Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is in response to the amendments filed 05 June 2026. Claims 1, 3, 4, 7, and 8 are amended. Claim 9 is newly added. Claims 1-9 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 27 May 2026 is being considered by the examiner.
Response to Arguments
Applicant' s arguments, see page 6, filed 05 June 2026, with respect to the rejections of Claims 1, 3, and 4 under 35 U.S.C. 112(b) have been fully considered and are persuasive. The rejections of Claims 1, 3, and 4 under 35 U.S.C. 112(b) have been withdrawn.
APPLICANT'S ARGUMENT: Applicant asserts (page 6, paragraph 3) that "Applicant respectfully requests that the rejections under 35 U.S.C. 112 be withdrawn."
EXAMINER'S RESPONSE: Examiner agrees. The rejections of Claims 1, 3, and 4 under 35 U.S.C. 112(b) have been withdrawn.
Applicant' s arguments, see page 6, filed 05 June 2026, with respect to the rejections of Claims 1-8 under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
APPLICANT'S ARGUMENT: Applicant asserts (page 6, paragraph 5) that "the Examiners confirmed that the amendments appear to overcome the rejections under 35 U.S.C. 103. Applicant respectfully requests that the rejections under 35 U.S.C. 103 be withdrawn."
EXAMINER'S RESPONSE: Examiner notes that, based on further search and consideration of the formally filed amendments, Applicant's arguments are now moot. Amended Claim 1 is now rejected in view of Xu in view of Mansouri in view of Wang.
Applicant' s arguments, see page 6, filed 05 June 2026, with respect to the rejection of Claim 5 on the ground of non-statutory double patenting have been fully considered and are persuasive. The provisional rejection of Claim 5 for non-statutory double patenting has been withdrawn.
APPLICANT'S ARGUMENT: Applicant argues (page 6, paragraph 7) that "Because the scope of claims may change during prosecution, Applicant will consider filing of a terminal disclaimer when at least one claim is found allowable."
EXAMINER'S RESPONSE: Examiner agrees. The provisional rejection of Claim 5 for non-statutory double patenting has been withdrawn.
Applicant's arguments, see page 7, filed 05 June 2026, with respect to the rejections of Claims 1-8 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
APPLICANT'S ARGUMENT: Applicant argues (page 7, paragraph 3) that "Amended claims do not recite any judicial exception. For example, the subject matter of amended claim 1 is directed to a method for diagnosing a fault or failure of a target device from timeseries operation data acquired from the target device by performing a specific combination of operations in a particular sequence."
EXAMINER'S RESPONSE: Examiner respectfully disagrees. Amended Claim 1 recites the mental process steps as indicated in the 35 U.S.C. 101 rejection below. As recited, the steps of diagnosing, selecting, extracting feature, predicting, identifying, and adjusting appear to be performable in the mind, or with the aid of pen and paper, similarly to the performance of an observation, evaluation, judgment, or opinion. As further indicated, the additional elements recited by amended Claim 1 do not appear to integrate the mental process steps or provide significantly more. Therefore, the claim is directed to the mental process steps.
APPLICANT'S ARGUMENT: Applicant argues (page 7, paragraph 4) that "Amended claims integrate any alleged judicial exception into a practical application. The claimed subject matter, by utilizing a learned neutral network, improves the computational efficiency at least by reducing the amount of time-series operation data that needs to be processed, as described in paragraphs [0077] and [0109] of the specification."
EXAMINER'S RESPONSE: Examiner respectfully disagrees. As indicated in the 35 U.S.C. 101 rejection below, the additional elements recited by amended Claim 1, when evaluated according to the criteria provided by MPEP 2106.05, do not appear to provide the argued improvement to the functioning of a computer or another technology. Therefore, the claimed additional elements do not amount to an inventive concept.
APPLICANT'S ARGUMENT: Applicant argues (page 7, paragraph 5) that "amended claims recite a technical implementation for solving a specific technical problem. In addition, the subject matter of amended claims recites a specific combination of operations, where such combination is not taught by the cited references. Therefore, Applicant respectfully submits that the additional elements now recited in amended claims amount to significantly more than any alleged judicial exception."
EXAMINER'S RESPONSE: Examiner respectfully disagrees. As indicated in the 35 U.S.C. 101 rejection below, the additional elements recited by amended Claim 1, when evaluated according to the criteria provided by MPEP 2106.05, do not appear to provide significantly more than the recited mental process steps.
Claim Rejections - 35 USC § 112
The rejections of Claims 1, 3, and 4 under 35 U.S.C. 112(b) or are withdrawn in light of arguments and/or amendments.
Double Patenting
The provisional rejection of Claim 5 on the ground of non-statutory double patenting is withdrawn in light of arguments and/or amendments.
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 1-9 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1
Step 1
Claim 1 recites a method of learning, and thus the claimed process falls within a statutory category of invention.
Step 2A Prong 1
The claim recites diagnosing a fault or failure of a target device from time-series operation data acquired from the target device, which is a mental process. The claim recites selecting a part of the time-series operation data, which is a mental process. The claim recites extracting a feature quantity based on the selected part of the time-series operation data, which is a mental process. The claim recites performing a prediction based on the feature quantity, which is a mental process. The claim recites identifying the domain of each sample, which is a mental process. The claim recites adjusting weight parameters of the neural network based on a prediction loss of the prediction layer and a domain identification loss of the domain identification layer so that the fault or failure of the target device is diagnosed ... wherein the weight parameters of the domain identification layer are adjusted so as to increase identification accuracy of the domain identification layer, which is a mental process. The claim recites the weight parameters of the feature selection layer and the feature extraction layer are adjusted ... so as to reduce a contribution of the domain to a prediction result of the prediction layer, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional element a neural network does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element the time-series operation data include samples each associated with a domain of the sample does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional elements a feature selection layer, a feature extraction layer, a prediction layer, a domain identification layer, and a gradient inversion layer provided between the feature extraction layer and the domain identification layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional elements providing the neural network with the time-series operation data acquired from the target device amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering"). The additional elements adjusted, via the gradient inversion layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
Step 2B
The additional element a neural network does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element the time-series operation data include samples each associated with a domain of the sample does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional elements a feature selection layer, a feature extraction layer, a prediction layer, a domain identification layer, and a gradient inversion layer provided between the feature extraction layer and the domain identification layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional elements providing the neural network with the time-series operation data acquired from the target device is well-understood, routine, conventional activity (see MPEP 2106.05(d), "storing and retrieving information in memory"). The additional elements adjusted, via the gradient inversion layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 2
Step 1
Regarding Claim 2, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites identifying the domain, which is a mental process. The claim recites a weight parameter of the domain identification layer is adjusted to increase an identification accuracy in the domain identification layer, which is a mental process. The claim recites weight parameters of the feature selection layer and the feature extraction layer are adjusted to reduce the identification accuracy in the domain identification layer, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element a domain identification layer invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 3
Step 1
Regarding Claim 3, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites calculating a degree of similarity between the domains of each sample of the time-series operation data, which is a mental process. The claim recites weight parameters of the feature selection layer and the feature extraction layer are adjusted to increase the degree of similarity between the domains calculated in the interdomain distance calculation layer, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element an interdomain distance calculation layer invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 4
Step 1
Regarding Claim 4, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites identifying the domain and an interdomain distance calculation layer for calculating a degree of similarity between the domains of each sample of the time-series operation data, which is a mental process. The claim recites a weight parameter of the domain identification layer is adjusted to increase an identification accuracy in the domain identification layer, which is a mental process. The claim recites weight parameters of the feature selection layer and the feature extraction layer are adjusted to reduce the identification accuracy in the domain identification layer, which is a mental process. The claim recites weight parameters of the feature selection layer and the feature extraction layer are adjusted ... to increase the degree of similarity between the domains calculated in the interdomain distance calculation layer, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element a domain identification layer invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 5
Step 1
Regarding Claim 5, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites reconstructing the selected input data on the basis of the feature quantity, which is a mental process. The claim recites the weight parameter ... is adjusted on the basis of a reconstruction error in the partial reconstruction layer, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element a partial reconstruction layer invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element the neural network does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 6
Step 1
Regarding Claim 6, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites the weight parameter ... is adjusted to predict ... by using the data, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional element the input data include data obtained from a device amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering"). The additional element an attribute information about a failure that may occur in the device and a failure that has occurred in the device does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element the neural network does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element an unexperienced failure that has not occurred in the device does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
Step 2B
The additional element the input data include data obtained from a device is well-understood, routine, conventional activity (see MPEP 2106.05(d), "receiving or transmitting data over a network"). The additional element an attribute information about a failure that may occur in the device and a failure that has occurred in the device does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element the neural network does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element an unexperienced failure that has not occurred in the device does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 7
Step 1
Claim 7 recites a feature selection apparatus, and thus the claimed machine falls within a statutory category of invention.
Step 2A Prong 1
The claim recites diagnosing a fault or failure of a target device from time-series operation data acquired from the target device, which is a mental process. The claim recites adjust weight parameters of a neural network based on a prediction loss of a prediction layer of the neural network and a domain identification loss of a domain identification layer of the neural network so that the fault or failure of the target device is diagnosed ... wherein the weight parameters of the domain identification layer are adjusted so as to increase identification accuracy of the domain identification layer, which is a mental process. The claim recites selects a part of the time-series operation data, which is a mental process. The claim recites the weight parameters of a feature selection layer and a feature extraction layer are adjusted ... so as to reduce a contribution of the domain to a prediction result of the prediction layer, which is a mental process. The claim recites selecting a part of the time-series operation data, which is a mental process. The claim recites extracting a feature quantity based on the selected part of the time-series operation data, which is a mental process. The claim recites performing a prediction based on the feature quantity, which is a mental process. The claim recites identifying the domain of each sample, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional elements performs learning invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element the time-series operation data include samples each associated with a domain of the sample does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional elements providing the neural network with the time-series operation data acquired from the target device amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering"). The additional elements using the learned neural network invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional elements adjusted, via the gradient inversion layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional elements the feature selection layer, the feature extraction layer, the prediction layer, the domain identification layer, and the gradient inversion layer provided between the feature extraction layer and the domain identification layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
Step 2B
The additional elements performs learning invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element the time-series operation data include samples each associated with a domain of the sample does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional elements providing the neural network with the time-series operation data acquired from the target device is well-understood, routine, conventional activity (see MPEP 2106.05(d), "storing and retrieving information in memory"). The additional elements using the learned neural network invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional elements adjusted, via the gradient inversion layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional elements the feature selection layer, the feature extraction layer, the prediction layer, the domain identification layer, and the gradient inversion layer provided between the feature extraction layer and the domain identification layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 8
Step 1
Claim 8 recites a feature selection method, and thus the claimed process falls within a statutory category of invention.
Step 2A Prong 1
The claim recites diagnosing a fault or failure of a target device from time-series operation data acquired from the target device, which is a mental process. The claim recites adjust weight parameters of a neural network based on a prediction loss of a prediction layer of the neural network and a domain identification loss of a domain identification layer of the neural network so that the fault or failure of the target device is diagnosed ... wherein the weight parameters of the domain identification layer are adjusted so as to increase identification accuracy of the domain identification layer, which is a mental process. The claim recites selecting a part of the time-series operation data, which is a mental process. The claim recites the weight parameters of a feature selection layer and a feature extraction layer are adjusted ... so as to reduce a contribution of the domain to a prediction result of the prediction layer, which is a mental process. The claim recites selecting a part of the time-series operation data, which is a mental process. The claim recites extracting a feature quantity based on the selected part of the time-series operation data, which is a mental process. The claim recites performing a prediction based on the feature quantity, which is a mental process. The claim recites identifying the domain of each sample, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional elements performs learning invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element the time-series operation data include samples each associated with a domain of the sample does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional elements providing the neural network with the time-series operation data acquired from the target device amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering"). The additional elements adjusted, via the gradient inversion layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional elements using the learned neural network invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional elements the feature selection layer, the feature extraction layer, the prediction layer, the domain identification layer, and the gradient inversion layer provided between the feature extraction layer and the domain identification layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
Step 2B
The additional elements performs learning invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element the time-series operation data include samples each associated with a domain of the sample does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional elements providing the neural network with the time-series operation data acquired from the target device is well-understood, routine, conventional activity (see MPEP 2106.05(d), "storing and retrieving information in memory"). The additional elements adjusted, via the gradient inversion layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional elements using the learned neural network invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional elements the feature selection layer, the feature extraction layer, the prediction layer, the domain identification layer, and the gradient inversion layer provided between the feature extraction layer and the domain identification layer invoke a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 9
Step 1
Regarding Claim 9, the rejection of Claim 1 is incorporated.
Step 2A Prong 1
The claim recites diagnosing a fault or failure ... from ... data, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2, Step 2B
The additional element of a target device does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment"). The additional element the selected part of the time-series operation data acquired from the target device does not amount to more than generally linking the use of a judicial exception to a particular field of use (see MPEP 2106.05(h), "limit the use of the abstract idea to a particular technological environment," where neither selection nor acquiring are recited as positive steps).
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 1, 2, and 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Xu, et al., "Adversarial domain adaptation for stance detection" (hereinafter "Xu") in view of Mansouri, et al., "A Deep Explainable Model for Fault Prediction Using IoT Sensors" (hereinafter "Mansouri") in view of Wang, et al., "Temporal convolution domain adaptation learning for crops growth prediction" (hereinafter "Wang").
Regarding Claim 1, Xu teaches:
A method of learning a neural network (Xu, p. 2, 2 Method: "we also use a Convolutional Neural Network (CNN) approach [7] for learning representations of claims and documents. ...¶ Label Prediction Component: This component uses a Multi-Layer Perceptron (MLP) with a fully-connected hidden layer followed by a softmax layer which employs cross entropy loss as the cost function") ... from ... data (Xu, p. 3, 3.1 Datasets: "We use the Fake News Challenge (FNC) dataset1 as target data. This data is collected from a variety of sources such as rumour sites, e.g. snopes.com, and Twitter accounts such as @Hoaxalizer. It contains around 50K claim-document pairs as training data with an imbalanced distribution over stance labels: 73% (unrelated), 18% (discuss), 7:3%(agree), 1:7%(disagree)") ... ,
wherein the ... data include samples each associated with a domain of the sample (Xu, p. 2, 2 Method: "Our domain adaptation component uses adversarial learning [4] to encourage the feature extraction component to select common -- rather than domain-specific -- features when input data is from multiple different domains. This allows the model to better leverage source domain data for better prediction on data from the target domain"), the neural network includes:
a feature selection layer for selecting a part of the time-series operation data (Xu, p. 2, 2 Method: "Our domain adaptation component uses adversarial learning [4] to encourage the feature extraction component to select common -- rather than domain-specific -- features when input data is from multiple different domains," where Xu's feature extraction component selecting features corresponds to the instant feature selection, as depicted in p. 2, Figure 1, "The architecture of our model with domain adaptation component for stance detection");
a feature extraction layer for extracting a feature quantity based on the selected part of the time-series operation data (Xu, p. 2, Figure 1, "The architecture of our model with domain adaptation component for stance detection," depicting a feature extraction component, and p. 2, Feature Extraction Component: "This component takes the input claim c and document d and converts them to their semantic representations and features")
a prediction layer for performing a prediction based on the feature quantity (Xu, p. 2, Figure 1, "The architecture of our model with domain adaptation component for stance detection," depicting the Label Prediction Component, and p. 2, Label Prediction Component: "This component uses a Multi-Layer Perceptron (MLP) .... This component will predict stance labels as agree, disagree, discuss, or unrelated for a given set of claim and document features");
a domain identification layer for identifying the domain of each sample (Xu, p. 2, Figure 1, "The architecture of our model with domain adaptation component for stance detection," depicting the Domain Adaptation Component corresponding to the instant domain identification, and p. 2, Domain Adaptation Component: "We introduce a domain classifier which includes a MLP followed by a softmax layer. Given a set of features for a claim-document pair, the domain classifier predicts which domain the features originated from");
a gradient inversion layer provided between the feature extraction layer and the domain identification layer (Xu, p. 2, Figure 1, "The architecture of our model with domain adaptation component for stance detection," depicting a Gradient Reversal Layer between the Feature Extraction Component and the Hidden Layer of the Domain Adaptation Component), and
the method comprises adjusting weight parameters of the neural network based on a prediction loss of the prediction layer (Xu, p. 2, Label Prediction Component: "This component uses a Multi-Layer Perceptron (MLP) with a fully-connected hidden layer followed by a softmax layer which employs cross entropy loss as the cost function") and a domain identification loss of the domain identification layer (Xu, p. 2, Domain Adaptation Component: "We introduce a domain classifier which includes a MLP followed by a softmax layer. ... The domain classifier is an adversary because the model—specifically, the feature extraction component ---attempts to maximize the domain classifier loss, while the domain classifier attempts to minimize it") ...,
wherein the weight parameters of the domain identification layer are adjusted so as to increase identification accuracy of the domain identification layer (Xu, p. 2, Domain Adaptation Component: "The domain classifier is an adversary because the model --specifically, the feature extraction component -- attempts to maximize the domain classifier loss, while the domain classifier attempts to minimize it. This is because a high domain classifier loss implies that the domain classifier is unable to accurately discern whether a set of features belongs to a source or target domain"), and
wherein the weight parameters of the feature selection layer and the feature extraction layer are adjusted ... so as to reduce a contribution of the domain to a prediction result of the prediction layer (Xu, p. 2, Domain Adaptation Component: "The domain classifier is an adversary because the model --specifically, the feature extraction component -- attempts to maximize the domain classifier loss, while the domain classifier attempts to minimize it. This is because a high domain classifier loss implies that the domain classifier is unable to accurately discern whether a set of features belongs to a source or target domain. This implies that the features extracted from the input examples are common to both the source and target domains as we desire") ... via the gradient inversion layer (Xu, p. 2, Domain Adaptation Component: "To achieve this adversarial effect, the features from the feature extraction component are passed to a gradient reversal layer before being passed to the domain classifier. The gradient reversal layer is a simple identity transform during forward propagation and multiplies the gradient by a negative constant (the gradient reversal constant) during backpropagation [4]. By adding the gradient reversal layer, the desired training behavior can be achieved through normal model training").
Xu teaches a method of learning a neural network from data.
Xu does not explicitly teach diagnosing a fault or failure of a target device from time-series operation data acquired from the target device and the fault or failure of the target device is diagnosed by providing the neural network with the time-series operation data acquired from the target device.
However, Mansouri teaches
diagnosing a fault or failure of a target device from time-series operation data acquired from the target device (Mansouri, p. 66933, Abstract: "IoT sensors and deep learning models can widely be applied for fault prediction. ... This paper first examines different deep learning techniques to carry out univariate time series analysis based on vibration sensors installed on four industrial bearings to predict a fault occurring in a predefined time window")
the fault or failure of the target device is diagnosed by providing the neural network with the time-series operation data acquired from the target device (Mansouri, p. 66935, I. Introduction: "To this end the current work evaluates several recurrent deep learning models for fault prediction in disintegrated bearings using vibration sensor readings" and p. 66935, II. Framework: "The first goal of this research is to predict a machine fault within the time window ahead. This binary classification task predicts whether a sequence of vibration readings leads to a fault or a normal condition," where Mansouri's predicted future fault and disintegrated bearings corresponds to the possible failure and failure that has occurred, respectively).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Xu regarding a method of learning a neural network from data with those of Mansouri regarding diagnosing a fault or failure of a target device from time-series operation data acquired from the target device and the fault or failure of the target device is diagnosed by providing the neural network with the time-series operation data acquired from the target device.
The motivation to do so would be to facilitate training of a model that balances improvements in explainability with minimization of selected features (Mansouri, p. 66933, Abstract: "instance-wise feature selection has been considered to highlight the most contributing features for its outputs regarding any input. In this problem, the main challenge is to propose a trainable feature selection model with the minimum number of selected features whilst its performance is close to the baseline model. This paper develops a novel explainable method called the Gumbel-Sigmoid eXplanator (GSX) to tackle these problems").
The Xu/Mansouri combination teaches diagnosing a fault or failure of a target device from time-series operation data acquired from the target device.
The Xu/Mansouri combination may not explicitly teach wherein the time-series operation data include samples each associated with a domain of the sample.
However, Wang teaches:
wherein the time-series operation data include samples each associated with a domain of the sample (Wang, p. 3, B. Data Pre-processing: "For the source domain, we develop a simulation model to generate a large amount of crop data. ... The output of the PCSE [Python Crop Simulation Environment] is considered as time-series data where each element in the series is a multi-dimensional vector. The vector represents all the features concerning weather conditions and the crop itself, including Leaf Area Index (LAI) which characterizes the plant growth situation" and p. 5, IV. Solution: "we modify the DANN [Domain-Adversarial Neural Networks] framework to make it applicable to our time-series prediction problem," where Wang's time-series elements correspond to the instant samples).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the Xu/Mansouri combination regarding diagnosing a fault or failure of a target device from time-series operation data acquired from the target device with those of Wang regarding wherein the time-series operation data include samples each associated with a domain of the sample.
The motivation to do so would be to facilitate model learning in circumstances where limited time-series data would otherwise challenge effective prediction (Wang, p. 1, Abstract: "Existing Deep Neural Nets on crops growth prediction mostly rely on availability of a large amount of data. In practice, it is difficult to collect enough high-quality data to utilize the full potential of these deep learning models. In this paper, we construct an innovative network architecture based on domain adaptation learning to predict crops growth curves with limited available crop data. This network architecture overcomes the challenge of data availability by incorporating generated data from the developed crops simulation model. We are the first to use the temporal convolution filters as the backbone to construct a domain adaptation network architecture which is suitable for deep learning regression models with very limited training data of the target domain").
Regarding Claim 7, Xu teaches:
A feature selection apparatus (Xu, p. 2, 2 Method: "Our domain adaptation component uses adversarial learning [4] to encourage the feature extraction component to select common --- rather than domain-specific -- features when input data is from multiple different domains," and p. 4, Table 1, "Results on the FNC test data. ... We show the results of the models based on the smallest loss for validation set across 5 independent runs," where a computing apparatus is inherent in Xu's processing of multiple model testing runs) that performs precisely those steps recited by the method of Claim 1. Claim 7 is rejected under the same rationale as Claim 1.
Regarding Claim 8, Xu teaches:
A feature selection method (Xu, p. 2, 2 Method: "Our domain adaptation component uses adversarial learning [4] to encourage the feature extraction component to select common --- rather than domain-specific -- features when input data is from multiple different domains") comprising: precisely those steps recited by the method of Claim 1. Claim 8 is rejected under the same rationale as Claim 1.
Regarding Claim 2, the rejection of Claim 1 is incorporated. The Xu/Mansouri/Wang combination teaches:
wherein the neural network further includes a domain identification layer for identifying the domain (Xu, p. 2, Figure 1, "The architecture of our model with domain adaptation component for stance detection," depicting the Domain Adaptation Component corresponding to the instant domain identification, and p. 2, Domain Adaptation Component: "We introduce a domain classifier which includes a MLP followed by a softmax layer. Given a set of features for a claim-document pair, the domain classifier predicts which domain the features originated from"),
a weight parameter of the domain identification layer is adjusted to increase an identification accuracy in the domain identification layer (Xu, p. 2, 2 Method, Domain Adaptation Component: "The domain classifier is an adversary because the model --specifically, the feature extraction component -- attempts to maximize the domain classifier loss, while the domain classifier attempts to minimize it. This is because a high domain classifier loss implies that the domain classifier is unable to accurately discern whether a set of features belongs to a source or target domain," where Xu's domain classifier loss reasonably suggests classifier weight updates as in normal model training, as in p. 2, 2 Method, Domain Adaptation Component: "The gradient reversal layer is a simple identity transform during forward propagation and multiplies the gradient by a negative constant (the gradient reversal constant) during backpropagation [4]. By adding the gradient reversal layer, the desired training behavior can be achieved through normal model training"), and
weight parameters of the feature selection layer and the feature extraction layer are adjusted to reduce the identification accuracy in the domain identification layer (Xu, p. 2, Domain Adaptation Component: "The domain classifier is an adversary because the model --specifically, the feature extraction component -- attempts to maximize the domain classifier loss, while the domain classifier attempts to minimize it. This is because a high domain classifier loss implies that the domain classifier is unable to accurately discern whether a set of features belongs to a source or target domain. This implies that the features extracted from the input examples are common to both the source and target domains as we desire").
Regarding Claim 6, the rejection of Claim 1 is incorporated. Mansouri further teaches:
wherein the input data include data obtained from a device (Mansouri, p. 66933, Abstract: "IoT sensors and deep learning models can widely be applied for fault prediction. ... This paper first examines different deep learning techniques to carry out univariate time series analysis based on vibration sensors installed on four industrial bearings to predict a fault occurring in a predefined time window") and an attribute information about a failure that may occur in the device and a failure that has occurred in the device (Mansouri, p. 66935, I. Introduction: "To this end the current work evaluates several recurrent deep learning models for fault prediction in disintegrated bearings using vibration sensor readings" and p. 66935, II. Framework: "The first goal of this research is to predict a machine fault within the time window ahead. This binary classification task predicts whether a sequence of vibration readings leads to a fault or a normal condition," where Mansouri's predicted future fault and disintegrated bearings corresponds to the possible failure and failure that has occurred, respectively),
the weight parameter of the neural network is adjusted (Mansouri, p. 66936, II. Framework: "The selector model (2), parametrized by
θ
, accepts an input and generates
m
independent probabilities with the same size as the input features" and p. 66937, Figure 2: "The GSX workflow. During the training, data passes through GSX and the baseline model simultaneously to calculate the error," depicting the selector model as a component of GSX) to predict an unexperienced failure that has not occurred in the device, by using the data obtained from the device (Mansouri, p. 66940, IV. Conclusion: "This paper develops an explainable deep learning model for the predictive preventive maintenance task, aiming to predict a fault happening in the near future by processing the preceding vibration signals. The primary dataset consists of chronological sequences of vibration readings and their related labels denoting whether they were faulty or not").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the Xu/Mansouri/Wang combination regarding learning a neural network that includes a feature selection layer, a feature extraction layer, and a prediction layer with the further teachings of Mansouri regarding wherein the input data include data obtained from a device and an attribute information about a failure that may occur in the device and a failure that has occurred in the device, the weight parameter of the neural network is adjusted to predict an unexperienced failure that has not occurred in the device, by using the data obtained from the device.
The motivation to do so would be to facilitate training a model with improved explainability over a longer sampling time window (Mansouri, p. 66933, Abstract: "hybrid models outperform other models when the time window increases. Then, instance-wise feature selection has been considered to highlight the most contributing features for its outputs regarding any input. In this problem, the main challenge is to propose a trainable feature selection model with the minimum number of selected features whilst its performance is close to the baseline model. This paper develops a novel explainable method called the Gumbel-Sigmoid eXplanator (GSX) to tackle these problems").
Regarding Claim 9, the rejection of Claim 8 is incorporated. The Xu/Mansouri/Wang combination has been shown to teach:
diagnosing a fault or failure of a target device from the selected part of the time-series operation data acquired from the target device (as recited in the rejection of Claim 1, Mansouri, p. 66933, Abstract: "IoT sensors and deep learning models can widely be applied for fault prediction. ... This paper first examines different deep learning techniques to carry out univariate time series analysis based on vibration sensors installed on four industrial bearings to predict a fault occurring in a predefined time window").
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Xu, et al., "Adversarial domain adaptation for stance detection" (hereinafter "Xu") in view of Mansouri, et al., "A Deep Explainable Model for Fault Prediction Using IoT Sensors" (hereinafter "Mansouri") in view of Wang, et al., "Temporal convolution domain adaptation learning for crops growth prediction" (hereinafter "Wang") in light of Sun, et al. (US 2023/0307908 A1, hereinafter "Sun").
Regarding Claim 3, the rejection of Claim 1 is incorporated.
The Xu/Mansouri/Wang combination teaches a method of learning a neural network that includes a feature selection layer, a feature extraction layer, a prediction layer, a domain identification layer, and a gradient inversion layer.
The Xu/Mansouri/Wang combination may not explicitly teach wherein the neural network further includes an interdomain distance calculation layer for calculating a degree of similarity between the domains of each sample of the time-series operation data.
However, Sun teaches:
wherein the neural network further includes an interdomain distance calculation layer for calculating a degree of similarity between the domains of each sample of the time-series operation data (Sun, [0071]: "the generator 800 is updated using a weighted function corresponding to the MSE loss value on source domain data and the MSE loss value on the feature space. The feature space MSE value aims at minimizing the gap between the source domain data 810 and the target domain data 820 feature space. Lastly, the discriminator 830 is updated using the binary cross-entropy loss, which computes the probability of the input feature space being obtained from the source or target domain data," where Sun's binary cross-entropy loss corresponds to the instant degree of similarity, which is used for training, as in Fig. 5A, Algorithm 1), and
weight parameters of the feature selection layer and the feature extraction layer are adjusted (Sun, Fig. 5A, Algorithm 1 Conv-EDNet+ training algorithm, lines 8 and 11, where
Θ
a
e
represents the encoder weight matrix, and [0060] "All the Conv-EDNet model blocks are jointly optimized. The model is trained for a pre-determined number of epochs (E), and the weights of all the different components are updated simultaneously to minimize the loss function (5)") to increase the degree of similarity between the domains calculated in the interdomain distance calculation layer (Sun, [0055]: "The
D
is trained to maximize the probability of correctly classifying the real samples (i.e., the measurements) and the generated samples (produced by
D
). In contrast,
G
is trained to produce output samples that are hard correctly distinguish by
D
," where Sun's adversarially hard to distinguish corresponds to the instant degree of similarity).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the Xu/Mansouri/Wang combination regarding a method of learning a neural network that includes a feature selection layer, a feature extraction layer, a prediction layer, a domain identification layer, and a gradient inversion layer with the teachings of Sun regarding the neural network further includes an interdomain distance calculation layer for calculating a degree of similarity between the domains, and weight parameters of the feature selection layer and the feature extraction layer are adjusted to increase the degree of similarity between the domains calculated in the interdomain distance calculation layer.
The motivation to do so would be to facilitate training a model when faced with limited labeled data from the model's target domain (Sun, [0011]: "We combine adversarial learning and joint adaptation concepts and modify the Conv-EDNet+ to perform the task of distribution gap minimization of the feature space and label space between the source domain and target domain. This approach enables training the model on synthetically obtained data and its application to real-world measurement data. By using limited labeled data in the source domain and unlabeled target domain data, the proposed network can generate satisfactory results for energy disaggregation using CPOW measurements").
Regarding Claim 4, the rejection of Claim 1 is incorporated. The Xu/Mansouri/Wang combination teaches:
the neural network further includes a domain identification layer for identifying the domain (Xu, p. 2, Figure 1, "The architecture of our model with domain adaptation component for stance detection," depicting the Domain Adaptation Component corresponding to the instant domain identification, and p. 2, Domain Adaptation Component: "We introduce a domain classifier which includes a MLP followed by a softmax layer. Given a set of features for a claim-document pair, the domain classifier predicts which domain the features originated from") ...,
a weight parameter of the domain identification layer is adjusted to increase an identification accuracy in the domain identification layer (Xu, p. 2, Domain Adaptation Component: "The domain classifier is an adversary because the model --specifically, the feature extraction component -- attempts to maximize the domain classifier loss, while the domain classifier attempts to minimize it. This is because a high domain classifier loss implies that the domain classifier is unable to accurately discern whether a set of features belongs to a source or target domain").
The Xu/Mansouri/Wang combination teaches the neural network further includes a domain identification layer for identifying the domain and a weight parameter of the domain identification layer is adjusted to increase an identification accuracy in the domain identification layer.
The Xu/Mansouri/Wang combination may not explicitly teach an interdomain distance calculation layer for calculating a degree of similarity between the domains of each sample of the time-series operation data and weight parameters of the feature selection layer and the feature extraction layer are adjusted to reduce the identification accuracy in the domain identification layer, and to increase the degree of similarity between the domains calculated in the interdomain distance calculation layer.
However, Sun teaches:
an interdomain distance calculation layer (Sun, [0071]: "To incorporate domain adaptation, we repurpose the discriminator network to perform the task of domain adaptation. The underlying idea is that the discriminator adversarially trains the model using the feature space instead of the disaggregated time-series used in Conv-EDNet+. The goal is to minimize the deviation between the source domain and the target domain feature space," where Sun's discriminator corresponds to the instant domain identification layer and to the distance layer) for calculating a degree of similarity between the domains of each sample of the time-series operation data (Sun, [0071]: "the generator 800 is updated using a weighted function corresponding to the MSE loss value on source domain data and the MSE loss value on the feature space. The feature space MSE value aims at minimizing the gap between the source domain data 810 and the target domain data 820 feature space. Lastly, the discriminator 830 is updated using the binary cross-entropy loss, which computes the probability of the input feature space being obtained from the source or target domain data," where Sun's binary cross-entropy loss corresponds to the instant degree of similarity, which is used for training, as in Fig. 5A, Algorithm 1),
weight parameters of the feature selection layer and the feature extraction layer are adjusted (Sun, Fig. 5A, Algorithm 1 Conv-EDNet+ training algorithm, and [0060] "All the Conv-EDNet model blocks are jointly optimized. The model is trained for a pre-determined number of epochs (E), and the weights of all the different components are updated simultaneously to minimize the loss function (5)") to reduce the identification accuracy in the domain identification layer, and to increase the degree of similarity between the domains calculated in the interdomain distance calculation layer (Sun, [0055]: "The
D
is trained to maximize the probability of correctly classifying the real samples (i.e., the measurements) and the generated samples (produced by
D
). In contrast,
G
is trained to produce output samples that are hard correctly distinguish by
D
," where Sun's adversarially hard to distinguish corresponds to the instant degree of similarity).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the Xu/Mansouri/Wang combination regarding the neural network further includes a domain identification layer for identifying the domain and a weight parameter of the domain identification layer is adjusted to increase an identification accuracy in the domain identification layer with those of Sun regarding an interdomain distance calculation layer for calculating a degree of similarity between the domains of each sample of the time-series operation data and weight parameters of the feature selection layer and the feature extraction layer are adjusted to reduce the identification accuracy in the domain identification layer, and to increase the degree of similarity between the domains calculated in the interdomain distance calculation layer.
The motivation to do so would be to facilitate training a model when faced with limited labeled data from the model's target domain (Sun, [0011]: "We combine adversarial learning and joint adaptation concepts and modify the Conv-EDNet+ to perform the task of distribution gap minimization of the feature space and label space between the source domain and target domain. This approach enables training the model on synthetically obtained data and its application to real-world measurement data. By using limited labeled data in the source domain and unlabeled target domain data, the proposed network can generate satisfactory results for energy disaggregation using CPOW measurements").
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Xu, et al., "Adversarial domain adaptation for stance detection" (hereinafter "Xu") in view of Mansouri, et al., "A Deep Explainable Model for Fault Prediction Using IoT Sensors" (hereinafter "Mansouri") in view of Wang, et al., "Temporal convolution domain adaptation learning for crops growth prediction" (hereinafter "Wang") in light of Abid, et al., "Concrete Autoencoders: Differentiable Feature Selection and Reconstruction" (hereinafter "Abid").
Regarding Claim 5, the rejection of Claim 1 is incorporated.
The Xu/Mansouri/Wang combination teaches the neural network includes a feature selection layer, a feature extraction layer, a prediction layer, a domain identification layer, and a gradient inversion layer.
The Xu/Mansouri/Wang combination may not explicitly teach wherein the neural network further includes a partial reconstruction layer for reconstructing the selected input data on the basis of the feature quantity, and the weight parameter of the neural network is adjusted on the basis of a reconstruction error in the partial reconstruction layer.
However, Abid teaches:
wherein the neural network further includes a partial reconstruction layer for reconstructing the selected input data on the basis of the feature quantity (Abid, p. 4, Figure 2: "Concrete autoencoder architecture and pseudocode. ... The architecture of the decoder remains the same during train and test time, namely that
x
^
=
f
θ
u
, where
u
is the vector consisting of each
u
i
," where Abid's decoder output layer corresponds to the instant partial reconstruction layer, as in p. 3, 3. Proposed Method: "The decoder of a concrete autoencoder, which serves as the reconstruction function .... In effect, then, the concrete autoencoder is a method for selecting a discrete set of features that are optimized for an arbitrarily-complex reconstruction function"), and
the weight parameter of the neural network is adjusted on the basis of a reconstruction error in the partial reconstruction layer (Abid, p. 4, Figure 2: "Concrete autoencoder architecture and pseudocode. (a) The architecture of a concrete autoencoder consists of ... arbitrary decoding layers (e.g. a deep feedforward neural network).... The architecture of the decoder remains the same during train and test time, namely that
x
^
=
f
θ
u
, where
u
is the vector consisting of each
u
i
," where Abid's decoder parameters
θ
reasonably suggest the instant weight parameter), as updated during training of p. 4, Algorithm I. Training a Concrete Autoencoder).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the Xu/Mansouri/Wang combination regarding the neural network includes a feature selection layer, a feature extraction layer, a prediction layer, a domain identification layer, and a gradient inversion layer with those of Abid regarding wherein the neural network further includes a partial reconstruction layer for reconstructing the selected input data on the basis of the feature quantity, and the weight parameter of the neural network is adjusted on the basis of a reconstruction error in the partial reconstruction layer.
The motivation to do so would be to facilitate a training scenario providing improved feature selection with reduced expense in collecting unneeded features (Abid, p. 1, Abstract: "We evaluate concrete autoencoders on a variety of datasets, where they significantly outperform state-of-the art methods for feature selection and data reconstruction. In particular, on a large-scale gene expression dataset, the concrete autoencoder selects a small subset of genes whose expression levels can be use to impute the expression levels of the remaining genes. In doing so, it improves on the current widely-used expert-curated L1000 landmark genes, potentially reducing measurement costs by 20%").
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.N.D./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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