condition of one or more continuous variables and/or one or more binary variables, including identify composite designs with lower areal weight (see abstract). the MINLP model may be extended to formulate a multi-objective optimization problem considering weight and a second objective that may represent the cost of manufacturing the composites (para [0007], where Apq and Dpq may be calculated from the referred equation and Apq and Dpq are defined as a summation of the transformed stiffness matrix for each ply i, Qpqi with each weighted by a respective ply geometric factor (see para [0038], [0060], [0064-0067]); and forming, by installing the first selected composite sandwich structure at the first location and the second selected composite sandwich structure at the second location, the structural element (para [0069], For example, the composite panels may be installed as shells for electronic devices such as cellular phones and laptop computers. As another example, the composite panels may be installed as door panels and bumpers on automobile vehicles, which includes at least a first and a second location to form the structural element). Mallapragada et al. further teaches the non-transitory computer readable medium along with the computing device comprising: one or more processors; a user interface of claims 19-20 (see fig. 7-8, para [0071).
Irisarri et al. and Mallapragada et al. are analogous art because they are from the same field of endeavor and that the model analyzes by Mallapragada et al. is similar to that of Irisarri et al. Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
6.2 Regarding claim 2, the combined teachings of Irisarri et al. and Mallapragada et al. teaches the step of forming the structural element at the first location using the first selected composite sandwich structure and at the second location using the second selected composite sandwich structure (see Irissari et al. page 3036, The model is divided into three sections, namely the lower cone (thereafter LC), the central cylinder (CC), and the upper cone (UC). Each of these substructures is made of a sandwich composite material. They are manufactured separately and assembled to form the final part using hybrid composite/metal flanges that are not optimized in this work. Further see Mallapragada et al. fig.4, para [0035], Then, at block 404, the method 400 may include selecting, by the processor, a first choice of one or more materials for the multiple ply layered composite and a second choice of characteristics of individual layers within the multiple ply layered composite, wherein the individual layer characteristics comprise at least fiber volume fraction and fiber orientation angle, and wherein the first choice and the second choice meets the at least one material requirement. Finally, at block 406, the method 400 may include manufacturing the multiple ply layered composite selected according to the optimized solution to the mixed integer nonlinear programming (MINLP) model.). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.3 As per claim 3, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein the first location and the second location both correspond to a skin of an aircraft (see Irisarri et al. abstract, An original strategy for the design of large-scale composite sandwich structures is developed. Such structures are typical of launcher and spacecraft structures, but the methodology remains applicable to any thin-walled sandwich structure (such as aircraft fuselage panels or watercraft hulls). Further see Mallapragada et al. para [0027] Individual layers of the composite panel 100 may include fibers dispersed in a resin/polymeric matrix. Such composite materials are useful in various commercial products such as consumer electronics, ballistic, aeronautic, and transportation products. [0030]). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.4 Regarding claim 4, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein the first composite sandwich structures are the same as the second composite sandwich structures (see Mallapragada et al. para [0025] A multiple ply layered composite is a composite material having multiple layers, in which each layer includes fibers embedded in a resin to form a matrix. Each layer may be different materials or some or all layers may be made from the same material. Further see Irissari et al. page 3036, Each substructure in the reference design is made of a constant stiffness sandwich composite. In the following, m0 is mass of the reference design, t 0 s(LC) is the thickness of the skin of the lower cone, and t0 c is the thickness of the core material which is the same in the three substructures, with t0 c /t0 s(LC) ≈ 19. T). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.5 With regards to claim 5, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein the first location corresponds to a skin of an aircraft and the second location corresponds to a stringer of the aircraft (see Irisarri et al. abstract, such structures are typical of launcher and spacecraft structures, but the methodology remains applicable to any thin-walled sandwich structure (such as aircraft fuselage panels or watercraft hulls). Further see Mallapragada et al. para [0027] Individual layers of the composite panel 100 may include fibers dispersed in a resin/polymeric matrix. Such composite materials are useful in various commercial products such as consumer electronics, ballistic, aeronautic, and transportation products. [0030]). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.6 As per claim 6, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein the first composite sandwich structures differ from each other with respect to one or more of a material stacking sequence, a quantity of plies, or an orientation stacking sequence (see Irisarri et al. page 3031, Thus, as such, the representation used here may not be used for hybrid laminates (two or more different kinds of layers). Further see Mallapragada et al. para [0025] A multiple ply layered composite is a composite material having multiple layers, in which each layer includes fibers embedded in a resin to form a matrix. Each layer may be different materials or some or all layers may be made from the same material. Each of the layers may include different percentage fiber versus resin. Further, each layer may contain the fibers to be oriented at a different angle with respect to a fixed x-axis. Any one or all of these characteristics may be controlled in a design to change the characteristics of the resulting composite.). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.7 With regards to claim 7, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein the second composite sandwich structures differ from each other with respect to one or more of a material stacking sequence, a quantity of plies, or an orientation stacking sequence (see Irisarri et al. page 3031, Thus, as such, the representation used here may not be used for hybrid laminates (two or more different kinds of layers). Further see Mallapragada et al. para [0025] A multiple ply layered composite is a composite material having multiple layers, in which each layer includes fibers embedded in a resin to form a matrix. Each layer may be different materials or some or all layers may be made from the same material. Each of the layers may include different percentage fiber versus resin. Further, each layer may contain the fibers to be oriented at a different angle with respect to a fixed x-axis. Any one or all of these characteristics may be controlled in a design to change the characteristics of the resulting composite.). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.8 Regarding claim 8, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein calculating the gradient vector comprises calculating the gradient vector of a tension rating of the structural element (see Mallapragada et al. para [0011], Embodiment 2 is the method of embodiment 1, further comprising manufacturing the multiple ply layered composite selected according to the optimized solution to the mixed integer nonlinear programming (MINLP) model. Embodiment 3 is the method of embodiment 1, wherein the step of optimizing a solution to the mixed integer nonlinear programming (MINLP) model comprises: defining a vector of constraint functions, g and h, by selecting values for a vector of continuous decision variables, x, and a vector of binary decision variables, y, wherein the constraint functions comprise at least one of functions for calculating the constitutive mechanical properties of each possible pair of fiber and matrix that can form an individual ply, functions for calculating a composite mechanical property, and a linear loading-deformation relation governing an aggregated mechanical response of the composite; and defining an objective function, f, that is to be minimized while satisfying the constraint functions. Further see Irisarri et al. page 3029-3031, see page 3029, column 1 and 2. Note that v is itself a vector, see "v is a vector of continuous design variables used to parametrize the homogenized stiffness of the skin material", at page 3029, column 2, paragraph 2. The optimization variables (v,ts,tc) are defined per-zone, see page 3029, column 1, paragraph 3: "The design variables[...] can be defined for the, whole composite structure, or for each of its sub-regions, so as to define a constant stiffness optimization or a variable stiffness optimization respectively. n2 is the number of sub-regions.". The design variable vector defined for the first sub-region represents the first vector (x) and the design variable vector defined for the second sub-region represents the second vector (y).). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.9 As per claim 9, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein calculating the gradient vector comprises calculating the gradient vector of a compression rating of the structural element (see Irisarri et al. page 3029, column 1 and 2. Note that v is itself a vector, see "v is a vector of continuous design variables used to parametrize the homogenized stiffness of the skin material", at page 3029, column 2, paragraph 2. The optimization variables (v,ts,tc) are defined per-zone, see page 3029, column 1, paragraph 3: "The design variables[...] can be defined for the, whole composite structure, or for each of its sub-regions, so as to define a constant stiffness optimization or a variable stiffness optimization respectively. n2 is the number of sub-regions.". The design variable vector defined for the first sub-region represents the first vector (x) and the design variable vector defined for the second sub-region represents the second vector (y). Considering that for an axially compressed thin elastic cylindrical shell made of an isotropic material, the linearized buckling equations lead to a critical load that is independent from the length of the cylinder (Koiter 1970), it is assumed here that the reference design inherits satisfying buckling behavior from the original configuration.). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.10 Regarding claim 10, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein calculating the gradient vector comprises calculating a gradient vector of one or more of an average of a component of an ABD matrix of the structural element, an average of a Young's modulus of the structural element, an average of a rigidity modulus of the structural element, or an average of a bulk modulus of the structural element (see Irisarri et al. page 3029, column 1 and 2. Note that v is itself a vector, see "v is a vector of continuous design variables used to parametrize the homogenized stiffness of the skin material", at page 3029, column 2, paragraph 2. The optimization variables (v,ts,tc) are defined per-zone, see page 3029, column 1, paragraph 3: "The design variables[...] can be defined for the, whole composite structure, or for each of its sub-regions, so as to define a constant stiffness optimization or a variable stiffness optimization respectively. n2 is the number of sub-regions.". The design variable vector defined for the first sub-region represents the first vector (x) and the design variable vector defined for the second sub-region represents the second vector (y). Also see section 3.1, The local stiffness of the material is governed by the set of stiffness tensors (A, D, F), which represent respectively the membrane, bending, and transverse stiffnesses, as well as ten. Further see Mallapragada et al. para [0012, [0038], 0041] For each ply i, the value of Q.sub.pq.sup.i may be related to the effective mechanical properties obtained from experimental characterization of the ply material, namely the stiffness modulus along (E.sub.1) and perpendicular (E.sub.2) to the fiber, Poisson's ratio (v.sub.12), and the shear modulus (G.sub.12) as shown in the equations below: [0057-0058]). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.11 As per claim 11, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein calculating the gradient vector comprises calculating the gradient vector of an average density of the structural element (see Irisarri et al. page 3028-3029, In the first optimization step, the thickness and macroscopic stiffness distributions of the laminated sandwich composites are optimized using continuous design variables and gradient-based optimization. Both the skin thickness and the stiffness are design variables, together with the core material density and its orientation. page 3029, column 1 and 2. Note that v is itself a vector, see "v is a vector of continuous design variables used to parametrize the homogenized stiffness of the skin material"; also see Mallapragada et al. para [0012], Embodiment 16 is the apparatus of embodiment 14, wherein the step of optimizing a solution to the mixed integer nonlinear programming (MINLP) model comprises: defining a vector of constraint functions, g and h, by selecting values for a vector of continuous decision variables, x, and a vector of binary decision variables, y, wherein the constraint functions comprise at least one of functions for calculating the constitutive mechanical properties of each possible pair of fiber and matrix that can form an individual ply, functions for calculating a composite mechanical property, and a linear loading-deformation relation governing an aggregated mechanical response of the composite; and defining an objective function, f, that is to be minimized while satisfying the constraint functions. [0060-0061]). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.12 Regarding claim 12, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein calculating the gradient vector comprises calculating the gradient vector of an average of a particular ply percentage (see Irisarri et al. page 3028, In the first optimization step, the thickness and macroscopic stiffness distributions of the laminated sandwich composites are optimized using continuous design variables and gradient-based optimization. Both the skin thickness and the stiffness are design variables, together with the core material density and its orientation. page 3029-3030, column 1 and 2. Note that v is itself a vector, see "v is a vector of continuous design variables used to parametrize the homogenized stiffness of the skin material"; also see Mallapragada et al. para [0025] A multiple ply layered composite is a composite material having multiple layers, in which each layer includes fibers embedded in a resin to form a matrix. Each layer may be different materials or some or all layers may be made from the same material. Each of the layers may include different percentage fiber versus resin.). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.13 With regards to claim 13, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein identifying the first values and the second values comprises identifying the first values and the second values that yield a minimum value of the property (see Irisarri et al. page 3029, In the first step, the composite material is seen as an equivalent homogenized material. The optimization consists in finding the local stiffness and thickness distributions that minimize the mass of the structure, while satisfying design constraints that most of the time directly derive from the specifications of the structure. The design variables are considered continuous and taking values in a convex space. As a result, gradientbased optimization algorithms shall be use; Mallapragada et al. para [0008], The step of selecting the first choice and the second choice may include solving a mixed integer nonlinear programming (MINLP) model by simultaneously considering the at least one material parameter and the characteristics of the individual layers and by predicting an aggregated stiffness of a composite having the considered at least one material parameters and the considered characteristics of the individual layers. The step of selecting may also include optimizing a solution to the mixed integer nonlinear programming (MINLP) model to select the multiple ply layered composite meeting the at least one material requirement having a minimal areal weight. [0034] The optimization tool 310 may solve a mixed integer nonlinear programming (MINLP) model 316 in view of the material characteristics 302 and the materials specifications 304 to find the optimal selections of the variables 312 and 314 that minimize the specified objective 306. [0066]). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.14 As per claim 14, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein identifying the first values and the second values comprises identifying the first values and the second values that yield a maximum value of the property (see Irisarri et al. page 3029, In the first step, the composite material is seen as an equivalent homogenized material. The optimization consists in finding the local stiffness and thickness distributions that minimize the mass of the structure, while satisfying design constraints that most of the time directly derive from the specifications of the structure. The design variables are considered continuous and taking values in a convex space. As a result, gradientbased optimization algorithms shall be use; Mallapragada et al. para [0046], [0059] Another constraint that may be imposed by the optimization tool includes user-specified maximum permissible values of the mid-plane strains (∈.sub.ii;ii=1, 2, 3) and curvatures (∈.sub.ii;ii=4, 5, 6). This constraint may be enforced by optimization tool using the following equations, which allow for positive and negative values of the maximum deformations: [0066]). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.15 Regarding claim 15, the combined teachings of Irisarri et al. and Mallapragada et al. teaches the step of identifying a maximum value of the first values (see Mallapragada et al. para [0011], embodiment 1, wherein the at least one materials requirements comprises at least one of matrix, fiber, maximum strain, symmetric composite, balanced composite, ply thickness, maximum number of plies, in-plane forces, bending moments, twisting moments, strains, and deflections. [0036] Referring back to block 404, the processor may solve a mathematical model to perform, is described in the method 400, other objectives or combinations of multiple objectives may be considered as part of the optimization process for designing and manufacturing a composite panel. [0046], For a fixed N, the binary variable y.sub.j.sup.N, selects the total number of plies that are in the optimal design for a composite. For example, y.sub.3.sup.5=1 indicates that a composite with six plies is selected from a design space that allows a maximum of ten plies.), wherein selecting the first selected composite sandwich structure comprises selecting the first selected composite sandwich structure that corresponds to the maximum value of the first values (see Mallapragada et al. para the selection of the first choice of materials and the second choice of layer characteristics. For example, the selection step may include the steps of solving a mixed integer nonlinear programming (MINLP) model by simultaneously considering the at least one material parameter and the characteristics of the individual layers and by predicting an aggregated stiffness of a composite having the considered at least one material parameter and the considered characteristics of the individual layers. The selection step 404 may also include optimizing a solution to the mixed integer nonlinear programming (MINLP) model to select the multiple ply layered composite meeting the at least one material requirement having a minimal areal weight. Although only a single objective, areal weight [0046], For a fixed N, the binary variable y.sub.j.sup.N, selects the total number of plies that are in the optimal design for a composite. For example, y.sub.3.sup.5=1 indicates that a composite with six plies is selected from a design space that allows a maximum of ten plies.). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.16 As per claim 16, the combined teachings of Irisarri et al. and Mallapragada et al. teaches the step of identifying a second maximum value of the second values, wherein selecting the second selected composite sandwich structure comprises selecting the second selected composite sandwich structure that corresponds to the second maximum value of the second values (see Mallapragada et al. para the selection of the first choice of materials and the second choice of layer characteristics. For example, the selection step may include the steps of solving a mixed integer nonlinear programming (MINLP) model by simultaneously considering the at least one material parameter and the characteristics of the individual layers and by predicting an aggregated stiffness of a composite having the considered at least one material parameter and the considered characteristics of the individual layers. The selection step 404 may also include optimizing a solution to the mixed integer nonlinear programming (MINLP) model to select the multiple ply layered composite meeting the at least one material requirement having a minimal areal weight. Although only a single objective, areal weight [0046], For a fixed N, the binary variable y.sub.j.sup.N, selects the total number of plies that are in the optimal design for a composite. For example, y.sub.3.sup.5=1 indicates that a composite with six plies is selected from a design space that allows a maximum of ten plies.). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.17 Regarding claim 17, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein the criterion is a first criterion (see Mallapragada et al. para [0026], Of these, only one or a few designs achieve a threshold value of certain performance criteria such as cost, weight, strength, and/or other objectives and are thus of practical interest because of manufacturing limitations and/or requirements for the composite panel.), the method further comprising: calculating a second gradient vector of a second property of the structural element with respect to the first components and the second components (see Irisarri et al. page 3029, abstract: "gradient based optimization that handles the large number of variables with a continuous representation of the composite mechanical behavior." The continuous representation of the mechanical behavior represents the property. figure 1 and page 3029, column 2, paragraph 1: "As a result, gradient based optimization algorithms shall be used." and equation (2). The optimization variables v, ts, tc represent the first components of a vector and correspond to design variables, skin thickness and core thickness, respectively, see page 3029, column 1 and 2. Note that v is itself a vector, see "v is a vector of continuous design variables used to parametrize the homogenized stiffness of the skin material", at page 3029, column 2, paragraph 2. The optimization variables (v,ts,tc) are defined per-zone, see page 3029, column 1, paragraph 3: "The design variables[...] can be defined for the, whole composite structure, or for each of its sub-regions, so as to define a constant stiffness optimization or a variable stiffness optimization respectively. n2 is the number of sub-regions.". The design variable vector defined for the first sub-region represents the first vector (x) and the design variable vector defined for the second sub-region represents the second vector (y).)), wherein identifying the first values and the second values comprises identifying the first values and the second values that also satisfy a second criterion (see Mallapragada et al. para [0064] Although the models described above include optimization of the composite material in view of one objective, areal weight, the optimization of the MINLP model in other embodiments may involve optimizing based on multiple objectives. For example, in addition to optimizing the composite design to obtain a composite that satisfies the materials requirements with the lowest areal weight, the optimization tool may optimize to obtain a trade-off between lowest areal weight and lowest cost.). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
5.18 As per claim 18, the combined teachings of Irisarri et al. and Mallapragada et al. teaches that wherein selecting the first selected composite sandwich structure and the second selected composite sandwich structure comprises selecting the first selected composite sandwich structure and the second selected composite sandwich structure such that the first selected composite sandwich structure and the second selected composite sandwich structure satisfy the first criterion and the second criterion (see Mallapragada et al. fig.3-5, para [0008], The composite designed according to the first choice and the second choice selected by the processor may meet the at least one material requirement received by the processor as predicted by a composite property prediction model. The step of selecting the first choice and the second choice may include solving a mixed integer nonlinear programming (MINLP) model by simultaneously considering the at least one material parameter and the characteristics of the individual layers and by predicting an aggregated stiffness of a composite having the considered at least one material parameters and the considered characteristics of the individual layers. The step of selecting may also include optimizing a solution to the mixed integer nonlinear programming (MINLP) model to select the multiple ply layered composite meeting the at least one material requirement having a minimal areal weight. [0063] FIG. 5 are graphs illustrating an improvement in composite material design possible with the MINLP model according to one embodiment of the disclosure. Bar 506 illustrates the areal weight of a composite material selected from a hybrid of materials T300/PP and AS/PP. As shown between the bars 502, 504, and 506, increasing the freedom of design selection by adding additional variables to the model provides for an increased possibility of optimization in terms of reducing areal weight. The MINLP model described above allows consideration of these additional variables and optimization of the composite material design based on these additional variables in a manner that allows designs not previously contemplated due to the limits of the prior art heuristics and trial-and-error approaches. In fact, the MINLP model may allow selecting optimal materials and layer characteristics in a matter of a few minutes, despite a large number of variables.). Therefore, it would have been obvious to a person of skilled in the art the time of filing of the applicant’s invention to combine the method of Mallapragada et al. with that of Irisarri et al. because Mallapragada et al. teaches the improvement in composite material design possible with the MINLP model (see para [0063]).
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
6.1 Chen et al. (USPG_PUB No. 2023/0351074) teaches a topology and material collaborative robust optimization design method for a composite material support structure.
6.2 Turng et al. (U.S. Patent No. 8,475,703) teaches a method of fabricating a composite incorporating fillers.
7. Claims 1-20 are rejected and THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571)272-8636. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 December 22, 2025
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