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 .
35 USC 101
The claims when viewed as a whole or in ordered combination integrates the abstract, idea, e.g. a judicial exception -mathematical calculation (see step “determining…” in representing claim 1) to a practical application, e.g. to diagnose abnormality of a mechanical device by providing notification of abnormal operation of the one or more components of the mechanical device when one or more of the components is determined to have abnormal operation.
Claim Rejections - 35 USC § 102
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 1-12 and 14-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vedual et al. (hereinafter “Vedula”) (USPAP. 20190095781).
Regarding claims 1, 16, and 20 Vedula discloses a method for abnormality diagnosis of a mechanical device having one or more moving components, comprising:
receiving, in a computer control system, motion of the one or more components (Pars. 29 and 30, rotating equipment) during operation of the mechanical device detected by one or more sensor devices (sensors 104 such as wireless sensor 104 can be a device, module, and/or subsystem that detects vibration of the rotating equipment 102) operatively associated with the mechanical device (Pars. 29-31);
determining, by the computer control system coupled to the one or more sensors, vibration measurement spectrums of the one or more components based on the detected motion of the one or more components (see Pars. 70-72), wherein one or more Machine Learning (ML) techniques are used (techniques Pars. 37, 38, 94-96) to determine abnormal operation of the one or more components by comparing a determined vibration measurement spectrum of a said component against that component's expected normal vibration measurement spectrum during operation of the mechanical device such that abnormal operation is determined when the determined vibration measurement spectrum of a said component exceeds its expected normal vibration measurement spectrum (spectrum at Pars. 70-72) by a threshold value (Pars. 30-39. Pars. 92, 93, 94: threshold values; 94-98. Also see step 316 diagnose faults using wavelet neural network at Fig. 3);
and providing, by the control system, notification of abnormal operation of the one or more components when one or more of the components is determined to have abnormal operation (Pars. 78 and 93. Also see Fig. 1).
Regarding claims 2 and 17, Vedula discloses wherein the one or more ML techniques utilizes both machine context data and process context data relating to operation of the mechanical device for determining abnormal operation of the one or more moving components whereby the machine context data includes one or more configured operating parameters for the one or more moving 31 components and the process context data relates to parameters of an output of the mechanical device (Par. 48).
Regarding claim 3, Vedula discloses wherein determining, by the control system using the one or more ML techniques, the expected normal vibration spectrum for the one or more moving components utilizing the machine and process context data relating to operation of the mechanical device (Pars. 70, 71, 96-98: When a fault occurs in the rotating equipment 102, some abnormal frequencies typically emerge in the frequency spectra of the digital signal, which can reflect the health conditions of the rotating equipment 102. Moreover, frequency spectra are often more sensitive to incipient faults since an imperceptible change will produce a spectrum line in corresponding frequency spectrum. Therefore, it may be useful for fault diagnosis to extract frequency-domain statistical features. In addition, these frequency-domain statistical features may contain some fault-related information that may not be present in the time domain statistical features. In other words, these statistical features in the frequency domain can be an effective compensation for those time-domain statistical features alone. At step 512, the server 106 trains the WNN for predicting the life percentage of the rotating equipment 102. In some embodiments, the server 106 can train the WNN using Levenberg-Marquardt (LM) algorithm based on the training set and the validation set. A person with skill in the art will appreciate that any suitable algorithm can be used to train the WNN. The over-fitting problem can be solved by the use of validation set. During training process of the WNN, the MSE for the training set and the validation set are calculated. Both of the mean square errors drop early in the training process, because the WNN can learn the relationship between the inputs and the outputs by modifying the trainable weights based on the training set. After a certain point, the mean square error for the validation set starts to increase because the WNN starts to model the noise in the training set. Thus, the training process may be stopped at this point, and the preferred WNN with optimum modeling and generalization capability is achieved.)
Regarding claims 4 and 18, Vedula discloses responsive to determination a said component is determined to have abnormal operation, determine adjustment to one or more operating parameters of the mechanical device such that the determined abnormal operating component is caused to operate within its expected normal vibration spectrum during operation of the mechanical device (Pars. 26, 70-72: adjustment if abnormal or deviation).
Regarding claim 5, Vedula discloses providing a control signal from the control system to the mechanical device including the determined adjusted operating parameters, causing adjustment to one or more operating parameters of the mechanical device such that the determined abnormal operating component is caused to operate within its expected normal vibration spectrum during operation of the mechanical device (Pars. 94-104).
Regarding claim 6, Vedula discloses wherein the one or more ML techniques includes utilization of one or more of a XG Boost algorithm and a neural network algorithmic technique (see Fig. 1, Pars. 78-87 for neural network technique).
Regarding claim 7, Vedula discloses training a Machine Learning (ML) model for determining the expected normal vibration spectrum for the one or more moving components utilizing the machine and process context data relating to operation of the mechanical device (Pars. 48 and 49).
Regarding claim 8, Vedula discloses training a Machine Learning (ML) model for determining the expected normal vibration spectrum for the one or more moving components utilizing the machine and process context data relating to operation of the mechanical device (Pars. 27, 78-87, 104).
Regarding claim 9, Vedula discloses wherein one or more of the optical sensor devices consist of a camera device (Par. 41: wireless sensor 104 at Figs. 1 and 2, can be designed to include a slot for plugging in a blink-up module such as 202 shown at Fig. 2 and the blink up module 202 can include a second processor and a photo-detector) operatively coupled to software for detecting motion of the one more components operative to detect at least vibration associated with each detected spatial point of the mechanical device (steps 508-518 Fig. 5: Pars. 87-100).
Regarding claim 10, Vedual discloses wherein the control system is operatively coupled to a plurality of geographically remote sensor systems, wherein each senor system has one or more sensor devices operatively associated with a respective mechanical device (see remote sensor system in Figs. 1 and 2).
Regarding claim 11, Vedual discloses wherein the one or more ML techniques includes applying images captured from the one or more optical sensors to a recurrent convolutional neural network for determining when a detected vibration measurement spectrum of a said component of the mechanical device exceeds its expected normal vibration measurement by a threshold value (Pars. 41-46).
Regarding claim 12, Vedual discloses including training, utilizing images captured from the one or more optical sensors (blink up module includes photo detector Par. 41), the recurrent convolutional neural network for determining the expected normal vibration spectrum for the one or more moving components (Par. 45: the blink-up module 202 can control the wireless sensor 104 and establish the connection of the wireless sensor 104 with the communication network 110. In some examples, circuitry of the wireless sensor 104 may be directly controlled by code executing within a virtual machine (not shown) that runs in the second processor (not shown) of the blink-up module 202. As a result, the second processor of the blink-up module 202 can control the wireless sensor 104. Using the virtual machine inside the blink-up module 202 can allow the wireless sensor 104 to not need a control processor. See Pars. 41-46).
Regarding claim 14, Vedual discloses wherein motion data detected from the one or more sensor devices is wirelessly transmitted via a communications network to the computer diagnostic control system remotely located from the mechanical device (see system at Fig. 1).
Regarding claim 15, Vedula discloses wherein determining when the determined vibration measurement spectrum of a said component exceeds its expected normal vibration measurement spectrum by a threshold value is performed in real-time (Par. 112 shows real measurements; Par. 33 user device is placed in an operating room of an industry receives data of detected faults and the RUL of the rotating equipment 102, which meets the “performed in real time” limitation).
Regarding claim 19, Vedula discloses wherein the one or more sensor devices include one or more optical sensor devices whereby each optical sensor device (Par. 41: wireless sensor 104 at Figs. 1 and 2, can be designed to include a slot for plugging in a blink-up module such as 202 shown at Fig. 2 and the blink up module 202 can include a second processor and a photo-detector) detects motion upon multiple spatial points on the mechanical device (Par. 45: the blink-up module 202 can control the wireless sensor 104 and establish the connection of the wireless sensor 104 with the communication network 110. In some examples, circuitry of the wireless sensor 104 may be directly controlled by code executing within a virtual machine (not shown) that runs in the second processor (not shown) of the blink-up module 202. As a result, the second processor of the blink-up module 202 can control the wireless sensor 104. Using the virtual machine inside the blink-up module 202 can allow the wireless sensor 104 to not need a control processor. See Pars. 41-46).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Vedula and Cella et al. (USPAP. 20200201292)(hereinafter “Cella”).
Regarding claim 13, Vedual does not explicitly disclose wherein the one or more moving components consist of a rotating drum associated with a mechanical device used for one or more of minerals, metals and materials output applications.
Cella teaches wherein the one or more moving components consist of a rotating drum associated with a mechanical device used for one or more of minerals, metals and materials output applications (Cella: Abstract; Pars. 54, 64-66, 415, 422).
It would have been obvious to one of ordinary skilled in the art at the time of filling the Application to modify Vedula's invention using Cella's invention to arrive at the claimed invention specified in claim to identify potential failures and provide improved prediction, diagnosis, yield, and efficiency (see Cella: Par. 245).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Busch et al. (USPAP. 20110113888) discloses an alignment device with one or two optoelectronic transmitting and/or receiving units and an evaluation unit. At least one optoelectronic transmitting and/or receiving unit contains an inclinometer. Furthermore, the transmitting and/or receiving unit is connected to a vibration sensor which can be the inclinometer. Both the result of the alignment process and also the result of the vibration measurement are communicated to the user as an easily understandable characteristic on a display of the evaluation unit. For vibration measurement at a non-rotating part of a machine, an accelerometer/inclinometer sensor may be used for measuring acceleration forces resulting from machine vibrations to be measured and for measuring gravity and an electronic evaluation unit determining the orientation of the sensor with regard to gravity from a stationary component of the sensor output and determining sensor orientation from evaluation of non-stationary components of sensor output (Abstract; Pars. 34-40).
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/PHUONG HUYNH/ Primary Examiner, Art Unit 2857 June 10, 2026
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