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 .
1. Claims 1-12 are presented for examination.
Claim Objections
2. Claims 1 and 9 are objected to because of the following informalities:
The space between “collecting”, “determining” and “generating” and “,” should be deleted Appropriate correction is required.
Claim Rejections - 35 USC § 112
3. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 5-8 and 11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5, “communication interface,” listed but not ben use or integrated with other limitation; merely lists a structure without defining how they connect, cooperate, or structurally integrate can is indefiniteness or omission of essential cooperative relationships, see MPEP § 2172. Appropriate correction is required.
Claims 3, 7 and 11, “different type of faults” is ambiguous and vague because the exact boundaries of the claim are unclear to a person of ordinary skill in the art. Specially the specification does not clearly define what constitutes each "type" of defect, the claim is considered insolubly ambiguous. See MPEP § 2173.05(d).
As claims 6-8 are directly or indirectly dependent on claim 5, thus those claims are also rejected at least by virtue of their dependency.
Claim Rejections - 35 USC § 101
4. 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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites collecting , via one or more hardware processors, data from a wind turbine as input data; generating , via the one or more hardware processors, a plurality of prime values associated with one or more parameters of the input data, by processing the input data using a dynamics model, wherein the plurality of prime values are an estimate of actual values of the one or more parameters of the input data; determining , using a fault model via the one or more hardware processors, correlation of a measured value of the one or more parameters of the input data with associated prime values in presence of a plurality of fault signatures; determining a likelihood function , via the one or more hardware processors, wherein the likelihood function indicates probability of the measured value of the one or more parameters given the associated prime values and fault signatures; generating , using a Bayesian inference model via the one or more hardware processors, a posterior information by combining the likelihood function with a prior information, wherein the posterior information is an updating of the prior information in light of one or more new observations indicated in the likelihood function; and generating , via the one or more hardware processors, one or more fault signature distributions and associated magnitude and uncertainty, by processing the generated posterior information, wherein the one or more fault signature distributions is indicative of one or more probable faults in at least one of a sensor and actuator of the wind turbine.
The limitation of generating , via the one or more hardware processors, a plurality of prime values associated with one or more parameters of the input data, by processing the input data using a dynamics model; generating, via the one or more hardware processors, one or more fault signature distributions and associated magnitude and uncertainty; and generating , using a Bayesian inference model via the one or more hardware processors, a posterior information by combining the likelihood function with a prior information, as drafted, is a process that, under its broadest reasonable interpretation, Mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I); but for the recitation of generic computer components (via the one or more hardware processors).
In addition, the limitations of determining, using a fault model via the one or more hardware processors, correlation of a measured value of the one or more parameters of the input data with associated prime values in presence of a plurality of fault signatures; and determining a likelihood function , via the one or more hardware processors, wherein the likelihood function, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (via the one or more hardware processors). That is, other than reciting “a hardware processor,” nothing in the claim element precludes the step from practically being performed in the mind or mathematical relationships. For example, but for the “hardware processor” language, “generating” and “determining” in the context of this claim encompasses the user manually calculating to determine the wind turbine defect. If a claim limitation, under its
broadest reasonable interpretation, covers performance of the limitation in the mind but for the
recitation of generic computer components, then it falls within the “Mental Processes” and “mathematical relationships grouping” of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim
recites additional element – using a processor to perform both “generating” and “determining” steps. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor
performing a generic computer function. such that it amounts no more than mere instructions to apply the exception using a generic computer component.
In addition, recites additional element “collecting data from a wind turbine as input data” is insignificant extra‑solution activity; the limitation of receiving data amounts to no more than insignificant pre-activity of receiving data. Further, the “receiving” step simply appends well-understood and conventional activity of receiving data over a network (see MPEP 2106.05(d)(II)(i): “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL! Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements 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 element of using a hardware processor to perform “determining”, “generating” and “collecting” steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
The analysis above applies to all statutory categories of invention. As such, the presentment of claim 1 otherwise styled as a system claim 5, adding a communication interface and memory and claim 9, a non-transitory machine-readable, for example, would be subject to the same analysis. Therefore, claims 5 and 9 are rejected for the same rational that applied to claim 1 Thus, the independent of claims 1, 5 and 9 are not patentable eligible.
As the dependent claims 2-4, 6-8 and 10-12 further limit the abstract idea of an analysis that can be performed mentally or certain methods of human activity that were already rejected in claims 1, 5 and 9, but fail to remedy the deficiencies of the parent claim as they do not impose any limitations that amount to significantly more than the abstract idea itself.
Regarding claims 2, 6 and 10, recite plurality of state parameters, wind data, and measured values from at least one of the sensors and the actuator, which is insignificant extra solution activity (see MPEP 2106.05(g). Thus, the claims are an abstract idea Thus, the claims are an abstract idea.
Regarding claims 3, 7 and 11 fault signature distributions capture a plurality of different types of faults associated with at least one of a sensor or actuator of the wind turbine, which is insignificant extra solution activity (see MPEP 2106.05(g). Thus, the claims are an abstract idea Thus, the claims are an abstract idea.
Regarding claims 4, 8 and 12 probable faults are in one or more sensors or actuators of the wind turbine, which is insignificant extra solution activity (see MPEP 2106.05(g). Thus, the claims are an abstract idea Thus, the claims are an abstract idea.
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.
5. 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.
5.1 Claim(s) 1-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yin et al. (CN 122595146 A) in view of Cerrada et al. (Mechanical Systems and Signal Processing) further in view of Ishioka et al. (US 8,433,539 B2).
Regarding claims 1, 5 and 9, YIN discloses collecting, via one or more hardware processors, data from a wind turbine as input data (Page 2, par. 1, collecting multidimensional sensor data of a water-cooling system of a wind turbine generator);
generating, via the one or more hardware processors, a plurality of prime values associated with one or more parameters of the input data (Page 9, Par. 4-6, parameter value higher than the confidence interval is 2, the parameter value lower than the confidence interval is 1, and the value in the normal range is 0. The discretized dataset will serve as input for subsequent bayesian network structure learning and parameter learning) by processing the input data using a dynamics model (page 9, par. 4-6, the measured sensor data X at any moment is input into a corresponding BLS model to obtain the predicted value of the target variable residual sequence and discretized data generation), wherein the plurality of prime values are an estimate of actual values of the one or more parameters of the input data (page12, Par. 1, the input variable of the sample calculating the predicted values of 8 target variables, obtaining residual errors by differencing the predicted values and the measured values, and discretizing the residual errors into 0, 1 and 2 state);
determining, using a fault model (Abstract, fault diagnosis) via the one or more hardware processors, correlation of a measured value Abstract, actual measurement value) of the one or more parameters of the input data with associated prime values (page 3, par. 1, the parameter value lower than the confidence interval is 1) in presence of a plurality of fault signatures (Abstract, calculating residual errors of an actual measurement value and a predicted value, and performing types of typical Fault time periods are marked according to the on-site operation and maintenance records, namely, a cooling liquid leakage Fault (Fault 1), a radiator scaling Fault (Fault 2), a water pump performance attenuation Fault (Fault 3) and a pipeline local blockage Fault (Fault 4), and the labels of the Fault time periods are confirmed by an on-site maintenance work order);
determining a likelihood function (page 3, par. 5, Maximum Likelihood Estimation), via the one or more hardware processors, wherein the likelihood function (page 3, par. 5, Maximum Likelihood Estimation) indicates probability of the measured value (page 2, par. 5-6, an actual measurement value) of the one or more parameters (page 3, par. the parameter value lower than the confidence interval is 1 (representing lower abnormality) (page 3, par. 6-7, a maximum likelihood estimate of the conditional probability distribution of a symptom node is obtained by counting how often states of the node appear with a parent node in a certain combination of states) given the associated prime values and the value in a normal range is 0 (representing normal), and fault signatures (page 4, par. 2, page 8, par. 2-3, the total number of the collected sensor variables is 12, and the sensor variables comprise cooling water inlet temperature T (C), cooling water outlet temperature T in environment temperature T amb (C), cooling water flow Q (L/min), water pump motor current I /min), converter inlet temperature T convin (C), converter outlet temperature T active power P (kW), cabin internal temperature T nacelle convout (C), generator winding temperature T (C) and cooling water pressure P out pump (C), (A), radiator fan rotating speed N gen fan (C), unit water (kPa), namely N=12); and
generating, using a Bayesian inference model (page 6, par. 8, Bayesian network diagnosis model) via the one or more hardware processors, a posterior information (Page 6, par. 9, obtaining the posterior probability of the fault nodes) by combining the likelihood function( page 6, par. 6-9, Maximum Likelihood Estimation) with a prior information (page 6, par. 8, set of historical data sets) (Abstract, page 3, par. 2, performing parameter learning by maximum likelihood estimation to establish a complete Bayesian network diagnosis model, performing real-time online fault diagnosis by using the model, and outputting diagnosis results by posterior probability reasoning. The posterior outputting diagnosis results by posterior probability reasoning from The prior probability learning of the failed node can be learned through maintenance records on site or corresponding rules of an expert, specifically, for a set of historical data sets); wherein the posterior information is an updating of the prior information in light of one or more new observations indicated in the likelihood function (page 6, par. 9-10 taking the observed state of the symptom nodes as the input of the established Bayesian network, obtaining the posterior probability of the fault nodes, and outputting a diagnosis result according to a set rule). processing the generated posterior information (page 11, par. 6-8, the posterior probability of the fault node also changes correspondingly).
Yin fails to disclose generating, via the one or more hardware processors, one or more fault signature distributions and associated magnitude and uncertainty, wherein the one or more fault signature distributions are indicative of one or more probable faults in at least one of a sensor and actuator of the wind turbine.
However, Cerrada discloses generating, via the one or more hardware processors (page 172, par. 1, signal processing), one or more fault signature distributions (Page 172, par. 2, analysis of the fault signature) and associated magnitude (page 171, par. 2, magnitude of the fault) and uncertainty (page 171, Par. 2, magnitude of the fault is not analyzed) (Abstracta, Page 173, par. 5, Fault size estimation or degradation assessment is usually based on the analysis of the fault signature), wherein the one or more fault signature distributions is indicative of one or more probable faults in at least one of a sensor and actuator of the wind turbine (Abstract, page 172, par. 2, page 173, par. 1-2, Fault size estimation or degradation assessment is usually based on the analysis of the fault signature. A fault signature is a vector of symptoms for each fault, that can be related to the extraction of specific features from the analyzed signals, such as vibration, AE, current or voltages. Sensor can provide useful information for detecting impulses within the frequency spectrum of the vibration signa. These features are direct measurements from sensors).
Yin and Cerrada are analogous art. They relate to Fault diagnosis of wind turbine generator. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify intelligent operation and maintenance of wind turbine generators, taught by Yin, incorporated with health condition monitoring, taught by Cerrada, for detecting and diagnosing their faults a wind turbine, or extracting the proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions. Finally, new challenges are highlighted in order to develop new contributions in a new field.
Yin and Cerrada fail to disclose one or more hardware processors, a communication interface and a memory. However, Ishioka discloses (column 5, line 65-column 6, line 14, the configuration of a wind turbine monitoring device includes a computer system (calculating system) formed of a CPU (central processing unit) 11, a main storage device 12 such as a RAM (Random Access Memory), an auxiliary storage device 13 such as a ROM (Read Only Memory) or an HDD (Hard Disk Drive), an input device 14 such as a keyboard or mouse, an output device 15 such as a monitor or printer, and a communication device for sending and receiving information by performing communication with an external device.
Ishioka, Yin and Cerrada are analogous art. They relate to Fault diagnosis of wind turbine generator. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Status monitoring of a wind turbine is automatically performed, and evaluation, taught by Ishioka, incorporated with the teaching of Yin and Cerrada, as stated above, in order to provides a wind turbine monitoring device, method, and program that can perform automatic monitoring of the wind turbine status and that can perform quantitative evaluation of that status based on an appropriate criterion.
Regarding claims 2, 6 and 10, Yin discloses the input data comprises a plurality of state parameters, wind data, and measured values from at least one of the sensor and the actuator (Page 2, par. 5-6, collecting multidimensional sensor data of a water cooling system are collected from a wind turbine generator system, and collected variables comprise fault nodes and symptom nodes. the measured sensor data X at any time is input into the corresponding BLS model to obtain the predicted value of the target variable).
Regarding claims 3, 7 and 11, Cerrada and Yin disclose:
Cerrada discloses one or more fault signature distributions capture a plurality of different types of faults associated with at least one of a sensor or actuator of the wind turbine (Abstract, page 172, par. 2, page 173, par. 1-2, fault size estimation or degradation assessment is usually based on the analysis of the fault signature. A fault signature is a vector of symptoms for each fault, that can be related to the extraction of specific features from the analyzed signals, such as vibration, AE, current or voltages. Sensor can provide useful information for detecting impulses within the frequency spectrum of the vibration signa. These features are direct measurements from sensors).
Yin discloses page 8, par. 2-3, the total number of the collected sensor variables is 12, and the sensor variables comprise cooling water inlet temperature T (C), cooling water outlet temperature T in environment temperature T amb (C), cooling water flow Q (L/min), water pump motor current I /min), converter inlet temperature T convin (C), converter outlet temperature T active power P (kW), cabin internal temperature T nacelle convout (C), generator winding temperature T (C) and cooling water pressure P out pump (C), (A), radiator fan rotating speed N gen fan (C), unit water (kPa), namely N=12.
Regarding claims 4, 8 and 12, Yin discloses the one or more probable faults are in one or more sensors or actuators of the wind turbine (page 7, par. 6, page 13, claim 2, fault diagnosis method for a water cooling system of a wind turbine generator provided output reliable posterior diagnosis conclusion through Bayesian network reverse reasoning under the incomplete evidence condition of partial sensor signal loss. Removing missing values and abnormal values caused by sensor faults, wherein a judgment criterion is a criterion, namely removing data points which deviate from the mean value by more than 3 times of standard deviation).
Citation Pertinent prior art
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rajagopal (US20200150192A1) discloses a fault detector that is arranged to monitor the indication of the number of high-frequency events and the generated frequency band information, and to generate a fault flag in response to the monitored indication of the number of high-frequency events and the generated frequency band information.
Neti (US20130049733A1) discloses a method detecting faults in a wind turbine generator based on current signature analysis and acquiring a set of electrical signals representative of an operating condition of a generator. Further, the electrical signals are processed to generate a normalized spectrum of electrical signals. A fault related to a gearbox or bearing or any other component associated with the generator is detected based on analyzing the current spectrum.
Hartman (US20090265295A1) discloses analysis of a process having parameter-based faults includes: a parameter value inputter configured for inputting values of at least one process parameter, a fault detector, configured for detecting the occurrence of a fault, a learning file creator associated with the parameter value inputter and the fault detector.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969).
Conclusion
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kidest Worku, whose telephone number is 571-272-3737. The examiner can normally be reached on Mon-Fri 9am to 5pm, ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ali Mohammad, can be reached on 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KIDEST WORKU/Primary Examiner, Art Unit 2119
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