Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
Acknowledgment is made of the Information Disclosure Statement dated 09/20/2023.
The information disclosure statement filed with the Foreign Reference KR-10-2022-0049165 A dated 09/20/2023 fails to comply with 37 CFR 1.98(a)(3)(i) because it does not include a concise explanation of the relevance, as it is presently understood by the individual designated in 37 CFR 1.56(c) most knowledgeable about the content of the information, of each reference listed that is not in the English language. It has been placed in the application file, but the information referred to therein has not been considered.
Drawings
The drawings have been received on 09/20/2023. These drawings are accepted.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are:
“drift pattern creating unit” in claims 1, 2 and 3.
“ensemble drift calibrating unit” in claims 1, 5, 6 and 8.
“similarity determining unit” in claims 1, 6 and 7.
“individual drift calibrating unit” in claims 1, 5 and 6.
“drift pattern classifying unit” in claims 4 and 5.
“storage module” in claim 8.
“dense layer” in claim 6.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(a) – Written Description
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-8 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.
There is not enough written description to describe what the units are. The written description fails to disclose the corresponding structure:
Claims 1, 2 and 3 recite a “drift pattern creating unit”. The scope of the claim encompasses all possible ways of creating drift patterns using a unit. In contrast, the specification, see e.g., published [0027], describes at best a particular way of detecting a latent factor of learning data and creating a possible drift pattern for the learning data based on the detected latent factor. For purposes of examination, Examiner will interpret unit as generic computing components.
Claims 1, 5, 6 and 8 recite a “ensemble drift calibrating unit”; however, the ensemble drift calibrating unit performs functions because it comprises of these other units. The ensemble drift calibrating unit does not have sufficient structure because the other unit structures do not have sufficient structure. For purposes of examination, Examiner will interpret unit as generic computing components.
Claims 1, 6 and 7 recite a “similarity determining unit”. The scope of the claim encompasses all possible ways of determining similarity using a unit. In contrast, the specification, see e.g., published [0027], describes at best a particular way of performing prelearning so as to determine similarity between the drift pattern and the recovered recovery data by reconstructing the input drift pattern. For purposes of examination, Examiner will interpret unit as generic computing components.
Claims 1, 5 and 6 recite an “individual drift calibrating unit”. The scope of the claim encompasses all possible ways of calibrating for drift using a unit. In contrast, the specification, see e.g., published [0027], describes at best a particular way of prelearning calibration information according to a loss function between the learning data and the drift pattern for each drift pattern. For purposes of examination, Examiner will interpret unit as generic computing components.
Claims 4 and 5 recite a “drift pattern classifying unit”. The scope of the claim encompasses all possible ways of classifying drift patterns using a unit. In contrast, the specification, see e.g., published [0038], describes at best a particular way of classifying a data pair including the learning data and the corresponding drift pattern according to a drift level. For purposes of examination, Examiner will interpret unit as generic computing components.
Claim 8 recites a “storage module”. The scope of the claim encompasses all possible ways of storing prelearned ensemble drift calibrating units. In contrast, the specification, see e.g., published [0058], describes at best a particular way of storing a prelearned ensemble drift calibrating unit 120 until abnormality in input data is detected. For purposes of examination, Examiner will interpret module as generic computing components.
Claim Rejections - 35 USC § 112(b)
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.
Claim limitation “drift pattern creating unit” in claims 1, 2 and 3 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For purposes of examination, Examiner will interpret unit as generic computing components.
Claim limitation “ensemble drift calibrating unit” in claims 1, 5, 6 and 8 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For purposes of examination, Examiner will interpret unit as generic computing components.
Claim limitation “similarity determining unit” in claims 1, 6 and 7 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For purposes of examination, Examiner will interpret unit as generic computing components.
Claim limitation “individual drift calibrating unit” in claims 1, 5 and 6 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For purposes of examination, Examiner will interpret unit as generic computing components.
Claim limitation “drift pattern classifying unit” in claims 4 and 5 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For purposes of examination, Examiner will interpret unit as generic computing components.
Claim limitation “storage module” in claim 8 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For purposes of examination, Examiner will interpret unit as generic computing components.
Claim limitation “dense layer” in claim 6 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For purposes of examination, Examiner will interpret unit as generic computing components.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ott et al. (Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift); hereinafter Ott in view of Upadhyay et al. (Generating Out of Distribution Adversarial Attack using Latent Space Poisoning); hereinafter Upadhyay in view of Yong et al. (Bayesian Autoencoders for Drift Detection in Industrial Environments); hereinafter Yong
Claim 1 is rejected over Ott, Upadhyay and Yong.
Regarding claim 1, Ott teaches an apparatus for calibrating prediction models of an inference service, including a computer program and a processor for executing the computer program, wherein the apparatus for calibrating prediction models of an inference service comprises: (Ott [page 3]: “Optimizing a transformation from source to target domain and selecting the transformation at inference (Section 3.2).”)
an instruction executing an individual drift calibrating unit configured to pre-learn calibration information according to a loss function between the learning data and (Ott [page 2]: “we train an optimal transformation T from the source to the target domain for each class with joint distribution adaptation methods, i.e., optimal transport and CORAL, from an adaptation set with few samples.”; and [page 4]: “we classify the transformed embedding with the target domain classifier with the cross-entropy (CE) loss.”)
wherein the individual drift calibrating units are connected in parallel, and the similarity determining units are connected in parallel to the individual drift calibrating units, respectively. (Ott [page 2]: “we extract features with the source domain model, transform features into the target domain for each class, compute the similarity to the target domain to select the best transformation, and classify the transformed embedding with the target domain model.”)
Ott does not appear to explicitly teach a drift pattern creating unit configured to detect a latent factor of learning data and create a possible drift pattern for the learning data based on the detected latent factor; and
However, Upadhyay teaches a drift pattern creating unit configured to detect a latent factor of learning data and create a possible drift pattern for the learning data based on the detected latent factor; and (Upadhyay [page 1]: “the latent space poisoning exploits the inclination of classifiers to model the independent and identical distribution of the training dataset and tricks it by producing out of distribution samples.”; Note: the out of distribution samples are the drift patterns)
It would have been obvious before the effective filing date to combine the domain adaptation covariate shift of Ott with the latent space poisoning of Upadhyay to improve control over latent space (Upadhyay, 1 Introduction). Ott and Upadhyay are analogous art because they both concern operating in latent space to shift data.
Ott does not appear to explicitly teach an ensemble drift calibrating unit including a similarity determining unit configured to perform prelearning to determine similarity between recovery data recovered by reconstructing the input drift pattern and the drift pattern,
However, Yong teaches an ensemble drift calibrating unit including a similarity determining unit configured to perform prelearning to determine similarity between recovery data recovered by reconstructing the input drift pattern and the drift pattern, (Yong [page 2]: “ensemble consists of M independent autoencoders and each j-th autoencoder contains a set of parameters,”; and [page 1]: “we have found the epistemic uncertainty to be less sensitive to sensor perturbations as compared to the reconstruction loss. By observing the reconstructed signals with the uncertainties, we gain interpretable insights”)
It would have been obvious before the effective filing date to combine the domain adaptation covariate shift of Ott with the drift simulation and ensembling of Yong to improve predictive modeling (Yong, [page 1]). Ott and Yong are analogous art because they both concern detecting drift.
Claim 9 is claim 1 in the form of a method and is rejected for the same reasons as claim 1 stated above.
Claims 2, 3, 4, 5, 8, 10, 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Ott, Upadhyay and Yong in view of Walters et al. (US20200012900A1); hereinafter Walters
Claim 2 is rejected over Ott, Upadhyay, Yong and Walters with the incorporation of claim 1.
Regarding claim 2, Ott does not appear to explicitly teach estimate a plurality of latent factors from the learning data using a variable auto encoder (VAE)—based generative model, and
for input noise data
However, Upadhyay teaches estimate a plurality of latent factors from the learning data using a variable auto encoder (VAE)—based generative model, and (Upadhyay [page 1]: “Then we employ our pre-trained JointVAE model to decode the new noisy latent space and obtain adversarial examples conditioned”)
for input noise data (Upadhyay [page 1]: “We train a disentangled variational autoencoder (-VAE) to model the data in latent space and then we add noise perturbations using a class-conditioned distribution function to the latent space under the constraint that it is misclassified to the target label”)
It would have been obvious before the effective filing date to combine the domain adaptation covariate shift of Ott with the latent space poisoning of Upadhyay to improve control over latent space (Upadhyay, 1 Introduction). Ott and Upadhyay are analogous art because they both concern operating in latent space to shift data.
Ott does not appear to explicitly teach create a drift pattern by transforming a covariate between the estimated latent factors [for input noise data].
However, Walters teaches create a drift pattern by transforming a covariate between the estimated latent factors [for input noise data]. (Walters [0099]: “system 100 can be configured to generate a difference matrix using a covariance matrix of the normalized reference dataset and a covariance matrix of the synthetic dataset.”)
It would have been obvious before the effective filing date to combine the domain adaptation covariate shift of Ott with the covariance matrices of Walters to improve quality of the synthetic data model (Walters [0053]). Ott and Walters are analogous art because they both concern using covariances to detect data drift.
Claim 3 is rejected over Ott, Upadhyay, Yong and Walters with the incorporation of claim 1.
Regarding claim 3, Ott does not appear to explicitly teach wherein drift pattern creating unit includes a plurality of VAEs for estimating a different number of latent factors.
However, Upadhyay teaches wherein drift pattern creating unit includes a plurality of VAEs for estimating a different number of latent factors. (Upadhyay [page 1]: “Then we employ our pre-trained JointVAE model to decode the new noisy latent space and obtain adversarial examples conditioned”)
It would have been obvious before the effective filing date to combine the domain adaptation covariate shift of Ott with the latent space poisoning of Upadhyay to improve control over latent space (Upadhyay, 1 Introduction). Ott and Upadhyay are analogous art because they both concern operating in latent space to shift data.
Claim 4 is rejected over Ott, Upadhyay, Yong and Walters with the incorporation of claim 1.
Regarding claim 4, Ott does not appear to explicitly teach a drift pattern classifying unit configured to classify a data pair including the learning data and the corresponding drift pattern according to a drift level,
wherein the drift level is determined using a rooted mean squared error (RMSE) of the data pair.
However, Walters teaches a drift pattern classifying unit configured to classify a data pair including the learning data and the corresponding drift pattern according to a drift level, (Walters [0010]: “The operations may include receiving event data and detecting data drift based on the predicted data and the event data.”)
wherein the drift level is determined using a rooted mean squared error (RMSE) of the data pair. (Walters [0184]: “the difference is determined using at least one of a Mean Absolute Error, a Root Mean Squared Error, a percent good classification, or the like.”)
It would have been obvious before the effective filing date to combine the domain adaptation covariate shift of Ott with the covariance matrices of Walters to improve quality of the synthetic data model (Walters [0053]). Ott and Walters are analogous art because they both concern using covariances to detect data drift.
Claim 5 is rejected over Ott, Upadhyay, Yong and Walters with the incorporation of claim 1.
Regarding claim 5, Ott does not appear to explicitly teach wherein the ensemble drift calibrating unit comprises:
respective individual drift calibrating units configured to perform prelearning independently using the data pair classified in the respective drift pattern classifying units.
However, Yong teaches wherein the ensemble drift calibrating unit comprises:
respective individual drift calibrating units configured to perform prelearning independently using the data pair classified in the respective drift pattern classifying units. (Yong [page 2]: “Assume our ensemble consists of M independent autoencoders and each j-th autoencoder contains a set of parameters,” and [page 3]: “To simulate virtual drifts scenarios, we create two datasets from the ‘healthy‘ test set and artificially inject a range of noise from 5-25% (i.e. injected noise of uniform distribution) and constant sensor drift of 5-25% of the mean in each one of the sensors (i.e. injected drift).”)
It would have been obvious before the effective filing date to combine the domain adaptation covariate shift of Ott with the drift simulation and ensembling of Yong to improve predictive modeling (Yong, [page 1]). Ott and Yong are analogous art because they both concern detecting drift.
Claim 8 is rejected over Ott, Upadhyay, Yong and Walters with the incorporation of claim 1.
Regarding claim 8, Ott does not appear to explicitly teach a storage module for storing prelearned ensemble drift calibrating unit until abnormality in input data is detected.
However, Walters teaches a storage module for storing prelearned ensemble drift calibrating unit until abnormality in input data is detected. (Walters [0197]: “Step 1918 includes storing updated model parameters and updated model hyperparameters (e.g., architectural or training hyperparameters) in memory (e.g., in a database such as database 105 or in model storage 109).”)
It would have been obvious before the effective filing date to combine the domain adaptation covariate shift of Ott with the covariance matrices of Walters to improve quality of the synthetic data model (Walters [0053]). Ott and Walters are analogous art because they both concern using covariances to detect data drift.
Dependent claim 10 is claim 2 in the form of a method and is rejected for the same reasons as claim 2 stated above. For the rejection of the limitations specifically pertaining to the method of claim 9, see the rejection of claim 9 above.
Dependent claim 11 is claim 3 in the form of a method and is rejected for the same reasons as claim 3 stated above. For the rejection of the limitations specifically pertaining to the method of claim 9, see the rejection of claim 9 above.
Dependent claim 12 is claim 4 in the form of a method and is rejected for the same reasons as claim 4 stated above. For the rejection of the limitations specifically pertaining to the method of claim 9, see the rejection of claim 9 above.
Claim 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ott, Upadhyay and Yong in view of Savari (Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection); hereinafter Savari
Claim 6 is rejected over Ott, Upadhyay, Yong and Savari with the incorporation of claim 1.
Regarding claim 6, Ott does not appear to explicitly teach wherein the similarity determining unit determines the similarity between input data input during service and recovery data recovered by reconstructing the input data, and
adjusts a weight of the individual drift calibrating unit according to the determined similarity, and
the ensemble drift calibrating unit further includes a dense layer configured to apply the weight to calibration information of the individual drift calibrating unit to sum up final calibration information to be applied to the input data.
However, Savari teaches wherein the similarity determining unit determines the similarity between input data input during service and recovery data recovered by reconstructing the input data, and (Savari [page 1, 1 Introduction]: “When using an autoencoder for outlier detection, the reconstruction error indicates the level of outlierness of the corresponding input.”)
adjusts a weight of the individual drift calibrating unit according to the determined similarity, and (Savari [page 3]: “the components are inlier-specialized and are better suited for detecting outliers. Following the above observation, the component that has smaller reconstruction errors has more impact on the final anomaly score. Thus, we build a weighted consensus function that assigns weights to each component based on the sum of reconstruction errors that the autoencoders generate on their corresponding sample.”)
the ensemble drift calibrating unit further includes a dense layer configured to apply the weight to calibration information of the individual drift calibrating unit to sum up final calibration information to be applied to the input data. (Savari, [page 2]: “the components are combined into a weighted sum that represents the final output of the boosted model.”)
It would have been obvious before the effective filing date to combine the domain adaptation covariate shift of Ott with the weight adjustment of Savari to improve performance of the autoencoder (Savari, page 3). Ott and Savari are analogous art because they both concern drift detection using autoencoders.
Dependent claim 13 is claim 6 in the form of a method and is rejected for the same reasons as claim 6 stated above. For the rejection of the limitations specifically pertaining to the method of claim 9, see the rejection of claim 9 above.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ott, Upadhyay, Yong and Savari in view of An et al. (Variational Autoencoder based Anomaly Detection using Reconstruction Probability); hereinafter An
Claim 7 is rejected over Ott, Upadhyay, Yong, Savari and An with the incorporation of claim 1.
Regarding claim 7, Ott does not appear to explicitly teach wherein the similarity determining unit is a VAE-based generative model
for more similarly reconstructing input data having the same data distribution as prelearned learning data.
However, An teaches wherein the similarity determining unit is a VAE-based generative model (An [page 2]: “A variational autoencoder is a probabilistic graphical model that combines variational inference with deep learning. Because VAE reduces dimensions in a probabilistically sound way, theoretical foundations are rm. The advantage of a VAE over an autoencoder and a PCA is that it provides a probability measure rather than a reconstruction error as an anomaly score, which we will call the reconstruction probability.”)
for more similarly reconstructing input data having the same data distribution as prelearned learning data. (An [page 4]: “After training, the autoencoder will reconstruct normal data very well, while failing to do so with anomaly data which the autoencoder has not encountered.”)
It would have been obvious before the effective filing date to combine the domain adaptation covariate shift of Ott with the reconstruction data of An to improve performance (An, page 15). Ott and An are analogous art because they both concern autoencoders and drift data.
Dependent claim 14 is claim 7 in the form of a method and is rejected for the same reasons as claim 7 stated above. For the rejection of the limitations specifically pertaining to the method of claim 9, see the rejection of claim 9 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID H TRAN whose telephone number is (703)756-1525. The examiner can normally be reached M-F 9:30 am - 5:30 pm.
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/DAVID H TRAN/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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