DETAILED ACTION
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 .
Response to Amendment/Status of Claims
Claims 1, 7, and 13 were amended.
Claims 4, 6, 10, 12, 16 and 18 were cancelled.
Claims 1-3, 5, 7-9, 11, 13-15, and 17 are pending and examined herein.
Claims 1, 7, and 13 are objected to.
Claims 1-3, 5, 7-9, 11, 13-15, and 17 are rejected under 35 U.S.C. 112(b).
Claims 1-3, 5, 7-9, 11, 13-15, and 17are rejected under 35 U.S.C. 101.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/20/2026 has been entered.
Response to Arguments
Applicant’s arguments, see pages 13-14, filed 04/20/2026, with respect to the 35 U.S.C. 112(b) rejection of claims 1-3, 5, 7-9, 11, 13-15, and 17 for the relative term “high-dimensional” have been fully considered and are persuasive.
Applicant’s arguments, see pages 14-15, filed 04/20/2026, with respect to the 35 U.S.C. 112(b) rejection of claims 1-3, 5, 7-9, 11, 13-15, and 17 for the term “cpGAN” have been fully considered and are persuasive. A cpGAN will be interpreted, see specification paragraph [0030], as "an architecture comprising of embedding, recovery, critic, supervisor, generator and discriminator."
Applicant’s arguments, see page 16, filed 04/20/2026, with respect to the 35 U.S.C. 112(b) rejection of claims 1-3, 5, 7-9, 11, 13-15, and 17 for the term “conditional temporal dynamics” have been fully considered and are persuasive.
Applicant’s arguments, see page 17, 04/20/2026, with respect to the 35 U.S.C. 112(b) rejection of claims 1-3, 5, 7-9, 11, 13-15, and 17 for lack of antecedent basis for the term “the imputed temporal data” have been fully considered and are persuasive.
Applicant's arguments filed 04/20/2026 regarding the 35 U.S.C. 112(b) rejection of claims 1-3, 5, 7-9, 11, 13-15, and 17 for the limitation "minimizing, via the one or more hardware processors, a difference between the one or more imputed high-dimensional target feature embeddings of the input training dataset and the one or more predicted imputed high-dimensional target feature embeddings" have been fully considered but they are not persuasive. The plain meaning of the term “minimizing a difference” entails making the two terms equal, which contradicts the specification, which, as Applicant explains, "consistently describes training neural-network modules (including the critic and supervisor) by optimizing defined loss functions, such as squared error, prediction loss, or divergence-based measures, between predicted outputs and reference values." Examiner has provided a suggested amendment in the 35 U.S.C. 112(b) rejection below.
Applicant's arguments filed 04/20/2026 regarding the 35 U.S.C. 112(b) rejection of claims 1-3, 5, 7-9, 11, 13-15, and 17 for the limitation “minimizing a first-order moment (
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to the fully-observed data, nor does it clearly link
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to the imputed data. Additionally, the plain meaning of the claim is unclear, regardless of what the specification or common practice suggests. Examiner has provided a suggested amendment in the 35 U.S.C. 112(b) rejection below.
Applicant’s arguments, see page 20, filed 04/20/2026, with respect to the 35 U.S.C. 112(b) rejection of claims 1-3, 5, 7-9, 11, 13-15, and 17 for the limitation “mean for the fully-observed data (
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is learned by minimizing (
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)” have been fully considered and are persuasive. The definition of each variable is provided and consistent in the specification.
Applicant’s arguments, see pages 20-21, filed 04/20/2026, with respect to the 35 U.S.C. 112(b) rejection of claims 1-3, 5, 7-9, 11, 13-15, and 17 for lack of antecedent basis for the term "the input temporal data" have been fully considered and are persuasive.
Applicant’s arguments, see pages 20-21, filed 04/20/2026, with respect to the 35 U.S.C. 112(b) rejection of claims 1-3, 5, 7-9, 11, 13-15, and 17 for the limitation "imputing, with the cpGAN, a low-dimensional multivariate industrial time-series data in a digital twin, a simulation of an industry machine or an industrial plant, or a sensor, a production unit, or a manufacturing unit" have been fully considered and are persuasive.
Applicant's arguments filed 04/20/2026 regarding the 35 U.S.C. 101 rejection of claims 1-18 have been fully considered but they are not persuasive.
Applicant argues "In response, Applicant submits that independent amended claims 1, 7, and 13 are patent eligible as they integrate a judicial exception in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(l) and 2106.05(a)) i.e., imputing low-dimensional incomplete multivariate industrial time-series data in a digital twin, thereby minimizes rubric based on an information theory for machine learning (ML) between empirical probability distributions of a reconcile data and a non-linear original data to preserve temporal dependencies and retain input feature-attributes and target-variable relationship and probability distributions of the original data."
MPEP 2106.05 states "It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements."
See 35 U.S.C. 112(b) rejection for interpretation and 35 U.S.C. 101 rejection for explanation as to why this limitation is an abstract idea. As this limitation is an abstract idea, it cannot show an improvement to technology.
Applicant further argues "Applicant submit that independent amended claims 1, 7, and 13 are patent-eligible as they integrate a judicial exception in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(l) and 2106.0S(a)) i.e., classifying the one or more imputed high-dimensional feature embeddings into at least one class type, represented as real or fake"
See 35 U.S.C. 101 rejection for explanation as to why this limitation is an abstract idea. As this limitation is an abstract idea, it cannot show an improvement to technology.
Applicant further argues "Applicant submits that independent amended claims 1, 7, and 13 are patent-eligible as they integrate a judicial exception in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(l) and 2106.0S(a)), i.e., imputing low-dimensional incomplete multivariate industrial time-series data using congeniality-preserving Generative Adversarial Networks (cpGAN)."
See 35 U.S.C. 101 rejection for explanation as to why this limitation is an abstract idea. As this limitation is an abstract idea, it cannot show an improvement to technology.
Applicant further argues "Applicant submit that independent amended claims 1, 7, and 13 are patent-eligible as they integrate a judicial exception in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(l) and 2106.0S(a)), i.e., generative imputation by the cpGAN fuses imperceptible noise, customized to an input process database, leveraged as a data anonymization technique, a privacy-preserving mechanism to prevent de-identification of industrial process-plants database by a third-party adversary, and as defenses to adversarial attacks."
MPEP 2106.05(a) states "An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107."
This limitation recites potential outcomes of the usage of the cpGAN rather than covering a particular solution to a problem or a particular way to achieve a desired outcome. Therefore, this limitation does not show an improvement to technology.
Applicant further argues "Applicant submits that amended claims 1, 7, and 13 apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP § 2106.0S(e)) i.e., cpGAN effectively captures temporal dynamics of data by minimizing supervised loss. Learnable parameters are transformed by joint training of the modules in the supervised-learning approach of reconstructing the input fully observed temporal data,
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through by minimizing the supervised loss."
Minimizing the supervised loss is an abstract idea. See 35 U.S.C. 101 rejection below. As this is an abstract idea, it cannot show an improvement to technology.
"Applicant submits that amended claims 1, 7, and 13 apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP § 2106.0S(e)) i.e., critic module preserves relationship between independent feature columns and the target variable in a real dataset during an adversarial training of
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to generate relationship preserving synthetic data,
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by minimizing the
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i.e., loss function for the target variable prediction."
Minimizing a loss is an abstract idea. See 35 U.S.C. 101 rejection below. As this is an abstract idea, it cannot show an improvement to technology. Note that “critic module preserves relationship between independent feature columns and the target variable in a real dataset during an adversarial training of
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to generate relationship preserving synthetic data,
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” is simply a result of the abstract idea “minimizing the
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i.e., loss function for the target variable prediction."
Applicant further argues "Applicant submits that the claimed subject matter is patent-eligible as it integrates a judicial exception in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(l) and 2106.0S(a)) i.e.,
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tries to minimize,
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which helps to learn {circumflex over (P)}({circumflex over (D)}n,1:T n) that best approximates
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."
Minimizing a loss is an abstract idea. See 35 U.S.C. 101 rejection below. As this is an abstract idea, it cannot show an improvement to technology.
Applicant further argues "Applicant submits that the claimed subject matter is patent-eligible as it integrates a judicial exception in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(l) and 2106.0S(a)) i.e., unbiased imputed data,
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is then beneficial for utilization in the downstream predictive analytics task and forecasting tasks."
Imputing data is an abstract idea. As this is an abstract idea, it cannot show an improvement to technology. The application of this to other tasks is extra-solution activity (See MPEP § 2106.05)g) ‘Insignificant application’, ex. i. – ii.), and does not meaningfully limit the claim. Additionally, it is unclear whether this purported improvement is present in the claims.
Applicant further argues "Applicant submits that the claimed subject matter is patent-eligible as it integrates a judicial exception in terms of improvement in functionality of the computer (MPEP §§ 2106.04(d)(l) and 2106.0S(a)) i.e., leveraging artificial intelligence systems to demonstrate the random missing data imputation-utility efficacy tradeoff for downstream tasks on the open-source industrial benchmark datasets."
It is unclear how “demonstrat[ing] the random missing data imputation-utility efficacy tradeoff for downstream tasks on the open-source industrial benchmark datasets” is an improvement to computers.
For similar reasons to those listed above, the contested limitations do not amount to significantly more than the judicial exception.
Applicant further provides quotations from the specification for asserted improvements. However, it is unclear as to which limitations in the claim reflect the purported improvement. For example, Applicant quotes "The embedding and recovery modules are trained jointly in a supervised learning approach to reconstruct the input training dataset. The supervisor module is trained in a supervised learning approach as a forecasting model to minimize the forecasting error predictions on the training dataset." However, the joint training of the embedding and recovery modules is not claimed.
Applicant’s arguments, see pages 31-34, filed 04/20/2026, with respect to the 35 U.S.C. 103 rejection of claims 1-3, 5, 7-9, 11, 13-15, and 17 have been fully considered and are persuasive. The 35 U.S.C. 103 rejection of claims 1-3, 5, 7-9, 11, 13-15, and 17 has been withdrawn.
Claim Objections
Claims 1, 7, and 13 objected to because of the following informalities:
In the limitation beginning with “predict[ing]”, “target variable” should be “a target variable”.
In the limitation beginning with “wherein last feature variable”, “target variable for prediction” should be “the target variable for prediction”.
In the limitation beginning with “wherein variable subset selection”, “features attributes from set…” should be “feature attributes from set…”.
In the limitation beginning with “wherein variable subset selection”, “input variables” should be “input variables for the critic neural network”.
In the limitation beginning with “wherein last feature variable”, “last feature variable” should be “a last feature variable.”
In the limitation beginning with "generat[ing] … one or more single-step ahead imputed high-dimensional feature embeddings", "through by minimizing the supervised loss" should be "by minimizing the supervised loss".
In the limitation beginning with "generat[ing] … one or more single-step ahead imputed high-dimensional feature embeddings", "the supervised-learning approach of reconstructing the input fully observed temporal data" should be "a supervised-learning approach of reconstructing the input fully observed temporal data”.
In the limitation beginning with "generat[ing] … one or more single-step ahead imputed high-dimensional feature embeddings", "fuses imperceptible noise, customized to an input process database, leveraged as a data anonymization technique, a privacy-preserving mechanism to prevent de-identification of industrial process-plants database by a third-party adversary, and as defenses to adversarial attacks" should be "fuses imperceptible noise, which is customized to an input process database, can also be leveraged as a data anonymization technique, a privacy-preserving mechanism to prevent de-identification of industrial process-plants database by a third-party adversary, and as a defense to adversarial attacks."
In the limitation beginning with “imput[ing], with the cpGAN, the low-dimensional incomplete multivariate industrial time-series data”, the limitation "wherein the digital twin representing a simulation of an industry machine or an industrial plant, or a sensor, a production unit, or a manufacturing unit" should be “wherein the digital twin represents a simulation of an industry machine, an industrial plant, a sensor, a production unit, or a manufacturing unit”.
Appropriate correction is required.
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 1-3, 5, 7-9, 11, 13-15, and 17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 7, and 13 recite the limitation "imputing, with the cpGAN, the low-dimensional incomplete multivariate industrial time-series data in a digital twin, thereby minimizes rubric based on an information theory for machine learning (ML) between empirical probability distributions of a reconcile data and a nonlinear original data." The specification does not support that the imputation of incomplete data in a digital twin thereby minimizes the rubric. Therefore, this is new matter. The limitations "imputing, with the cpGAN, the low-dimensional incomplete multivariate industrial time-series data in a digital twin” and "minimizes rubric based on an information theory for machine learning (ML) between empirical probability distributions of a reconcile data and a nonlinear original data" are, however separately supported. Therefore, for purposes of examination, the "minimizes rubric based on an information theory for machine learning (ML) between empirical probability distributions of a reconcile data and a nonlinear original data" will be interpreted as independent of "imputing, with the cpGAN, the low-dimensional incomplete multivariate industrial time-series data in a digital twin”.
Dependent claims 2-3, 5, 8-9, 11, 14-15, and 17 fail to resolve the issue and are rejected with the same rationale.
Claims 1-3, 5, 7-9, 11, 13-15, and 17 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.
Claims 1 and 13 recite the limitation "predicting … one or more imputed high-dimensional target feature embeddings of the input training dataset using the one or more imputed high-dimensional feature embeddings, wherein the critic module corresponds to a critic neural network comprises a
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or
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.” There is insufficient antecedent basis for “the critic module” in the claim. [0050] of the specification states that the critic module is invoked for the prediction. However, it is unclear whether the critic module implements the prediction in the claim. For purposes of examination, the limitation will be interpreted as if the critic module implements the prediction.
Claims 1, 7, and 13 recites the limitation "the target variable prediction” in the paragraph beginning with “a loss function for the target variable prediction.” There is insufficient antecedent basis for this limitation in the claim. As there is insufficient antecedent basis for this limitation, it is unclear if predicting "one or more imputed high-dimensional target feature embeddings" corresponds to the target variable prediction. For purposes of examination, the limitations will be treated as if they do not correspond.
Claims 1, 7, and 13 recite the limitation "the generative imputation by the cpGAN" in the paragraph beginning with "generat[ing] … one or more single-step ahead imputed high-dimensional feature embeddings." There is insufficient antecedent basis for this limitation. Therefore, it is unclear whether the limitation refers to the generation of one or more single-step ahead imputed high-dimensional feature embeddings, the generation of an imputed synthetic noise, or the generation of the one or more imputed high-dimensional feature embeddings.
Claims 1, 7, and 13 recite the limitation "wherein learnable parameters are transformed by joint training of the modules" in the paragraph beginning with "generat[ing] … one or more single-step ahead imputed high-dimensional feature embeddings." There is insufficient antecedent basis for “the modules”. Therefore, it is unclear as to which modules “the modules” refers to. For purposes of examination, “the modules” will be interpreted as any modules.
The term “imperceptible” in claims 1, 7, and 13 is a relative term which renders the claim indefinite. The term “imperceptible” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Therefore, the amount of noise required to be “imperceptible” is rendered indefinite.
Claims 1, 7, and 13 recite the limitation "minimiz[ing] … a difference between the one or more imputed high-dimensional target feature embeddings of the input training dataset and the one or more predicted imputed high-dimensional target feature embeddings;" It is unclear what minimizing a difference entails. One interpretation is making the one or more target feature embeddings equal to the one or more predicted imputed high-dimensional target feature embeddings. Another interpretation is minimizing a loss function. Therefore, this limitation is indefinite. For purposes of examination, this will be interpreted as minimizing a loss function involving high-dimensional target feature embeddings.
Examiner notes that this limitation seems to refer to the minimization of the loss function
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, which is already claimed in the limitation “wherein the critic module preserves relationship between independent feature columns and the target variable in a real dataset during an adversarial training of
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to generate relationship preserving synthetic data,
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by minimizing the
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.” Therefore, Examiner recommends that the limitation "minimiz[ing] … a difference between the one or more imputed high-dimensional target feature embeddings of the input training dataset and the one or more predicted imputed high-dimensional target feature embeddings" be removed. If this limitation does not refer to the minimization of the loss function
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, Examiner recommends that the limitation in claim 1 be amended to recite “minimizing, via the one or more hardware processors, [the respective loss function]”.
Claims 1, 7, and 13 recite the limitation “minimizing a first-order moment (
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”. It is unclear what is minimized: the first-order moment(s) and the second-order moment(s), the difference between the first-order of the fully-observed data and second-order moment for the fully-observed data and the difference between the first-order moment of the imputed data and second-order moment for the imputed data, the difference between the first-order moment of the fully-observed data and the first-order moment of the imputed data and the difference between the second-order moment of the fully-observed data and the second-order moment of the imputed data and/or any other combination. If a difference is minimized, it is again unclear what minimizing a difference entails. One interpretation is making the moments equal to each other, thus minimizing the difference. Another interpretation is minimizing a loss function involving the first-order moment and the second-order moment. Therefore, this limitation is indefinite. For purposes of examination, this limitation will be interpreted as minimizing a loss function involving a first-order moment (mean) and a second-order moment (variance).
Examiner recommends that this limitation in claim 1 be amended to recite “minimizing, via the one or more hardware processors, a loss function
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, wherein
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=
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+
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Claims 1, 7, and 13 recite the limitation "wherein underlying probability distributions of [[the]] input temporal data,
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is learned by minimizing (
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refers to. It is noted that
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corresponds to a specific loss function, however,
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appears to be undefined in the specification, and it is further unclear what the combination (
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refers to. It is further noted that this limitation appears to have support from specification paragraph [0059], however, [0059] seems to state that
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learns from the minimization of
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is minimized. For purposes of examination, minimizing (
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will be interpreted as minimizing
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.
Claims 1, 7, and 13 recites the limitation "the original data” in the paragraph beginning with "imputing, with the cpGAN, the low-dimensional incomplete multivariate industrial time-series data in a digital twin.” There is insufficient antecedent basis for this limitation in the claim. As such, it is unclear whether “the original data” refers to “the low-dimensional incomplete multivariate industrial time-series data” or “the input training dataset”.
Claims 1, 7, and 13 recite the limitation "imputing, with the cpGAN, the low-dimensional incomplete multivariate industrial time-series data in a digital twin, thereby minimizes rubric based on an information theory for machine learning (ML) between empirical probability distributions of a reconcile data and a nonlinear original data." The plain meaning of "minimizes rubric based on an information theory for machine learning (ML) between empirical probability distributions of a reconcile data and a nonlinear original data" is unclear. According to the Merriam Webster dictionary, a rubric is "1 a : an authoritative rule especially : a rule for conduct of a liturgical service b (1) : name, title specifically : the title of a statute (2) : something under which a thing is classed : category … the sensations falling under the general rubric, "pressure." —F. A. Geldard c : an explanatory or introductory commentary : gloss specifically : an editorial interpolation 2 : a heading of a part of a book or manuscript done or underlined in a color (such as red) different from the rest 3 : an established rule, tradition, or custom 4 : a guide listing specific criteria for grading or scoring academic papers, projects, or tests." It is unclear how any of the potential definitions of “rubric” between empirical probability distributions of a reconcile data and a nonlinear original data would be minimized. Therefore, the metes and bounds of this limitation is unclear to one of ordinary skill in the art. For purposes of examination, this limitation will be interpreted as “minimizing a loss between empirical probability distributions of a reconcile data and a nonlinear original data.”
Dependent claims 2-3, 5, 8-9, 11, 14-15, and 17 fail to resolve the issues and are rejected with the same rationales.
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 1-3, 5, 7-9, 11, 13-15, and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject
matter. The analysis of claims 1-3, 5, 7-9, 11, 13-15, and 17, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine,
manufacture, or composition of matter). Claims 1-3 and 5 are directed to a process, claims 7-9 and 11 are directed to a machine, and claims 13-15 and 17 are directed to a manufacture. All claims are directed to statutory categories and analysis proceeds.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Regarding claim 1, the following claim elements are abstract ideas:
transforming, … the cluster independent random noise by using the one or more associated cluster labels corresponding to the input training dataset to obtain a cluster dependent random noise; (One could practically transform data in the human mind given pen and paper. This is a mental process)
generating, … an imputed synthetic noise based on (i) a mask variable, (ii) one or more feature embeddings of the input training dataset, (iii) a flipped mask variable, and (iv) the obtained cluster dependent random noise; (One could practically generate imputed synthetic noise in the human mind given pen and paper, i.e. applying a mapping. This is a mental process.)
generating, … one or more imputed high-dimensional feature embeddings using the generated imputed synthetic noise; (One could practically generate imputed high-dimensional feature embeddings in the human mind given pen and paper, i.e. applying a mapping. This is a mental process.)
classifying the one or more imputed high-dimensional feature embeddings into at least one class type, represented as real or fake (Classifying embeddings into real or fake can be practically performed in the human mind. This is a mental process.)
predicting, … one or more imputed high-dimensional target feature embeddings of the training dataset using the one or more imputed high-dimensional feature embeddings, wherein the critic module corresponds to a critic neural network comprises a
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, for determining target variable, where * refers to
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or
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; (One could practically predict target feature embeddings in the human mind given pen and paper, i.e. applying a mapping. This is a mental process.
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is a mathematical equation, which is a mathematical concept.)
a loss function for the target variable prediction is represented as:
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, and (This is a mathematical equation, which is a mathematical concept.)
wherein the critic module preserves relationship between independent feature columns and the target variable in a real dataset during an adversarial training of
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to generate relationship preserving synthetic data,
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by minimizing the
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; (Minimizing a loss function is a mathematical calculation, which is a mathematical concept.)
generating, … one or more single-step ahead imputed high-dimensional feature embeddings using the one or more predicted imputed target high-dimensional feature embeddings, (One could practically generate single-step ahead imputed high-dimensional feature embeddings in the human mind given pen and paper, i.e. applying a mapping. This is a mental process.) wherein the cpGAN effectively captures temporal dynamics of data by minimizing supervised loss, wherein learnable parameters are transformed by joint training of the modules in the supervised-learning approach of reconstructing the input fully observed temporal data,
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through by minimizing the supervised loss, (Minimizing a loss function is a mathematical calculation, which is a mathematical concept.) and wherein the generative imputation by the cpGAN fuses imperceptible noise, customized to an input process database (Fusing noise, for example adding a small number to a data point, can be practically performed in the human mind. This is a mental process.)
generating, … an imputed training data using the one or more single-step ahead imputed high- dimensional feature embeddings. (One could practically generate imputer training data in the human mind given pen and paper, i.e. applying a mapping. This is a mental process.)
minimizing, via the one or more hardware processors, a difference between the one or more imputed high-dimensional target feature embeddings of the input training dataset and the one or more predicted imputed high-dimensional target feature embeddings; (See 112(b) rejection for interpretation. Minimizing a loss function is a mathematical calculation, which are mathematical concepts.)
minimizing a first-order moment (
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, and mean for the fully-observed data (
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is computed by, (See 112(b) rejection for interpretation. Minimizing a loss function is a mathematical calculation, which are mathematical concepts.)
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, (See 112(b) rejection for interpretation. These are mathematical equations, which are mathematical concepts.)
wherein underlying probability distributions of the input temporal data,
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is learned by minimizing (
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) (See 112(b) rejection for interpretation. Minimizing a loss function is a mathematical calculation, which are mathematical concepts.)
imputing, …, the low-dimensional incomplete multivariate industrial time-series data in a digital twin, thereby minimizes rubric based on an information theory for machine learning (ML) between empirical probability distributions of a reconcile data and a non-linear original data to preserve temporal dependencies and retain input feature-attributes and target-variable relationship and probability distributions of the original data (One could practically impute data in the human mind. This is a mental process. See 35 U.S.C. 112(b) rejection for interpretation. Minimizing a loss is a mathematical calculation, which is a mathematical concept.)
The following claim elements are additional elements which, taken alone or in combination with
the other additional elements, do not integrate the judicial exception into a practical application nor
amount to significantly more than the judicial exception:
A processor implemented method, comprising: (This recites a generic computer part with a generic computer function. This is mere instructions to apply an exception. See MPEP § 2106.05(f).)
obtaining, via one or more hardware processors, an input training dataset, a cluster independent random noise and one or more associated cluster labels corresponding to the input training dataset; (Obtaining data is the known process of receiving data; this is mere instructions to apply an exception.)
via one or more hardware processors, (This recites a generic computer part used to implement abstract ideas; this is mere instructions to apply an exception.)
wherein the critic neural network function takes as input realizations of
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or
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and outputs
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or
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, (This is the insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP § 2106.05(g), ‘Selecting a particular data source or type of data to be manipulated’, ex. i. – iv.)
wherein variable subset selection comprises features attributes from set,
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as input variables, (This is the insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP § 2106.05(g), ‘Selecting a particular data source or type of data to be manipulated’, ex. i. – iv.)
wherein last feature variable in
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(
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denoted by a superscript
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denotes target carriable for prediction, and (This is the insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP § 2106.05(g), ‘Selecting a particular data source or type of data to be manipulated’, ex. i. – iv.)
leveraged as a data anonymization technique, a privacy-preserving mechanism to prevent de-identification of industrial process-plants database by a third-party adversary, and as defenses to adversarial attacks (This generally links the judicial exceptions to the field of cybersecurity. This is a field of use limitation.)
upon invoking a supervisor module comprised in a congeniality preserving Generative Adverial Networks (cpGAN), and the cpGAN leverages a supervisor neural network function (
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to retain conditional temporal dynamics of an fully-observed data (
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) in the imputed temporal data; (See 112(b) rejection for interpretation. The BRI of a supervisor module is any software module, which amounts to mere instructions to apply an exception. A supervisor neural network function, interpreted as a self-supervised neural network, is a known process/component in machine learning. This amounts to mere instructions to apply an exception. A GAN is a known process/component in machine learning, which amounts to mere instructions to apply an exception.)
with the cpGAN (A GAN is a known process/component in machine learning, which amounts to mere instructions to apply an exception.)
wherein the digital twin representing, a simulation of an industry machine or an industrial plant, or a sensor, a production unit, or a manufacturing unit. (This generally links the use of the judicial exceptions to the particular field of industry/manufacturing. This is a field of use limitation.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following abstract idea:
wherein the cluster dependent random noise is obtained from the cluster independent random noise that is sampled from a Gaussian distribution and the one or more associated cluster labels corresponding to the input training dataset. (Again, one could practically transform the cluster independent random noise in the human mind given pen and paper. One could also sample a Gaussian distribution to obtain cluster independent random noise and use the corresponding cluster labels to transform the cluster independent random noise in the human mind with pen and paper. This is a mental process.)
Claim 2 does not recite any additional elements.
Regarding claim 3, the rejection of claim 1 is incorporated herein. Further, claim 3 recites the following abstract idea:
wherein the flipped mask variable is obtained based on a difference between a pre-defined value and the mask variable. (One could practically obtain a flipped mask based on the difference between a pre-defined value and the mask variable in the human mind with the aid of pen and paper. This is a mental process.)
Claim 3 does not recite any additional elements.
Regarding claim 5, the rejection of claim 1 is incorporated herein. Further, claim 5 recites the following abstract idea:
further comprising validating the imputed training data based on a comparison of the imputed training data and the input training dataset. (One could practically compare data in the human mind and validate it. This is the mental process of evaluation.)
Regarding claim 7, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A system, comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: (All components recited are generic computer parts, configured in a generic way, and the processor does the known process of carrying out instructions. This is mere instructions to apply an exception.)
… by using a generator module comprised in the cpGAN … (Programming modules are a known computer process. This is mere instructions to apply an exception.)
… by using a critic module comprised in the cpGAN … (Programming modules are a known computer process. This is mere instructions to apply an exception.)
… by using a supervisor module comprised in the cpGAN … (Programming modules are a known computer process. This is mere instructions to apply an exception.)
… by using a recovery module comprised in the cpGAN … (Programming modules are a known computer process. This is mere instructions to apply an exception.)
The remainder of claim 7 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Claims 8-9 and 11 recite substantially similar subject matter to claims 2-3 and 5 respectively and are rejected with the same rationale, mutatis mutandis.
Regarding claim 13, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause: (This recites generic computer components and functions; this is mere instructions to apply an exception.)
The remainder of claim 13 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Claims 14-15 and 17 recite substantially similar subject matter to claims 2-3 and 5 respectively and are rejected with the same rationale, mutatis mutandis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA THUY PHAM whose telephone number is (571)272-2605. The examiner can normally be reached Monday - Friday, 9 A.M. - 5:00 P.M..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.T.P./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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