DETAILED ACTION
This action is in response to the application filed on 7/17/2024.
Claims 1-14 are pending.
Acknowledgment is made of a claim for foreign priority. All of the certified copies of the priority documents have been received.
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 .
Means plus Function - 35 USC § 112
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.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Since the claim limitation(s) “implementing means” and “comparing means”, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim(s) has/have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the at least one corresponding structure described in the specification for each of the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation:
“comparing means“ : Element 14 See Fig. 2
“implementing means” : Element 12 See Fig. 2
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Mapping Notation
In this office action, following notations are being used to refer to the paragraph numbers or column number and lines of portions of the cited reference.
In this office action, following notations are being used to refer to the paragraph numbers or column number and lines of portions of the cited reference.
[0005] (Paragraph number [0005])
C5 (Column 5)
Pa5 (Page 5)
S5 (Section 5)
Furthermore, unless necessary to distinguish from other references in this action, “et al.” will be omitted when referring to the reference.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-14 are rejected under 35 U.S.C. 102(a2) as being anticipated by Nemani et al. (US 20240370009 A1)
1. A method for predicting the remaining life of a bearing, the method comprising: measuring vibrations of the bearing, and
“[0026]…The industrial equipment may include industrial mechanical equipment and the time-series data may include machine sensor data collected from at least one sensor. The industrial equipment may include a bearing such as a roller element bearing. The sensor data may include data such as shaft rotation speed and loading conditions associated with a roller element bearing. The sensor data may include vibration data and the at least one sensor comprises an accelerometer.”
implementing a neural network configured to determine the remaining life of the bearing from the measured vibrations.
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment, and applying a jointly trained health predictor (HP-JT) to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment. The jointly trained health predictor (HP-JT) may be trained using real data and augmented with generated data.”
2. A method for training a neural network configured to determine the remaining life of a bearing from measured vibrations, the method comprising:
for at least a training bearing, obtaining at least one training set comprising vibration measurements of the training bearing and a measured remaining life of the training bearing associated to the vibration measurements, implementing the neural network to determine a first output remaining life from the vibration measurements of the training set, performing a first comparison between the measured remaining life of the training set and the first output remaining life, and
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment, and applying a jointly trained health predictor (HP-JT) to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment. The jointly trained health predictor (HP-JT) may be trained using real data and augmented with generated data.”
tuning weights of the neural network according to the result of the first comparison.
“[0113] The RUL prediction results for all the test bearings, as a result of the five-fold cross-validation, are summarized in Table 6. The bearings are sorted in ascending order of the total prognostic duration ΔT, defined as ΔT=t.sub.EOL−t.sub.FPT+1. For each bearing in Table 6, the model with the least prediction error is highlighted in bold. The cumulative RMSE.sub.RUL is calculated by doing a weighted average of the individual bearing RMSE.sub.RUL scaled by ΔT.”
3. The method according to claim 2, further comprising:
for at least one validation bearing, obtaining at least one validation set comprising vibration measurements of the validation bearing and a measured remaining life of the validation bearing associated to the vibration measurements, implementing the neural network to determine a second output remaining life from the vibration measurements of the validation set, performing a second comparison between the measured remaining life of the validation set and the second output remaining life,
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment, and applying a jointly trained health predictor (HP-JT) to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment. The jointly trained health predictor (HP-JT) may be trained using real data and augmented with generated data.”
wherein tuning weights of the neural network further comprises tuning weights of the neural network according to the result of the second comparison.
“[0113] The RUL prediction results for all the test bearings, as a result of the five-fold cross-validation, are summarized in Table 6. The bearings are sorted in ascending order of the total prognostic duration ΔT, defined as ΔT=t.sub.EOL−t.sub.FPT+1. For each bearing in Table 6, the model with the least prediction error is highlighted in bold. The cumulative RMSE.sub.RUL is calculated by doing a weighted average of the individual bearing RMSE.sub.RUL scaled by ΔT.”
4. A method according to claim 2, wherein each comparison between the said remaining life and the said output remaining life comprises determining the mean absolute error between the said remaining life and the said output remaining life.
“[0114] To better compare the prediction results, we analyze the mean absolute error (MAE) that quantifies the magnitude of the prediction error, and also include the mean error that quantifies the overall direction of the prediction error (overestimation or underestimation). At the same level of prediction accuracy, underestimating the bearing RUL is often more desirable than overestimating it in industry settings because overestimation brings misleading confidence to the end user and may cause unexpected machine failure. FIG. 9(a) summarizes the MAE and mean error of RUL prediction by various models.”
5. A method according to claim 3, wherein each comparison between the said remaining life and the said output remaining life comprises determining the mean absolute error between the said remaining life and the said output remaining life.
“[0114] To better compare the prediction results, we analyze the mean absolute error (MAE) that quantifies the magnitude of the prediction error, and also include the mean error that quantifies the overall direction of the prediction error (overestimation or underestimation). At the same level of prediction accuracy, underestimating the bearing RUL is often more desirable than overestimating it in industry settings because overestimation brings misleading confidence to the end user and may cause unexpected machine failure. FIG. 9(a) summarizes the MAE and mean error of RUL prediction by various models.”
6. The device for predicting the remaining life of a bearing, the device comprising: a memory storing a neural network configured to determine a remaining life of the bearing from measured vibrations of the bearing, and implementing means configure to implement the neural network.
“[0026]…The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment, and applying a jointly trained health predictor (HP-JT) to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment.”
7. The device according to claim 6, wherein the neural network comprises at least one stack of three layers, a dense layer and at least one recurrent layer comprising at least one recurrent unit, the stack of three layers comprising a convolutional layer, a batch normalisation layer and a max pooling layer.
“[0059] This disclosure proposes a GAN-based LSTM predictor for predicting remaining useful life (RUL) in industrial equipment. Specifically, a Jointly Trained Health Predictor (HP-JT) method is disclosed to forecast the future behavior of a set of higher-level health indicators that are computed from raw sensor data.”
“[0071] The disclosed HP-JT predictor is formed by an LSTM layer followed by a dense layer. As shown in FIG. 4, the first step of offline predictor training is to pre-train the HP-JT predictor. The input of the HP-JT is the higher-level feature values starting from the previous k−1 timesteps to the current time. The output of the predictor model is the higher-level feature value at the next time step. In this step, the mean squared error loss is adopted to optimize the parameters of the predictor.”
8. The device according to claim 7, where the recurrent unit comprises a long short-term memory unit.
“[0059] This disclosure proposes a GAN-based LSTM predictor for predicting remaining useful life (RUL) in industrial equipment. Specifically, a Jointly Trained Health Predictor (HP-JT) method is disclosed to forecast the future behavior of a set of higher-level health indicators that are computed from raw sensor data.”
9. The device according to claim 6, wherein: the implementing means is configured to implement the neural network to determine a first output remaining life from vibration measurements of at least one training set, the training set comprising vibration measurements of a training bearing and a measured remaining life of the training bearing associated to the vibration measurements,
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment,..”
the device further comprises: comparing means configured to perform a first comparison between the measured remaining life of the training set and the first output remaining life, and
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment, and applying a jointly trained health predictor (HP-JT) to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment. The jointly trained health predictor (HP-JT) may be trained using real data and augmented with generated data.”
tuning means configured to tune weights of the neural network according to the result of the first comparison.
“[0114] To better compare the prediction results, we analyze the mean absolute error (MAE) that quantifies the magnitude of the prediction error, and also include the mean error that quantifies the overall direction of the prediction error (overestimation or underestimation). At the same level of prediction accuracy, underestimating the bearing RUL is often more desirable than overestimating it in industry settings because overestimation brings misleading confidence to the end user and may cause unexpected machine failure. FIG. 9(a) summarizes the MAE and mean error of RUL prediction by various models.”
10. The device according to claim 9, wherein:
the implementing means is configured to implement the neural network to determine a second output remaining life from vibration measurements of at least one validation set, the validation set comprising vibration measurements of a validation bearing and a measured remaining life of the validation bearing associated to the vibration measurements,
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment,..”
the comparing means is configured to perform a second comparison between the measured remaining life of the validation set and the second output remaining life, and
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment, and applying a jointly trained health predictor (HP-JT) to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment. The jointly trained health predictor (HP-JT) may be trained using real data and augmented with generated data.”
the tuning means is configured to tune weights of the neural network according to the result of the second comparison.
“[0114] To better compare the prediction results, we analyze the mean absolute error (MAE) that quantifies the magnitude of the prediction error, and also include the mean error that quantifies the overall direction of the prediction error (overestimation or underestimation). At the same level of prediction accuracy, underestimating the bearing RUL is often more desirable than overestimating it in industry settings because overestimation brings misleading confidence to the end user and may cause unexpected machine failure. FIG. 9(a) summarizes the MAE and mean error of RUL prediction by various models.”
11. The device according to claim 8, wherein: the implementing means is configured to implement the neural network to determine a first output remaining life from vibration measurements of at least one training set, the training set comprising vibration measurements of a training bearing and a measured remaining life of the training bearing associated to the vibration measurements,
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment,..”
the device further comprises: comparing means configured to perform a first comparison between the measured remaining life of the training set and the first output remaining life, and
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment, and applying a jointly trained health predictor (HP-JT) to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment. The jointly trained health predictor (HP-JT) may be trained using real data and augmented with generated data.”
tuning means configured to tune weights of the neural network according to the result of the first comparison.
“[0114] To better compare the prediction results, we analyze the mean absolute error (MAE) that quantifies the magnitude of the prediction error, and also include the mean error that quantifies the overall direction of the prediction error (overestimation or underestimation). At the same level of prediction accuracy, underestimating the bearing RUL is often more desirable than overestimating it in industry settings because overestimation brings misleading confidence to the end user and may cause unexpected machine failure. FIG. 9(a) summarizes the MAE and mean error of RUL prediction by various models.”
12. The device according to claim 11, wherein: the implementing means is configured to implement the neural network to determine a second output remaining life from vibration measurements of at least one validation set, the validation set comprising vibration measurements of a validation bearing and a measured remaining life of the validation bearing associated to the vibration measurements,
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment,..”
the comparing means is configured to perform a second comparison between the measured remaining life of the validation set and the second output remaining life, and
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment, and applying a jointly trained health predictor (HP-JT) to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment. The jointly trained health predictor (HP-JT) may be trained using real data and augmented with generated data.”
the tuning means is configured to tune weights of the neural network according to the result of the second comparison.
“[0114] To better compare the prediction results, we analyze the mean absolute error (MAE) that quantifies the magnitude of the prediction error, and also include the mean error that quantifies the overall direction of the prediction error (overestimation or underestimation). At the same level of prediction accuracy, underestimating the bearing RUL is often more desirable than overestimating it in industry settings because overestimation brings misleading confidence to the end user and may cause unexpected machine failure. FIG. 9(a) summarizes the MAE and mean error of RUL prediction by various models.”
13. A bearing device comprising: a bearing, a sensor configured to measure vibrations of the bearing, and a device according to claim 5 connected to the sensor.
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment, and applying a jointly trained health predictor (HP-JT) to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment. The jointly trained health predictor (HP-JT) may be trained using real data and augmented with generated data.”
14. A bearing device comprising: a bearing, a sensor configured to measure vibrations of the bearing, and a device according to claim 12 connected to the sensor.
“[0026] According to another aspect, a method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment, and applying a jointly trained health predictor (HP-JT) to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment. The jointly trained health predictor (HP-JT) may be trained using real data and augmented with generated data.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Niu et al. (US 20240085274 A1) and Sjögren et al. (US 20210334656 A1) disclose relevant art related to the subject matter of the present invention.
A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action. An extension of time may be obtained under 37 CFR 1.136(a). However, in no event, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAE N NOH whose telephone number is (571)270-0686. The examiner can normally be reached on Mon-Fri 8:30AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Vaughn can be reached on (571) 272-3922. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAE N NOH/
Primary Examiner
Art Unit 2481
Prosecution Insights