DETAILED ACTION
1. This office action is in responsive to the applicant’s arguments filed on 11/19/25.
2. The present application is being examined under the first inventor to file provisions of the AIA .
3. Claims 1-20 are currently pending.
4. Claims 1, 5, 8, 12 and 15-20 are previously presented. Claims 2-4, 6-7, 9-11 and 13-14 are original.
Response to Arguments
Response: 35 U.S.C. § 103
5. Applicants argue:
The applicant argues that the Kuna-Ciskal et al. reference doesn’t teach the limitation of
claim 1 that states “wherein during each iteration of the iterative optimization process, the optimization algorithm enforces a stability constraint on the one or more parameters across one or more predetermined strain ranges for one or more deformations of interest”. The applicant also argues that the Kuna-Ciskal et al. reference doesn’t disclose material stability analysis. (Remarks: pages 12-13)
6. Examiner Response:
The examiner notes that the Kuna-Ciskal et al. reference teaches a failure criterion that is
introduced with the Drucker’s material stability to analyze during each iteration step the stress
occurring on the material. The examiner considers the Drucker’s material stability to be the
stability constraint, since the Drucker’s material stability is the failure criteria that is used in the
iteration steps, see Pg. 689 1st – 4th paragraph of the Kuna-Ciskal et al. reference “The (6_6)
matrix of the components of the fourth-rank tensor, etc.”. Also, the Kuna-Ciskal et al. reference
teaches a Murakami–Kamiya MK model of the elastic damage material that incorporates a
modified strain tensor to analyze the continuous transition of the stress-strain response from the
crack opening and closure, see Pgs. 684-685 last paragraph “Following Murakami and Kamiya
[19] assumptions, both terms of the free energy, etc.” of Kuna-Ciskal et al. reference. The
examiner further notes that with the strain tensor being considered to be the or more parameters
that the stability constraint is enforced on demonstrates that enforcing a stability constrain in an
optimization algorithm is taught by the Kuna-Ciskal et al. reference, since the Drucker’s material
stability is used for the failure criteria in iterative steps, see Pg. 688 last paragraph of the Kuna-
Ciskal et al. reference, “In the case if incremental form of the constitutive law is used, etc.” and
Pg. 689 1st – 4th paragraph of the Kuna-Ciskal et al. reference “The (6_6) matrix of the
components of the fourth-rank tensor, etc.”.
Also, the Kuna-Ciskal et al. reference teaches the stress-strain associated with the
material instability. The examiner considers the material instability and stability to be the
predetermined strain ranges, since on the stress-strain curve, the damage evolution causes the
elastic modulus in the constitutive equation to drop, until a critical point in a sense of the
material instability in the strain-stress curve is met and the local failure criterion is satisfied, see
Pg. 686, 2nd paragraph and Fig. 3 of the Kuna-Ciskal et al. reference. This demonstrates that the
Kuna-Ciskal et al. reference teaches a material stability analysis.
7. Applicants argue:
The applicant argues that the Kuna-Ciskal et al. reference doesn’t teach the limitation of
claim 1 that states “wherein the one or more predetermined strain ranges includes one or
more of lower and upper threshold for nominal strain, volume ratio, and shear strain”,
where the Kuna-Ciskal et al. reference doesn’t disclose an iterative process that optimizes one or
more parameters for a material model. (Remarks: pages 15-16)
8. Examiner Response:
As stated above in section 6 of the current office action, with the strain tensor being
considered to be the or more parameters that the stability constraint is enforced on demonstrates
that enforcing a stability constrain in an optimization algorithm is taught by the Kuna-Ciskal et
al. reference, since the Drucker’s material stability is used for the failure criteria in iterative
steps, see Pg. 688 last paragraph of the Kuna-Ciskal et al. reference, “In the case if incremental
form of the constitutive law is used, etc.” and Pg. 689 1st – 4th paragraph of the Kuna-Ciskal et al.
reference “The (6_6) matrix of the components of the fourth-rank tensor, etc.”.
Also, the applicant argues that the Kuna-Ciskal et al. reference doesn’t teach the strain
ranges. The examiner notes that there’s a deformation process in the damaged material. The
examiner considers the deformation of the material, where material instability is being met and
the local failure criterion is satisfied as being the threshold for the nominal strain and shear
strain, since the deformation process is characterized by a non-linear stress-strain curve, see
Kuna-Ciskal et al. Pg. 682, 1st paragraph, “Deformation process in the damaged material is
characterised by a non-linear stress–strain curve that precedes the local failure.”, Kuna-Ciskal
et al. Pg. 686, 2nd paragraph, “When exposed to tension or compression, the MK
constitutive phenomenological model is capable of capturing unilateral damage response. Under
the uniaxial tension condition the damage component D11 is dominant, whereas the other two
components D22 and D33 are negligible. By contrast, under the uniaxial compression the
transverse damage components D22 ¼ D33 become predominant, but a non-negligible axial
component D11 is also visible (cf. Fig. 3). In contrast to other models that are time-dependent
(cf. e.g. [9]), damage evolution described by the MK model results from the stress and strain
growth, but it is time independent and does not occur under the constant stress condition. Note
also that in the MK model below the damage threshold A0(ð0, ε0) none damage nucleation or
growth occurs.”, and Fig. 3. The examiner also notes that with the deformation process being
characterized by a non-linear stress-strain curve demonstrates that there’s a lower and upper
threshold.
Also, the examiner notes that the state of the damage to the representative volume
element (RVE) is represented by the topology, size, orientation and number of micro-defects. The examiner considers the distribution of micro-defects within the representative volume element (RVE) to be the threshold of the volume ratio, since the correlation of the distribution of micro-defects are measured by the change of the effective constitutive modules, stiffness Λ (Ɗ) or compliance Λ-1 (Ɗ), see Pg. 682, 3rd paragraph “Continuum damage mechanics (CDM) approach provides the constitutive and damage, etc.” and Fig. 1.
9. Applicants argue:
The applicant argues that the Kuna-Ciskal et al. reference doesn’t teach the limitation of
claim 1 that states “determining if the updated material model violates a stability constraint”
(Remarks: pages 15-16)
10. Examiner Response:
The examiner notes that the Kuna-Ciskal et al. reference teaches applying a modified
strain tensor. The modified strain tensor being applied ensures that the continuous transition of
the stress–strain response from crack opening to closure, which influences the diagonal
components of the stiffness matrix if the constitutive law is written in the principal damage
directions. Also, on Pg. 696, sec. 6 Final remarks #3 it states “By the use of local approach to
fracture LAF and FEM the crack growth in a concrete specimen may successfully be simulated
until the ultimate fracture mechanism is achieved. Crack is modelled as the assembly of failed
elements in the FE mesh the stiffness of which and stress are reduced to zero.”. With a
simulation consistently occurring until the ultimate fracture mechanism is achieved,
demonstrates that the material model is being updated. Further, equation 21 of the Kuna-Ciskal
et al. reference is directed towards a Druckers stability postulate, which is another way a stability
criterion is recited, see Kuna-Ciskal et al. reference, Pg. 689, 3rd paragraph, “The local tangent
stiffness matrix is used for the quasi-Newton algorithm for first iteration step of solving the non-
linear equation (17) as long as the local failure criterion (23) holds. The stiffness of the element
in the FE mesh that has come to failure is next reduced to zero. As a consequence, the failed
element is completely released from stress and the appropriate stress redistribution occurs in the
neighbouring elements to ensure the global equilibrium. Note that the above failure criterion
(23) assumes the brittle failure mechanism.”.
Claim Rejections - 35 USC § 103
11. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hoffman (U.S. PGPub 2006/0010427) in view of online reference CDM based modelling of damage and fracture mechanisms in concrete under tension and compression, written by Kuna-Ciskal et al.
With respect to claim 1, Hoffman discloses “A computer simulation system” as [Hoffman (paragraph [0097] “Once a material is selected for use in a system component, the designer can identify the vulnerability of the system through modeling and simulation.”)];
“a memory storing an experimental test data set” as [Hoffman (paragraph [0102] “FIG. 10 shows a distributed network architecture including a network server 1004 and a network storage system 1008. The application resides in the storage system 1008. A computer 1012 is connected to the network server 1004 via the Internet, an intranet or any other communication link.”, Fig. 10)];
“the experimental test data set includes experimentally-obtained stress-strain data for a sample of a physical material subjected to one or more deformation modes during a real-world experiment” as [Hoffman (paragraph [0071] “In step 404, a material is subjected to a test to determine a material property (e.g., hardness, ultimate strength, etc.). The material may be steel, aluminum alloy, copper alloy or any other material of interest. In step 408, the test data is collected and recorded.”)];
“wherein deformation includes at least one of tension or compression” as [Hoffman (paragraph [0024] “An example of a materials characterization test is a `uniaxial tensile test` performed to characterize the elastic-plastic response of a metal. A small cylindrical sample of the metal is strain-loaded in an axial direction until it breaks. A one-dimensional load-displacement response is recorded in a stress-versus-strain plot. Within the context of several plasticity models, this test is sufficient for characterizing the material response of a given metal with a continuously increasing load. If the designer wants to simulate a cyclical load or a reverse load, then a materials characterization test that produces this response is needed.”, Hoffman paragraph [0063] “The example test template above shows that a material can be completely characterized by one type of test, a tensile test. In some cases, multiple test types are required to completely characterize a material's response.”)];
“and a processor configured to execute instructions stored in the memory, which, when executed by the processor, cause the processor to at least” as [Hoffman (paragraph [0102] “FIG. 10 shows a distributed network architecture including a network server 1004 and a network storage system 1008. The application resides in the storage system 1008. A computer 1012 is connected to the network server 1004 via the Internet, an intranet or any other communication link.”, Fig. 10)];
“display, to a user, a graphical user interface configured to allow the user to import the experimental test data set” as [Hoffman (paragraph [0105] “Being an object-oriented programming language, Java provides many features common to modern programming languages like inheritance, polymorphism, and interfaces. These features provide greater flexibility to a software architecture. For example, software components like the material model driver can be designed around a common interface so that, to the application, all material model drivers look the same.”)];
“receive an identification of the test data set for material model calibration” as [Hoffman (paragraph [0074] “In step 416, the response of the constitutive model is generated under input conditions similar to the input conditions in step 404. In step 420, the response of the constitutive model is compared to the test data and a parameter estimation process is used to calculate the model parameters. Once the model parameters are calculated, they are recorded and incorporated into the constitutive model to provide a graphical representation of the model response. The complete set of model parameters required to numerically model the response of a particular material is called a property set.”, The examiner notes that the constitutive model is used to calibrate the original test model data)];
“identify a material model, the material model includes one or more parameters of a parameter set to be calibrated during the material model calibration, the parameter set starting with a set of initial parameter values” as [Hoffman (paragraph [0074] “In step 416, the response of the constitutive model is generated under input conditions similar to the input conditions in step 404. In step 420, the response of the constitutive model is compared to the test data and a parameter estimation process is used to calculate the model parameters. Once the model parameters are calculated, they are recorded and incorporated into the constitutive model to provide a graphical representation of the model response. The complete set of model parameters required to numerically model the response of a particular material is called a property set.”)];
“calibrate the one or more parameters of the material model using an iterative optimization process comprising an optimization algorithm” as [Hoffman (paragraph [0027] “The parameter estimation process is typically an iterative one. The loads applied during the tests are applied to the model and the model response is compared to the test response. The parameters are changed until the model response closely matches that of the test data. A variety of numerical optimization methods have been developed that automate the parameter estimation process.”, Hoffman (paragraph [0081] “FIG. 5 illustrates the parameter estimation process in accordance with one embodiment of the invention. The process uses a material model driver (MMD) to drive a constitutive model over the load path applied in a test. The process further uses an iterative numerical optimization algorithm to estimate a property set that produces the best possible match to test data.”, Hoffman et al. paragraphs [0082] – [0085] “In step 504, test data is received by the application. In step 508, loading conditions are extracted from the test data. In step 512, an initial guess is made of all parameters in the property set, etc.”, A numerical optimization algorithm is executed to generate an updated property set, where the algorithm varies the property set associated constitutive model until the model response most closely matches the test data)];
“terminate the iterative optimization process, the optimization process generates a calibrated material model that is stable across the one or more predetermined strain ranges for the one or more deformations of interest” as [Hoffman (paragraph [0027] “The parameter estimation process is typically an iterative one. The loads applied during the tests are applied to the model and the model response is compared to the test response. The parameters are changed until the model response closely matches that of the test data. A variety of numerical optimization methods have been developed that automate the parameter estimation process.”)];
“assign a component of the calibrated material model of a simulation model based on input from the user to a real-world equivalent of the component being made of the physical material” as [Hoffman (paragraph [0003] “A designer may select a material for use in a component based on one or more design requirements. The designer typically has an in-depth understanding of materials, and is capable of making an appropriate material selection based on the design requirements”, Hoffman et al. paragraph [0007] “The simulation-based design begins with a finite element model 104 of the component. The finite element model 104 is one form of a physical model of a component.”, Hoffman et al. paragraph [0008] “Next, the finite element model 104 is discretized into regular-shaped elements, called the discretized finite element model 108.”, Hoffman et al. paragraph [0009] “Next, a simulation code, called a finite element analysis (FEA) code 112, is used to solve the mathematical equations of the discretized finite element model 108.”, Hoffman et al. [0016] “A property set is a complete set of model parameters required to numerically model the response of a particular material. The property set for a particular species of material is generally obtained through some estimation process that requires test data”, Hoffman paragraph [0027] “The parameter estimation process is typically an iterative one. The loads applied during the tests are applied to the model and the model response is compared to the test response. The parameters are changed until the model response closely matches that of the test data. A variety of numerical optimization methods have been developed that automate the parameter estimation process”, Hoffman paragraph [0096] “The application provides a variety of capabilities to support materials selection process. Using the application, a designer can document the association of a material with a component, including metadata such as notes documenting the design intent behind the materials selection and appropriate references.”, Hoffman et al. paragraph [0045] “A material list window 228 is a virtual `shopping list` that allows a user to select model-based property sets from the application database and build simulation code input”. Also, in paragraphs [0007] – [0009], [0016] and [0045] of the Hoffman et al. reference it teaches the simulation code is the simulation model ([0007] - [0009]) of the actual physical object (as described in Fig.1 & above) into which the material property set (from the constitutive material model is integrated). This is where assigning maps the property set of calibrated material model is used in the simulation model (simulation code))];
“and perform a simulation that includes the component, the simulation using the calibrated material model and stable set of parameters to simulate response of the real- world equivalent during the simulation” as [Hoffman et al. (paragraph [0007] “The simulation-based design begins with a finite element model 104 of the component. The finite element model 104 is one form of a physical model of a component.”, Hoffman et al. paragraph [0008] “Next, the finite element model 104 is discretized into regular-shaped elements, called the discretized finite element model 108.”, Hoffman et al. paragraph [0009] “Next, a simulation code, called a finite element analysis (FEA) code 112, is used to solve the mathematical equations of the discretized finite element model 108.”, Hoffman et al. [0016] “A property set is a complete set of model parameters required to numerically model the response of a particular material. The property set for a particular species of material is generally obtained through some estimation process that requires test data”, Hoffman et al. paragraph [0045] “A material list window 228 is a virtual `shopping list` that allows a user to select model-based property sets from the application database and build simulation code input.”, Hoffman et al. paragraph [0074] “In step 416, the response of the constitutive model is generated under input conditions similar to the input conditions in step 404. In step 420, the response of the constitutive model is compared to the test data and a parameter estimation process is used to calculate the model parameters. Once the model parameters are calculated, they are recorded and incorporated into the constitutive model to provide a graphical representation of the model response.”, Fig. 1 flow, Constitutive Material Model (Calibrated property set) -> simulation code (model), In paragraphs [0007] – [0009], [0016] and [0045] of the Hoffman et al. reference it teaches the simulation code is the simulation model ([0007] - [0009]) of the actual physical object (as described in Fig.1 & above) into which the material property set (from the constitutive material model is integrated). This is where assigning maps the property set of calibrated material model is used in the simulation model (simulation code))];
“the stable set of parameters includes a current set of parameter values at each iteration of the iterative optimization process” as [Hoffman (paragraph [0027] “The parameter estimation process is typically an iterative one. The loads applied during the tests are applied to the model and the model response is compared to the test response. The parameters are changed until the model response closely matches that of the test data. A variety of numerical optimization methods have been developed that automate the parameter estimation process.”)];
“and wherein each iteration of the iterative optimization process includes: generating an error between the material model response and the test data set” as [Hoffman (paragraph [0027] “The parameter estimation process is typically an iterative one. The loads applied during the tests are applied to the model and the model response is compared to the test response. The parameters are changed until the model response closely matches that of the test data. A variety of numerical optimization methods have been developed that automate the parameter estimation process.”, Hoffman paragraph [0087] “The numerical optimization routine then compares the ideal model response to the test data and computes the error. The algorithm then iteratively searches the mathematical space of the model, adjusting the model parameters until the best fit is achieved.”)];
“computing an updated set of parameter values for the material model based on the error, wherein the updated set of parameter values for the material model are within the one or more predetermined strain ranges for one or more deformations of interest” as [Hoffman et al. (paragraph [0027] “The parameter estimation process is typically an iterative one. The loads applied during the tests are applied to the model and the model response is compared to the test response. The parameters are changed until the model response closely matches that of the test data. A variety of numerical optimization methods have been developed that automate the parameter estimation process.”, Hoffman et al. paragraph “[0047] When the model response matches the test data within a user-specified tolerance, the property set is extracted. The graphical comparison module 316 graphically compares the model response to the test data.”., Hoffman et al. paragraph [0087] “The numerical optimization routine then compares the ideal model response to the test data and computes the error. The algorithm then iteratively searches the mathematical space of the model, adjusting the model parameters until the best fit is achieved.”, The model response being compared to the test data. When the model response matches the test data within a user-specified tolerance, the property set is extracted.)];
“and determining if an end condition is met to exit the iterative optimization process, wherein the end condition includes at least one of a change in solution is less than a predetermined threshold, a change in value of the objective function is less than a predetermined threshold, and a number of iterations exceeds a predetermined threshold” as [Hoffman (paragraph [0085] “The numerical optimization algorithm generates a new property set after each iteration. The new property set is then provided to the material model driver to generate the model response. When the model response most closely matches the test data (i.e., the model response converged with the test data), in step 824 the property set is extracted as the final property set.”, The examiner considers the new property set to be the change in solution that is less than a predetermined threshold, since a new property set is generated after each iteration, where it’s provided to the material model driver to generate a model response.)];
While Hoffman teaches identifying a material model, where the material model includes one or more parameters of a parameter set to be calibrated during the material model calibration, Hoffman doesn’t explicitly disclose “wherein during each iteration of the iterative optimization process, the optimization algorithm enforces a stability constraint on the one or more parameters across one or more predetermined strain ranges for one or more deformations of interest, wherein the one or more predetermined strain ranges includes one or more of lower and upper threshold for nominal strain, volume ratio, and shear strain; determining if the updated material model violates a stability constraint; if the stability constraint is violated, applying a penalty function to the objective function by adding an error value of the error to the penalty function to generate a modified objective function to be used at the next iteration; and wherein the stability constraint is based, at least in part, on Drucker’s stability criterion.”
Kuna-Ciskal et al. discloses “wherein during each iteration of the iterative
optimization process, the optimization algorithm enforces a stability constraint on the one or more parameters across one or more predetermined strain ranges” as [Kuna-Ciskal et al. (Pg. 684 4th -6th paragraph “In order to properly describe the unilateratl, etc.”, Kuna-Ciskal et al. Pgs. 684-685 last paragraph “Following Murakami and Kamiya [19] assumptions, both terms of the free energy, etc.”, Kuna-Ciskal et al. Pg. 686, 2nd paragraph “When exposed to tension or compression, the MK constitutive phenomenological model is capable of capturing unilateral damage response. Under the uniaxial tension condition the damage component D11 is dominant, whereas the other two components D22 and D33 are negligible. By contrast, under the uniaxial compression the transverse damage components D22 ¼ D33 become predominant, but a non-negligible axial component D11 is also visible (cf. Fig. 3). In contrast to other models that are time-dependent (cf. e.g. [9]), damage evolution described by the MK model results from the stress and strain growth, but it is time independent and does not occur under the constant stress condition. Note also that in the MK model below the damage threshold A0 (σ0, ε0) none damage nucleation or growth occurs. On the non-linear pre-peak stress–strain curve the damage evolution cause the elastic modulus in the constitutive equation to drop, until the critical point in a sense of the material instability in the σ – ε curve Af (σf, εf) is met and, hence, the local failure criterion is satisfied.”, Kuna-Ciskal et al. Pg. 689, 3rd paragraph, “The local tangent stiffness matrix is used for the quasi-Newton algorithm for first iteration step of solving the non-linear equation (17) as long as the local failure criterion (23) holds. The stiffness of the element in the FE mesh that has come to failure is next reduced to zero. As a consequence, the failed element is
completely released from stress and the appropriate stress redistribution occurs in the
neighbouring elements to ensure the global equilibrium. Note that the above failure criterion
(23) assumes the brittle failure mechanism.”, Eqns. 20-22, The elastic strain tensor is modified in
order to describe the unilateral damage response in tension or compression of the material. Also,
a general failure criterion is introduced to analyze during each iteration step the stress occurring
on the material. The general failure criterion is introduced with the Drucker’s material stability.
The examiner considers the Drucker’s material stability to be the stability constraint, since the
Drucker’s material stability is the failure criteria that are used in the iteration step. Also, the
examiner considers the strain tensor to be the one or more parameters that the stability constraint
is enforced on, since the Drucker’s material stability is used with the fourth rank tensor in
determining the stress-strain of the damage material. Further, the examiner considers the
material instability and stability to be the predetermined strain ranges, since on the stress-strain
curve, the damage evolution causes the elastic modulus in the constitutive equation to drop, until
a critical point in a sense of the material instability in the strain-stress curve is met and the local
failure criterion is satisfied)];
“for one or more deformations of interest, wherein the one or more predetermined
strain ranges includes one or more of lower and upper threshold for nominal strain,
volume ratio, and shear strain” as [Kuna-Ciskal et al. (Pg. 682, 1st paragraph, “Deformation
process in the damaged material is characterised by a non-linear stress–strain curve that
precedes the local failure.”, Kuna-Ciskal et al. Pg. 682, 3rd paragraph “Continuum damage
mechanics (CDM) approach provides the constitutive and damage, etc.”, Kuna-Ciskal et al. Pg.
686, 2nd paragraph, “When exposed to tension or compression, the MK constitutive
phenomenological model is capable of capturing unilateral damage response. Under the
uniaxial tension condition the damage component D11 is dominant, whereas the other two
components D22 and D33 are negligible. By contrast, under the uniaxial compression the
transverse damage components D22 ¼ D33 become predominant, but a non-negligible axial
component D11 is also visible (cf. Fig. 3). In contrast to other models that are time-dependent
(cf. e.g. [9]), damage evolution described by the MK model results from the stress and strain
growth, but it is time independent and does not occur under the constant stress condition. Note
also that in the MK model below the damage threshold A0(ð0, ε0) none damage nucleation or
growth occurs.”, Figs. 1 and 3, The examiner considers the deformation of the material,
where material instability is being met and the local failure criterion is satisfied as being the
threshold for the nominal strain and shear strain, since the deformation process is characterized
by a non-linear stress-strain curve)];
“determining if the updated material model violates a stability constraint” as [Kuna-Ciskal et al. (Pg. 689, 3rd paragraph, “The local tangent stiffness matrix is used for the quasi-Newton algorithm for first iteration step of solving the non-linear equation (17) as long as the local failure criterion (23) holds. The stiffness of the element in the FE mesh that has come to failure is next reduced to zero. As a consequence, the failed element is completely released from stress and the appropriate stress redistribution occurs in the neighbouring elements to ensure the global equilibrium. Note that the above failure criterion (23) assumes the brittle failure mechanism.”, Eqn. 21, Equation 21 is directed towards a Druckers stability postulate, which is another way a stability criterion is recited)];
“if the stability constraint is violated, applying a penalty function to the objective function by adding an error value of the error to the penalty function to generate a modified objective function to be used at the next iteration” as [Kuna-Ciskal et al. (Pg. 689, 3rd paragraph, “The local tangent stiffness matrix is used for the quasi-Newton algorithm for first iteration step of solving the non-linear equation (17) as long as the local failure criterion (23) holds. The stiffness of the element in the FE mesh that has come to failure is next reduced to zero. As a consequence, the failed element is completely released from stress and the appropriate stress redistribution occurs in the neighbouring elements to ensure the global equilibrium. Note that the above failure criterion (23) assumes the brittle failure mechanism.”, Eqn. 21, The examiner notes that the modification of the objective function comprising an error value of Hoffman reference with the penalty function added to the objective function to be used in the next iteration of Kuna-Ciskal reference corresponds to the broadest reasonable interpretation of “applying a penalty function to the objective function by adding an error value of the error to the penalty function to generate a modified objective function to be used at the next iteration as described above”.)];
“and wherein the stability constraint is based, at least in part, on Drucker’s stability criterion.” as [Kuna-Ciskal et al. (Pg. 689, 3rd paragraph, “The local tangent stiffness matrix is used for the quasi-Newton algorithm for first iteration step of solving the non-linear equation (17) as long as the local failure criterion (23) holds. The stiffness of the element in the FE mesh that has come to failure is next reduced to zero. As a consequence, the failed element is completely released from stress and the appropriate stress redistribution occurs in the neighbouring elements to ensure the global equilibrium. Note that the above failure criterion (23) assumes the brittle failure mechanism.”, Eqn. 21, Equation 21 is directed towards a Druckers stability postulate, which is another way a stability criterion is recited)];
Hoffman and Kuna-Ciskal et al. are analogous art because they are from the same field endeavor of analyzing a material.
Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art to modify the teachings of Hoffman of identifying a material model, where the material model includes one or more parameters of a parameter set to be calibrated during the material model calibration by incorporating wherein during each iteration of the iterative optimization process, the optimization algorithm enforces a stability constraint on the one or more parameters across one or more predetermined strain ranges for one or more deformations of interest, wherein the one or more predetermined strain ranges includes one or more of lower and upper threshold for nominal strain, volume ratio, and shear strain; determining if the updated material model violates a stability constraint; if the stability constraint is violated, applying a penalty function to the objective function by adding an error value of the error to the penalty function to generate a modified objective function to be used at the next iteration; and wherein the stability constraint is based, at least in part, on Drucker’s stability criterion as taught by Kuna-Ciskal et al. for the purpose of predicting anistropic damage growth and crack propagation in elastic brittle materials.
Hoffman in view of Kuna-Ciskal et al. teaches wherein during each iteration of the iterative optimization process, the optimization algorithm enforces a stability constraint on the one or more parameters across one or more predetermined strain ranges for one or more deformations of interest, wherein the one or more predetermined strain ranges includes one or more of lower and upper threshold for nominal strain, volume ratio, and shear strain; determining if the updated material model violates a stability constraint; if the stability constraint is violated, applying a penalty function to the objective function by adding an error value of the error to the penalty function to generate a modified objective function to be used at the next iteration; and wherein the stability constraint is based, at least in part, on Drucker’s stability criterion.
The motivation for doing so would have been because Kuna-Ciskal et al. teaches that by utilizing the Drucker’s stability criterion as optimization, it allows for accurate testing of the stability of the materials and specifically allows for a “failed element is completely released from stress and the appropriate stress redistribution occurs in the neighbouring elements to ensure the global equilibrium.” (Kuna-Ciskal (Pg. 689, 3rd paragraph, “The local tangent stiffness matrix, etc.”)).
With respect to claim 2, the combination of Hoffman and Kuna-Ciskal et al. discloses the system of claim 1 above, and Hoffman further discloses “display a stability calibration pane within the graphical user interface, the stability calibration pane allows the user to enable or disable the stability constraint violation determination during the iterative process.” as [Hoffman (Figs. 7-9, The user has the ability to modify the property values, formatted input, various tests and the simulation input. The aspect of activating and deactivating the constraint can refer to the changing of one of these values)];
With respect to claim 3, the combination of Hoffman and Kuna-Ciskal et al. discloses the system of claim 1 above, and Hoffman further discloses “display a stability calibration pane within the graphical user interface, the stability calibration pane allows the user to identify a range for use during the stability constraint violation determination, the range identifies a range of values within which the material model is to be evaluated for stability.” as [Hoffman (paragraph [0015] “The constitutive model approximates physical observations of a real material's response over a suitably restricted range.”, Figs. 7-9)];
With respect to claim 4, the combination of Hoffman and Kuna-Ciskal et al. discloses the system of claim 1 above, and Hoffman further discloses “wherein the graphical user interface further allows the user to select the experimental test data set as a subset of the imported data.” as [Hoffman (paragraph [0045] “When a product is selected from the catalog (by a double-click), various information about the product including test data, property sets for a variety of constitutive models, are displayed in the product information window (216). A parameter estimation window 220 shows a model response after the model parameters have been calculated. The parameter estimation window shows that the model response matches the test data after the parameter estimation process. A units conversion window 224 allows a user to select the units that the user want to view data in. All data displayed are automatically converted to the selected units system. A material list window 228 is a virtual `shopping list` that allows a user to select model-based property sets from the application database and build simulation code input.”, Hoffman paragraph [0073] “In order to assist the application to select an appropriate constitutive model from the library, information about the material and the tests are provided to the application. For example, the application may require information about the type of material (e.g., steel), the type of response (e.g., stress versus strain characteristics), and test conditions (e.g., temperature). Based on the information, the application selects a constitutive model that generally approximates the material response”)];
With respect to claim 5, the combination of Hoffman and Kuna-Ciskal et al. discloses the system of claim 1 above, and Kuna-Ciskal et al. further discloses “wherein each iteration of the iterative optimization process further includes: terminating the constraint violation process for the current iteration of the optimization process when the material model with the updated set of parameters does not violate the stability constraint.” as [Kuna-Ciskal et al. (Pg. 689, 3rd paragraph, “The local tangent stiffness matrix is used for the quasi-Newton algorithm for first iteration step of solving the non-linear equation (17) as long as the local failure criterion (23) holds. The stiffness of the element in the FE mesh that has come to failure is next reduced to zero. As a consequence, the failed element is completely released from stress and the appropriate stress redistribution occurs in the neighbouring elements to ensure the global equilibrium. Note that the above failure criterion (23) assumes the brittle failure mechanism”, The local tangent stiffness matrix is used for the quasi-Newton algorithm for first iteration step of solving the non-linear equation (17) as long as the local failure criterion (23) holds, which demonstrates that constraint violation process is terminated)];
With respect to claim 6, the combination of Hoffman and Kuna-Ciskal et al. discloses the system of claim 5 above, and Kuna-Ciskal et al. further discloses “wherein determining that the material model with the updated set of parameters violates the stability constraint includes determining that a material stiffness matrix of the material model is positive definite.” as [Kuna-Ciskal et al. (Pg. 689, 2nd paragraph, “According to the Sylvester criterion the symmetric matrix [H] of the nth order is positive definite if and only if det[Hk] > 0 (k =1; 2; . . . ; n) (23) where [Hk] is the (k x k) minor of the matrix [H] (cf. [31])”, Eqn. 23)];
With respect to claim 7, the combination of Hoffman and Kuna-Ciskal et al. discloses the system of claim 6 above, and Kuna-Ciskal et al. further discloses “wherein determining that a material stiffness matrix of the material model is positive definite includes using Sylvester’s criterion.” as [Kuna-Ciskal et al. (Pg. 689, 2nd paragraph, “According to the Sylvester criterion the symmetric matrix [H] of the nth order is positive definite if and only if det[Hk] > 0 (k =1; 2; . . . ; n) (23) where [Hk] is the (k x k) minor of the matrix [H] (cf. [31])”, Eqn. 23)];
With respect to claim 8, Hoffman discloses “A method of calibrating a material model for use in a computer simulation” as [Hoffman (paragraph [0097] “Once a material is selected for use in a system component, the designer can identify the vulnerability of the system through modeling and simulation.”)];
The other limitations of the claim recite the same substantive limitations as claim 1 above, and are rejected using the same teachings.
With respect to claims 9-14, the claims recite the same substantive limitations as claims 2-7 above, and are rejected using the same teachings.
With respect to claim 15, Hoffman discloses “A non-transitory machine-readable storage medium having computer-executable instructions embodied thereon” as [Hoffman (paragraph [0102] “FIG. 10 shows a distributed network architecture including a network server 1004 and a network storage system 1008. The application resides in the storage system 1008. A computer 1012 is connected to the network server 1004 via the Internet, an intranet or any other communication link.”, Fig. 10)];
The other limitations of the claim recite the same substantive limitations as claim 1 above, and are rejected using the same teachings.
With respect to claims 16-20, the claims recite the same substantive limitations as claims 2-3 and 5-7 above, and are rejected using the same teachings.
Conclusion
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.
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/BERNARD E COTHRAN/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188