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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-10, 11-20, are directed to a method, apparatus of maintenance prediction model. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 11 these claims recite
obtaining target vibration signal data and target equipment data of a target industrial equipment; obtaining a target 2D short-time Fourier transform (STFT) image by preprocessing the target vibration signal data; and obtaining a remaining life of the target industrial equipment and derivation basis information on a basis for deriving the remaining life based on the target 2D STFT image and the target equipment data by using the maintenance prediction model that has been trained and built in advance.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they could be performed mentally, and they are also disclosed as a human user performing these functions, simply using a computer as a tool-see spec, page 8-10, Fig. 1, etc. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 11 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 11: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). Further the claimed invention appears to be something that can be performed by head and hand (Gottschalk v. Benson). The claimed solution is not necessarily rooted in computer technology in order to overcome a problem (DDR v. Hotels.com). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of “obtaining…”, obtaining…”, “obtaining…”, etc. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 12
Claim 2, 12 merely recite other additional elements that define a GAN which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3-9, 13-19
Claim 3-9, 13-19 merely recite other additional elements that define obtaining the remaining life of the equipment which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 10, 20
Claim 10, 20 merely recite other additional elements that define the vibration signal data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
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.
Claims 1-5, 10-15, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dev et al. (Dev) US 2024/0184282 in view of SCHOCKAERT et al. (Schockaert) US 2024/0248468
In regard to claim 1, Dev disclose A predictive maintenance method for industrial equipment based on a maintenance prediction model that is explainable in a time-frequency domain, the method comprising: ([0056][0077]-[0088] [0101]-[0107][0156] a method for industrial equipment based on a maintenance prediction model in a time frequency domain and output a prediction of one or more operating conditions)
obtaining target vibration signal data and target equipment data of a target industrial equipment; ([0005][0006] [0048]-[0051] vibration are measured and other sensor data (accelerometers) are sensed of a industrial equipment)
obtaining a target 2D short-time Fourier transform (STFT) image by preprocessing the target vibration signal data; (Fig. 3, [0080]-[0090] [0102][0103][0168]-[0173] obtain FFT image from preprocessing vibration data obtained) and
obtaining the operating condition of the target industrial equipment and derivation basis information on a basis for deriving the operating condition based on the target 2D STFT image and the target equipment data by using the maintenance prediction model that has been trained and built in advance. ([0048]-[0056][0067]-[0068][0080]-[0095] [0102][0103][0122] [0150]-[0156] [0168]-[0173] predicting an anomalous operating condition of the industrial equipment and derive base information from the normal operating condition for deriving the anomalous operation condition based on the STFT image with time series feature extraction using FFT with subsample time windows and equipment data by using the trained maintenance prediction model. Note: please further define the image using the functional language and how the prediction is obtained to help move forward the prosecution, call to discuss if necessary)
But Dev fail to explicitly disclose “obtaining a remaining life of the target industrial equipment and derivation basis information on a basis for deriving the remaining life based on the image and the data by the model.”
Schockaert disclose obtaining a remaining life of the target industrial equipment and derivation basis information on a basis for deriving the remaining life based on the image and the data by the model. ([0023] [0144]-[0145] [0251]-[0252] obtaining the remaining useful life and derivation base information based on the image and data by the model)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Schockaert’s method of predictive maintenance into Dev’s invention as they are related to the same field endeavor of predictive maintenance for equipment. The motivation to combine these arts, as proposed above, at least because Schockaert’s predicting a remaining life for the equipment would help to provide more prediction detail into Dev’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more prediction detail with a remaining life for the equipment would help to improve the accuracy of prediction and therefore improve user experience using the device.
In regard to claim 2, Dev and Schockaert disclose The predictive maintenance method of claim 1,
Dev disclose wherein the maintenance prediction model includes: and an encoder neural network that maps the 2D STFT image in a distribution. ([0086]-[0090] [0172]-[0186] encoder model correlate image data to a distribution corresponding to various operating conditions)
But Dev fail to explicitly disclose a generative adversarial network (GAN) including a generator that generates the 2D STFT image based on a distribution and a discriminator that discriminates the 2D STFT image generated through the generator;”
Schockaert disclose a generative adversarial network (GAN) including a generator that generates the 2D STFT image based on a distribution and a discriminator that discriminates the 2D STFT image generated through the generator; ([0251]-[0252] GAN and with generator that generate image based on a distribution and a discriminator the discriminate the image generated)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Schockaert’s method of predictive maintenance into Dev’s invention as they are related to the same field endeavor of predictive maintenance for equipment. The motivation to combine these arts, as proposed above, at least because Schockaert’s GAN would help to provide more prediction model into Dev’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more prediction model with GAN would help to improve the accuracy of prediction and therefore improve user experience using the device.
In regard to claim 3, Dev and Schockaert disclose The predictive maintenance method of claim 2,
Dev disclose inputting the target 2D STFT image and the target equipment data to the maintenance prediction model; ([0005][0006] [0084]-[0094]input the image and data to the model) and
obtaining the operating condition and the derivation basis information using a reconstructed 2D STFT image which is output data of the maintenance prediction model. ([0048]-[0056][0067]-[0068][0080]-[0095] [0102][0103][0122] [0142] [0150]-[0156] [0168]-[0173] predicting an anomalous operating condition of the industrial equipment and derive base information from the normal operating condition for deriving the anomalous operation condition based on the STFT image which is reconstructed with time series feature extraction using FFT with subsample time windows and the anomalous operating condition is the output of the predictive model)
But Dev fail to explicitly disclose “wherein the obtaining of the remaining life and the derivation basis information includes: obtaining the remaining life and the derivation basis information using the image.”
Schockaert disclose wherein the obtaining of the remaining life and the derivation basis information includes: obtaining the remaining life and the derivation basis information using the image. ([0023] [0144]-[0145] [0251]-[0252] obtaining the remaining useful life and derivation base information based on the image)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Schockaert’s method of predictive maintenance into Dev’s invention as they are related to the same field endeavor of predictive maintenance for equipment. The motivation to combine these arts, as proposed above, at least because Schockaert’s predicting a remaining life for the equipment would help to provide more prediction detail into Dev’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more prediction detail with a remaining life for the equipment would help to improve the accuracy of prediction and therefore improve user experience using the device.
In regard to claim 4, Dev and Schockaert disclose The predictive maintenance method of claim 3,
Dev disclose wherein the obtaining of the remaining life and the derivation basis information further includes:
inputting the target 2D STFT image and the target equipment data to the encoder neural network including a label embedding layer that transfers equipment data as a condition, ([0005][0006] [0040]-[0047] [0084]-[0094] [0123] [0152] [0153][0185]-[0190] input the image and data to the encoder and a layer which transfer the data as a labeled condition) and
obtaining a target distribution corresponding to the target 2D STFT image, which is an output of the encoder neural network; (Fig. 20, [0168]-[0170] [0183]-[0189] obtain a distribution corresponding to the image which is output of the encoder)
But Dev fail to explicitly disclose “inputting a discrete vector obtained based on the target distribution to the generator including a label embedding layer that converts the equipment data into a weight to be multiplied by a feature map channel to be transferred, and obtaining the reconstructed 2D STFT image which is an output of the generator; and obtaining the remaining life and the derivation basis information based on the target 2D STFT image and the reconstructed 2D STFT image.”
Schockaert disclose inputting a discrete vector obtained based on the target distribution to the generator including a label embedding layer that converts the equipment data into a weight to be multiplied by a feature map channel to be transferred, and obtaining the reconstructed 2D STFT image which is an output of the generator; and obtaining the remaining life and the derivation basis information based on the target 2D STFT image and the reconstructed 2D STFT image. (Fig. 1A, 1B, [0061] [0080]-[0084] [0100]-[0106] [0189]-[0190] [0251]-[0252] input vectors obtained based on the distribution to the generator with a layer that converts data into a weight and multiply the box in the right which is a node and obtaining the reconstructed 2D STFT image which is an output of the generator; ([0023] [0144]-[0145] [0251]-[0252] obtain the image with is generated by the generator) and obtaining the remaining life and the derivation basis information based on the target 2D STFT image and the reconstructed 2D STFT image. ([0023] [0144]-[0147] [0251]-[0252] obtaining the remaining useful life and derivation base information based on the discriminated images)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Schockaert’s method of predictive maintenance into Dev’s invention as they are related to the same field endeavor of predictive maintenance for equipment. The motivation to combine these arts, as proposed above, at least because Schockaert’s GAN would help to provide more prediction model into Dev’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more prediction model with GAN would help to improve the accuracy of prediction and therefore improve user experience using the device.
In regard to claim 5, Dev and Schockaert disclose The predictive maintenance method of claim 4,
But Dev fail to explicitly disclose “wherein the obtaining of the remaining life and the derivation basis information further includes obtaining the remaining life based on an anomaly score obtained based on the target 2D STFT image and the reconstructed 2D STFT image.”
Schockaert disclose wherein the obtaining of the remaining life and the derivation basis information further includes obtaining the remaining life based on an anomaly score obtained based on the target 2D STFT image and the reconstructed 2D STFT image. ([0023] [0144]-[0147] [0251]-[0252] the remaining life is obtained based on various scores which can be calculated and derived based on the data available which is well known skill for the people in the art)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Schockaert’s method of predictive maintenance into Dev’s invention as they are related to the same field endeavor of predictive maintenance for equipment. The motivation to combine these arts, as proposed above, at least because Schockaert’s predicting a remaining life for the equipment would help to provide more prediction detail into Dev’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more prediction detail with a remaining life for the equipment would help to improve the accuracy of prediction and therefore improve user experience using the device,
In regard to claim 10, Dev and Schockaert disclose The predictive maintenance method of claim 1,
Dev disclose wherein the target vibration signal data includes data representing a vibration signal for a predetermined direction of the target industrial equipment, ([0094] [0180]-[0185] the vibration data along the x, y or z axes) and the target equipment data includes data representing a discrete value for a process average drill press force of the target industrial equipment and a discrete value for the number of drilling processes of the target industrial equipment. ([0070]-[0078] [0112] [0123] [0135] [0150]-[0151] drilling machines with drill press and collecting a number of cycles of drill press values)
In regard to claims 11-15, 20, claims 11-15, 20 are apparatus claims corresponding to the method claims 1-5, 10 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-5, 10.
Claims 6, 8-9, 16, 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Dev et al. (Dev) US 2024/0184282 and SCHOCKAERT et al. (Schockaert) US 2024/0248468 as applied to claim 1, further in view of Sakuma et al. (Sakuma) US 2024/0119357
In regard to claim 6, Dev and Schockaert disclose The predictive maintenance method of claim 5,
But Dev and Schockaert fail to explicitly disclose “wherein the obtaining of the remaining life and the derivation basis information further includes obtaining the anomaly score based on a mean squared error (MSE) representing a difference between the target 2D STFT image and the reconstructed 2D STFT image, a score of the discriminator, and an MSE representing a distance on the target distribution obtained through the encoder neural network.”
Sakuma disclose wherein the obtaining of the remaining life and the derivation basis information further includes obtaining the anomaly score based on a mean squared error (MSE) representing a difference between the target 2D STFT image and the reconstructed 2D STFT image, a score of the discriminator, and an MSE representing a distance on the target distribution obtained through the encoder neural network. ([0037]-[0041][0052]-[0058] [0061]-[0070] [0091]-[0099]obtain an abnormality score based on a MSE and representing difference between the two data (image) and other evaluations results of the metrics, such as prediction accuracy (error) value (factor) and an MSE representing a distance on the distribution from the model)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Sakuma’s method of predictive maintenance into Schockaert and Dev’s invention as they are related to the same field endeavor of predictive maintenance for equipment. The motivation to combine these arts, as proposed above, at least because Sakuma’s error predicting with MSE would help to provide more error prediction calculation method into Schockaert and Dev’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more error prediction calculation with MSE would help to improve the accuracy of prediction and therefore improve user experience using the device.
In regard to claim 8, Dev and Schockaert disclose The predictive maintenance method of claim 4,
But Dev and Schockaert fail to explicitly disclose “wherein the obtaining of the remaining life and the derivation basis information further includes obtaining the derivation basis information based on an MSE representing a difference between the target 2D STFT image and the reconstructed 2D STFT image.”
Sakuma disclose wherein the obtaining of the remaining life and the derivation basis information further includes obtaining the derivation basis information based on an MSE representing a difference between the target 2D STFT image and the reconstructed 2D STFT image. ([0037]-[0041][0052]-[0058] [0061]-[0070] [0091]-[0099]obtain derivation basis information such as abnormality score based on a MSE and representing difference between the two data (image))
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Sakuma’s method of predictive maintenance into Schockaert and Dev’s invention as they are related to the same field endeavor of predictive maintenance for equipment. The motivation to combine these arts, as proposed above, at least because Sakuma’s error predicting with MSE would help to provide more error prediction calculation method into Schockaert and Dev’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more error prediction calculation with MSE would help to improve the accuracy of prediction and therefore improve user experience using the device.
In regard to claim 9, Dev and Schockaert disclose The predictive maintenance method of claim 8,
But Dev and Schockaert fail to explicitly disclose “wherein the obtaining of the remaining life and the derivation basis information further includes obtaining the derivation basis information by visualizing the MSE representing the difference between the target 2D STFT image and the reconstructed 2D STFT image as a residual plot.”
Sakuma disclose wherein the obtaining of the remaining life and the derivation basis information further includes obtaining the derivation basis information by visualizing the MSE representing the difference between the target 2D STFT image and the reconstructed 2D STFT image as a residual plot. (Fig. 10, 12, [0037]-[0041][0052]-[0058] [0061]-[0073] [0091]-[0099] plotting the MSE representing difference between the two data (image))
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Sakuma’s method of predictive maintenance into Schockaert and Dev’s invention as they are related to the same field endeavor of predictive maintenance for equipment. The motivation to combine these arts, as proposed above, at least because Sakuma’s error predicting with MSE would help to provide more error prediction calculation method into Schockaert and Dev’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more error prediction calculation with MSE would help to improve the accuracy of prediction and therefore improve user experience using the device.
In regard to claims 16, 18-19, claims 16, 18-19 are apparatus claims corresponding to the method claims 6, 8-9 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 6, 8-9.
Claims 7, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dev et al. (Dev) US 2024/0184282 and SCHOCKAERT et al. (Schockaert) US 2024/0248468, Sakuma et al. (Sakuma) US 2024/0119357 as applied to claim 1, further in view of Sudo et al. (Sudo) US 2021/0272704
In regard to claim 7, Dev and Schockaert, Sakuma disclose The predictive maintenance method of claim 6,
But Dev and Schockaert, Sakuma fail to explicitly disclose “wherein the remaining life is inversely proportional to the anomaly score.”
Sudo disclose wherein the remaining life is inversely proportional to the anomaly score. (Fig. 17, [0075] [0135] [0202]-[0208] the more of the anomaly score, the more of deterioration of the target (the more of the slope), the less of the remaining life (failure operating time subtract the current operating time)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Sudo’s method of condition monitoring into Sakuma, Schockaert and Dev’s invention as they are related to the same field endeavor of predictive maintenance for equipment. The motivation to combine these arts, as proposed above, at least because Sudo’s remaining life calculation would help to provide more prediction calculation method into Sakuma, Schockaert and Dev’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing remaining life calculation would help to improve the accuracy of prediction and therefore improve user experience using the device.
In regard to claim 17, claims 17 is an apparatus claim corresponding to the method claim 7 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20210055719 A1 2021-02-25 ZHENG et al.
SYSTEM FOR PREDICTIVE MAINTENANCE USING GENERATIVE ADVERSARIAL NETWORKS FOR FAILURE PREDICTION
ZHENG et al. disclose Example implementations involve a system for Predictive Maintenance using Generative Adversarial Networks for Failure Prediction. Through utilizing three processes concurrently and training them iteratively with data-label pairs, example implementations described herein can thereby generate a more accurate predictive maintenance model than that of the related art. Example implementations further involve shared networks so that the three processes can be trained concurrently while sharing parameters with each other… see abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights