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 .
Specification
The lengthy specification has not been checked to the extent necessary to
determine the presence of all possible minor errors. Applicant's cooperation is
requested in correcting any errors of which applicant may become aware in the
specification.
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
Claims 10 and 14 are objected to because of the following informalities:
Claim 10 recites “… identify one or more of the plurality of input parameters of the model that impact the plurality of second results ;”
Claim 14 recites “The method according to claim 10, wherein the propagation is a layer-wise relevance propagation .”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
As to claim 1,
Step 1: Claim 1 is directed to a system. Therefore, the claim is eligible under Step 1 for being directed to apparatus.
Step 2A Prong One
Claim 1 recites
a storage device configured to: store a plurality of first results generated by one or more perturbations of a structure; and store a plurality of input parameters of a model of the structure, (data storing, generic computer function)
wherein the model is generated by a finite element modeling; (mere instructions to apply an exception)
a processor configured to: (generic computer function)
generate a plurality of second results by exercising the model with the one or more perturbations; (mere instructions to apply an exception)
train a neural network to replicate the plurality of second results, (mere instructions to apply an exception)
wherein the neural network includes a plurality of input nodes and a plurality of output nodes, a subset of the plurality of input nodes represent the plurality of input parameters of the model, and the plurality of output nodes represent the plurality of second results; (data description)
invert the neural network; run a propagation through the neural network as inverted from the plurality of output nodes to the plurality of input nodes to identify one or more of the plurality of input parameters of the model that impact the plurality of second results; (mere instructions to apply an exception)
and generate a plurality of gradient values that represent how the plurality of second results deviate from the plurality of first results; (mental process)
and an output device configured to present a gradient graph of the plurality of gradient values. (output data, insignificant activity)
The claimed concept is a method of determining gradient values by evaluating model data based on mathematic relationship directed to “Mental Process” and/or “Mathematical Concepts” grouping. These limitations can be performed in a human mind or using pen and paper.
Therefore, claim 1 is an abstract idea.
Step 2A Prong Two
The output step of a model is recited at a high level of generality (i.e. as a general means of outputting data) and amounts to mere data outputting, which is a form of insignificant extra-solution activity.
The claim recites additional elements such as “storage device and processor”. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. See applicant’s specification [0045-0048] Fig. 3 for generic computer description.
Claim 1 recites “wherein the model is generated by a finite element modeling; … generate a plurality of second results by exercising the model with the one or more perturbations; … invert the neural network; run a propagation through the neural network as inverted from the plurality of output nodes to the plurality of input nodes to identify one or more of the plurality of input parameters of the model that impact the plurality of second results;” which amounts to mere instructions to apply an exception in accordance with MPEP 2106.05(f) (1) and (3). For example, the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.
The judicial exception is not integrated into a practical application.
Step 2B:
The same analysis of Step 2A Prong Two applies here in 2B. The present claim does not recite any limitation that would integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(d). In particular, the claim limitations do not recite a combination of additional elements that tie or “integrate the invention into a practical application”.
Thus, claim 1 is not patent eligible. Same conclusion for dependent claims of claim 1. See below.
2. The system according to claim 1, wherein the processor is further configured to: receive a query of an area of interest in the gradient graph; (receive input)
and identify a subset of the plurality of input parameters of the model that are associated with the area of interest in the gradient graph. (mental process)
3. The system according to claim 2, wherein the processor is further configured to: reduce the plurality of gradient values within the area of interest by automatically adjusting one or more of the plurality of input parameters in the subset. (mental process)
4. The system according to claim 3, wherein the plurality of gradient values in the area of interest determine if the one or more of the plurality of input parameters are increased or decreased. (mental process)
5. The system according to claim 1, wherein the propagation is a layer-wise relevance propagation. (data description)
6. The system according to claim 1, wherein the neural network is a graph neural network. (data description)
7. The system according to claim 1, wherein the neural network comprises: an input layer that includes the plurality of input nodes; an output layer that includes the plurality of output nodes; and at least one hidden layer that couples the input layer to the output layer. (data description)
8. The system according to claim 7, wherein one or more of a plurality of output edges in the at least one hidden layer loops back to one or more of a plurality of input edges in the at least one hidden layer. (data description)
9. The system according to claim 1, wherein the output device is one or more of a display and a printer. (generic computer function)
Same conclusion for independent claims 10, 19 and dependent claims. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In particular, the claim limitations do not recite a combination of additional elements that tie or “integrate the invention into a practical application”.
Thus, claims 1-20 are not patent eligible.
Allowable Subject Matter
Claims 1-20 are objected to but would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101.
The following is a statement of reasons for the indication of allowable subject matter:
Haudrich et al (US 20050234839 A1) teaches a method of performing aeroelastic analysis using a neural network. Input parameters, such as mass and location, contributing to aeroelastic characterization are determined and constrained. A model of a structure to be analyzed can be constructed. The model can include a number of locations where the input parameters can be varied. The aeroelastic characteristic of the structure can be analyzed using a finite element model to determine a number of output characteristics, each of which can correspond to at least one of a plurality of input samples. A neural network can be generated for determining the aeroelastic characteristic based on input parameters. The input sample/output characteristic pairs can be used to train the neural network. The weights and bias values from the trained neural network can be used to generate a non-linear transfer function that generates the aeroelastic characteristic in response to input parameters.
Chau et al (US 20220101063 A1) teaches a method of predicting performance of a hardware arrangement or a neural network model includes the following steps of obtaining one or more of a first hardware arrangement or a first neural network model, obtaining a first graphical model comprising a first plurality of nodes corresponding to the obtained first hardware arrangement or the obtained first neural network model, wherein each node of the first plurality of nodes corresponds to a respective component or device of the first plurality of interconnected components or devices or a respective operation of the first plurality of operations; extracting, based on the first graphical model, a first graphical representation of the obtained first hardware arrangement or the obtained first neural network model; predicting, based on the first graphical representation, performance of the obtained first hardware arrangement or the obtained first neural network model; and outputting the predicted performance.
Horesh et al (US 11531902 B2) teaches a method for generating and managing, including simulating and training, deep tensor neural networks are presented. A deep tensor neural network comprises a graph of nodes connected via weighted edges. A network management component (NMC) extracts features from tensor-formatted input data based on tensor-formatted parameters. NMC evolves tensor-formatted input data based on a defined tensor-tensor layer evolution rule, the network generating output data based on evolution of the tensor-formatted input data. The network is activated by non-linear activation functions, wherein the weighted edges and non-linear activation functions operate, based on tensor-tensor functions, to evolve tensor-formatted input data. NMC trains the network based on tensor-formatted training data, comparing output training data output from the network to simulated output data, based on a defined loss function, to determine an update. NMC updates the network, including weight and bias parameters, based on the update, by application of tensor-tensor operations.
Seskin et al (US 20060229753 A1) teaches a method for designing a product includes obtaining data records relating to one or more input variables and one or more output parameters associated with the product. One or more input parameters may be selected from the one or more input variables, and a computational model indicative of interrelationships between the one or more input parameters and the one or more output parameters based on the data records may be generated. The method further includes providing a set of constraints to the computational model representative of a compliance state for the product and using the computational model to generate statistical distributions for the one or more input parameters and the one or more output parameters, based on the set of constraints, that represent a design for the product.
These references, taken either alone or in combination with the prior art of record, fail to disclose instructions, including:
Claims 1, 10 and 19: “train a neural network to replicate the plurality of second results, wherein the neural network includes a plurality of input nodes and a plurality of output nodes, a subset of the plurality of input nodes represent the plurality of input parameters of the model, and the plurality of output nodes represent the plurality of second results;
invert the neural network; run a propagation through the neural network as inverted from the plurality of output nodes to the plurality of input nodes to identify one or more of the plurality of input parameters of the model that impact the plurality of second results; and
generate a plurality of gradient values that represent how the plurality of second results deviate from the plurality of first results;”
in combination with the remaining elements and features of the claimed invention.
Conclusion
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/CHUEN-MEEI GAN/Primary Examiner, Art Unit 2189