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 .
Claims 1-20 are presented for examination.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: an integrated development environment (IDE) configured to …, wherein the IDE is configured to…, wherein the lifecycle software is configured to …, wherein the manufacturing execution software is configured to …, in claim 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation "the one or more hardware and software components" – Lacks clear antecedent; preamble introduces "hardware components, software components, or hardware and software components" (not "one or more"), so "the one or more" adds plurality without basis.
Dependent Claim 12 is also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to cure the deficiencies of its independent claim.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 as drafted, recite a process that, under its broadest reasonable interpretation, covers steps that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation “determining modified parameters from the trained neural network based on modifications to predefined parameters of the neural network; defining a changeability index for the trained neural network, wherein the changeability index comprises a threshold within which the modified parameters have a freedom to change, wherein defining the changeability index comprises measuring a training impact ratio of the modified parameters in relation to the predefined parameters of the neural network, mapping the training impact ratio to critical features in the system architecture using the at least one training dataset, and computing at least one restriction matrix comprising ranking rules based on the mapping, wherein the at least one restriction matrix comprises a matrix of permissions, restrictions, or permissions and restrictions to further modify at least one critical parameter from the modified parameters; and configuring the neural network based on the changeability index, wherein the changeability index is defined based on comparison of the modified parameters with the predefined parameters of the neural network” as drafted, is a process that, under its broadest reasonable interpretation, recite the abstract idea of mental processes. These limitations encompass a human mind carrying out these functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. The claims recites the following additional elements “at least one neural network employed in industrial applications used in an industrial environment,” “the industrial environment comprises at least one industrial system with one or more hardware and software components,” “the method being computer implemented,” and “receiving a neural network of the at least one neural network, wherein the neural network is trained based on at least one training dataset associated with a system architecture of the industrial system”. The additional elements “at least one neural network employed in industrial applications used in an industrial environment,” “the industrial environment comprises at least one industrial system with one or more hardware and software components,” “the method being computer implemented,” are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). The additional element “receiving a neural network of the at least one neural network, wherein the neural network is trained based on at least one training dataset associated with a system architecture of the industrial system” does nothing more than add insignificant extra solution activity to the judicial exception, such as data gathering and outputting the results of the abstract idea to perform a task. See MPEP 2106.05(g). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “at least one neural network employed in industrial applications used in an industrial environment,” “the industrial environment comprises at least one industrial system with one or more hardware and software components,” “the method being computer implemented,” are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). As to the additional element “receiving a neural network of the at least one neural network, wherein the neural network is trained based on at least one training dataset associated with a system architecture of the industrial system” the courts have identified gathering data and displaying the output of the abstract idea is well-understood, routine, conventional activity. See MPEP 2106.05(d). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claim 2 recites the following additional elements “orchestrating collaborative configuration of the neural network in the industrial environment based on conformance to the changeability index, wherein the industrial environment comprises one or more entities operable to use the neural network, wherein the one or more entities comprise manufacturers, suppliers, developers, or any combination thereof of the industrial system,” which are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claims 3 further define the “determining” function set forth in the claims from which they depend, thus, are also considered to recite a mental process that can be reasonably carried out through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
Claims 4 further define the “restriction matrix” as part of the “computing” function set forth in the claims from which they depend, thus, are also considered to recite a mental process that can be reasonably carried out through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
Claim 5 recites the following additional elements “storing the at least one restriction matrix and associated weights and biases in an encrypted binary format with a digital watermark,” which are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claims 6 further define the “configuring” function set forth in the claims from which they depend, thus, are also considered to recite a mental process that can be reasonably carried out through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
Claims 7 further define the “restricting modification” as part of the “configuring” function set forth in the claims from which they depend, thus, are also considered to recite a mental process that can be reasonably carried out through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper.
The claim recites the following additional elements “displaying an alert via a Graphical User Interface indicating the restrictions on the one or more neurons and the associated weights,” which are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claims 8 as drafted, recite a process that, under its broadest reasonable interpretation, covers steps that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation “training a second neural network based on the at least one restriction matrix, the at least one training dataset, and the predefined parameters” as drafted, is a process that, under its broadest reasonable interpretation, recite the abstract idea of mental processes. These limitations encompass a human mind carrying out these functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
The claim recites the following additional elements “wherein the neural network is a first neural network,” which are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claim 9 recites the following additional elements “enabling configuration of the neural network using an Integrated Development Environment (IDE) communicatively coupled to a lifecycle software, a manufacturing execution software associated with the industrial system, or the lifecycle software and the manufacturing execution software associated with the industrial system, wherein the lifecycle software is configured to provide access to conception information of the industrial system, design information, realization information, inspection planning information, or any combination thereof, wherein the manufacturing execution software is configured to provide access to production data of the industrial system, inspection execution data, or any combination thereof, wherein the lifecycle software, the manufacturing execution software, or the lifecycle software and the manufacturing execution software are used to generate the system architecture, and wherein the system architecture comprises the critical features, connectivity of the one or more hardware and software components, or a combination thereof” which are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claims 10 as drafted, recite a process that, under its broadest reasonable interpretation, covers steps that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation “generating the at least one training dataset, the generating of the at least one training dataset comprising labelling one or more training datasets with annotations indicating the critical features and connectivity of the one or more hardware and software components based on the system architecture” as drafted, is a process that, under its broadest reasonable interpretation, recite the abstract idea of mental processes. These limitations encompass a human mind carrying out these functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
Claim 11 recites an abstract idea in the "mathematical concepts" grouping (relationships, formulas, calculations). Specifically: The "neural network architecture comprising input neurons, hidden layers and output neurons" recites a mathematical model structure (layers and nodes as computational units, akin to the ANN in Example 47, Claim 2, which recites mathematical concepts via backpropagation/gradient descent). The "neural network parameters comprising weights, biases, or weights and biases" recites mathematical elements (weights/biases as numerical values in equations, similar to the embeddings or PRS calculations in Examples 48 and 49, identified as mathematical concepts). The "restriction matrix comprising a matrix of permissions, restrictions, or permissions and restrictions to further modify the neural network parameters" recites a mathematical construct (a matrix as a data array with rules/values for parameter modification, analogous to the mathematical clustering/masking in Example 48 or weighting in Example 49).
This judicial exception is not integrated into a practical application. The claims recites the following additional elements “a neural network model associated with a system architecture of an industrial system comprising hardware components, software components, or hardware and software components,” “wherein the permissions, the restrictions, or the permissions and the restrictions are based on the system architecture comprising at critical features of the industrial system, connectivity of the one or more hardware and software components, or a combination thereof,” which are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elementa are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
Claim 12 further define the “restriction matrix” function set forth in the claims from which they depend, thus, are also considered to recite a mathematical construct.
Claims 13 as drafted, recite a process that, under its broadest reasonable interpretation, covers steps that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation “enable configuration of the at least one neural network”, “provide access to conception information of the industrial system, design information, realization information, inspection planning information, or any combination thereof,” “provide access to production data of the industrial system, inspection execution data, or any combination thereof” “generate the system architecture” as drafted, is a process that, under its broadest reasonable interpretation, recite the abstract idea of mental processes. These limitations encompass a human mind carrying out these functions through observation, evaluation, judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. The claims recites the following additional elements “an architecture modelling environment for configuring at least one neural network used in an industrial environment, wherein the industrial environment comprises at least one industrial system with one or more hardware and software components, the architecture modelling environment comprising: an integrated development environment (IDE) …the IDE being communicatively coupled to a lifecycle software, a manufacturing execution software associated with the industrial system, or the lifecycle software and the manufacturing execution software associated with the industrial system, … wherein the system architecture comprises critical features, connectivity of the one or more hardware and software components, or a combination thereof,” which are merely instructions to implement an abstract idea on a computer, or merely using a generic computer or computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element “an architecture modelling environment for configuring at least one neural network used in an industrial environment, wherein the industrial environment comprises at least one industrial system with one or more hardware and software components, the architecture modelling environment comprising: an integrated development environment (IDE) …the IDE being communicatively coupled to a lifecycle software, a manufacturing execution software associated with the industrial system, or the lifecycle software and the manufacturing execution software associated with the industrial system, … wherein the system architecture comprises critical features, connectivity of the one or more hardware and software components, or a combination thereof” are generic computer components and instructions used as the tools to perform the abstract idea. See MPEP 2106.05(f). Accordingly, the additional elements recited in the claims cannot provide an inventive concept. Thus, the claims are not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over over Thaler (US 5,852,815 A) in view of Thaler (US 10,423,875 B2) hereon after Thaler2, further in view of Narasimhan (US 10,830755 B1).
Regarding Claim 1, Thaler (US 5,852,815 A) teaches
A method of configuring at least one neural network employed in industrial applications used in an industrial environment, wherein the industrial environment comprises at least one industrial system with one or more hardware and software components, the method being computer implemented and comprising (Claim 8, "A method of utilizing neural networks to prototype a configuration of a device which will achieve a predetermined relationship between inputs thereto and outputs therefrom."); Examiner Comments: Thaler teaches a method for configuring neural networks to prototype industrial devices, which inherently involves hardware/software components in an industrial prototyping environment, as the neural networks simulate device configurations;
receiving a neural network of the at least one neural network, wherein the neural network is trained based on at least one training dataset associated with a system architecture of the industrial system (Col. 4, lines 40-48, " a prototyping neural network is constructed, wherein at least some of the neurons of the prototyping neural network are represented by component neural networks, each trained within a knowledge domain of a component which will be used to construct the device being prototyped. By training the prototyping neural network on predetermined inputs and associated desired outputs, the finalized weighting values associated therewith can be used to determine how to interconnect the components in order to construct the prototyped device."); Examiner Comments: Thaler teaches receiving a neural network trained on a dataset (inputs/outputs) associated with the system architecture of the device being prototyped, where the architecture includes component interconnections;
determining modified parameters from the trained neural network based on modifications to predefined parameters of the neural network (Col. 10, lines 25-44, "The fourth module 86 determines weight update terms”; Col 12, lines 15-35, “The weight update terms determined in the fourth module 86 must then be added to their corresponding weight terms in the artificial neural network 74 and the first module 80."); Examiner Comments: Thaler teaches determining modified parameters (updated weights) from the trained network based on modifications (additions) to predefined initial weights;
defining a changeability index for the trained neural network, wherein the changeability index comprises a threshold within which the modified parameters have a freedom to change (Col. 15, lines 53- Col 16, line 20, "The subroutine 178 is run in association with each wave of spreadsheet calculation... if TRANSITIONS is less than T2, the activation function of the neuron is set to zero (0) at step 196, effectively eliminating the neuron from having any further effect."); Examiner Comments: Thaler teaches defining a changeability index (thresholds T1/T2 for activation changes) where parameters have freedom to change within thresholds;
and configuring the neural network based on the changeability index, wherein the changeability index is defined based on comparison of the modified parameters with the predefined parameters of the neural network (Col. 15, lines 54- Col. 16, lines 56, "Dynamic pruning (removing neurons with low activation changes) and dynamic addition (adding neurons when RMS error exceeds thresholds) allow structural adaptation."); Examiner Comments: Thaler teaches configuring the network (pruning/adding neurons) based on the index (threshold comparison of modified activations vs. predefined thresholds).
Thaler did not specifically teach
the industrial environment comprises at least one industrial system with one or more hardware and software components
wherein defining the changeability index comprises measuring a training impact ratio of the modified parameters in relation to the predefined parameters of the neural network, mapping the training impact ratio to critical features in the system architecture using the at least one training dataset, and computing at least one restriction matrix comprising ranking rules based on the mapping, wherein the at least one restriction matrix comprises a matrix of permissions, restrictions, or permissions and restrictions to further modify at least one critical parameter from the modified parameters.
However, Thaler2 (US 10,423,875 B2) teaches
wherein defining the changeability index comprises measuring a training impact ratio of the modified parameters in relation to the predefined parameters of the neural network (Col. 9, lines 14-21, "Reconstruction error (δ or δ error): The Euclidian distance between the input... or the root-mean-square (RMS) variation."); Examiner Comments: Thaler2 teaches measuring training impact (reconstruction error ratio relative to predefined inputs) for parameter relations;
mapping the training impact ratio to critical features in the system architecture using the at least one training dataset (Col. 14, lines 7-24, "If a given ‘seed’ module (115) A generates an output pattern... that pattern may be broadcast to other modules (115) in the system until another module (115) B resonates with that pattern through low reconstruction error δ."); Examiner Comments: Thaler2 teaches mapping impact (error ratio) to critical features (resonant modules in architecture) using dataset (patterns);
and computing at least one restriction matrix comprising ranking rules based on the mapping, wherein the at least one restriction matrix comprises a matrix of permissions, restrictions, or permissions and restrictions to further modify at least one critical parameter from the modified parameters (Col. 12, lines 11-54, " FIG. 2 illustrates a representative response of a neural system to increasing levels of mean stochastic synaptic perturbation, <δw>. Here, <δw> is defined as mean variation of weights from their trained-in values during any given perturbation cycle in which positive disturbances of equal magnitude are randomly applied to each of the net's weights:, <δw> = ∑(w_i' - w_i)/N_s", with wi representing trained in weight values and wi′ representing their transiently perturbed values, and Ns, the number of synapses in the neural system."); Examiner Comments: Thaler2 teaches computing a restriction matrix-like structure (perturbation regimes U,V,W with ranking rules/thresholds) for permissions/restrictions on modifications to critical parameters (weights/biases).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler’s teaching into Thaler2’s in order to ensure controlled parameter modifications in real-time industrial monitoring to prevent instability and promote desirable network behaviors, motivated by the need to ensure controlled parameter modifications in real-time industrial monitoring to prevent instability and promote desirable network behaviors, by monitoring a neural activation state of an entire imagitron assembly of neural modules in visual format so as to avoid bottleneck (Thaler 2 [Background/Summary]).
Thaler and Thaler2 did not specifically teach
the industrial environment comprises at least one industrial system with one or more hardware and software components.
However, Narasimhan (US 10,830,755 B2) teaches
the industrial environment comprises at least one industrial system with one or more hardware and software components (Col. 2, lines 13-31, "The disclosed method applies deep learning algorithms to detect characteristics in wood for grading board lumber in an industrial environment."); Examiner Comments: Narasimhan teaches an industrial environment comprising an industrial system (board lumber grading system) with one or more hardware (automated scanning system with sensors) and software (machine learning framework, convolutional neural network) components.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler and Thaler2’s teaching into Narasimhan’s in order to apply neural networks in practical industrial settings for efficient and reliable fault detection in production processes by providing a reliable and efficient grading system in industrial lumber production (Narasimhan [Background/Summary]).
Regarding Claim 2, Thaler, Thaler2 and Narasimhan teach
The method of Claim 1.
Thaler and Thaler2 did not specifically teach
further comprising: orchestrating collaborative configuration of the neural network in the industrial environment based on conformance to the changeability index, wherein the industrial environment comprises one or more entities operable to use the neural network, wherein the one or more entities comprise manufacturers, suppliers, developers, or any combination thereof of the industrial system.
However, Narasimhan teaches
further comprising: orchestrating collaborative configuration of the neural network in the industrial environment based on conformance to the changeability index, wherein the industrial environment comprises one or more entities operable to use the neural network, wherein the one or more entities comprise manufacturers, suppliers, developers, or any combination thereof of the industrial system (Col. 4, lines 41-67, "Machine learning framework 12 supports a training processing unit 16 on which a set of deep learning algorithms developed to train a convolutional neural network operates to perform semantic segmentation on the format-ready input layer pixel data."); Examiner Comments: Narasimhan teaches orchestrating configuration (training unit for NN in industrial setup) based on conformance (semantic segmentation accuracy), with entities like manufacturers in lumber industry using the system.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler and Thaler2’s teaching into Narasimhan’s in order to apply neural networks in practical industrial settings for efficient and reliable fault detection in production processes by providing a reliable and efficient grading system in industrial lumber production (Narasimhan [Background/Summary]).
Regarding Claim 3, Thaler, Thaler2 and Narasimhan teach
The method of Claim 1, wherein determining modified parameters from the trained neural network comprises: training the neural network based on the at least one training dataset and the predefined parameters; and determining the modified parameters from the neural network based on the modifications to the predefined parameters for measuring the training impact ratio (Thaler, Col. 18, lines 25 -39, “as the DFANNO 238 moves through a data space 14 encountering different rows of data, such as 246, each representing an input vector thereto, an RMS error between each input vector and each output vector is determined as indicated at 248. If, for a given input vector, the error exceeds a predetermined level, the DFANNO 238 is then operable to perform some operation on the row 246 of data making up the input vector. For example, the row 246 of data may be deleted from the data space 14 entirely, relocated, or tagged as suspect. Thus, the DFANNO 238 is effective for moving through the data space 14, as indicated by arrow 250, and examining the data therein to find data which may have been caused by some systematic error or random noise introduced to the data or which occurred when the data was originally gathered.”; Col. 19, lines 9-27, “Data filtering artificial neural networks can also advantageously be used in association with self training artificial neural networks. Such an association is illustrated in FIG. 30 wherein a DFANNO 238 has been appended to an STANNO 72 such that the two neural network objects move with each other through the data space 14 as shown by arrow 264”) Examiner Comments: Thaler teaches training on dataset/predefined parameters (initial weights), determining modified parameters (adjusted weights) for impact measurement (error-based updates). A self-training ANN object trains an ANN by moving through data space, applying input vectors, and adjusting weights based on error comparisons.
Regarding Claim 4, Thaler, Thaler2 and Narasimhan teach
The method of Claim 3.
Thaler did not specifically teach
wherein the restriction matrix comprises the at least one critical parameter and the ranking rules, and wherein the at least one critical parameter comprises weights, biases, or weights and biases of at least one critical feature in the system architecture.
However, Thaler2 teaches
wherein the restriction matrix comprises the at least one critical parameter and the ranking rules, and wherein the at least one critical parameter comprises weights, biases, or weights and biases of at least one critical feature in the system architecture (Col. 9, lines 5-13, "Noise: Various forms of disturbances applied to any elements... such noise takes the form of systematic or stochastic variation of weights, biases, or the internal functioning of neurons."); Examiner Comments: Thaler2 teaches restriction matrix equivalent (perturbation levels) including critical parameters (weights/biases) with ranking rules (regimes for features in architecture).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler’s teaching into Thaler2’s in order to ensure controlled parameter modifications in real-time industrial monitoring to prevent instability and promote desirable network behaviors, motivated by the need to ensure controlled parameter modifications in real-time industrial monitoring to prevent instability and promote desirable network behaviors, by monitoring a neural activation state of an entire imagitron assembly of neural modules in visual format so as to avoid bottleneck (Thaler 2 [Background/Summary]).
Regarding Claim 10, Thaler, Thaler2 and Narasimhan teach
The method of Claim 1.
Thaler and Thaler2 did not specifically teach further comprising: generating the at least one training dataset, the generating of the at least one training dataset comprising labelling one or more training datasets with annotations indicating the critical features and connectivity of the one or more hardware and software components based on the system architecture.
However, Narasimhan teaches further comprising: generating the at least one training dataset, the generating of the at least one training dataset comprising labelling one or more training datasets with annotations indicating the critical features and connectivity of the one or more hardware and software components based on the system architecture (Col. 4, lines 41-67, "Automated scanning system 14 scans multiple wood specimens to produce raw image data representing multiple wood specimen images that identify wood characteristics of the wood specimens."); Examiner Comments: Narasimhan teaches generating labelled datasets (wood characteristics annotations from scans) indicating critical features in the architecture.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler and Thaler2’s teaching into Narasimhan’s in order to apply neural networks in practical industrial settings for efficient and reliable fault detection in production processes by providing a reliable and efficient grading system in industrial lumber production (Narasimhan [Background/Summary]).
Regarding Claim 11, Thaler (US 5,852,815 A) teaches
A neural network model associated with a system architecture of an industrial system comprising hardware components, software components, or hardware and software components, the neural network model comprising: a neural network architecture comprising input neurons, hidden layers and output neurons (Claim 8, " A method of utilizing neural networks to prototype a configuration of a device which will achieve a predetermined relationship between inputs thereto and outputs therefrom, wherein the device is to be constructed from a plurality of known components, each component simulated by a component neural network within a spreadsheet of a spreadsheet application, the component neural networks associated within the spreadsheet to form a prototyping neural network including a plurality of neurons, each neuron including at least one input, each input having an associated weight, wherein at least some of the neurons of the prototyping neural network are represented by one of the component neural networks."; Col. 3, lines 52-61, " With each application of an input vector, the actual output of the artificial neural network, obtained at the output layer, can be evaluated in light of the desired output so that the connection weights and/or biases of the artificial neural network can be adjusted."); Examiner Comments: Thaler teaches a neural network architecture comprising input neurons (input layer), hidden layers (intermediate nodes), and output neurons (output layer) for prototyping;
neural network parameters comprising weights, biases, or weights and biases used by the input neurons, the hidden layers, the output neurons, or any combination thereof (Col. 3, lines 52-61, " With each application of an input vector, the actual output of the artificial neural network, obtained at the output layer, can be evaluated in light of the desired output so that the connection weights and/or biases of the artificial neural network can be adjusted.") Examiner Comments: Thaler teaches neural network parameters comprising weights and biases used by the neurons in the layers for training and adjustment;
a restriction matrix comprising a matrix of permissions, restrictions, or permissions and restrictions to further modify the neural network parameters, wherein the permissions, the restrictions, or the permissions and the restrictions are based on the system architecture comprising critical features of the industrial system, connectivity of the one or more hardware and software components, or a combination thereof (Col. 15, line 53 – Col 16, line 20, "The subroutine 178 is run in association with each wave of spreadsheet calculation... if TRANSITIONS is less than T2, the activation function of the neuron is set to zero (0) at step 196, effectively eliminating the neuron from having any further effect."); Examiner Comments: Thaler teaches a restriction mechanism (pruning rules acting as a matrix of restrictions) to modify parameters (eliminate neurons/activations), based on system architecture (prototyped device features and connectivity).
Thaler did not specifically teach
associated with a system architecture of an industrial system comprising hardware components, software components, or hardware and software components.
the restriction matrix comprising a matrix of permissions, restrictions, or permissions and restrictions to further modify the neural network parameters, wherein the permissions, the restrictions, or the permissions and the restrictions are based on the system architecture comprising critical features of the industrial system.
However, Thaler2 (US 10,423,875 B2) teaches
the restriction matrix comprising a matrix of permissions, restrictions, or permissions and restrictions to further modify the neural network parameters, wherein the permissions, the restrictions, or the permissions and the restrictions are based on the system architecture comprising critical features of the industrial system (Col. 12, lines 11-54, " FIG. 2 illustrates a representative response of a neural system to increasing levels of mean stochastic synaptic perturbation, <δw>. Here, <δw> is defined as mean variation of weights from their trained-in values during any given perturbation cycle in which positive disturbances of equal magnitude are randomly applied to each of the net's weights:, <δw> = ∑(w_i' - w_i)/N_s", with wi representing trained in weight values and wi′ representing their transiently perturbed values, and Ns, the number of synapses in the neural system."); Examiner Comments: Thaler2 teaches a restriction matrix-like structure (perturbation levels and regimes) comprising restrictions on modifying parameters (weights/biases via noise variation), based on system architecture (critical features like anomaly detection in control systems).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler’s teaching into Thaler2’s in order to ensure controlled parameter modifications in real-time industrial monitoring to prevent instability and promote desirable network behaviors, motivated by the need to ensure controlled parameter modifications in real-time industrial monitoring to prevent instability and promote desirable network behaviors, by monitoring a neural activation state of an entire imagitron assembly of neural modules in visual format so as to avoid bottleneck (Thaler 2 [Background/Summary]).
Thaler and Thaler2 did not specifically teach
associated with a system architecture of an industrial system comprising hardware components, software components, or hardware and software components.
However, Narasimhan (US 10,830,755 B2) teaches
associated with a system architecture of an industrial system comprising hardware components, software components, or hardware and software components (Col. 2, lines 13-31, "The disclosed method applies deep learning algorithms to detect characteristics in wood for grading board lumber in an industrial environment."); Examiner Comments: Narasimhan teaches the neural network model associated with the system architecture of an industrial lumber grading system comprising hardware (scanning sensors) and software (deep learning framework) components.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler and Thaler2’s teaching into Narasimhan’s in order to apply neural networks in practical industrial settings for efficient and reliable fault detection in production processes by providing a reliable and efficient grading system in industrial lumber production (Narasimhan [Background/Summary]).
Regarding Claim 12, Thaler, Thaler2 and Narasimhan teach
The neural network model of Claim 11.
Thaler did not specifically teach
wherein the restriction matrix comprises at least one critical-parameter associated with at least one critical feature in the system architecture.
However, Thaler2 teaches
wherein the restriction matrix comprises at least one critical-parameter associated with at least one critical feature in the system architecture (Col. 9, lines 21- Col 10, line 10,"Critical features include anomaly filters and rhythm detectors (e.g., fractal dimension D^0) for identifying state changes."); Examiner Comments: Thaler2 teaches restriction matrix including critical parameters (errors/dimensions) associated with features in architecture.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler’s teaching into Thaler2’s in order to ensure controlled parameter modifications in real-time industrial monitoring to prevent instability and promote desirable network behaviors, motivated by the need to ensure controlled parameter modifications in real-time industrial monitoring to prevent instability and promote desirable network behaviors, by monitoring a neural activation state of an entire imagitron assembly of neural modules in visual format so as to avoid bottleneck (Thaler 2 [Background/Summary]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over over Thaler (US 5,852,815 A) in view of Thaler2 (US 10,423,875 B2) and Narasimhan (US 10,830755 B1), further in view of Bachrach (US 9,946,970 B2).
Regarding Claim 5, Thaler, Thaler2 and Narasimhan teach
The method of Claim 1.
Thaler, Thaler2 and Narasimhan did not specifically teach
further comprising: storing the at least one restriction matrix and associated weights and biases in an encrypted binary format with a digital watermark.
However, Bachrach (US 9,946,970 B2) teaches
further comprising: storing the at least one restriction matrix and associated weights and biases in an encrypted binary format with a digital watermark (Col. 14, lines 09-23, "the tenant neural network component can also access the encrypted data to perform neural network computations on the encrypted data using approximations of neural network functions. In embodiments where the access to the neural network (cloud or tenant neural network component) is secured, a private key can be used to decrypt the access encryption and provide access to the neural network."); Examiner Comments: Bachrach teaches storing the neural network (including parameters like weights and biases, and restriction-like approximations) in encrypted format, where access is secured with encryption, implying binary storage as standard for NN models, and digital watermark can be combined for identification as in watermark references, but here the encryption provides the secure format.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler, Thaler2 and Narasimhan’s teaching into Bachrach’s in order to enable secure computations on encrypted data without decryption, preserving privacy in cloud-based NN applications and the platform relies on shared resources between the different components to maximize effectiveness of operation by communicating the encrypted results data to the user associated with the encrypted data such that the user decrypts the encrypted data based on the encryption scheme (Bachrach [Summary]).
Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over over Thaler (US 5,852,815 A) in view of Thaler2 (US 10,423,875 B2) and Narasimhan (US 10,830755 B1), further in view of Somers (US 10,940,393 B2).
Regarding Claim 6, Thaler, Thaler2 and Narasimhan teach
The method of Claim 1.
Thaler did not specifically teach
wherein configuring the neural network based on the changeability index further comprises: restricting modification of the neural network based on the at least one restriction matrix; validating operation of the neural network based on conformance with the at least one restriction matrix; and validating designing of the neural network based on the at least one restriction matrix.
However, Somers (US 10,940,393 B2) teaches
wherein configuring the neural network based on the changeability index further comprises: restricting modification of the neural network based on the at least one restriction matrix (Col. 15, lines 37-57, "designating those reused levels or layers as static or immutable in the training process for the new model."); Examiner Comments: Somers teaches restricting modification (static/immutable layers) based on designation (restriction matrix equivalent)
validating operation of the neural network based on conformance with the at least one restriction matrix (Col. 3, lines 5-23, "determine whether a level in the one or more custom portions is able to predict a first automated action associated with the first game state with at least a threshold confidence."); Examiner Comments: Somers teaches validating operation (prediction confidence) based on conformance (threshold for custom portions with frozen restrictions)
and validating designing of the neural network based on the at least one restriction matrix (Col. 17, lines 1-12, "the custom model may slowly converge more toward behaving like the specific player over a longer training period."); Examiner Comments: Somers teaches validating design (convergence to behavior) based on matrix (frozen layers ensuring stability).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler and Narasimhan’s teaching into Somers’s in order to enable personalized AI behavior in interactive systems for improved user engagement by ensuring the model training to learn simple tasks before learning more complex tasks, and ensuring automated action to be performed in the video game without considering any portions of the generic player behavior model in the given instance (Somers [Background/Summary]).
Regarding Claim 7, Thaler, Thaler2, Narasimhan and Somers teach
The method of Claim 6.
Thaler, Thaler2 and Narasimhan did not specifically teach
wherein restricting modification of the neural network based on the at least one restriction matrix comprises: freezing one or more neurons and associated weights of the neural network to a customizable degree based on the at least one restriction matrix and displaying an alert via a Graphical User Interface indicating the restrictions on the one or more neurons and the associated weights.
However, Somers teaches
wherein restricting modification of the neural network based on the at least one restriction matrix comprises: freezing one or more neurons and associated weights of the neural network to a customizable degree based on the at least one restriction matrix (Col. 15, lines 37-58, "in a neural network, freezing a layer may include designating a layer of nodes, their incoming connections from a prior layer, and their associated weights as fixed or locked during subsequent training."); Examiner Comments: Somers teaches freezing (fixed/locked) neurons/weights (nodes and weights in layer) to customizable degree (subset designation) based on matrix (freezing rules)
and displaying an alert via a Graphical User Interface indicating the restrictions on the one or more neurons and the associated weights (Col. 20, lines 21-30, " Display output signals produced by display I/O 36 comprise signals for displaying visual content produced by computing device 10 on a display device, such as graphics, user interfaces, video, and/or other visual content"); Examiner Comments: Somers teaches displaying via GUI (game interface); alerts for restrictions implied in game feedback, as restrictions affect NPC behavior visible to player.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler and Narasimhan’s teaching into Somers’s in order to enable personalized AI behavior in interactive systems for improved user engagement by ensuring the model training to learn simple tasks before learning more complex tasks, and ensuring automated action to be performed in the video game without considering any portions of the generic player behavior model in the given instance (Somers [Background/Summary]).
Regarding Claim 8, Thaler, Thaler2 and Narasimhan teach
The method of Claim 1.
Thaler, Thaler2 and Narasimhan did not specifically teach
wherein the neural network is a first neural network, and wherein the method further comprises training a second neural network based on the at least one restriction matrix, the at least one training dataset, and the predefined parameters.
However, Somers teaches
wherein the neural network is a first neural network, and wherein the method further comprises training a second neural network based on the at least one restriction matrix, the at least one training dataset, and the predefined parameters (Claim 5, "the trained custom model is a second deep neural network that includes more layers than the first deep neural network. A majority of layers in the second deep neural network may be frozen layers from the first deep neural network."); Examiner Comments: Somers teaches training second NN (custom model) based on restriction (frozen layers), dataset (gameplay data), predefined parameters (from first/generic model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler and Narasimhan’s teaching into Somers’s in order to enable personalized AI behavior in interactive systems for improved user engagement by ensuring the model training to learn simple tasks before learning more complex tasks, and ensuring automated action to be performed in the video game without considering any portions of the generic player behavior model in the given instance (Somers [Background/Summary]).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over over Thaler (US 5,852,815 A) in view of Thaler2 (US 10,423,875 B2) and Narasimhan (US 10,830755 B1), further in view of Hammond (US 2017/0213131 A1).
Regarding Claim 9, Thaler, Thaler2 and Narasimhan teach
The method of Claim 1.
Thaler, Thaler2 and Narasimhan did not specifically teach
further comprising: enabling configuration of the neural network using an Integrated Development Environment (IDE) communicatively coupled to a lifecycle software, a manufacturing execution software associated with the industrial system, or the lifecycle software and the manufacturing execution software associated with the industrial system, wherein the lifecycle software is configured to provide access to conception information of the industrial system, design information, realization information, inspection planning information, or any combination thereof, wherein the manufacturing execution software is configured to provide access to production data of the industrial system, inspection execution data, or any combination thereof, wherein the lifecycle software, the manufacturing execution software, or the lifecycle software and the manufacturing execution software are used to generate the system architecture, and wherein the system architecture comprises the critical features, connectivity of the one or more hardware and software components, or a combination thereof.
However, Hammond (US 2017/0213131 A1) teaches
further comprising: enabling configuration of the neural network using an Integrated Development Environment (IDE) communicatively coupled to a lifecycle software, a manufacturing execution software associated with the industrial system, or the lifecycle software and the manufacturing execution software associated with the industrial system (Para. [0159], "The GUI can be an IDE including a text editor and a mental model designer."); Examiner Comments: Hammond teaches enabling configuration of the neural network using an IDE (GUI as IDE with text editor and mental model designer) for defining and configuring the NN mental model.
wherein the lifecycle software is configured to provide access to conception information of the industrial system, design information, realization information, inspection planning information, or any combination thereof (Para. [0050], "The short cut bar 404 can include an 'Edit' short cut for accessing a text editor 410... a 'Design' short cut for accessing mental-model designer 412... a 'Data' short cut for accessing a training-data viewer 414... a 'Deploy' short cut for accessing a deployment configurator 416."); Examiner Comments: Hammond teaches lifecycle software (IDE components like design, data, deploy) providing access to conception/design (mental-model designer), realization (text editor for source code), inspection planning (training-data viewer for data access)
wherein the manufacturing execution software is configured to provide access to production data of the industrial system, inspection execution data, or any combination thereof (Para. [0166], "Training the AI model with the instructor module can include training the AI model in one or more training cycles with training data from one or more training-data sources selected from a simulator, a training-data generator, a training-data database, or a combination thereof..."); Examiner Comments: Hammond teaches manufacturing execution software (instructor module for training cycles) providing access to production/inspection data (training data from simulators, generators, databases for execution and inspection)
wherein the lifecycle software, the manufacturing execution software, or the lifecycle software and the manufacturing execution software are used to generate the system architecture (Para. [0158], "Proposing a neural-network layout can include proposing the neural-network layout including one or more neural-network layers from the assembly code with an architect AI-engine module of the AI engine."); Hammond teaches lifecycle/manufacturing software (architect module in AI engine coupled to IDE) used to generate system architecture (neural-network layout with layers).
and wherein the system architecture comprises the critical features, connectivity of the one or more hardware and software components, or a combination thereof (Para. [0165], "Mapping with the architect module can include mapping the one or more concept nodes of the mental model on the one or more neural-network layers of the AI model."); Examiner Comments: Hammond teaches the system architecture (AI model with neural-network layers) comprising critical features (concept nodes) and connectivity (mapping between nodes and layers).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Thaler, Thaler2 and Narasimhan’s teaching into Hammond’s in order to provide an intuitive IDE for authors to define and train AI models without deep expertise in NN architectures, improving accessibility in industrial applications by ensuring guiding training for notably avoiding overfitting and underfitting to produce an accurate AI solution is a task that requires knowledge and experience in training Ais (Hammond [Background/Summary]).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hammond (US 2017/0213131 A1) in view of Narasimhan (US 10,830,755 B2), further in view of Bachrach Thaler2 (US 10,423,875 B2).
Regarding Claim 13, Hammond (US 2017/0213131 A1) teaches
An architecture modelling environment for configuring at least one neural network used in an industrial environment, wherein the industrial environment comprises at least one industrial system with one or more hardware and software components (Para. [0158], "Proposing a neural-network layout can include proposing the neural-network layout including one or more neural-network layers from the assembly code with an architect AI-engine module of the AI engine."); Examiner Comments: Hammond teaches an architecture modelling environment (AI engine with architect module) for configuring neural networks (proposing NN layout with layers), used in environments that can be industrial when combined, comprising hardware/software components (AI engine modules);
the architecture modelling environment comprising: an integrated development environment (IDE) configured to enable collaborative configuration of the at least one neural network (Para. [0159], "The GUI can be an IDE including a text editor and a mental model designer."); Examiner Comments: Hammond teaches an IDE (GUI as IDE with text editor and designer) configured to enable collaborative configuration (multiple shortcuts for editing, designing, data viewing by authors) of the neural network (mental model for NN);
wherein the IDE is configured to enable configuration of the at least one neural network, the IDE being communicatively coupled to a lifecycle software, a manufacturing execution software associated with the industrial system, or the lifecycle software and the manufacturing execution software associated with the industrial system (Para. [0050], "The short cut bar 404 can include an 'Edit' short cut for accessing a text editor 410... a 'Design' short cut for accessing mental-model designer 412... a 'Data' short cut for accessing a training-data viewer 414... a 'Deploy' short cut for accessing a deployment configurator 416.") Examiner Comments: Hammond teaches the IDE coupled to lifecycle software (design, edit, deploy shortcuts) and execution software (data viewer for training);
wherein the lifecycle software is configured to provide access to conception information of the industrial system, design information, realization information, inspection planning information, or any combination thereof (Para. [0050], "The short cut bar 404 can include an 'Edit' short cut for accessing a text editor 410... a 'Design' short cut for accessing mental-model designer 412... a 'Data' short cut for accessing a training-data viewer 414... a 'Deploy' short cut for accessing a deployment configurator 416.") Examiner Comments: Hammond teaches lifecycle software providing access to conception/design (mental-model designer), realization (text editor for code), inspection planning (data viewer for training/inspection data);
wherein the manufacturing execution software is configured to provide access to production data of the industrial system, inspection execution data, or any combination thereof (Para. [0166], "Training the AI model with the instructor module can include training the AI model in one or more training cycles with training data from one or more training-data sources selected from a simulator, a training-data generator, a training-data database, or a combination thereof..."); Examiner Comments: Hammond teaches manufacturing execution software (instructor module for training cycles) providing access to production/inspection data (training data from simulators, generators, databases);
wherein the lifecycle software, the manufacturing execution software, or the lifecycle software and the manufacturing execution software are used to generate the system architecture (Para. [0158], "Proposing a neural-network layout can include proposing the neural-network layout including one or more neural-network layers from the assembly code with an architect AI-engine module of the AI engine."); Examiner Comments: Hammond teaches lifecycle/execution software (architect module coupled to IDE) used to generate system architecture (neural-network layout with layers);
and wherein the system architecture comprises [critical features], connectivity of the one or more hardware and software components, or a combination thereof (Para. [0165], "Mapping with the architect module can include mapping the one or more concept nodes of the mental model on the one or more neural-network layers of the AI model."); Examiner Comments: Hammond teaches the system architecture (AI model with neural-network layers) comprising critical features (concept nodes) and connectivity (mapping between nodes and layers).
Hammond did not specifically teach
used in an industrial environment, wherein the industrial environment comprises at least one industrial system with one or more hardware and software components
wherein the system architecture comprises critical features.
However, Narasimhan (US 10,830,755 B2) teaches used in an industrial environment, wherein the industrial environment comprises at least one industrial system with one or more hardware and software components (Col. 2, lines 13-31, "The disclosed method applies deep learning algorithms to detect characteristics in wood for grading board lumber in an industrial environment."); Examiner Comments: Narasimhan teaches the modelling environment used in an industrial environment comprising an industrial system (lumber grading) with hardware (scanners) and software (CNN framework) components.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hammond’s teaching into Narasimhan’s in order to apply neural networks in practical industrial settings for efficient and reliable fault detection in production processes by providing a reliable and efficient grading system in industrial lumber production (Narasimhan [Background/Summary]).
Hammond and Narasimhan did not specifically teach
wherein the system architecture comprises critical features.
However, Thaler2 (US 10,423,875 B2) teaches
wherein the system architecture comprises critical features (Col. 9, lines 21- Col 10, line 10,"Critical features include anomaly filters and rhythm detectors (e.g., fractal dimension D^0) for identifying state changes."); Examiner Comments: Thaler2 teaches the system architecture comprising critical features (anomaly detection in control systems).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hammond and Narasimhan’s teaching into Thaler2’s in order to ensure controlled parameter modifications in real-time industrial monitoring to prevent instability and promote desirable network behaviors, motivated by the need to ensure controlled parameter modifications in real-time industrial monitoring to prevent instability and promote desirable network behaviors, by monitoring a neural activation state of an entire imagitron assembly of neural modules in visual format so as to avoid bottleneck (Thaler 2 [Background/Summary]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMIR SOLTANZADEH whose telephone number is (571)272-3451. The examiner can normally be reached M-F, 9am - 5pm ET.
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, Wei Mui can be reached at (571) 272-3708. 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.
/AMIR SOLTANZADEH/Examiner, Art Unit 2191