DETAILED ACTION
This office action is in response to the claims filed on 12/13/2021.
Claims 1-104 are pending.
Claims 1-84 are cancelled.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/22/2022, 05/13/2022, 04/27/2023, and 09/12/2024 are in compliance with the provisions of 37 CFR 1.97 and have been considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 85-104 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Independent claims 85 and 95 are directed towards a method and an apparatus, respectively. Therefore, these claims, as well as their dependent claims, are directed towards one of the four statutory categories (process, machine (i.e. apparatus), manufacture, or composition of matter).
With respect to claim 85:
2A Prong 1:
Generating an approximation comprising a system of equations wherein the approximation represents a physical process of a component of a system to be modeled (mental process - generating an approximating system of equations to represent a physical process can be performed in the human mind, or by a human using a pen and paper – see MPEP 2106.04(a)(2)(III)).
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
Storing the trained surrogate for later use (adding insignificant extra-solution activity to the judicial exception – mere data gathering, see MPEP 2106.05(g)).
Training a surrogate based on the approximation (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Storing the trained surrogate for later use(MPEP 2106.05(d)(II) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer).
Training a surrogate based on the approximation (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 86:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The approximation is generated based on input received from a user via a graphical user interface (GUI), the surrogate is trained based on input received from the user via the GUI, and the trained surrogate is stored based on input received from the user via the GUI (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The approximation is generated based on input received from a user via a graphical user interface (GUI), the surrogate is trained based on input received from the user via the GUI, and the trained surrogate is stored based on input received from the user via the GUI (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 87:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The trained surrogate is stored in a library of trained surrogate components (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The trained surrogate is stored in a library of trained surrogate components (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 88:
2A Prong 1:
The trained surrogate is a nonlinear reduced approximation of the component of the system to be modeled (mental process - developing a nonlinear reduced approximation can be performed in the human mind, or by a human using a pen and paper – see MPEP 2106.04(a)(2)(III)).
2A Prong 2: No additional elements beyond the judicial exception.
2B: No additional elements beyond the judicial exception.
With respect to claim 89:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The surrogate is a neural network (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The surrogate is a neural network (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 90:
2A Prong 1:
The system of equations is a system of differential-algebraic equations (mathematical concept - a differential-algebraic equation can be considered a mathematical concept - see MPEP 2106.04(a)(2)(I)).
2A Prong 2: No additional elements beyond the judicial exception.
2B: No additional elements beyond the judicial exception.
With respect to claim 91:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The trained surrogate recreates the dynamics of the physical process of the system to be modeled (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The trained surrogate recreates the dynamics of the physical process of the system to be modeled (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 92:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The trained surrogate is configured to be used as a representation of a physical process in a second system to be modeled that represents a second physical process (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The trained surrogate is configured to be used as a representation of a physical process in a second system to be modeled that represents a second physical process (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 93:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The trained surrogate is configured to be used in multiple simulations without retraining the trained surrogate (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The trained surrogate is configured to be used in multiple simulations without retraining the trained surrogate (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 94:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The trained surrogate is configured to be combined with other trained surrogates to build a composed system that can be solved using a differential-equation solver (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The trained surrogate is configured to be combined with other trained surrogates to build a composed system that can be solved using a differential-equation solver (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 95:
2A Prong 1:
Generating an approximation comprising a system of equations wherein the approximation represents a physical process of a component of a system to be modeled (mental process - generating an approximating system of equations to represent a physical process can be performed in the human mind, or by a human using a pen and paper – see MPEP 2106.04(a)(2)(III)).
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
Storing the trained surrogate for later use (adding insignificant extra-solution activity to the judicial exception – mere data gathering, see MPEP 2106.05(g)).
Training a surrogate based on the approximation (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Storing the trained surrogate for later use(MPEP 2106.05(d)(II) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer).
Training a surrogate based on the approximation (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 96:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The approximation is generated based on input received from a user via a graphical user interface (GUI), the surrogate is trained based on input received from the user via the GUI, and the trained surrogate is stored based on input received from the user via the GUI (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The approximation is generated based on input received from a user via a graphical user interface (GUI), the surrogate is trained based on input received from the user via the GUI, and the trained surrogate is stored based on input received from the user via the GUI (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 97:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The trained surrogate is stored in a library of trained surrogate components (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The trained surrogate is stored in a library of trained surrogate components (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 98:
2A Prong 1:
The trained surrogate is a nonlinear reduced approximation of the component of the system to be modeled (mental process - developing a nonlinear reduced approximation can be performed in the human mind, or by a human using a pen and paper – see MPEP 2106.04(a)(2)(III)).
2A Prong 2: No additional elements beyond the judicial exception.
2B: No additional elements beyond the judicial exception.
With respect to claim 99:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The surrogate is a neural network (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The surrogate is a neural network (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 100:
2A Prong 1:
The system of equations is a system of differential-algebraic equations (mathematical concept - a differential-algebraic equation can be considered a mathematical concept - see MPEP 2106.04(a)(2)(I)).
2A Prong 2: No additional elements beyond the judicial exception.
2B: No additional elements beyond the judicial exception.
With respect to claim 101:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The trained surrogate recreates the dynamics of the physical process of the system to be modeled (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The trained surrogate recreates the dynamics of the physical process of the system to be modeled (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 102:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The trained surrogate is configured to be used as a representation of a physical process in a second system to be modeled that represents a second physical process (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The trained surrogate is configured to be used as a representation of a physical process in a second system to be modeled that represents a second physical process (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 103:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The trained surrogate is configured to be used in multiple simulations without retraining the trained surrogate (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The trained surrogate is configured to be used in multiple simulations without retraining the trained surrogate (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
With respect to claim 104:
2A Prong 1: No judicial exceptions introduced beyond those present in the claim’s inherited limitations.
2A Prong 2: The additional elements recited in the claim do not integrate the judicial exception into a practical application.
Additional elements:
The trained surrogate is configured to be combined with other trained surrogates to build a composed system that can be solved using a differential-equation solver (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
The trained surrogate is configured to be combined with other trained surrogates to build a composed system that can be solved using a differential-equation solver (can be considered merely reciting the words 'apply it' (or an equivalent) with the judicial exception - see MPEP 2106.05(f)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 85-104 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (WO 2020197533 A1), hereafter Wang, in view of Ferreira (US 20200265324 A1), hereafter Ferreira.
Regarding claim 85, Wang teaches:
A method of generating a surrogate to be used in component-based modeling in scientific computing, the method comprising (Wang [0031] teaches a method for generating a predictive model surrogate to be used for dynamically simulating a power system, which examiner equates to “a method of generating a surrogate to be used in modeling in scientific computing, the method comprising” as claimed. Wang [0059-0060] teaches the surrogate model is used in component based systems, which examiner equates to “a surrogate to be used in component-based modeling” as claimed).
Generating an approximation comprising a system of equations, wherein the approximation represents a physical process of a component of a system to be modeled (Wang [0059-0060] teaches the use of a system of equations to approximate the parameters of a system, which examiner equates to “generating an approximation comprising a system of equations”. Wang [0059] also teaches the system represents an active power grid, which examiner equates to “the approximation represents a physical process of a component of a system to be modeled” as claimed).
Training a surrogate based on the approximation (Wang [0061] teaches the approximation as described above in Wang [0059-0060] is used to set the cost function for training of a surrogate neural network, which examiner equates to “training a surrogate based on the approximation” as claimed).
Wang does not explicitly teach a method of generating a surrogate for a library to be used in component-based modeling in scientific computing, the method comprising; storing the trained surrogate for later use. Wang only goes so far as to teach a method of generating a surrogate used in component-based modeling in scientific computing, the method comprising, lacking explicit mention of a library type storage system for the surrogate models.
However, Ferreira teaches a method of generating a model for a library; storing the trained model for later use.
A method of generating a model for a library (Ferreira teaches a knowledge system for building machine learning models which generates and stores models for later usage, which examiner equates to “a method of generating a model for a library” as claimed).
Storing the trained model for later use (Ferreira [0026] teaches the knowledge system stores generated models for later reusage, which examiner equates to “storing the trained model for later use” as claimed).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Wang and Ferreira before them, to include Ferreira’s specific feature/module in the system of Wang performing surrogate model generation for physical system simulation. One would have been motivated to make such a combination as model substructure storage is a well-known practice in the art for model management (see Ferreira [0002]).
Regarding claim 86, Wang in view of Ferreira teaches the elements of claim 85 as outlined above. Wang in view of Ferreira also teaches:
The approximation is generated based on input received from a user via a graphical user interface (GUI) (Ferreira [0019-0020] teaches the knowledge system generates new models (or approximation over Wang) based on the input received from a user, which examiner equates to “the approximation is generated based on input received from a user” as claimed. Ferreira [0027] teaches the user interfaces with the knowledge graph system via a graphical user interface, which examiner equates to “via a graphical user interface (GUI)” as claimed).
The surrogate is trained based on input received from the user via the GUI (Ferreira [0028] teaches the user defined input can dictate how the model (or surrogate model over Wang) is trained, which examiner equates to “the surrogate is trained based on input received from the user via the GUI” as claimed).
The trained surrogate is stored based on input received from the user via the GUI (Ferreira [0026] teaches the model (or surrogate model over Wang) can be stored by the model retriever component based on query input by the user, which examiner equates to “the trained surrogate is stored based on input received from the user via the GUI” as claimed).
Regarding claim 87, Wang in view of Ferreira teaches the elements of claim 85 as outlined above. Wang in view of Ferreira also teaches:
The trained surrogate is stored in a library of trained surrogate components (Ferreira [0026] teaches the model (or surrogate model over Wang) is stored in a knowledge system of models and associated components, which examiner equates to “the trained surrogate is stored in a library of trained surrogate components” as claimed).
Regarding claim 88, Wang in view of Ferreira teaches the elements of claim 85 as outlined above. Wang in view of Ferreira also teaches:
The trained surrogate is a nonlinear reduced approximation of the component of the system to be modeled (Wang [0052] teaches the surrogate model can be used in system model parameter identification, which examiner equates to “the trained surrogate is an approximation of the component of the system to be modeled” as claimed. Wang [0072] teaches the features for said model can include non-linear principal components, which examiner equates to “the trained surrogate is a nonlinear approximation” as claimed. Wang [0047] teaches a process of data pre-conditioning in which identification of model parameters are assessed based on sensitivity and dependency, which examiner notes is feasibly a process of system reduction (i.e. principal component analysis (PCA). As such, examiner equates the model parameter assessment as taught to “a reduced approximation” as claimed).
Regarding claim 89, Wang in view of Ferreira teaches the elements of claim 85 as outlined above. Wang in view of Ferreira also teaches:
The surrogate is a neural network (Wang [0033] teaches the surrogate can be a neural network, which examiner equates to “the surrogate is a neural network” as claimed).
Regarding claim 90, Wang in view of Ferreira teaches the elements of claim 85 as outlined above. Wang in view of Ferreira also teaches:
The system of equations is a system of differential-algebraic equations (Wang [0091] teaches one of the components in the predictive system can be a differential algebraic equation based model, which examiner equates to “the system of equations is a system of differential-algebraic equations” as claimed).
Regarding claim 91, Wang in view of Ferreira teaches the elements of claim 85 as outlined above. Wang in view of Ferreira also teaches:
The trained surrogate recreates the dynamics of the physical process of the system to be modeled (Wang [0031] teaches the surrogate model can act as a dynamic simulation engine of the power system activity, which examiner equates to “the trained surrogate recreates the dynamics of the physical process of the system to be modeled” as claimed).
Regarding claim 92, Wang in view of Ferreira teaches the elements of claim 85 as outlined above. Wang in view of Ferreira also teaches:
The trained surrogate is configured to be used as a representation of a physical process in a second system to be modeled that represents a second physical process (Wang [0031] teaches the surrogate model can replace the dynamic simulation engine, which Wang [0029] teaches can be used in multiple simulations of power systems, which examiner equates to “the trained surrogate is configured to be used as a representation of a physical process in a second system to be modeled that represents a second physical process” as claimed).
Regarding claim 93, Wang in view of Ferreira teaches the elements of claim 85 as outlined above. Wang in view of Ferreira also teaches:
The trained surrogate is configured to be used in multiple simulations without retraining the trained surrogate (as discussed in the analysis of claim 92, Wang [0031] teaches the surrogate model can replace the dynamic simulation engine, which Wang [0029] teaches can be used in multiple simulations of power systems. Wang [0031] also teaches the surrogate model can be retrained to calibrate, however it is feasible that the model of Wang could be reused without need for retraining, which examiner equates to “the trained surrogate is configured to be used in multiple simulations without retraining the trained surrogate” as claimed).
Regarding claim 94, Wang in view of Ferreira teaches the elements of claim 85 as outlined above. Wang in view of Ferreira also teaches:
The trained surrogate is configured to be combined with other trained surrogates to build a composed system that can be solved using a differential-equation solver (Wang [0073] teaches the surrogate model can be an ensemble model comprising multiple different model types, which examiner equates to “the trained surrogate is configured to be combined with other trained surrogates to build a composed system” as claimed. Wang [0091] teaches the surrogate model can work in tandem with a differential algebraic equation based model to solve for calibration parameters (see Wang [0049]), which examiner equates to “the trained surrogate is configured to be combined with other trained surrogates to build a composed system that can be solved using a differential-equation solver”).
Regarding claim 95, Wang teaches:
A system for generating a surrogate for a library to be used in component-based modeling in scientific computing, the system having at least one processor configured for (Wang [0031] teaches a system for generating a predictive model surrogate to be used for dynamically simulating a power system, which examiner equates to “a system for generating a surrogate to be used in modeling in scientific computing” as claimed. Wang [0059-0060] teaches the surrogate model is used in component based systems, which examiner equates to “a surrogate to be used in component-based modeling” as claimed. Wang [0007] teaches the system comprises a processor, which examiner equates to “the system having at least one processor” as claimed).
Generating an approximation comprising a system of equations, wherein the approximation represents a physical process of a component of a system to be modeled (Wang [0059-0060] teaches the use of a system of equations to approximate the parameters of a system, which examiner equates to “generating an approximation comprising a system of equations”. Wang [0059] also teaches the system represents an active power grid, which examiner equates to “the approximation represents a physical process of a component of a system to be modeled” as claimed).
Training a surrogate based on the approximation (Wang [0061] teaches the approximation as described above in Wang [0059-0060] is used to set the cost function for training of a surrogate neural network, which examiner equates to “training a surrogate based on the approximation” as claimed).
Wang does not explicitly teach a method of generating a surrogate for a library to be used in component-based modeling in scientific computing, the method comprising; storing the trained surrogate for later use. Wang only goes so far as to teach a method of generating a surrogate used in component-based modeling in scientific computing, the method comprising, lacking explicit mention of a library type storage system for the surrogate models.
However, Ferreira teaches a method of generating a model for a library; storing the trained model for later use.
A method of generating a model for a library (Ferreira teaches a knowledge system for building machine learning models which can generate and store models for later usage, which examiner equates to “a method of generating a model for a library” as claimed).
Storing the trained model for later use (Ferreira [0026] teaches the knowledge system stores generated models for later reusage, which examiner equates to “storing the trained model for later use” as claimed).
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Wang and Ferreira before them, to include Ferreira’s specific feature/module in the system of Wang performing surrogate model generation for physical system simulation. One would have been motivated to make such a combination as model substructure storage is a well-known practice in the art for model management (see Ferreira [0002]).
Regarding claim 96, the present claim recites similar limitations as corresponding claim 86 and is rejected for similar reasons as claim 86 using similar teachings and rationale.
Regarding claim 97, the present claim recites similar limitations as corresponding claim 87 and is rejected for similar reasons as claim 87 using similar teachings and rationale.
Regarding claim 98, the present claim recites similar limitations as corresponding claim 88 and is rejected for similar reasons as claim 88 using similar teachings and rationale.
Regarding claim 99, the present claim recites similar limitations as corresponding claim 89 and is rejected for similar reasons as claim 89 using similar teachings and rationale.
Regarding claim 100, the present claim recites similar limitations as corresponding claim 90 and is rejected for similar reasons as claim 86 using similar teachings and rationale.
Regarding claim 101, the present claim recites similar limitations as corresponding claim 91 and is rejected for similar reasons as claim 86 using similar teachings and rationale.
Regarding claim 102, the present claim recites similar limitations as corresponding claim 92 and is rejected for similar reasons as claim 92 using similar teachings and rationale.
Regarding claim 103, the present claim recites similar limitations as corresponding claim 93 and is rejected for similar reasons as claim 93 using similar teachings and rationale.
Regarding claim 104, the present claim recites similar limitations as corresponding claim 94 and is rejected for similar reasons as claim 94 using similar teachings and rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Matt Chapman whose telephone number is (703) 756-1604. The examiner can normally be reached on Monday through Friday from 8:30AM to 6:00PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle, can be reached at (571) 272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.G.C./Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
Prosecution Insights