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 .
Claims 1-18 have been examined.
Claim Objections
Claim 1 is objected to because of the following informalities: The claim includes “deep leaning.” This appears once on line 10 and once on lines 10-11. This appears to be a clerical error which should be “deep learning.” Appropriate correction of both instances is required.
Claims 7 and 13 are objected to for similar reasons as indicated with respect to claim 1 above. Appropriate correction is required.
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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 20220051796 by Zhu et al. ("Zhu") in view of U.S. Patent Application Publication 20180054376 by Hershey et al. ("Hershey") and U.S. Patent Application Publication 20210374279 by Zheng et al. ("Zheng").
In regard to claim 1, Zhu discloses:
1. A method for remaining useful life (RUL) prediction of an aircraft engine based on gaussian process regression (GPR) integrated deep learning (GIDL), comprising: See Zhu, Fig. 1, broadly depicting a method for prediction based upon GPR and deep learning.
partitioning observation data into training data, validation data, and testing data; Zhu,¶ 0077, “We split our dataset to 70% for a training set, 15% validation set and 15% test set. We tested our method on approximately 4,000 observation windows.”
training a generative GPR model using the training data to obtain a trained GPR model; Zhu, ¶ 0006, “Thus, a method is provided in which Gaussian process regression is used to generate synthetic vital sign data at regularly spaced intervals, which is provided as input to a recurrent neural network (RNN).” Also ¶ 0051, “Since it is desired to model vital sign data of the entire patient population, log-normal distributions are applied as priors for the three hyperparameters based on clinical judgment. The model is optimized by minimizing the negative log likelihood with respect to the hyperparameters.”
using the trained GPR model as a synthetic data generator to generate synthetic data; Zhu ¶ 0040, “A Gaussian process model is applied to the continuous variables and used to generate a time series of synthetic vital sign data.”
performing an averaging process to integrate the synthetic data and the training data to obtain integrated data; Zhu, ¶ 0042, “The output from the Gaussian process regression 303 and the step function modelling 304 is a posterior mean and a posterior variance for each of the components of the vital sign information processed. … Synthetic vital sign data may then be generated at a plurality t of regularly spaced time points (e.g. t=12) to define a feature space 305 to be used as input to step S4 of FIG. 1.”
generating a plurality of data minibatches from the integrated data; feeding the plurality of data minibatches into a deep [learning] model to train the deep [learning] model; Zhu, ¶ 0091, “RNNs All of the RNNs used in step S4 of FIG. 1 were trained for 200 epochs with early stopping using the validation set to avoid overfitting, 50 steps per epoch and a batch size of 50 sequences of the same length.”
obtaining … prediction from the trained deep learning model based on the validation data; and Zhu, ¶ 0035, “Each EWS may, for example, comprise a binary output indicating whether an observation set of a patient is within 24 hours of a composite outcome …” Also ¶ 0084, “The scaling and shifting operations are obtained through the training set and then applied to the validation and test sets.”
Zhu does not expressly disclose: RUL prediction. This is taught by Hershey. See Hershey, ¶ 0001, “For example, it may be helpful to predict a Remaining Useful Life (“RUL”) of an electro-mechanical system, such as an aircraft engine, to help plan when the system should be replaced.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hershey’s aircraft engine RUL with Zhu’s RNN model in order to help plan when a system should be replaced as suggested by Hershey.
using the RUL prediction for further parameter training of … the deep learning model. Zhu, ¶ 0129, “All hyperparameters of the model were optimised empirically using a balanced training and validation set, referred to as DO,1B.”
Zhu does not expressly disclose … further parameter training of the generative GPR model and … However, this is taught by Zheng. See Zheng, Fig. 1E, wherein element 132 depicts downstream loss used to generate new synthetic knowledge. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zheng’s feedback based synthetic knowledge generation with Zhu’s GPR model in order to satisfy a stopping condition as suggested by Zheng (see ¶ 0046).
In regard to claim 2, Zhu also discloses:
2. The method according to claim 1, further including:
obtaining sensing data from sensors Zhu, Fig. 2 and ¶ 0036, “… vital sign information may be provided on an automatic basis by a sensor system 12 …”
Zhu does not expressly disclose: of the aircraft engine; This is taught by Hershey. See Hershey ¶ 0027, “For example, it may be helpful to predict a Remaining Useful Life (“RUL”) of an electro-mechanical system, such as an aircraft engine, to help plan when the system should be replaced.”
Zhu also discloses:
inputting the sensing data into the trained deep learning model to provide RUL prediction of the aircraft engine; and Zhu, Fig. 1, elements S2-S5, depicting event prediction.
Zhu does not expressly disclose:
determining a scheduling strategy for maintenance of the aircraft engine according to the RUL prediction of the aircraft engine, wherein the maintenance of the aircraft engine is performed according to the scheduling strategy. This is taught by Hershey. See ¶ 0001, “For example, it may be helpful to predict a Remaining Useful Life (“RUL”) of an electro-mechanical system, such as an aircraft engine, to help plan when the system should be replaced.” Also ¶ 0029, “A digital twin may estimate a remaining useful life of a twinned physical system using sensors, communications, modeling, history, and computation.” Also ¶ 0079, “The process may bring the system off-line in a scheduled, orderly, and cost-beneficial manner thereby reducing any unscheduled down time.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Hershey’s aircraft engine maintenance scheduling with Zhu’s prediction in order to help plan when the system should be replaced as suggested by Hershey.
In regard to claim 3, Zhu also discloses:
3. The method according to claim 1, wherein: the generative GPR model is first trained with initial hyperparameters and further tuned empirically using the training data and the testing data. Zhu, ¶ 0051, “Since it is desired to model vital sign data of the entire patient population, log-normal distributions are applied as priors for the three hyperparameters based on clinical judgment. The model is optimized by minimizing the negative log likelihood with respect to the hyperparameters. The GPR models may be built for example using GPy, which is a GP framework written in python.” Also ¶ 0129, “All hyperparameters of the model were optimised empirically using a balanced training and validation set, …”
In regard to claim 4, Zhu also discloses:
4. The method according to claim 1, wherein: training the generative GPR model using the training data includes obtaining a posterior distribution based on standard Bayesian update. Zhu, ¶ 0042, “In some embodiments, the generation of the EWS in step S4 uses the posterior variances generated by the pre-processing of step S3 in addition to the posterior means generated by the pre-processing of step S3. Thus, the mean and variance of each component of the vital sign information generated by the Gaussian process model at each time point tin the assessment window may be used as input to step S4.” Not that posterior mean and variance are derived from a posterior distribution.
In regard to claim 5, Zhu also discloses:
5. The method according to claim 4, after obtaining the posterior distribution, further including: sampling data from the posterior distribution. See Zhu, ¶ 0042, as cited above. Sampling from a posterior distribution is inherent in calculation of posterior mean and variance.
In regard to claim 6, Zhu and Hershey also teach:
6. The method according to claim 1, wherein: RUL is calculated as a first passage time when a health status value of the aircraft engine exceeds a predefined failure threshold. Hershey ¶ 0029, “It may provide an answer in a time frame that is useful, that is, meaningfully prior to a projected occurrence of a failure event or suboptimal operation.” Also ¶ 0053 “If differences between the sensor values at time=t and the UPM predictions fall outside of the tolerance envelopes, then a report issues at 360.” Also ¶ 0107, “predicting RUL or the time to failure of a twinned physical system.”
In regard to claim 7, Zhu discloses:
7. A system, comprising: a memory, configured to store program instructions for performing a method for remaining useful life (RUL) prediction of an aircraft engine based on gaussian process regression (GPR) integrated deep learning (GIDL); and a processor, coupled with the memory and, when executing the program instructions, configured for: Zhu, ¶ 0034, “The one or more computer programs may be provided in the form of media or data carriers, optionally non-transitory media, storing computer readable instructions. When the computer readable instructions are read by the computer, the computer performs the required method steps.” Also Fig. 2 and ¶ 0037, “In the schematic configuration of FIG. 2, the vital sign data is received by a data receiving unit 8 of the data processing apparatus 5. The data processing apparatus 5 may further comprise a processor 10 configured to carry out steps of the method.”
All further limitations of claim 7 have been addressed in the above rejection of claim 1.
In regard to claims 8-12, parent claim 7 is addressed above.
All further limitations of claims 8-12 have been addressed in the above rejections of claims 2-6, respectively.
In regard to claim 13, Zhu discloses:
13. A non-transitory computer-readable storage medium, containing program instructions for, when being executed by a processor, performing a method for remaining useful life (RUL) prediction of an aircraft engine based on gaussian process regression (GPR) integrated deep learning (GIDL), the method comprising: See Zhu, ¶ 0034, “The one or more computer programs may be provided in the form of media or data carriers, optionally non-transitory media, storing computer readable instructions. When the computer readable instructions are read by the computer, the computer performs the required method steps.” Also Fig. 2 and ¶ 0037, “In the schematic configuration of FIG. 2, the vital sign data is received by a data receiving unit 8 of the data processing apparatus 5. The data processing apparatus 5 may further comprise a processor 10 configured to carry out steps of the method.”
All further limitations of claim 13 have been addressed in the above rejection of claim 1.
In regard to claims 14-18, parent claim 13 is addressed above.
All further limitations of claims 14-18 have been addressed in the above rejections of claims 2-6, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Oh, E., & Lee, H. (2020). An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network. Symmetry, 12(4), 669. <doi.org/10.3390/sym12040669>
“An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network” by Oh et al. See top of p. 8, “Missing values are predicted based on GPR … Finally, GAN is used to estimate missing values.” See p. 7 under Fig. 5, “As shown in Figure 5, the missing values of the original data are indicated as not available (na). First, if na exists, it is replaced with the average value of the attribute data that are missing.” See p. 14, 2nd paragraph, “The deep neural network (DNN) is trained and tested to produce interpolated data, as shown in the red box in Figure 9b.” See p. 14, 2nd paragraph, “A pass (1) or fail (0) is diagnosed using the DNN model.” See p. 14, 2nd paragraph, “The deep neural network (DNN) is trained and tested to produce interpolated data, as shown in the red box in Figure 9b.” Also p. 17, section 5, “The generated data are used as training data and help to overcome any data imbalances in the input data set” See p. 14, 1st paragraph, “Training data are divided into 59,000 negative classes of APS-based faults and 1000 positive cases. Test data are divided into 15,625 negative cases and 375 positive cases.”
U.S. Patent Application Publication 20190156151 by Wrenninge et al. ("Wrenninge") Wrenninge ¶ 0076, “training a model based on the synthetic image dataset. … Approaches to train the models can include: … Gaussian process regression.” Wrenninge, ¶ 0022, “This performance data can be used to tune the model, to seek out additional real world data having specified parameters based on the results of the performance analysis, or for any other suitable purpose.” ¶ 0083, “In variations, Block S700 can include generating a performance metric that is used to modify the synthetic image dataset (e.g., wherein the synthetic image dataset is modified based on the relationship between the performance metric and a threshold).”
U.S. Patent Application Publication 20200210848 by Kour et al. ("Kour") Kour ¶ 0050, “A common approach to assess the performance of such statistical components is to partition the data-set into training, validation, and testing subsets; …”
U.S. Patent Application Publication 20220077944 by Zou et al. ("Zou") Zou ¶ 0025, “The Gaussian process regression model (GP) can be used to provide a coarse estimation of RSS values.” Zou, ¶ 0031, “WiGAN may generate realistic and fine-grained RSS values in constrained space (e.g., on a table of cubicles, conference rooms, and personal offices) using its generator G with the output of the GPR model GP learned using the data collected in free space (e.g., corridors, open space).”
U.S. Patent Application Publication 20210073671 by Puri et al. ("Puri") Puri ¶ 0092, “For example, the digital synthetic data system 106 can utilize an average value between the modified training sample values to generate a combined synthetic training sample value.” Puri, Fig. 6, depicting a machine learning training process. Also ¶ 0038, e.g. “deep learning.”
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/James D. Rutten/Primary Examiner, Art Unit 2121
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