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 .
Response to Arguments
The Amendment filed on February 19, 2026, has been entered. The examiner acknowledges the amendments to claims 1-4, 13-16, 18-19.
Rejections under 35 U.S.C § 112(b): Applicant’s amendments to claim 15 have rendered that claim not indefinite, thus rejection under 35 U.S.C § 112(b) for that claim is withdrawn.
Rejections under 35 U.S.C. § 101: Applicant’s amendments disclose a practical application for the invention, thus the rejections under 35 U.S.C. § 101 are withdrawn.
Rejections under 35 U.S.C. § 103: Applicant’s amendments to the claims provide additional and necessary detail to the technical functionality of the invention. Additional search did uncover prior art teaching those amended elements.
Applicant argues that energy loss data related to harmonic frequency data is not included in the prior art. Additional search discloses how changes in vibration patterns at dominant frequencies lead to increased DC flow and eventual saturation of the transformer. The DC causes an increase in vibration intensity and additional losses through higher harmonics and reduction of reactive power. In view of these findings, applicant’s arguments are not compelling and the rejections under 35 U.S.C. § 103 will not be withdrawn.
Claim Rejections – 35 U.S.C. § 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 non-statutory subject matter. The claims, 1-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-20 are directed to a process (method), machine (system), and product/article of manufacture, which are statutory categories of invention.
Step 2A
Claims 1-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of analyzing transformer monitoring sensor data to generate replacement data informing a decision on the optimal time for replacing a transformer.
Claim 1: A method comprising:
receiving, sensor data during operation of the transformer; (following rules or instructions, observation, evaluation, judgement, opinion)
generating, energy loss data representative of a predicted energy loss of the transformer based at least in part on: (i) the sensor data and (ii) load harmonic frequency information based on vibroacoustic signal data the load harmonic frequency information (following rules or instructions, observation, evaluation, judgement, opinion), being generated by:
transforming the vibroacoustic signal data from a time domain to a frequency domain using a Fast Fourier transformation (FFT) to identify one or more vibroacoustic harmonic frequencies, (following rules or instructions, observation, evaluation, judgement, opinion), and
determining harmonic distortion of an electrical load associated with the transformer based on the one or more vibroacoustic harmonic frequencies, (following rules or instructions, observation, evaluation, judgement, opinion);
training, a failure rate prediction model using failure data, resulting in a trained failure rate prediction model that calculates probability distribution data indicative of a time at which a failure of the transformer is most likely to occur; (following rules or instructions, observation, evaluation, judgement, opinion), and
generating, replacement data representative of an optimal time for replacing the transformer based at least in part on: (i) the energy loss data, (ii) the probability distribution data, (iii) specification data for the transformer, and (iv) aging conditions data representative of an operational age of the transformer determined from timeseries temperature data associated with the transformer, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion).
Additional limitations employ the method to receive temperature data, top-oil and hot-spot, from the transformer while in operation, (following rules or instructions, observation, evaluation, judgement, opinion - claim 2), generating aging conditions data from heat transfer equations, (following rules or instructions, observation, evaluation, judgement, opinion - claim 3), where replacement data is based on aging conditions data, insulation degradation, (following rules or instructions, observation, evaluation, judgement, opinion - claim 4), generating core energy loss data representing predicted core energy loss of a core of the transformer, (following rules or instructions, observation, evaluation, judgement, opinion - claim 5), where generating core loss data is based on transformer specifications, (following rules or instructions, observation, evaluation, judgement, opinion claim 6), generating winding energy loss data representing predicted loss of the winding structure in the transformer, (following rules or instructions, observation, evaluation, judgement, opinion claim 7), where generating the winding energy loss data is based on received data, (following rules or instructions, observation, evaluation, judgement, opinion – claim 8), where energy loss data is energy loss data is based in part on core energy loss data and winding energy loss data, (following rules or instructions, observation, evaluation, judgement, opinion – claim 9), where the failure rate model comprises a maximum likelihood estimation algorithm to train a Weibull distribution model resulting in the trained failure rate prediction model, (following rules or instructions, observation, evaluation, judgement, opinion – claim 10),
where training the Weibull distribution model comprises determining a shape parameter and scale coefficient, (following rules or instructions, observation, evaluation, judgement, opinion – claim 11), where generating the replacement data comprises generating replacement data for the optimal time for replacing a fleet of transformers under budget and cost constraints based on total cost of ownership data for the fleet of transformers containing the one transformer, (following rules or instructions, observation, evaluation, judgement, opinion – claim 12).
Each of these claimed limitations employ: organizing human activity in the form of fundamental economic principles and practices based on mitigating risk and calculating costs, following rules or instructions, or performing mental processes including, observation, evaluation, judgement, and opinion.
Claims 13-20 recite similar abstract ideas as those identified with respect to claims 1-12.
Thus, the concepts set forth in claims 1-20 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-20 recite additional limitations which are hardware or software elements such as a computer, one or more sensors, a transformer monitoring system associated with a transformer, one or more temperature sensors, a computer program product comprising one or more computer readable storage media, a processor, a data processing system, a network from a remote processing system, a server data processing system, a remote data processing system, a computer system comprising a processor and one or more computer readable storage media, these limitations are sufficient to qualify as a practical application (MPEP § 2106.05 (f) & (h)).
The claimed invention integrates the abstract idea of analyzing transformer monitoring sensor data to assess the state of the generator and generate replacement data informing a decision on the optimal time for replacing a transformer. The sensor provides a non-invasive means to monitor and diagnose the functional performance aspects of the transformer while maintaining power delivery functionality. The network provides connectivity and enables data collection, while the processing elements enable advanced mathematical calculations to deliver real-time monitoring and processing data to inform decisions on the maintenance and management of the transformer. These are all improvements to the technology.
Since the limitations in the claims 1-20 present a practical application of the exception, they translate the described invention into a patent eligible application, thus the claims are directed to statutory subject matter and are not rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9, 12-14, 16-20 are rejected under 35 U.S.C. § 103 as being taught by
Sadeghnia, (US 20240103098 A1), hereafter Sadeghnia, “Method and Device for Analysing the State, Condition, and Power Quality of Transformers in Power Grids,” in view of Secic, "Vibro-Acoustic Methods in the Condition Assessment of Power Transformers: A Survey,” IEEE Access, vol. 7, pp. 83915-83931, 2019, in further view of Foros, “Health Index, Risk and Remaining Lifetime Estimation of Power Transformers,” IEEE Transactions on Power Delivery, vol. 35, no. 6., Dec 2020.
Regarding Claim 1, Sadeghnia teaches A computer-implemented method, (a computer implemented method of analysing the state of power transformers in power grids, [0012]), comprising:
receiving, by a transformer monitoring system associated with a transformer, sensor data from one or more sensors during operation of the transformer; (the method is capable of retrieving at least one vibroacoustic signal from at least one sensor, [0025]),
generating, by the transformer monitoring system, energy loss data representative of a predicted energy loss of the transformer based at least in part on: (i) the sensor data from the one or more sensors, (the method is capable of detecting unwanted loads which is related to the power transformer and/or indicates and/or predicts which error may occur in the power transformer, [0012]),
training, by the transformer monitoring system, a failure rate prediction model using failure data, resulting in a trained failure rate prediction model that calculates probability distribution data indicative of a time at which a failure of the transformer is most likely to occur; (monitoring the power transformers in real time, and predicting failures before the failures become destructive for the power transformer, [0011]), and
Sadeghnia does not teach:
and (ii) load harmonic frequency information based on vibroacoustic signal data retrieved by the one or more sensors, the load harmonic frequency information being generated by:
transforming the vibroacoustic signal data from a time domain to a frequency domain using a Fast Fourier transformation (FFT) to identify one or more vibroacoustic harmonic frequencies, and
determining harmonic distortion of an electrical load associated with the transformer based on the one or more vibroacoustic harmonic frequencies;
generating, by the transformer monitoring system, replacement data representative of an optimal time for replacing the transformer based at least in part on: (i) the energy loss data, (ii) the probability distribution data, (iii) specification data for the transformer, and (iv) aging conditions data representative of an operational age of the transformer determined from timeseries temperature data received from one or more temperature sensors associated with the transformer.
Secic teaches, (i) the sensor data from the one or more sensors, (vibro-acoustic sensor
mounted on the OLTC tank as close as possible to the OLTC, [p.83917]), and (ii) load harmonic frequency information based on vibroacoustic signal data retrieved by the one or more sensors, Motion of the OLTC contacts will cause vibrations that can be sensed on the OLTC housing, [p.83917], it is possible to detect faults in the OLTC tap selector and the diverter switch by means of vibration measurements, [p.83917], Experimental results demonstrate that for the proper diagnostics of transformer core and windings based on vibro-acoustic measurements it is usually enough to observe the frequency spectrum in the range up to 1 kHz [14], [15], although harmonics up to 2 kHz were observed in [16] and [17], [p.83918]), the load harmonic frequency information being generated by:
transforming the vibroacoustic signal data from a time domain to a frequency domain using a Fast Fourier transformation (FFT) to identify one or more vibroacoustic harmonic frequencies, (the extraction of relevant features from the gathered data set and decision- making process based on observing their values in: the time domain, the frequency domain and the time-frequency domain, [p.83939], authors manually identified five different regions in the
frequency spectrum using FFT which are separately used to distinguish contacts in good and bad shape, and contacts with increased electrical arcing, [p.83921], and
determining harmonic distortion of an electrical load associated with the transformer based on the one or more vibroacoustic harmonic frequencies; (the sum of the first four harmonics, the total harmonic distortion and the ratio between the amplitudes of the 50 Hz
and 100 Hz harmonic are sufficient to establish good diagnostics and the estimation of the remaining service life of the transformer; [p.83922]),
training, by the transformer monitoring system, a failure rate prediction model using failure data, resulting in a trained failure rate prediction model that calculates probability distribution data indicative of a time at which a failure of the transformer is most likely to occur; (monitoring solutions to inform about potential failure even before it happens as well as provide ongoing maintenance recommendations, [p.83916], mathematical modelling and system identification followed by the decision-making process based on the selected model, [p.83919]),
generating, by the transformer monitoring system, replacement data representative of an optimal time for replacing the transformer based at least in part on: (i) the energy loss data, (ii) the probability distribution data, (iii) specification data for the transformer, and (iv) Secic also teaches, aging conditions data representative of an operational age of the transformer determined from timeseries temperature data received from one or more temperature sensors associated with the transformer, Motion of the OLTC contacts will cause vibrations that can be sensed on the OLTC housing. Ageing of the contacts or early occurrence of a defect (deformation) cause changes in the vibration pattern. Therefore, by analysing these signals it is possible to establish a diagnostic method which can be used to evaluate the condition of the OLTC contacts, without disassembling its chamber. It is possible to detect faults in the OLTC tap selector and the diverter switch by means of vibration measurements, [p.83917]).
It would have been obvious before the earliest effective filing date of this application to modify Sadeghnia’s computer-implemented transformer monitoring system with the teachings of Secic for transforming data to a frequency domain with the motivation to provide less invasive diagnosis in the field monitoring for transformers as recited in Secic, [Abstract].
One of ordinary skill in the art would recognize the benefit in including the features of Secic, and since the claimed invention is a combination of existing elements, performing the same functions delivering more efficient and less disruptive monitoring of critical infrastructure.
Foros teachings include, generating, by the transformer monitoring system, replacement data representative of an optimal time for replacing the transformer based at least in part on: (i) the energy loss data, (ii) the probability distribution data, (iii) specification data for the transformer, and (iv) aging conditions data representative of an operational age of the transformer determined from timeseries temperature data received from one or more temperature sensors associated with the transformer, (There are typically strong temperature gradients in transformers, and this causes the DP-value to vary within the transformer. For condition monitoring purposes it is desirable to estimate the DP-value at the location in the transformer where the paper degrades fastest, i.e., at the winding temperature hot-spot. For older transformers the hot-spot temperature must be estimated from other temperature measurements such as top oil temperature or ambient temperature. Using the IEC temperature model [23], while for simplicity assuming that the load varies slowly enough that the transformer is approximately in steady state, the hot-spot temperature oil temperature rise above ambient temperature at rated load, H is the hot-spot factor, gr the average winding temperature with K as a load factor and R is the ration of load losses at rated current with no-load losses, [p.2614], and the statistics-based model extrapolates historic scrapping data for several transformers to estimate a transformer end-of-life distribution. By relating the calculated health index of the transformer in question to the statistics for the population of scrapped transformers, the condition-dependent probability of breakdown and remaining life are estimated. Finally, the profitability and optimal timing of maintenance or replacement is analyzed, Foros, [p.2613]).
It would have been obvious before the earliest effective filing date of this application to modify Sadeghnia’s computer-implemented transformer monitoring system with the teachings of Foros for breakdown estimation, predicting remaining transformer life, and maintenance, with the motivation to provide more effective data analysis and application to the field of transformer operations as recited in Foros [p.2613].
One of ordinary skill in the art would recognize the benefit in including the features of Foros and since the claimed invention is a combination of existing elements, performing the same functions delivering optimization of transformer operation.
Claims 13, 14, 18 are rejected for reasons corresponding to those provided for Claim 1. In these claims, a computer program product comprising computer readable storage media storing program instructions does not change the rational for the rejections under 35 U.S.C §
103 or the referenced prior art (Sadeghnia teaches a computer-readable storage medium comprising instructions that are executable by a processor of a device [Claim 10]).
Regarding Claim 2, Sadeghnia teaches, The computer-implemented method of claim 1, wherein the time series temperature data includes top-oil temperature data and hot-spot temperature data of windings of the transformer, wherein the thermal model is capable of providing temperature information related to the state of the top-oil temperature and/or the hotspot temperature in the power transformer, [0040]).
Claims 16, 19 are rejected for reasons corresponding to claim 2. The addition of a computer program product comprising one or more computer readable storage media,
and program instructions collectively store on the storage media and executable by a processor does not change the reasons for rejections cited under 35 U.S.C. § 103 or the referenced prior art (Sadeghnia teaches a computer-readable storage medium comprising instructions that are executable by a processor of a device [Claim 10]).
Regarding claim 3, Sadeghnia continues to teach, The computer-implemented method of claim 2, wherein the generating of the aging conditions data comprises solving one or more heat transfer equations based on the time-series temperature data to determine the operational age of the transformer, the one or more heat transfer equations modeling a top-oil temperature rise and a hot-spot temperature rise in the windings of the transformer, a method and a detection system for analysing the state of power transformers in grids. The method and detection system provides a solution for preventing overload in power transformers by given a plurality of information regarding the health of the power transformer, [0073], providing a thermal model based on the one or more load harmonic frequencies, wherein the thermal model is capable of providing temperature information related to the state of the top-oil temperature and/or the hotspot temperature in the power transformer, [0043], the computer program product may provide real time measurement. The computer program product provides one or more sequences of vibroacoustic signals, where each sequence of vibroacoustic signal or signals is to be analysed, [0069], and the thermal model is a computation or a formula formed as a result of an algorithm that takes some values as input and produces some value as output. The thermal model may be one or more trained model or models based on the input from vibroacoustic signal retrieved by the sensors, [0044], wherein the thermal model is capable of providing temperature information related to the state of the top-oil temperature and/or the hotspot temperature in the power transformer, [0040]).
Regarding claim 4, The computer-implemented method of claim 3, wherein the
Secic teaches the generating of the replacement data, (provide insights into the health condition of the monitored equipment at any moment, or inform about potential failure before it even happens. It can also provide the ongoing maintenance recommendations, [83927]), is further based at least in part on the aging conditions data,
While Sadeghnia teaches wherein the aging conditions data accounts for insulation degradation associated with the hotspot temperature over time, (transformer failures are common and costly. There are many causes of failure: insulation failures, design and/or manufacturing errors, oil contamination, overloading, line surge, loose connections, moisture, and other man-made or natural causes, [0004], and the analysed data frame information may be related or indirectly related to the state of the transformer, such as DC offset, DC load, phase imbalance, top-oil and hot-spot temperatures, loss of life and core saturation, [0108].
It would have been obvious before the earliest effective filing date of this application to modify Sadeghnia’s computer-implemented transformer monitoring system with the teachings of Secic for generating transformer maintenance insight or replacement data as recited in Secic [83927].
One of ordinary skill in the art would recognize the benefit in including the features of Secic, and since the claimed invention is a combination of existing elements, performing the same functions delivering more value from the sensor monitoring systems.
Regarding claim 5, The computer-implemented method of claim 1, further comprising:
generating, by the transformer monitoring system, core energy loss data representative of a predicted core energy loss of a core of the transformer, Sadeghnia teaches, (the electromagnetic signal is converted into an electromagnetic data, which can be used to specify and/or predict error which may occur in the power transformer. The electromagnetic data may be compared to the amplitude value and said phase angle related to the harmonic frequencies to specify and/or predict error, [0029], The temperature data may be compared to the amplitude value and said phase angle related to the harmonic frequencies and/or the electromagnetic data to indicate and/or predict which error may occur in the power transformer, [0030], The analysed information may comprise processed data related to amplitude value and the phase angle related to said one or more harmonic frequencies. The analysed information may also comprise processed data related to electromagnetic data and/or temperature data. The analysed information indicates and/or predicts which error may occur in the power transformer, [0031], and harmonic is the distortion in the waveform of the voltage and current. It is the integral multiple of some reference waves. The harmonic wave increases the core and copper loss of the transformer and hence reduces their efficiency, [0094]).
Regarding claim 6, The computer-implemented method of claim 5, wherein the generating of the core energy loss data is based at least in part on the specification data of the transformer. Sadeghnia teaches, (The analysed information may comprise processed data related to amplitude value and the phase angle related to said one or more harmonic frequencies. The analysed information may also comprise processed data related to electromagnetic data and/or temperature data. The analysed information indicates and/or predicts which error may occur in the power transformer, [0031], and the system may perform a calibration, when initialising the installation, such that the system is ready for use. The system may even be configured to self-calibrate sensors and values used in the system etc. The automatic calibration may be calibrated to a specific power transformer, [0032]).
Regarding claim 7, The computer-implemented method of claim 5, further comprising:
generating, by the transformer monitoring system, winding energy loss data representative of a predicted winding energy loss of a winding structure of the transformer, Sadeghnia teaches, (the analysed information may also comprise processed data related to electromagnetic data and/or temperature data. The analysed information indicates and/or predicts which error may occur in the power transformer, [0031], and harmonic is the distortion in the waveform of the voltage and current. It is the integral multiple of some reference waves. The harmonic wave increases the core and copper loss of the transformer and hence reduces their efficiency, [0094]).
Regarding claim 8, The computer-implemented method of claim 7, wherein the generating of the winding energy loss data is based at least in part on the sensor data from the one or more sensors. Sadeghnia teaches, (FIG. 2: Illustration of measurement when attaching sensors relative to the power transformer 1, [0090]; the retrieved signal from the sensors 7, 8, 9 may be sent to an external computer before or after providing the noise reduction, [0092]; FIG. 3: A simplified block diagram illustrating a signal analysing process. The vital part of the method for analysing the state of power transformers in power grids is highlighted using dotted line, and the harmonic is the distortion in the waveform of the voltage and current. It is the integral multiple of some reference waves. The harmonic wave increases the core and copper loss of the transformer and hence reduces their efficiency, [0094]).
Regarding claim 9, The computer-implemented method of claim 7, wherein the generating of the energy loss data is further based at least in part on the core energy loss data and the winding energy loss data, Sadeghnia teaches, (the harmonic wave increases the core and copper loss of the transformer and hence reduces their efficiency, [0094]).
Regarding claim 12, The computer-implemented method of claim 1, wherein the generating of the replacement data comprises generating replacement data representative of optimal times for replacing a fleet of transformers under budget and operational constraints based at least in part on total cost of ownership data for the fleet of transformers, the fleet of transformers comprising said transformer. Sadeghnia does not teach, Foros teaches, (the estimated winding degradation, health index and failure probability together provide a good basis for assessing whether the transformer needs maintenance or replacement and when
it should be done. A technical-economic cost-benefit model is proposed here, enabling a systematic way to carry out such assessments, [ ], the model can be used to estimate the optimal timing of measures. Foros, [p.2617]; The benefit of maintenance and replacement is given in terms of the resulting improvement of the transformer condition. This reduces the probability of failure and may also give reduced operational and maintenance costs. In case of replacement, the new condition becomes as new. [ ] From this, the health index and thus the apparent age and failure probability after the maintenance or replacement is calculated using the models in the above sections, [p2617]; costs are calculated for each year by spreading them equally throughout the lifetime of the transformer or measure, respectively, and then summing up over the analysis period. Also, these terms are adjusted for the annual probability of failure Pn given in the previous section. The profitability of a measure is finally calculated as the difference between the total costs in the analysis period with and without implementing the
measure during the period, [p.2617]; and the results are shown in a risk plot for the transformers in Fig. 4. This illustrates that the model is well suited for comparing transformers in a population and for identifying high-risk transformers.
Sadeghnia and Foros are both considered to be analogous to the claimed invention because both are in the field of power transformer analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the transformer data collection and analysis techniques of Sadeghnia with the overall cost-benefit analysis of Foros to provide key decision support for transformer managers, enabling them to identify transformers in poor condition, and to follow-up and prioritize transformers for maintenance and replacement, Foros, [Abstract].
Claims 17, 20 are rejected for reasons corresponding to those provided for Claim 12. In these claims, a computer program product comprising computer readable storage media storing program instructions (claim 12) and a computer system comprising a processor and one or more computer readable storage media, storing program instructions (claim 20), does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art (Sadeghnia teaches a computer-readable storage medium comprising instructions that are executable by a processor of a device [Claim 10]).
Claims 10-11 are rejected under 35 U.S.C. § 103 as being taught by Sadeghnia, (US
20240103098 A1), hereafter Sadeghnia, (US20240103098A1), “Method and Device for Analysing the State, Condition, and Power Quality of Transformers in Power Grids,” in view of Secic, "Vibro-Acoustic Methods in the Condition Assessment of Power Transformers: A Survey,” IEEE Access, vol. 7, pp. 83915-83931, 2019, in further view of Foros, “Health Index, Risk and Remaining Lifetime Estimation of Power Transformers,” IEEE Transactions on Power Delivery, vol. 35, no. 6., Dec 2020 in view of Michael, (US20230123527A1), “Distributed Client Server System for Generating Predictive Machine Learning Models.”
Regarding claim 10, The computer-implemented method of claim 1, wherein the training of the failure rate prediction model comprises using a maximum likelihood estimation algorithm to train a Weibull distribution model resulting in the trained failure rate prediction model. Sadeghnia does not teach, Michael teaches, (the maintenance records 302 may include one or more of a description of work orders, notifications, task lists 904, repair records, goods movement 305, or bill of materials, [0074]; the maintenance record data 302 may be consolidated via a data consolidation process 308 in order to perform preliminary calculations, [0075]; in some embodiments of the invention, the age calculation function 316 calculates a distribution of the age at replacement of each part, by part type, across the portfolio of operating assets collected by the data aggregator 315, [0110]; a fitter, such as the Weibull fitter 317, may be applied to the distribution of age at replacement of all parts of a defined part type, obtained from the age calculation function 316. FIGS. 14a and 14b show examples of fitting a Weibull probability distribution function or a Weibull cumulative distribution function to the distribution of age at replacement of all parts, respectively, [0112];
in some embodiments of the invention, a part failure and remaining useful life determination module 300 applies the trained machine learning models to new transactional and sensor data to predict the probability of part failure and/or the remaining useful life of the part, [0101]).
Sadeghnia and Michael are both considered to be analogous to the claimed invention because both are in the field of failure analysis of components. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the electrical power transformer data collection and analysis techniques of Sadeghnia with the probability distribution fitting techniques of Michael, to determine the failure pattern of the part
using a failure pattern analysis module.
Regarding claim 11, The computer-implemented method of claim 10, wherein the training of the Weibull distribution model comprises determining a shape parameter and a scale coefficient, Sadeghnia does not teach, Michael teaches, (In some embodiments of the invention, the Weibull fitting may produce one or more of three parameters, shape, scale and location, that may be used to construct probability and cumulative density functions associated with failure patterns, [ ] the shape parameter enables the identification of three fundamental failure patterns; premature failure, random failure, and wear out failure), [0113]).
Sadeghnia and Michael are both considered to be analogous to the claimed invention because both are in the field of failure analysis of components. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the electrical power transformer data collection and analysis techniques of Sadeghnia with the probability distribution fitting techniques of Michael, to determine the failure pattern of the part
using a failure pattern analysis module and provide recommendations for maintenance interventions associated with the failure patterns, [0113].
Claim 15 is rejected under 35 U.S.C. § 103 as being taught by Sadeghnia, (US
20240103098 A1), hereafter Sadeghnia, “Method and Device for Analysing the State, Condition, and Power Quality of Transformers in Power Grids,” in view of Secic, "Vibro-Acoustic Methods in the Condition Assessment of Power Transformers: A Survey,” IEEE Access, vol. 7, pp. 83915-83931, 2019, in further view of Foros, “Health Index, Risk and Remaining Lifetime Estimation of Power Transformers,” IEEE Transactions on Power Delivery, vol. 35, no. 6., Dec 2020, in further view of Michael, (US20230123527A1), hereafter Michael, “Distributed Client Server System for Generating Predictive Machine Learning Models,” in further view of Odibat, (US20220215325A1), hereafter Odibat, “Automated Identification of Changed-Induced Incidents.”
Regarding claim 15, The computer program product of claim 13, wherein the stored program instructions are stored in a computer readable storage device Sadeghnia teaches, (A computer-readable storage medium comprising instructions that are executable by a processor of a device, [claim 10], in a server data processing system, Sadeghnia does not teach, (Michael teaches, (a client-server system that performs machine learning based information fusion to predict part failure likelihood, Michael, [Abstract]),
Sadeghnia and Michael are both considered analogous to the claimed invention as both are in the field of network analysis and remote processing. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the transformer data collection and analysis techniques of Sadeghnia with the network configuration of Michael to provide a communication interface 252 that connects to a network 253 for communicating with other computing devices, [0070].
and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising:
program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use, Sadeghnia does not teach, Odibat teaches, (wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising: program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use, [claim 14.])
Sadeghnia and Odibat are both considered analogous to the claimed invention as both are in the field of network analysis and remote processing. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the transformer data collection and analysis techniques of Sadeghnia with the remote operations service provisioning techniques of Odibat so computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, provide remote services, [0124]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as
set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703)756-1822. The examiner can normally be reached M-F 8-4:30.
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Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at (866) 217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call (800) 786-9199 (IN USA OR CANADA) or (571) 272-1000.
/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624