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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/02/2026 has been entered.
Response to Arguments
Applicant's arguments filed 06/02/2026 have been fully considered.
Regarding applicant’s remarks directed to the rejection of claims under 35 USC § 103, the arguments are directed to newly amended limitations that were not previously examined by the examiner. Therefore, applicants arguments are rendered moot. The examiner refers to the rejection under 35 USC § 103 in the current office action for more details.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over KR Pub No. KR20200043196A Baek et al. (“Baek”) in view of Rao, Aniruddha Rajendra, et al. "Spatio-temporal functional neural networks." 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. (“Rao”) in further view of Peter, W. Tse, Wen-xian Yang, and H. Y. Tam. "Machine fault diagnosis through an effective exact wavelet analysis." Journal of sound and vibration 277.4-5 (2004): 1005-1024. (“Tse”) and further in view of by Q. Wang, S. Zheng, A. Farahat, S. Serita and C. Gupta, "Remaining Useful Life Estimation Using Functional Data Analysis," 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), San Francisco, CA, USA, 2019, pp. 1-8, doi: 10.1109/ICPHM.2019.8819420 (“Wang”)
In regards to claim 1 and analogous claim 11,
Baek teaches A method for predicting a characteristic of a system, comprising: measuring, at a sample rate, data relating to an operation of the system over a first time period; producing a two-dimensional (2D) time-and-frequency first input data set by applying a wavelet transform to the measured data; and generating a set of one or more values associated with one or more system characteristics by processing the 2D time-and-frequency first input data set [using a functional neural network (FNN)],
(Baek, Abstract, “A residual life prediction device is disclosed [A method for predicting a characteristic of a system]. The residual life prediction apparatus uses a data conversion unit that converts each of a plurality of one-dimensional vibration signals into a two-dimensional image, and uses a plurality of two-dimensional images generated by the data conversion unit as input data. Predictive model generation unit that trains an artificial neural network (ANN) model that outputs HI), and predicts the residual useful life (RUL) of a predicted facility or a predicted component using the trained ANN model [generating a set of one or more values ie RUL associated with one or more system characteristics by processing the 2D time-and-frequency first input data set; wherein the 2D data is input to the FNN of Rao] And a residual life prediction unit, and the data transformation unit generates the two-dimensional images by performing continuous wavelet transformation (CWT) [producing a two-dimensional (2D) time-and-frequency first input data set by applying a wavelet transform to the measured data] on each of the one-dimensional vibration signals.”)
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(Baek, Description para. 27, “FIG. 3 shows a contour plot of the wavelet power spectrum generated by the vibration signal and continuous wavelet transform (CWT). The vibration signal sampled at 0.1 s intervals at a frequency of 25.6 kHz has 2560 data points [measuring, at a sample rate, data relating to an operation of the system over a first time period].”
)
Baek teaches and using the set of one or more values to perform a predictive maintenance task that improves a performance of one or more components of the system.
(Baek, Description para. 2, “Residual life prediction technology is being actively researched in the field of health predictive management (PHM), and has recently emerged as a more important factor in the emergence of smart factories. Residual life prediction can be useful mainly in the aviation industry, nuclear power generation, automotive industry, high-tech industries such as semiconductor / display, etc. It is mainly applied to facilities or parts that are expected to be fatally damaged in the event of a sudden abnormality or failure, and continuously monitors to collect and analyze data to predict the failure and life of the machine to predict predictive maintenance (PdM). . Proper use of the residual life prediction technology can reduce unnecessary equipment maintenance, reduce maintenance costs, and predict failures to improve safety [using the set of one or more values to perform a predictive maintenance task that improves a performance of one or more components of the system]. In particular, vibration monitoring is widely used because it is simple and accurate to estimate the overall condition of the machine. Therefore, the present invention proposes a method and system for predicting residual life based on a vibration signal, and incorporates a deep learning technique that greatly contributes to improving the accuracy of recent data analysis to improve the shortcomings in existing studies. We want to improve the performance of the residual life prediction.”)
However, Baek does not explicitly teach:
using a functional neural network (FNN)…
Rather, Baek discloses that the ANN may not be limited to the type of ANN
(Baek, Description para. 20, “The predictive model generator 300 may generate a predictive model capable of predicting a residual life (RUL) of equipment or parts. Specifically, the prediction model generator 300 may generate an ANN (Artificial Neural Network) model, and train the ANN model using the two-dimensional images generated by the data converter 100. The ANN may be a convolutional neural network (CNN), but the scope of the present invention is not necessarily limited to the type of artificial neural network.”)
wherein the 2D time-and-frequency first input data set comprises a plurality of component signals at different frequency scales
Rather, Baek discloses that the CWT transform may be a Morlet-based CWT
(Baek, Description para. 26, “In the present invention, a continuous wavelet transform (CWT) is used to extract features (eg, image features) from a vibration signal. According to an embodiment, the continuous wavelet transform may be a Morlet-based CWT.”)
and wherein the FNN comprises a first layer of functional neurons that receive the plurality of component signals as functional covariates;
Tse teaches wherein the 2D time-and-frequency first input data set comprises a plurality of component signals at different frequency scales,
(Tse, pg. 7 para. 1, “According to the definition of Eq. (2), the results of the Morlet CWT [wherein the 2D time-and-frequency first input data set comprises a plurality of component signals at different frequency scales] should only exist as a temporal waveform x1(t) from 0.075 to 0.1 s at frequency level 200 Hz, and another temporal waveform x2(t) from 0.125 to 0.15 s at frequency level 100 Hz. However, as shown in Fig. 4, the unexpected waveforms appear also at levels of 220 and 180 Hz, which are adjacent to 200 Hz.”; see annotated figure 4 below
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Rao teaches using a functional neural network (FNN)… and wherein the FNN comprises a first layer of functional neurons that receive the plurality of component signals as functional covariates;
Examiner’s note: Examiner interprets the FNN in light para. [0034-0035] of the specification and figure 4 and 5 of the instant application.
(Rao, Section II A., “The goal of spatio-temporal regression is to build a mapping from time series covariates to a real-valued scalar response, leveraging both the spatial and temporal dependencies among data samples to minimize the prediction error. Suppose that we observe N samples scattered over a d dimensional spatial region D ⊂ Rd, d ∈ Z+. For each subject i ∈ {1,2,...,N}, R covariates are continuously recorded within a compact time interval T ⊆ R. Note that the measuring timestamps can vary across different covariates and different data samples. Hence, the subject and feature indexes need to be reflected in the following notations. In particular, the measuring times of the r-th feature of subject i are denoted as T(i,r) = [T(i,r) 1 , ..., T(i,r) j , ..., T(i,r) M(i,r) ]T , with M(i,r) being the number of observations and the measuring timestamps T(i,r) j ∈ T for i = 1,...,N;r = 1,...,R;j = 1, ..., M(i,r). The corresponding covariate data are represented by X(i,r) = [X(i,r) 1 , ..., X(i,r) j , ..., X(i,r) M(i,r) ]T [receive the plurality of component signals as functional covariates; wherein the component signals are provided by Baek in view of Tse] . The real-valued response variable for the i-th subject is Yi ∈ R and the spatial information is presented by Si ∈ D. In summary, the observed data is {X(i,1),...,X(i,R),Yi,Si}N i=1.”)
(Rao, Fig. 2, Section II B. Functional Data Analysis and Functional Neural Network [using a functional neural network (FNN)], “Functional data analysis (FDA) is a rapidly growing branch of statistics specialized in representing and modeling dynamically varying data over a continuum (e.g., time) [23], [24] . FDA models uniquely treat X(i,r), the observed r-th feature of subject i over time, as discretized observations from a continuous underlying curve X(i,r)(t),t ∈ T [wherein the FNN comprises a first layer of functional neurons; see fig. 2] . Under the regression setting, the conventional Functional Linear Models (FLM) [20], [21] assume and learn the unknown real-valued parameters in the mapping
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where b ∈ R is the unknown intercept, and βr is a finite dimensional vector that quantifies the parameter function Wr(βr,t), for r = 1,...,R.”
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Baek and Wang are both considered to be analogous to the claimed invention because they are in the same field of predictive maintenance using neural networks. Rao is considered analogous to the claimed invention because they are in the same field of functional neural networks and functional data analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Baek to incorporate the teachings of Rao in order to substitute the ANN of Baek with the FNN of Rao as functional models have the advantage of being capable of handling versatile data formats, and effectively capturing the time varying correlation between the covariates and the response (Rao, Abstract, “Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields. The spatio-temporal regression problem is of paramount importance from both the methodology development and real-world application perspectives.”) (Rao, Section II B., “… FLMs possess several advantages over the alternative temporal regression models such as RNN and LSTM: 1) FLMs are capable of handling versatile data formats. In particular, the time series covariates can be regular or irregular. Also, the number of observations as well as the measuring timestamps can be different across features and across subjects [21]. 2) FLMs effectively capture the timely varying correlation between the covariates and the response, while the sequential deep learning models typically use the same set of parameters across all timestamps [23], [24].”); further motivation is found for utilizing functional models to estimate RUL in Wang wherein providing functional models further provide the benefit of discovering the complex relationships between the sensor measurements and the RUL as functional analysis is more suitable to the nature of the equipment (Wang, Section II B., “Functional MLP, an algorithm that enables non-linear learning for the functional regression problem, is capable of discovering complex relationships between the continuous sensor curves and the RUL value. Due to the complicated nature of equipment, we believe that functional MLP is more suitable for the RUL estimation problem.”) (Wang, Abstract, “FDA explicitly incorporates both the correlations within the same equipment and the random variations across different equipment's sensor time series into the model. FDA also has the benefit of allowing the relationship between RUL and sensor variables to vary over time. We implement functional MLP on the benchmark NASA C-MAPSS data and evaluate the performance using two popularly-used metrics. Results show the superiority of our algorithm over all the other state-of-the-art methods.”)
Tse is considered to be analogous to the claimed invention because they are in the same field of machine fault diagnosis using wavelet analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Baek to incorporate the teachings of Tse in order to provide an exact wavelet analysis that improves upon CWT by providing the desirable result of no overlapping, distortion or redundant information. (Tse, Section 1.2, “Idealistically, each of the three temporal features of the raw signal should only appear in one scale that has the same frequency content and defined time frame as shown in Fig. 3. However, due to the problems of overlapping in adjacent scales and the distortion of the signal, the three temporal features appear in all three scales of the defined time frame as shown in Fig. 2. For the benefit of vibration-based machine fault diagnosis, the decomposed features that are obtained from an effective analyzing tool should possess all of the information on the amplitude, time and frequency exactly as they are in the original raw signal. That is, after the decomposition of the raw signal, each temporal feature should appear only in its expected scale and time frame exactly as it is displayed in the raw signal. The ideal results should have no overlapping, distortion or redundant information. Such a clear and precise result is called exact analysis. The aim of this paper is to develop an effective algorithm that will allow CWTs to achieve such a desirable result.”)
In regards to claim 2 and analogous claim 12,
Baek in view of Tse, Rao and Wang teach The method of claim 1,
Baek teaches wherein generating the set of one or more values associated with the one or more system characteristics comprises: providing the 2D time-and-frequency first input data set to the FNN as input;
(Baek, Abstract, “The residual life prediction apparatus uses a data conversion unit that converts each of a plurality of one-dimensional vibration signals into a two-dimensional image, and uses a plurality of two-dimensional images generated by the data conversion unit as input data [providing the 2D time-and-frequency first input data set to the FNN as input]”)
However, Baek does not explicitly teach generating, using the FNN, an output data set; and providing the output data set as a second input to a fully connected neural network (FCNN), wherein the generated set of one or more values associated with the one or more system characteristics is the output of the FCNN
Rao teaches generating, using the FNN, an output data set; and providing the output data set as a second input to a fully connected neural network (FCNN), wherein the generated set of one or more values associated with the one or more system characteristics is the output of the FCNN.
(Rao, Section II B., “The fundamental idea of FNN is to embed the FLM in Eq (4) into the fully connected neural network structure in deep learning [15]. In particular, the architecture of FNN is described as follows. The first layer of FNN consists of novel functional neurons. The functional neurons take the functional covariates Xi(t) = [X(i,1)(t),...,X(i,R)(t)]T as input and calculate [generating, using the FNN, an output data set]
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where b and Wr(βr,t) are the same as those in Eq (4), β =[β1,...,βR]T, and U(·) is a nonlinear activation function from R to R. The achieved scalar values H(Xi(t),β) are supplied into subsequent layers of numerical neurons (e.g., the inputs and outputs are both scalar values) [providing the output data set as a second input to a fully connected neural network (FCNN) ie numerical neurons] for further manipulations till the output layer that holds the response variable [wherein the generated set of one or more values associated with the one or more system characteristics is the output of the FCNN ie the response variable Yi]. An example FNN with three functional neurons on the first layer and two numerical neurons on the second layer is given in Fig 2.
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”)
In regards to claim 3 and analogous claim 13,
Baek in view of Tse, Rao and Wang teach The method of claim 2,
Rao teaches wherein the output data set is associated with a set of latent features of the 2D time-and-frequency input data set.
(Rao, Section II B., “Functional neural network (FNN) is introduced by [15] and later investigated further by [16], [22], with the purpose of learning complex mappings [wherein the output data set is associated with a set of latent features ie complex mappings of the 2D time-and-frequency input data set] between functional covariates {X(i,r)(t)}R r=1 and scalar responses Yi…”)
In regards to claim 4 and analogous claim 14,
Baek in view of Tse, Rao and Wang teach The method of claim 1,
Baek teaches wherein measuring the data relating to the operation of the system comprises continuously measuring the data, at the sample rate, over the first time period.
(Baek, Description para. 1, “The present invention relates to a technique for predicting a residual useful life (RUL) by analyzing vibration signals collected from a facility or a part, and in particular, it is possible to continuously collect signals by attaching a vibration sensor”)
In regards to claim 5 and analogous claim 15,
Baek in view of Tse, Rao and Wang teach The method of claim 1,
Tse teaches wherein the first time period is divided into a set of time-windows at each of a plurality of scales with each scale in the plurality of scales corresponding to a range of frequencies represented in the 2D time-and-frequency first input data set.
(Tse, pg. 7 para. 1, “According to the definition of Eq. (2), the results of the Morlet CWT should only exist as a temporal waveform x1(t) from 0.075 to 0.1 s at frequency level 200 Hz, and another temporal waveform x2(t) from 0.125 to 0.15 s at frequency level 100 Hz. However, as shown in Fig. 4, the unexpected waveforms appear also at levels of 220 and 180 Hz, which are adjacent to 200 Hz.”; see annotated figure 4 below wherein Tse also teaches a time window (see time axes wherein a time window is 0.5 seconds)
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In regards to claim 6 and analogous claim 16,
Baek in view of Tse, Rao and Wang teach The method of claim 5,
Tse teaches wherein applying the wavelet transform to the measured data to produce the 2D time-and-frequency first input data set comprises: applying, to each set of time-windows at each of the plurality of scales, a wavelet associated with the range of frequencies corresponding to the scale in the plurality of scales.
(Tse, pg. 7 para. 1, “According to the definition of Eq. (2), the results of the Morlet CWT [applying, to each set of time-windows at each of the plurality of scales, a wavelet (provided by the CWT) associated with the range of frequencies corresponding to the scale in the plurality of scales] should only exist as a temporal waveform x1(t) from 0.075 to 0.1 s at frequency level 200 Hz, and another temporal waveform x2(t) from 0.125 to 0.15 s at frequency level 100 Hz. However, as shown in Fig. 4, the unexpected waveforms appear also at levels of 220 and 180 Hz, which are adjacent to 200 Hz.”; see annotated figure 4 below
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In regards to claim 7 and analogous claim 17,
Baek in view of Tse, Rao and Wang teach The method of claim 6,
Tse teaches wherein each set of time-windows is a set of equal-size time- windows and the wavelet associated with each range of frequencies spans the equal-size time- windows in the set of equal-size time-windows associated with the range of frequencies.
(Tse, pg. 7 para. 1, “According to the definition of Eq. (2), the results of the Morlet CWT should only exist as a temporal waveform x1(t) from 0.075 to 0.1 s at frequency level 200 Hz, and another temporal waveform x2(t) from 0.125 to 0.15 s at frequency level 100 Hz. However, as shown in Fig. 4, the unexpected waveforms appear also at levels of 220 and 180 Hz, which are adjacent to 200 Hz.”; see annotated figure 4 below wherein the equal-size time-windows is 0.5
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In regards to claim 8 and analogous claim 18,
Baek in view of Tse, Rao and Wang teach The method of claim 1, further comprising:
Baek teaches measuring, at the sample rate, additional data relating to the operation of the system over a second time period; and producing an additional 2D time-and-frequency third input data set by applying the wavelet transform to the measured additional data relating to the operation of the system over the second time period, wherein generating the set of one or more values associated with the one or more system characteristics further comprises processing the additional 2D time-and-frequency third input data set [using the FNN.]
(Baek, Abstract, “A residual life prediction device is disclosed. The residual life prediction apparatus uses a data conversion unit that converts each of a plurality of one-dimensional vibration signals into a two-dimensional image, and uses a plurality of two-dimensional images generated by the data conversion unit as input data. Predictive model generation unit that trains an artificial neural network (ANN) model that outputs HI), and predicts the residual useful life (RUL) of a predicted facility or a predicted component using the trained ANN model [wherein generating the set of one or more values associated with the one or more system characteristics further comprises processing the additional 2D time-and-frequency third input data set; wherein the 2D data is input to the FNN of Rao] And a residual life prediction unit, and the data transformation unit generates the two-dimensional images by performing continuous wavelet transformation (CWT) [producing an additional 2D time-and-frequency third input data set by applying the wavelet transform to the measured additional data relating to the operation of the system over the second time period] on each of the one-dimensional vibration signals.”)
(Baek, Description para. 27, “FIG. 3 shows a contour plot of the wavelet power spectrum generated by the vibration signal and continuous wavelet transform (CWT). The vibration signal sampled at 0.1 s intervals at a frequency of 25.6 kHz has 2560 data points [measuring, at the sample rate, additional data relating to the operation of the system over a second time period; this would be another vibration signal].”
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However, Baek does not explicitly teach using the FNN
Rao teaches using the FNN
(Rao, Fig. 2, Section II B. Functional Data Analysis and Functional Neural Network [using the FNN], “Functional data analysis (FDA) is a rapidly growing branch of statistics specialized in representing and modeling dynamically varying data over a continuum (e.g., time) [23], [24] . FDA models uniquely treat X(i,r), the observed r-th feature of subject i over time, as discretized observations from a continuous underlying curve X(i,r)(t),t ∈ T . Under the regression setting, the conventional Functional Linear Models (FLM) [20], [21] assume and learn the unknown real-valued parameters in the mapping
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where b ∈ R is the unknown intercept, and βr is a finite dimensional vector that quantifies the parameter function Wr(βr,t), for r = 1,...,R.”
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Baek and Wang are both considered to be analogous to the claimed invention because they are in the same field of predictive maintenance using neural networks. Rao is considered analogous to the claimed invention because they are in the same field of functional neural networks and functional data analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Baek to incorporate the teachings of Rao in order to substitute the ANN of Baek with the FNN of Rao as functional models have the advantage of being capable of handling versatile data formats, and effectively capturing the time varying correlation between the covariates and the response (Rao, Abstract, “Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields. The spatio-temporal regression problem is of paramount importance from both the methodology development and real-world application perspectives.”) (Rao, Section II B., “… FLMs possess several advantages over the alternative temporal regression models such as RNN and LSTM: 1) FLMs are capable of handling versatile data formats. In particular, the time series covariates can be regular or irregular. Also, the number of observations as well as the measuring timestamps can be different across features and across subjects [21]. 2) FLMs effectively capture the timely varying correlation between the covariates and the response, while the sequential deep learning models typically use the same set of parameters across all timestamps [23], [24].”); further motivation is found for utilizing functional models to estimate RUL in Wang wherein providing functional models further provide the benefit of discovering the complex relationships between the sensor measurements and the RUL as functional analysis is more suitable to the nature of the equipment (Wang, Section II B., “Functional MLP, an algorithm that enables non-linear learning for the functional regression problem, is capable of discovering complex relationships between the continuous sensor curves and the RUL value. Due to the complicated nature of equipment, we believe that functional MLP is more suitable for the RUL estimation problem.”) (Wang, Abstract, “FDA explicitly incorporates both the correlations within the same equipment and the random variations across different equipment's sensor time series into the model. FDA also has the benefit of allowing the relationship between RUL and sensor variables to vary over time. We implement functional MLP on the benchmark NASA C-MAPSS data and evaluate the performance using two popularly-used metrics. Results show the superiority of our algorithm over all the other state-of-the-art methods.”)
In regards to claim 9 and analogous claim 19,
Baek in view of Tse, Rao and Wang teach The method of claim 1,
Baek teaches wherein the measured data is one of vibration data, acoustic data, or other time-varying data relating to an operation of the system.
(Baek, Description para. 1, “The present invention relates to a technique for predicting a residual useful life (RUL) by analyzing vibration signals collected from a facility or a part, and in particular, it is possible to continuously collect signals by attaching a vibration sensor.”)
In regards to claim 10 and analogous claim 20,
Baek in view of Tse, Rao and Wang teach The method of claim 1,
Baek teaches wherein the one or more system characteristics comprises at least one of a remaining useful life of the system, a probability of failing within a second time period following the first time period, or a detected anomaly.
(Baek, Abstract, “A residual life prediction device is disclosed.”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL: Ashish, R. (Dec. 23, 2018). Lecture 5: Transforms, Fourier and wavelets [PowerPoint slides]. https://web.archive.org/web/20181223112625/https://www.cs.cornell.edu/courses/cs5540/2010sp/lectures/Lec5.Transforms.pdf
(Ashish, Slide 28 disclose time-frequency analysis similar to the time frequency analysis shown in fig. 1 of the instant application
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/J.T.T./Examiner, Art Unit 2129
/SCHYLER S SANKS/Primary Examiner, Art Unit 2129
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