Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
Claims 1-21 are currently pending.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/30/2025 has been entered.
Response to Amendment
This action is in response to the RCE filled on 9/30/2025. The amendment has been entered. Claims 1, 2, 7, 8, 10, 11, 17, and 18 have been amended, and claims 20 and 21 have been newly added. Claims 1-21 are pending, with claims 1 and 10 being independent in the instant application.
Response to Arguments
Applicant's Arguments/Remarks filed on 9/30/2025 on page 8-11 regarding 35 U.S.C. 103 rejections have been fully considered and are found persuasive in view
of the amended claims and presented Arguments/Remarks by the Applicant regarding the prior art Barszcz.
Applicant stated in page 12-13 on Arguments/Remarks: “Rao '707 does not teach a system, such as the one featured in amended claim 1 - one that creates a calibrated model (i.e., a model that "comprises a multi-dimensional interface boundary to receive one or more input value and produce one or more output value based on a modeled behavior, without having to perform iterative computations in real time during the simulation of the system, and wherein the calibrated model comprises a functional representation of the result data") of a selected component selected from a plurality of physics computation modeled components …”.
Examiner respectfully disagrees with this argument/remark. The prior art Rao teaches the above-mentioned limitation (Rao disclosed in page 5 para [0050-0051, 0055 and 0060]) The disclosure by Rao teaches the claim limitation “the calibrated model comprises a multi-dimensional interface boundary to receive one or more input value and produce one or more output value based on a modeled behavior”, since a user specified tolerance of a solution of the system as input is received/obtained through input devices of the computing device. The constant is determined as a function of the error field, a residual of the second system of equations, correspond to claim element “output” produced based on modeled behavior of physical system and the error (as output) is expressed in computational grid point in the mesh that represents the solution domain of the physical system. Further, it is understood from the disclosure above that no real-time iteration being mentioned/disclosed by Rao, while performing the simulation of the system. Therefore, Rao teaches the whole limitation.
A new ground of rejections is necessitated by Applicant's claim amendments. Therefore, the previous rejections regarding 35 U.S.C.103 are being amended in this current office action. (See analysis below Claim Rejections-35 U.S.C. 103).
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Objections
Claim 11 is objected to because of the following informalities:
Claim 11 is dependent on claim 10. Claim 10 recited about “calibrated model comprises a multi-dimensional interface boundary”. However, claim 11 only recited “identifying the interface boundary for the calibrated model”, in 1st limitation of claim 11. Examiner assumes a typo has been occurred in this scenario, therefore appropriate correction is required (e.g., identifying the multi-dimensional interface boundary for the calibrated model”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1,2,4-6,9-11,14-16, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (Pub. No. US2017/0185707A1), in view of a Journal “Calibration and Uncertainty Analysis for Computer Simulations with Multivariate Output” by John McFarland et al. (hereinafter McFarland, journal published on 2008).
Regarding Claim 1, Rao teaches a system comprising: a processor and a memory configured to store non-transitory instructions that, when executed by the processor, (Rao disclosed in page 1 para [0011]: “An embodiment of the present invention is directed to a computer system for providing a simulation of a physical real-world System. Such a computer system comprises a processor and memory with computer code instructions stored thereon. The processor and the memory, with the computer code instructions, are configured to cause the system to generate a system of equations …”).
Rao teaches perform the steps of: receiving a selection of a selected component from a plurality of modeled components in a system, wherein each of the plurality of modeled components is a physics computation modeled component; (Rao disclosed in page 4 para [0041]: “For transient solvers, convergence comes into play when sub-iterations are being done between every time increment. The most widely used technique when performing these sub-iterations is to set a pre-determined number of sub-iterations. This pre-determined number requires calibration by trial and error as well as user judgment.” In page 5 para [0049]: “FIG. 1 is a flowchart of a method 110 for providing a simulation of a physical real-world system … In an embodiment, the system of equations is a discrete representation of the real-world system. This discrete representation of the real-world physical system represents the continuum as a finite set of points in space. In such an example, the real-world physical system may be governed by partial differential equations and a discrete version of these equations can govern each of these points in space. … Embodiments of the method 110 may simulate a variety of systems. … the system of equations may be a system of partial differential equations that indicates properties of the physical system. For example, the system of equations may contain data/properties reflecting the mass, stiffness, size, etc., of the real-world system.”).
Rao teaches receiving a setup for a virtual experiment for the selected component using a corresponding one of the physics computation modeled components for the selected component; (Rao disclosed in page 4-5 paras [0048-0049]: “A major advantage of using embodiments of the present invention in a linear solver and a transient solver is the increased performance that can be achieved. … Using embodiments of the present invention, it is now possible to stop the iterative loop when the solution is within a certain percent of the converged solution. Another advantage is the ability to allow the users to set up design experiments without determining calibrations of the stopping criteria for each and every design variation. FIG. 1 is a flowchart of a method 110 for providing a simulation of a physical real-world system. … In an embodiment, the system of equations is a discrete representation of the real-world system. This discrete representation of the real-world physical system represents the continuum as a finite set of points in space. In such an example, the real-world physical system may be governed by partial differential equations and a discrete version of these equations can govern each of these points in space.”
The disclosure above “stop the iterative loop when the solution is within a certain percent of the converged solution and allow the users to set up design experiments without determining calibrations of the stopping criteria for each design variation” corresponds to claim limitation “receiving a setup for a virtual experiment”. Further the disclosure “providing a simulation of a physical real-world system; discrete representation of the real-world physical system represents the continuum as a finite set of points in space; and real-world physical system may be governed by partial differential equations” corresponds to claim limitations “physics computation modeled components for the selected component” (“a finite set of points in space” is equivalent to selected component since discrete version of differential equations can govern each of these points)).
Rao teaches defining a plurality of input parameters for the virtual experiment; (Rao disclosed in page 5 para [0050]: “the method 110 continues and simulates the real-world System at step 112. According to an embodiment, simulating the system at step 112 includes first obtaining a user specified tolerance of a solution of the system of equations at step 112a. The tolerance provided at step 112a indicates how accurate of a solution the user desires. Because the simulation is performed at step 112 by iteratively solving the system of equations, at each iteration a solution is determined. … Thus, the tolerance provided at step 112a indicates how far the solution for a given iteration needs to be from the final solution for the current iterating to stop. The user may specify the tolerance at step 112a through any means known in the art. For example, the user may specify the tolerance in response to a prompt by a computing device implementing the method 110. In the such an embodiment, the user may provide the tolerance through use of any variety of input devices of the computing device.”
The disclosure “the tolerance provided at simulated step 112a indicates how accurate of a solution the user desires; The user may specify the tolerance at step 112a through any means known in the art e.g., by using any variety of input devices of the computing device” corresponds to the limitation “defining a plurality of input parameters for the virtual experiment”).
Rao teaches identifying an output parameter to be modeled by a calibrated model of the selected component; (Rao disclosed in page 4 para [0041]: “For transient solvers, convergence comes into play when sub-iterations are being done between every time increment. The most widely used technique when performing these sub-iterations is to set a pre-determined number of sub-iterations. This pre-determined number requires calibration by trial and error as well as user judgment.” In page 5-6 para [0054-0055]: “first, a converged solution to this second system of equations is determined and second, a plurality of iterations are per formed and an error field is calculated, where the error field is the difference between the converged solution and the iterative solutions. This embodiment may perform any number of iterations to determine the error field. … In an embodiment, the aforementioned error field is used to determine the experimental constant used in simulating the real-world system at step 112. In such an embodiment, the constant is determined as a function of the error field, a residual of the second system of equations, …”. Further in page 6 para [0056]: “According to an embodiment, the constant used at step 112b is determined prior to iteratively solving the first system of equations at step 112b. For example, the constant may be determined ahead of time and in turn, used for simulating a plurality of real-world systems using the method 110.”
“The error field” in above disclosure is the difference between the converged solution and the iterative solutions. The constant is determined as a function of the error field and constant used at simulation step is determined prior to iteratively solving the first system of equations and also the constant can be determined ahead of time and in turn, used for simulating a plurality of real-world systems. Therefore, these scenarios correspond the limitation “identifying an output parameter to be modeled by the calibrated model”. Further, the disclosure “performing these sub-iterations is to set a pre-determined number of sub-iterations. This pre-determined number requires calibration by trial and error as well as user judgment” corresponds to claim element “selected component”).
Rao teaches executing the virtual experiment for the defined input parameters and over a predefined range of values for the varied input parameter; (Rao disclosed in page 6 para [0056]: “In an embodiment, if a constant is determined using one iterative technique that constant can be used for simulating another system of equations using any other iterative technique.” In para [0058]: “In equation (1) A is the operator, u* is the exact solution, and b is the right hand side of known values, e.g., properties of the system being simulated.” In para [0060]: “The error, which can be expressed by equation (4) is a list in computer memory that has a value for every computational grid point in the mesh that represents the solution domain where the physical system represented by equation (1) is being solved. … To illustrate, consider an example where the real-world physical system is described by a finite set of points (computational grid points). Each point is governed by the discrete version of the partial differential equation. In such an example, a solution at each point is determined, e.g., the velocity at each point, and for a given iteration there is a guess for each point, and thus, there is an error for each point …”.
The disclosure above “the real-world physical system is described by a finite set of points (computational grid points)” corresponds to the input parameters in virtual experiment (e.g., constant is determined using one iterative technique that constant can be used as input for simulating another system of equations using any other iterative technique). Therefore, the error expressed by equation (4) is a list in computer memory, has a value for every computational grid point in the mesh, corresponds to the claim limitation “a predefined range of values for the varied input parameter is executed in virtual experiment”. Since each point is governed by the discrete version of the partial differential equation and the error expressed by equation (4) is a list or has a value and it has been discussed earlier in para [0055] that the constant is determined as a function of the error field, therefore, the claim limitation “executing the virtual experiment for the defined input parameters”, is taught in this scenario).
Rao teaches recording result data from the virtual experiment; and producing the calibrated model of the selected component based upon the result data from the virtual experiment; (Rao disclosed in page 6-7 para [0062-0063]: “FIG. 2 is a flow chart of a method 220 that can be used in a process for determining the constant C according to an embodiment. The method 220 begins a step 221 by discretizing a problem. Next, the discretized problem is iteratively solved for approximately infinite iterations, … To summarize the method 220, a given problem is run for a very large number of iterations to safely conclude the solution is converged. Thus, the final solution field u* from equation (1) is obtained and stored. The same problem is then re-run and the error, given by equation (4) is computed at every iteration. Where u* is the final solution from step 222 and u is the solution for each iteration. … For example, in an embodiment equation (8) is used to determine the experimental constant. This process can further, be repeated for various physical problems with various types of meshes and a constant can be determined …”.
The disclosure “the final solution field u* from equation (1) is obtained and stored in the method 220 (a flow chart in FIG. 2)” corresponds to the claim limitation “recording result data from the virtual experiment”. Further, the disclosure “the same problem is then re-run and the error, given by equation (4) is computed at every iteration. Where u* is the final solution from step 222 and u is the solution for each iteration; this process can further, be repeated for various physical problems with various types of meshes” corresponds to the claim limitation “producing the calibrated model based upon the result data from the virtual experiment”).
wherein Rao teaches each of the plurality of physics computation modeled components comprises a computer-based simulation based on derived mathematical expressions modeling the behavior of a corresponding one of the modeled components based on underlying physical properties of the corresponding one of the plurality of the modeled components, (Rao disclosed in page 5 para [0049]: “FIG. 1 is a flowchart of a method 110 for providing a simulation of a physical real-world system … In an embodiment, the system of equations is a discrete representation of the real-world system. This discrete representation of the real-world physical system represents the continuum as a finite set of points in space. In such an example, the real-world physical system may be governed by partial differential equations and a discrete version of these equations can govern each of these points in space. … Embodiments of the method 110 may simulate a variety of systems. … the system of equations may be a system of partial differential equations that indicates properties of the physical system. For example, the system of equations may contain data/properties reflecting the mass, stiffness, size, etc., of the real-world system.”).
The disclosure “the system of equations represents a real-world physical system; the system of equations may be a system of partial differential equations that indicates properties of the physical system; system of equations may contain data/properties reflecting the mass, stiffness, size, etc., of the real-world system” corresponds to claim limitation “a computer-based simulation based on derived mathematical expressions modeling the behavior of the system”. The disclosure “system of equations is a discrete representation of the real-world system; this discrete representation of the real-world physical system represents the continuum as a finite set of points in space” corresponds to claim limitation “plurality of physics computation modeled components … the behavior of a corresponding one of the modeled components based on underlying physical properties of the corresponding one of the plurality of the modeled components”).
and wherein Rao teaches the calibrated model comprises a multi-dimensional interface boundary to receive one or more input value and produce one or more output value based on a modeled behavior, without having to perform iterative computations in real time during the simulation of the system, (Rao disclosed in page 5 para [0050-0051]: “the method 110 continues and simulates the real-world System at step 112. According to an embodiment, simulating the system at step 112 includes first obtaining a user specified tolerance of a solution of the system of equations at step 112a. … For example, the user may specify the tolerance in response to a prompt by a computing device implementing the method 110. … Given the tolerance provided at step 112a, the method 110 continues and iteratively solves the system of equations until convergence is reached, where convergence is reached when a solution to the system of equations for a given iteration is within the user specified tolerance of the solution of the system of equations … According to an embodiment, the solution of the system of equations is determined to be within the user specified tolerance based on use of an experimentally determined constant, an estimate of a minimum eigenvalue of the system of equations for the given iteration, and a residual of the system of equations for the given iteration.”
In page 6 para [0055]: “In an embodiment, the aforementioned error field is used to determine the experimental constant used in simulating the real-world system at step 112. In such an embodiment, the constant is determined as a function of the error field, a residual of the second system of equations, and an estimate of the minimum eigenvalue of the second system of equations.” Further, in para [0060]: “The error, which can be expressed by equation (4) is a list in computer memory that has a value for every computational grid point in the mesh that represents the solution domain where the physical system represented by equation (1) is being solved. … To illustrate, consider an example where the real-world physical system is described by a finite set of points (computational grid points). Each point is governed by the discrete version of the partial differential equation. In such an example, a solution at each point is determined, e.g. the velocity at each point, and for a given iteration there is a guess for each point, and thus, there is an error for each point, which is represented by the array e.” The disclosure above teaches the claim limitation “the calibrated model comprises a multi-dimensional interface boundary to receive one or more input value and produce one or more output value based on a modeled behavior”, since a user specified tolerance of a solution of the system as input is received/obtained through input devices of the computing device. The constant is determined as a function of the error field, a residual of the second system of equations, correspond to claim element “output” produced based on modeled behavior of physical system and the error (as output) is expressed in computational grid point in the mesh that represents the solution domain of the physical system. Further, it is understood from the disclosure above that no real-time iteration being mentioned/disclosed by Rao, while performing the simulation of the system. Therefore, Rao teaches the whole limitation).
and wherein Rao teaches the calibrated model comprises a functional representation of the result data. (Examiner would construe the claim term “functional representation” (as per Specification of current application page 17 2nd para (last line)) as the response surface converted into a continuous representation. Rao disclosed in page 6-7 para [0063]: “To summarize the method 220, a given problem is run for a very large number of iterations to safely conclude the solution is converged. Thus, the final solution field u from equation (1) is obtained and stored. The same problem is then re-run and the error, given by equation (4) is computed at every iteration. Where u* is the final solution from step 222 and u is the solution for each iteration. A norm of the error field, such as the volume weighted norm, is computed and this volume weighted norm of the error filed can be used to determine the constant used in embodiments. For example, in an embodiment equation (8) is used to determine the experimental constant. This process can further, be repeated for various physical problems with various types of meshes and a constant can be determined that roughly bounds the error within 1 order of magnitude.”
The disclosure of ‘mesh’ is a functional representation of the result data, resulted from the calibrated model, since the given problem is run for a very large number of iterations to safely conclude the solution is converged and same problem is then re-run and the error, given by equation (4) is computed at every iteration. It has been disclosed in page 1 para [0004]: “The advent of CAD and CAE systems allows for a wide range of representation possibilities for objects. One such representation is a finite element analysis model. … A finite element model is a system of points called nodes which are interconnected to make a grid, referred to as a mesh.” Therefore, it is understood the finite element model is a system of points called nodes which are interconnected to make a grid or mesh is converted to a continuous representation of a physical object).
wherein Rao teaches the virtual experiment comprises a process for execution of the test setup for the virtual experiment using the physics computation modeled component model of the selected component, (Rao disclosed in page 4-5 paras [0048-0049]: “A major advantage of using embodiments of the present invention in a linear solver and a transient solver is the increased performance that can be achieved. … Using embodiments of the present invention, it is now possible to stop the iterative loop when the solution is within a certain percent of the converged solution. Another advantage is the ability to allow the users to set up design experiments without determining calibrations of the stopping criteria for each and every design variation. FIG. 1 is a flowchart of a method 110 for providing a simulation of a physical real-world system. … In an embodiment, the system of equations is a discrete representation of the real-world system. This discrete representation of the real-world physical system represents the continuum as a finite set of points in space. In such an example, the real-world physical system may be governed by partial differential equations and a discrete version of these equations can govern each of these points in space.”
The disclosure above “stop the iterative loop when the solution is within a certain percent of the converged solution and allow the users to set up design experiments without determining calibrations of the stopping criteria for each design variation” corresponds to claim limitation “virtual experiment comprises a process for execution of the test setup for the virtual experiment”. Further the disclosure “providing a simulation of a physical real-world system; discrete representation of the real-world physical system represents the continuum as a finite set of points in space; and real-world physical system may be governed by partial differential equations” corresponds to claim limitations “physics computation modeled components of the selected component” (“a finite set of points in space” is equivalent to selected component since discrete version of differential equations can govern each of these points)).
wherein Rao teaches executing the virtual experiment further comprises executing a series of passes each corresponding to one of a plurality of input values of the varied input parameter over the predefined value range, (Rao disclosed in page 6 para [0056]: “In an embodiment, if a constant is determined using one iterative technique that constant can be used for simulating another system of equations using any other iterative technique.” In para [0058]: “In equation (1) A is the operator, u* is the exact solution, and b is the right hand side of known values, e.g., properties of the system being simulated.” In para [0060]: “The error, which can be expressed by equation (4) is a list in computer memory that has a value for every computational grid point in the mesh that represents the solution domain where the physical system represented by equation (1) is being solved. … To illustrate, consider an example where the real-world physical system is described by a finite set of points (computational grid points). Each point is governed by the discrete version of the partial differential equation. In such an example, a solution at each point is determined, e.g., the velocity at each point, and for a given iteration there is a guess for each point, and thus, there is an error for each point, which is represented by the array e”.
The disclosure above “the real-world physical system is described by a finite set of points (computational grid points)” corresponds to the input parameters in virtual experiment (e.g., constant is determined using one iterative technique that constant can be used as input for simulating another system of equations using any other iterative technique). Therefore, the error expressed by equation (4) is a list in computer memory, has a value for every computational grid point in the mesh, corresponds to the claim limitation “a predefined range of values for the varied input parameter is executed in virtual experiment”. Since each point is governed by the discrete version of the partial differential equation; a solution at each point is determined, for a given iteration there is a guess for each point, and thus, there is an error for each point, which is represented by the array; therefore, the claim limitation “executing a series of passes each corresponding to plurality of input values”, is taught in this scenario).
and wherein Rao teaches the result data comprises an output value of the output parameter for each pass of the series of passes, (Rao disclosed in page 6 para [0060]: “The error, which can be expressed by equation (4) is a list in computer memory that has a value for every computational grid point in the mesh that represents the solution domain where the physical system represented by equation (1) is being solved. … The errors can be compressed to a single number using a norm. In order to increase functionality of embodiments, the error is represented in a form without any mesh dependencies. In other words, the norm represents the error without being dependent upon physical characteristics of the system being simulated. … Further, embodiments of the present invention may vary the norm determination method based upon the approach used to discretize the real-world system. The norm on the error according to an embodiment is given by equation (6) … were || indicates a norm of a field and the summation is over all the grid values of that field. Vi can be the volume of the domain subregion surrounding the corresponding meshpoint value, …”.
The disclosure “errors can be compressed to a single number using a norm; the norm represents the error;” correspond to the output parameter. Further, the disclosure “|e| indicates a norm of a field and the summation is over all the grid values of that field; Vi can be the volume of the domain subregion surrounding the corresponding meshpoint value” correspond to the claim limitation “result data comprises an output value of the output parameter for each pass of the series of passes” (passes in equation (6) is i=1 to N)).
However, Rao doesn’t explicitly teach the limitation “conducting a simulation of the system in which the calibrated model of the selected component interacts with the physics computation modeled components that were not selected,”
McFarland teaches conducting a simulation of the system in which the calibrated model of the selected component interacts with the physics computation modeled components that were not selected, (McFarland disclosed in page 1255 section II B.: “In many cases, the computer simulation may output the response quantity of interest (e.g., temperature) at a large number of time instances and/or spatial locations. … we propose an algorithm based on the greedy-algorithm concept, for selecting among a set of candidate training points. … choosing among available surrogate-model training points by iteratively adding points one at a time, and the point added at each step is that point corresponding to the largest prediction error. … The greedy point-selection approach is outlined next. Let us denote the total number of available points by mt, the set containing the selected points …, the set containing the points not yet selected by Ω, …”).
Therefore, Rao and McFarland are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao and McFarland to modify performing simulation of a system with calibrated model of selected component of Rao, to include performing simulation of a system with selected component interacts with the computation of non-selected component of McFarland. The suggestion/motivation for doing so would have been obvious by McFarland because “This work explored the use of Bayesian model calibration as a tool for calibrating a computational simulation with experimental observations while accounting for the uncertainty that is introduced in the process. In particular, we proposed an iterative point-selection process that allows one to build efficient Gaussian process surrogates for an analysis code that may have highly multivariate output (for example, time-history response). Further, we showed how a variety of uncertainties associated with the calibration process can be accounted for in the resulting estimates. This includes uncertainty associated with the use of surrogate models, both characterized and uncharacterized observation and modeling errors, and prescribed input parameter uncertainties.” (McFarland disclosed in page 1263 section V (left col.)). Therefore, it would have been obvious to combine Rao with McFarland to obtain the invention as specified in the instant claim(s).
Regarding claim 2, Rao and McFarland teach the system of claim 1, wherein Rao teaches executing the stored non- transitory instructions by the processor further performs the steps of: identifying the multi-dimensional interface boundary for the calibrated model of the selected component; (Rao disclosed in page 6 para [0060]: ““The error, which can be expressed by equation (4) is a list in computer memory that has a value for every computational grid point in the mesh that represents the solution domain where the physical system represented by equation (1) is being solved. … To illustrate, consider an example where the real-world physical system is described by a finite set of points (computational grid points). Each point is governed by the discrete version of the partial differential equation. In such an example, a solution at each point is determined, e.g. the velocity at each point, and for a given iteration there is a guess for each point, and thus, there is an error for each point, which is represented by the array e.”
It has been discussed in page 5 para [0050-0051] that a user specified tolerance of a solution of the system as input is received/obtained through input devices of the computing device. The constant is determined as a function of the error field, a residual of the second system of equations, correspond to claim element “output” produced based on modeled behavior of physical system and the error (as output) is expressed in computational grid point in the mesh that represents the solution domain of the physical system. This scenario corresponds to the claim limitation “identifying the multi-dimensional interface boundary for the calibrated model of the selected component.”).
Rao teaches generating a response surface corresponding to the interface boundary; and identifying a plurality of points on the response surface as output points. (Rao disclosed in page 6 para [0055]: “In an embodiment, the aforementioned error field is used to determine the experimental constant used in simulating the real-world system at step 112. In such an embodiment, the constant is determined as a function of the error field, a residual of the second system of equations, and an estimate of the minimum eigenvalue of the second system of equations.” Further, in para [0060]: “The error, which can be expressed by equation (4) is a list in computer memory that has a value for every computational grid point in the mesh that represents the solution domain where the physical system represented by equation (1) is being solved. … To illustrate, consider an example where the real-world physical system is described by a finite set of points (computational grid points). Each point is governed by the discrete version of the partial differential equation. In such an example, a solution at each point is determined, e.g. the velocity at each point, and for a given iteration there is a guess for each point, and thus, there is an error for each point, which is represented by the array e.”
The disclosure above “the error has a value for every computational grid point in the mesh that represents the solution domain where the physical system represented and the real-world physical system is described by a finite set of points (computational grid points)” teaches the claim limitation “generating a response surface corresponding to the interface boundary”. Further, the disclosure above “Each point is governed by the discrete version of the partial differential equation, a solution at each point is determined, e.g. the velocity at each point, and for a given iteration there is a guess for each point, and thus, there is an error for each point” corresponds to claim limitation “identifying a plurality of points on the response surface as output points”).
Regarding claim 4, Rao and McFarland teach the system of claim 2, wherein Rao teaches the recorded result data comprises output values for each of the plurality of output points for each pass of the series of passes. (Rao disclosed in page 6 para [0060]: “The error, which can be expressed by equation (4) is a list in computer memory that has a value for every computational grid point in the mesh that represents the solution domain where the physical system represented by equation (1) is being solved. … The errors can be compressed to a single number using a norm. In order to increase functionality of embodiments, the error is represented in a form without any mesh dependencies. In other words, the norm represents the error without being dependent upon physical characteristics of the system being simulated. … Further, embodiments of the present invention may vary the norm determination method based upon the approach used to discretize the real-world system. The norm on the error according to an embodiment is given by equation (6) … were || indicates a norm of a field and the summation is over all the grid values of that field. Vi can be the volume of the domain subregion surrounding the corresponding meshpoint value, …”.
The disclosure “errors can be compressed to a single number using a norm; the norm represents the error;” correspond to the output parameter. Further, the disclosure “|e| indicates a norm of a field and the summation is over all the grid values of that field; Vi can be the volume of the domain subregion surrounding the corresponding meshpoint value” correspond to the claim limitation “result data comprises an output value of the output parameter for each pass of the series of passes” (passes in equation (6) is i=1 to N)).
Regarding claim 5, Rao and McFarland teach the system of claim 1, wherein Rao teaches the virtual experiment further comprises the test setup for the physics computation model of the selected component. (Rao disclosed in page 4-5 paras [0048-0049]: “A major advantage of using embodiments of the present invention in a linear solver and a transient solver is the increased performance that can be achieved. … Using embodiments of the present invention, it is now possible to stop the iterative loop when the solution is within a certain percent of the converged solution. Another advantage is the ability to allow the users to set up design experiments without determining calibrations of the stopping criteria for each and every design variation. FIG. 1 is a flowchart of a method 110 for providing a simulation of a physical real-world system. … In an embodiment, the system of equations is a discrete representation of the real-world system. This discrete representation of the real-world physical system represents the continuum as a finite set of points in space. In such an example, the real-world physical system may be governed by partial differential equations and a discrete version of these equations can govern each of these points in space.”
The disclosure above “stop the iterative loop when the solution is within a certain percent of the converged solution and allow the users to set up design experiments without determining calibrations of the stopping criteria for each design variation” corresponds to claim limitation “test setup for a virtual experiment”. Further the disclosure “providing a simulation of a physical real-world system; discrete representation of the real-world physical system represents the continuum as a finite set of points in space; and real-world physical system may be governed by partial differential equations” corresponds to claim limitations “physics computation model of the selected component” (“a finite set of points in space” is equivalent to selected component since discrete version of differential equations can govern each of these points)).
Regarding claim 6, Rao and McFarland teach the system of claim 1, wherein Rao teaches the calibrated model comprises a functional representation of the response of the component to varying input parameters as per the virtual experiment. (Rao disclosed in page 6 para [0060]: “The error, which can be expressed by equation (4) is a list in computer memory that has a value for every computational grid point in the mesh that represents the solution domain where the physical system represented by equation (1) is being solved. … To illustrate, consider an example where the real-world physical system is described by a finite set of points (computational grid points). Each point is governed by the discrete version of the partial differential equation. In such an example, a solution at each point is determined, e.g., the velocity at each point, and for a given iteration there is a guess for each point, and thus, there is an error for each point …”. In page 6-7 para [0063]: “To summarize the method 220, a given problem is run for a very large number of iterations to safely conclude the solution is converged. Thus, the final solution field u from equation (1) is obtained and stored. The same problem is then re-run and the error, given by equation (4) is computed at every iteration. Where u* is the final solution from step 222 and u is the solution for each iteration. A norm of the error field, such as the volume weighted norm, is computed and this volume weighted norm of the error filed can be used to determine the constant used in embodiments. For example, in an embodiment equation (8) is used to determine the experimental constant. This process can further, be repeated for various physical problems with various types of meshes and a constant can be determined that roughly bounds the error within 1 order of magnitude.”
The disclosure above “the real-world physical system is described by a finite set of points (computational grid points)” corresponds to the input parameters in virtual experiment (e.g., constant is determined using one iterative technique that constant can be used as input for simulating another system of equations using any other iterative technique). The error expressed by equation (4) is a list in computer memory, has a value for every computational grid point in the mesh, corresponds to the claim limitation “a predefined range of values for the varied input parameter is executed in virtual experiment”. Further, the disclosure of ‘mesh’ is a functional representation of the result or response, resulted from the calibrated model, since the given problem is run for a very large number of iterations to safely conclude the solution is converged and same problem is then re-run and the error, given by equation (4) is computed at every iteration).
Regarding claim 10, the same ground of rejection is made as discussed in claim 1 for substantially similar rationale, therefore claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Rao and McFarland as discussed above for substantially similar rationale. In addition, claim 10 recites following limitations:
Rao teaches a computer-implemented method, (Rao disclosed in page 1 para [0008]: “An embodiment of the present invention provides a computer implemented method of providing a simulation of a physical real-world system. Such an embodiment begins by generating a system of equations in computer memory where the system of equations includes a discrete representation of the real-world system being simulated.”).
Regarding claim 14, Rao and McFarland teach the method of claim 10, further Rao teaches the step of replacing the physics computation model of the selected component with the calibrated model. (Rao disclosed in page 6 para [0060]: “consider an example where the real-world physical system is described by a finite set of points (computational grid points). Each point is governed by the discrete version of the partial differential equation. In such an example, a solution at each point is determined, e.g., the velocity at each point, and for a given iteration there is a guess for each point, and thus, there is an error for each point, … Further, embodiments of the present invention may vary the norm determination method based upon the approach used to discretize the real-world system”. In para [0062]: “The method 220 begins a step 221 by discretizing a problem. Next, the discretized problem is iteratively solved for approximately infinite iterations, i.e., until a digital machine precision converged solution is reached, at step 222. Then, at step 223, the discretized problem is once again iteratively solved and at each iteration the difference between the converged solution determined at step 222 and the solution for the given iteration is taken to form the error field.” Here, the physics computation model of the selected component (e.g., the real-world physical system is described by a finite set of points (computational grid points), where each point is governed by the discrete version of the partial differential equation) is replaced with the calibrated model (e.g., the approach used to discretize the real-world system; the discretized problem is iteratively solved for approximately infinite iterations, until a digital machine precision converged solution is reached)).
Regarding claims 11,15 and 16, Rao and McFarland teach the method of claim 10, are incorporating the rejections of claims 2, 5 and 6 respectively, because claims 11, 15 and 16 have substantially similar claim language as claims 2, 5 and 6, therefore claims 11, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Rao and McFarland as discussed above for substantially similar rationale.
Regarding claim 20, Rao and McFarland teach the system of claim 1, wherein Rao teaches the memory is configured to store additional non-transitory instructions that, when executed by the processor, perform the step of enabling the selection of the selected component from a plurality of modeled components in the system. (Rao disclosed in page 4 para [0041 and 0045]: “For transient solvers, convergence comes into play when sub-iterations are being done between every time increment. The most widely used technique when performing these sub-iterations is to set a pre-determined number of sub-iterations. This pre-determined number requires calibration by trial and error as well as user judgment. … In such an embodiment, the user only needs to specify the desired degree of convergence in terms of a relative error threshold. Then, the steady state iterative loop exits when the relative error estimate reaches this user specified degree of convergence.” In page 5 para [0049]: “FIG. 1 is a flowchart of a method 110 for providing a simulation of a physical real-world system … In an embodiment, the system of equations is a discrete representation of the real-world system. This discrete representation of the real-world physical system represents the continuum as a finite set of points in space. In such an example, the real-world physical system may be governed by partial differential equations and a discrete version of these equations can govern each of these points in space.”).
Regarding claim 21, Rao and McFarland teach the system of claim 1, wherein Rao teaches the setup for the virtual experiment is provided by a first human user, and the plurality of input parameters are defined by a second human user who is different than the first human user. (Rao disclosed in page 4 para [0041]: “For transient solvers, convergence comes into play when sub-iterations are being done between every time increment. The most widely used technique when performing these sub-iterations is to set a pre-determined number of sub-iterations. This pre-determined number requires calibration by trial and error as well as user judgment. … Thus, the user must be conservative and limit the size of the time step or specify a larger than required number of iterations.” This disclosure teaches the limitation “the setup for the virtual experiment is provided by a first human user”.
In page 5 para [0050]: “The tolerance provided at step 112a indicates how accurate of a solution the user desires. … Thus, the tolerance provided at step 112a indicates how far the solution for a given iteration needs to be from the final solution for the current iterating to stop. … the user may specify the tolerance in response to a prompt by a computing device implementing the method 110. In the such an embodiment, the user may provide the tolerance through use of any variety of input devices of the computing device.” This disclosure teaches the limitation “the plurality of input parameters are defined by a second human user who is different than the first human user”. In first scenario above, a pre-determined number of sub-iterations requires calibration by trial and error as well as user judgment, to the setup for the virtual experiment. Further, in 2nd scenario/disclosure above user can specify or provide any variety of input (e.g., user can define/specify a tolerance level or stopping criteria for a current iteration)).
Claims 3,7-9,12,13, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rao and McFarland and further in view of a Journal “A stopping criterion for the iterative solution of partial differential equations” by Kaustubh Rao (hereinafter Rao_NPL, journal available online 2017).
Regarding claim 3, Rao and McFarland teach the system of claim 2, wherein Rao teaches producing the calibrated model further comprises the steps of: providing a modeling function of the continuous representation. (Rao disclosed in page 6-7 para [0063]: “To summarize the method 220, a given problem is run for a very large number of iterations to safely conclude the solution is converged. Thus, the final solution field u from equation (1) is obtained and stored. The same problem is then re-run and the error, given by equation (4) is computed at every iteration. Where u* is the final solution from step 222 and u is the solution for each iteration. A norm of the error field, such as the volume weighted norm, is computed and this volume weighted norm of the error filed can be used to determine the constant used in embodiments. For example, in an embodiment equation (8) is used to determine the experimental constant. This process can further, be repeated for various physical problems with various types of meshes and a constant can be determined that roughly bounds the error within 1 order of magnitude.”
The disclosure of ‘mesh’ is a continuous representation resulted from the calibrated model, since the given problem is run for a very large number of iterations to safely conclude the solution is converged and same problem is then re-run and the error, given by equation (4) is computed at every iteration. It has been disclosed in page 1 para [0004]: “The advent of CAD and CAE systems allows for a wide range of representation possibilities for objects. One such representation is a finite element analysis model. … A finite element model is a system of points called nodes which are interconnected to make a grid, referred to as a mesh.” Therefore, it is understood the finite element model is a system of points called nodes which are interconnected to make a grid or mesh is converted to a continuous representation of a physical object).
However, Rao and McFarland do not explicitly teach the limitation “converting a discrete representation of the interface boundary to a continuous representation;”
Rao_NPL teaches converting a discrete representation of the interface boundary to a continuous representation; (Rao_NPL disclosed in page 268 section 2.2 (2nd para): “For a given PDE problem, different mesh types (triangles, quads, etc.) and different mesh resolutions, produce roughly the same norm value … A good definition for a norm of a field variable ||x|| should produce nearly the same value irrespective of the underlying discretization of that variable. The better norms to use for PDE variables, and therefore for this work, are integral norms, LVn, represented in this work with the superscript, V … In theory, computing this norm requires having a prescribed interpolation method available for the discrete unknowns (to be able to produce continuous functions that can be integrated) … This norm then becomes effectively a discrete volume weighted norm. … For a cell or element based unknown this would be the cell/element volume. For a node based unknown it would the dual-volume surrounding that node (which is usually the summation of some fraction of all the cell/element volumes touching that node).” The “cell/element volumes” is a discrete volume, corresponds to the claim element “response surface”, which has a discretized representation, is able to produce continuous functions that integrate prescribed interpolation method available for the discrete unknowns).
Therefore, Rao, McFarland and Rao_NPL are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of to modify the executing the virtual experiment using varied input parameters of Rao, to include converting a discrete representation of the interface boundary to a continuous representation of Rao_NPL. The suggestion/motivation for doing so would have been obvious by Rao_NPL because “this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. This hybrid method uses the good extrapolation estimates to precompute and store the ratio of the error to the residual” (Rao_NPL disclosed in page 283 section 7). Therefore, it would have been obvious to combine Rao_NPL with Rao and McFarland to obtain the invention as specified in the instant claim(s).
Regarding claim 7, Rao and McFarland teach the system of claim 4, further comprising compiling the recorded result data, wherein Rao teaches compiling the recorded result data from the virtual experiment into the calibrated model (Rao disclosed in page 6 para [0063]: “To summarize the method 220, a given problem is run for a very large number of iterations to safely conclude the solution is converged. Thus, the final solution field u from equation (1) is obtained and stored. The same problem is then re-run and the error, given by equation (4) is computed at every iteration. Where u* I s the final solution from step 222 and u is the solution for each iteration.” The result data is recorded in above disclosure e.g., “the final solution field u from equation (1) is obtained and stored”, further the claim limitation “compiling the recorded result data from the virtual experiment” is taught above “same problem is then re-run and the error, given by equation (4) is computed at every iteration”).
further Rao_NPL teaches the step of storing the output value corresponding to a virtual experiment pass for each output point in an array. (Rao_NPL disclosed in page 283 section 7 (2nd para-last para): “PDE context was found to be just as important for matrix norms. The popular PDE discretization methods, such as FV and FE methods, produce a discretization in which the matrix (and also the residual) has a cell/element volume in it … PDE context (and mesh independence) was used again when showing how the Rayleigh Quotient can be used to estimate the smallest singular value (of the volume weighted Jacobian). ... Finally, this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. ... The only free parameter in the extrapolation error estimate is the number of prior data points to use for that extrapolation. We use this one free parameter to our advantage by computing multiple error estimates with different numbers of data points … We have used the fact the methods are fundamentally different to develop a hybrid method that reverts to the classic estimator … This hybrid method uses the good extrapolation estimates to precompute and store the ratio of the error to the residual, R, so that this constant is available if/when the reversion to classical (residual based) estimation is needed.”
The current work (in this prior art) related to error extrapolation from current iterative progress worked well and the presented algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. Further, the developed hybrid method uses the good extrapolation estimates to precompute and store the ratio of the error to the residual, which is similar concept to the claim element “step of storing the output value corresponding to a virtual experiment pass for each output point in an array” (since matrix along with the residual having a cell/element volume used in PDE discretization method, as discussed above)).
Regarding claim 8, Rao and McFarland teach the system of claim 4, further comprising compiling the recorded result data, wherein Rao teaches compiling the recorded result data from the virtual experiment into the calibrated model (Rao disclosed in page 6 para [0063]: “To summarize the method 220, a given problem is run for a very large number of iterations to safely conclude the solution is converged. Thus, the final solution field u from equation (1) is obtained and stored. The same problem is then re-run and the error, given by equation (4) is computed at every iteration. Where u* I s the final solution from step 222 and u is the solution for each iteration.” The result data is recorded in above disclosure e.g., “the final solution field u from equation (1) is obtained and stored”, further the claim limitation “compiling the recorded result data from the virtual experiment” is taught above “same problem is then re-run and the error, given by equation (4) is computed at every iteration”).
Further Rao_NPL teaches the step of fitting each output value corresponding to a virtual experiment pass for each output point to a polynomial curve. (According to the conventional meaning in the art Examiner would construe the claim element “polynomial curve” as nonlinear. Rao_NPL disclosed in page 270 section 3 (in 2nd para): “The presented test cases all involve solutions of the incompressible Navier–Stokes equations or the incompressible Reynolds Averaged Navier–Stokes (RANS) equations that include turbulence. We will show error estimates for both the velocity components and for the turbulence model quantities. … The iterative method used for these tests is a segregated solver in which each field variable is solved uncoupled from the others sequentially inside each non-linear iteration.” Further, in page 277-278 section 5.2: “The proposed smooth extrapolation approach therefore uses a least squares best-fit line through the data to extrapolate the slope and the intercept … the goal is to curve fit a line on a log–linear plot … where eb is the best fit for the slope α, and ea is the best fit line’s approximation for the most recent solution increment … Note that M+2 is the number of data points being used in the curve fit. So M=0 for the 2-increment extrapolation (of the previous section). And M=2 for a 4 data-point smoothed extrapolation. M is the number of interior (or extra) smoothing data points.”
The iterative method used in test cases above disclosure is solved using sequentially with each non-linear iteration, corresponds to the claim limitation “a virtual experiment pass for each output point” (error estimation for turbulence model). Further, the proposed smooth extrapolation approach uses a least squares best-fit line, to curve fit a line on a log–linear plot where ea is the best fit line’s approximation for the most recent solution increment and M+2 is the number of data points as output point being used in the curve fit and this curve would be considered as “polynomial curve”).
Regarding claim 9, Rao and McFarland teach the system of claim 1, further Rao_NPL teaches the steps of: selecting a reduced subset of the calibrated model data corresponding to a reduction of at least one of the group consisting of an output parameter, an input parameter, and a predefined value range of the varied input parameter; (Rao_NPL disclosed in page 277-278 section 5.2: “There is ambiguity in what sort of average to use. There also remains a strong (and noisy) dependence on the most recent solution increment, … Much of this ambiguity can be removed by noting that the goal is to curve fit a line on a log–linear plot. The proposed smooth extrapolation approach therefore uses a least squares best-fit line through the data to extrapolate the slope and the intercept … the goal is to curve fit a line on a log–linear plot … where eb is the best fit for the slope α, and ea is the best fit line’s approximation for the most recent solution increment … Note that M+2 is the number of data points being used in the curve fit. So M=0 for the 2-increment extrapolation (of the previous section). And M=2 for a 4 data-point smoothed extrapolation. M is the number of interior (or extra) smoothing data points.” Here, the number of interior (or extra) smoothing data points corresponds to the claim element “an output parameter” which is reduced by fitting a curve line on a log–linear plot and the proposed smooth extrapolation approach uses a least squares best-fit line through the data to extrapolate).
and Rao_NPL teaches removing data from the calibrated model that is not associated with the reduced subset of the calibrated model data. (Rao_NPL disclosed in page 269 section 2.4: “Cell/element volumes creep into the analysis in one more place. They appear in the Jacobian itself. We believe there is one version of the Jacobian that is particularly useful, especially in the context of convergence estimates. Specifically, for the case of PDE problems, one very particular scaling of the Jacobian matrix has a minimum singular value that is essentially independent of the mesh size and the discretization type (triangle, quad, etc.) … The scaling ambiguity of a Jacobian is clear. Each equation in the original system f(¯x) =0 can be multiplied by a non-zero weight and the solution of the system, ¯x, will remain unchanged. But the row in the Jacobian corresponding to that equation will change (it will be multiplied by the weight). In this work, we are only interested in this simple act of weighting each equation … The multiplicative scaling ambiguity in the equations is critical for systems that come from discretized PDEs. … On a simple 2D Cartesian mesh a finite difference (FD) discretization of Laplace’s equation produces a ‘neighbor stencil’ for the Jacobian that is not the same as the stencil that a finite volume (FV) or finite element (FE) method produces.”).
Regarding claims 12,13 and 17-19, Rao and McFarland teach the method of claim 10, is incorporating the rejections of claims 3,4, and 7-9 respectively, because claims 12,13, and 17-19 have substantially similar claim language as claims 3,4, and 7-9, therefore claims 12,13, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rao, McFarland and Rao_NPL as discussed above for substantially similar rationale.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. A Journal “A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/ validation data in watershed modeling” by Haw Yen et al. developed a framework entitled the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT), utilizing Bayesian inferences, an input error model and modified goodness-of-fit statistics to incorporate uncertainty in parameter, model structure, input data, and calibration/validation data in watershed modeling. Accounting for the major sources of uncertainty associated with watershed modeling produces more realistic predictions, improves the quality of calibrated solutions, and consequently reduces predictive uncertainty. IPEAT is an innovative tool to investigate and explore the significance of uncertainty sources, which enhances watershed modeling by improved characterization and assessment of predictive uncertainty. IPEAT framework is a flexible tool to enhance watershed modeling with improved confidence even during validation period.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NUPUR DEBNATH whose telephone number is (571)272-8161. The examiner can normally be reached M-F 10:00 am-4 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rehana Perveen can be reached on (571)272-3676. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NUPUR DEBNATH/Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
Prosecution Insights