DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 were amended and are still pending in this Application.
Request for Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/02/2025 has been entered.
Response to Amendments/Remarks
Applicant’s argument/remarks, on pages 8-10, with respect to rejections to claims 1-20 under 35 USC § 102(a) and 103(a) have been fully considered and they are respectfully persuasive. Therefore the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made, see the new rejection below.
On page 8, The Applicant argues that:
“Claim 1 recites that the power system model is "trained, using a training dataset comprising historical data associated with at least one historical operational state of the power system including at least one of a component failure event or a non-nominal operational state, to detect anomalous operational states." This step requires that the training data itself includes instances of component failures or non-nominal operational states-that is, the model learns to recognize failures by being trained on data that contains such failure events… Nowhere does Spalt describe or suggest training its model on data comprising component failure events or non-nominal operational states. Spalt' s model learns the normal behavior of the grid to predict optimal settings-it does not learn from labeled failure data to recognize when components are deviating from nominal parameters or approaching failure… Spalt' s model is trained on normal operational data, and anomalies are detected simply when real-time values deviate from the model's predictions of normal behavior.” These arguments are respectfully partially persuasive.
The Examiner appreciates the Applicant’s interpretation of the claimed subject matter. However, the claimed subject matter as claimed is broader that the Applicant’s narrow interpretation. For instance, the claims do not recite “training data itself includes instances of component failures or non-nominal operational states-that is, the model learns to recognize failures by being trained on data that contains such failure events”.
0033 operational state (e.g., non-nominal operation, failure, overload, underload, short circuit, overheating) of a component or a portion of a component. In some embodiments, sensing modules 130-138 may include voltage sensors; The original published disclosure recites in 0060 “historical data can include images, videos, audio, logs, maintenance records, operational cycles, faults, environmental conditions, weather records, social media data, device specifications, and the like, or some combination thereof. In some embodiments, the training dataset contains data associated with a performance characteristic or an operational state of a component in a power system. For example, in some embodiments, the training dataset may include thermal images associated with an inverter overload. For example, in some embodiments, the training dataset may include weather data associated with voltage drops at specific components. For example, in some embodiments, the training dataset may include audio recordings of exploding transformers on transmission lines associated with customer reports of loss of service or damaged devices. For example, in some embodiments, the training dataset may include auto-ranging meter logs related to an appliance failure”. 0064 In some embodiments, in Step 412, the power system model may predict future or existing component failure, non-nominal operation of a component, and/or abnormal behavior in the power system based on the historical data and the real-time data/sensing data using detection/prediction module 308…0064 In some embodiments, in Step 412, the power system model may predict future or existing component failure, non-nominal operation of a component, and/or abnormal behavior in the power system based on the historical data and the real-time data/sensing data using detection/prediction module 308. 0066 The received image or video is then provided to the power system model to predict an operational state of the transmission line (e.g., a bolted fault, a ground fault, a phase-to-phase fault, or a high impedance fault).
Thus, the data used for training the model simply includes historical data “associated” with failure, non-nominal operation. The term “associated” is very broad and can be interpreted in the broadest reasonable interpretation as data used for determining states of the system including failures and non-nominal conditions. The term “operational state” is exemplified as: operational state (e.g., non-nominal operation, failure, overload, underload, short circuit, overheating) of a component or a portion of a component (0033), an operational state of the transmission line (e.g., a bolted fault, a ground fault, a phase-to-phase fault, or a high impedance fault) (0066). Thus, operational states is a label or classification for the system based/associated with inputs/data used to train a model. Thus, the disclosure suggests that a model is trained with data associated to operational states and not operational states as argued.
Spalt teaches a model trained with historical data to predict operational states of components of grid system. It is very common and well known to train machine learning models with historical datasets associated with faults/failures, non-nominal events. In the broadest reasonable interpretation, even if a model it is trained with historical data for components in a normal operating states and this data is used as a trend to predict failure in the components, the training dataset is somehow associated to failure events or non-nominal operation states since this data is used to perform the prediction of the failure event or non-nominal condition of the component/system. However, for purposes of compact prosecution, the Examiner has provided a new rejection with respect to this limitations since this limitation is very well known in the state of the art of machine learning for prediction operational states of assets.
On page 9, the Applicant further argues that:
“Claim 1 further recites that the prediction identifies "the respective component predicted to experience the future non-nominal operation or component failure." Thus, the power system will output a prediction that specifically identifies which component will fail or experience non-nominal operation. Spalt does not describe this capability. Spalt's anomaly detection operates at the system level…”. Spalt' system makes a general determination that something may be wrong somewhere on the grid and speculates that the cause "may be" a faulty device-it does not identify which specific component is predicted to fail. The amended claims require more: the prediction must identify the particular component that is predicted to experience failure or non-nominal operation”. These arguments are respectfully unpersuasive.
The term “component” is not defined in the independent claims. However, Spalt clearly teaches 0117 “The grid controller 218 can generate and provide alerts, notifications, reports or other information to facilitate operation, management or maintenance of the utility grid 100… For example, the report or notification can indicate that a device on the utility grid 100 is nearing failure or has failed”; also, [0108] “The data processing system 202 can determine when faults may occur on the utility grid 100, such as due to excess power usage during peak energy consumption (e.g., brownouts due to increased air conditioning use, or increased electric vehicle charging”. Also, [0110] ] “the data processing system 202 can perform anti-islanding detection, which can refer to a condition in which a distributed generator (e.g., a gas power generator or solar inverter device on the utility grid 100) continues to power a location even though electrical grid power is no longer present…”, this suggests predicting an operational state/bad condition such as islanding of component such as generator or inverter based on data collected. Thus, Spalt teaches determining and predicting an operational states including a non-nominal states which can be a value out of a range or value that exceed a thresholds, a failures of a specific device in the grid, or location on the grid.
On pages 9-10, the Applicant further argues:
“Claim 1 further recites that the control circuitry adjusts operational parameters "in response to the prediction to mitigate the detected or predicted non-nominal operation or prevent the component failure." When read in conjunction with the component-specific identification requirement, this establishes a control paradigm where a protective action is taken in direct response to a prediction that identifies a specific component at risk. Spalt describes-at best-control actions in the context of optimization. The control commands in Spalt are generated to "achieve the desired outcome for the utility grid 100,"such as adjusting "tap setting to increase or decrease voltage level." (Id. at ,i 112). While Spalt mentions that its system "can automatically disable the failed or failing device using a control command" in ,i 117, this passage describes action taken after a device "is nearing failure or has failed"-not a protective action taken in response to a prediction that identifies a specific component before failure occurs”. These arguments are respectfully unpersuasive.
As recognized by the Applicant, Spalt clearly teaches these argued limitations in [0117] “For example, the report or notification can indicate that a device on the utility grid 100 is nearing failure or has failed. The administrator of the utility grid 100, responsive to the notification, can replace or repair the device. In some cases, the grid controller 218 can automatically disable the failed or failing device using a control command”. Thus, Spalt citation reads in the argued limitations.
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.
Claims 1-3, 5, 7-11, 13, 16, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Spalt et al (US 20210373518) in view of McElhinney et al (US 20160155315).
As per claim 1, Spalt teaches a system (see Fig. 1) comprising:
a sensor configured to determine at least one performance characteristic of a component of a power system, the sensor further configured to transmit the at least one characteristic (see Fig. 1 metering devices 118a..n or 120a sense grid characteristics and transmit the measured grid characteristics; also, see [0045] “monitoring devices 118a-118n that can be coupled through optional potential transformers 120a-120n to secondary utilization circuits 116. The monitoring or metering devices 118a-118n can detect (e.g., continuously, periodically, based on a time interval, responsive to an event or trigger) measurements and continuous voltage signals of electricity supplied to one or more electrical devices 119 connected to circuit 112 or 116 from a power source 101 coupled to bus 102”; thus, the sensor measure characteristic of components such as lines, power to vehicles…also, see [0053],[0054] and [0070] meters/sensors at inverters);
a processor operatively connected to the sensor (see Fig. 2 processing system 202, including a processing module optimizer 216 and model 214 and controller 218; and see [0014] “The system can include one or more processors. The one or more processors can identify a first plurality of signals detected by one or more devices located at one or more portions of a utility grid”; also, see [0059-0060] and [0061]), the processor configured to receive the at least one characteristic (see [0070] “The data processing system 202 can receive, and store in the utility grid data 22,…voltage…current… temperature”; also, see [0069] and [0071], [0075-0076]) and apply a power system model to the at least one characteristic to determine at least one operational state of the component (see Fig. 2 model 224 and 214; also, see [0078] “…using the received utility grid data to generate an input matrix for input into the machine learning model generator 214 or the machine learning model…”; also, see [0104] and [0105] “… For example, the circuit optimizer 216 can be configured to facilitate Volt/VAR optimization and voltage management; system-state prediction,…; fault, location, isolation, and system restoration; anti-islanding detection; also, see [0107] and [0108] “The data processing system 202 can determine when faults may occur on the utility grid 100, such as due to excess power usage during peak energy consumption (e.g., brownouts due to increased air conditioning use, or increased electric vehicle charging). The data processing system 202 can detect that such a fault is imminent based on detecting a trend in the input matrix using the machine learning model, and pre-emptively adjust signals of the utility grid 100 by generating commands to devices on the utility grid 100”; also, see [0111] “…If the real-time measured do not match the predicted signals, then the data processing system 202 can determine that the behavior of the utility grid 100 may be anomalous. The data processing system 202 can determine the anomalous behavior may be the result of a faulty device on the utility grid 100, or other fault, failure or attack on the utility grid 100…”; also, see [0109] and [0110] “…The data processing system 202, using the machine learning model and an input matrix, can detect when such islanding may occur based on predicting output values for signals such as frequency and voltage… whether to classify the predicted output variables or signals as an instance of islanding…”; also, see [0103], [0105], [0117], [0122]; the operational state of a generator or inverter such as its frequency and voltage or islanding is determined and outputted by the processor by using the model), the power system model trained, using a training dataset comprising historical data associated with at least one historical operational state of the power system (see [0010] “The method can include the data processing system training the machine learning model using a historical input matrix generated from historical signals and historical statistical metrics. The data processing system can apply one or more weights to a dimension of the historical input matrix to adjust an impact values of the dimension have on the machine learning model”; also, see [0019], [0040], [0094] and [0101]), to detect anomalous operational states (see [0105] “0105] … For example, the circuit optimizer 216 can be configured to facilitate Volt/VAR optimization and voltage management; system-state prediction,…; fault, location, isolation, and system restoration; anti-islanding detection; also, see [0106] “…The circuit optimizer 216 can also account for general signals, such as time of day, weather, or transmission resources. Using the machine learning model, the circuit optimizer 216 can determine whether the predicted output for a signal falls within a desired range…”; also, see [0108] and [0111] “The data processing system 202 can use the machine learning model to verify behavior of the utility grid 100…If the real-time measured do not match the predicted signals, then the data processing system 202 can determine that the behavior of the utility grid 100 may be anomalous. The data processing system 202 can determine the anomalous behavior may be the result of a faulty device on the utility grid 100, or other fault, failure or attack on the utility grid 100”) and outputting a prediction indicative of a predicted future non-nominal operation or component failure for a respective component (see [0012] and see [0102] and [0106] “…The circuit optimizer 216 can also account for general signals, such as time of day, weather, or transmission resources. Using the machine learning model, the circuit optimizer 216 can determine whether the predicted output for a signal falls within a desired range….”; also, see [0110] “The data processing system 202 can perform anti-islanding detection, which can refer to a condition in which a distributed generator (e.g., a gas power generator or solar inverter device on the utility grid 100) continues to power a location even though electrical grid power is no longer present…. The data processing system 202, using the machine learning model and an input matrix, can detect when such islanding may occur based on predicting output values for signals such as frequency and voltage… For example, the data processing system 202, using the machine learning model, can include an output signal in the output matrix that corresponds to a classification of islanding (e.g., binary classification indicating whether islanding is present, or not present, or a likelihood that islanding is present, or a confidence score associated with the binary classification). The data processing system 202 can predict islanding based on predicted output signals, or an output matrix generated via the machine learning model can include an islanding classification signal or variable”; also, see [0041] “The data processing system, using the input matrix and machine learning model generator, can predict a corresponding output matrix. The model can predict a new output for a future time period, or predict an output value for a variable for which data does not exist in a current time period”; also, see [0108]); and
a control circuitry operatively connected to the processor (see Fig. 2 grid controller 218 connected to control circuitry to effect changes in the grid; see [0072] and [0106]), to adjust an operational parameter of the respective component and another component of the power system based on the prediction to mitigate a detected or predicted non-nominal operation or prevent the component failure (see [0073] “Commands 226 data structure can include control commands to control devices on the utility grid 100, alerts, notifications, reports, instructions, or other actions that can be performed by the data processing system 202 responsive to, or based on, utility grid data 222. Examples of commands 226 can include a command to increase or decrease a tap setting of a voltage regulating transformer 106a on the primary circuit 112, or change a setpoint on the primary circuit 112 or secondary circuit 116. Additional actions or determinations can include determining a topology of a utility grid 100 (e.g., main branches, secondary branches, customer sites or other connections or couplings of the utility grid 10…”; also, see [0106] and [0111]-[0112] “The data processing system 202 (e.g., via grid controller 218) can generate one or more commands to control one or more of the components to achieve the desired outcome for the utility grid 100. The data processing system 202 can compare the value for the signal of the utility grid 100 predicted by the circuit optimizer 216 via the machine learning model with a threshold, and generate the command to control the component on the utility grid 100 based on the comparison (e.g., adjust tap setting to increase or decrease voltage level).”; also, see [0113] and [0115]; also, see [0073] “…Examples of commands 226 can include a command to increase or decrease a tap setting of a voltage regulating transformer 106a on the primary circuit 112, or change a setpoint on the primary circuit 112 or secondary circuit 116…”; also, see [0106] “…Thus, the circuit optimizer 216, using the model, can determine a signal at the substation that facilitates VVO, and provide the value to the grid controller 218 to generate one or more commands to instruct one or more devices on the utility grid 100 to realize the predicted value to facilitate VVO”; also, see [0107], also, see page 19 claim 11; also, see [0117] “…For example, the report or notification can indicate that a device on the utility grid 100 is nearing failure or has failed…”; and [0133-0135]), by modifying a control setpoint of a voltage regulating transformer or by either modifying a control setpoint of the voltage regulating transformer to maintain operation within nominal parameters or by disconnecting a protection device to isolate the component predicted to fail (see [0112] “The data processing system 202 can compare the value for the signal of the utility grid 100 predicted by the circuit optimizer 216 via the machine learning model with a threshold, and generate the command to control the component on the utility grid 100 based on the comparison (e.g., adjust tap setting to increase or decrease voltage level).”; also, see [0117] “…can automatically disable the failed or failing device using a control command. Types of control commands can include, for example, activating or deactivating a device, changing a parameter of the device, setting a parameter of the device, adjusting operation of the device, disabling or turning off a device…”; also, see [0111-0112] and [0133-0138]).
While Spalt teaches training model with historical data associated with an operational state (see [0010], [0019], [0040], [0094] and [0101]), Spalt does not explicitly teach the historical data associated with at least one historical operational state of the power system including at least one of a component failure event or a non-nominal operational state.
However, McElhinney teaches a system and method comprising a model trained with historical data, wherein the historical data associated with at least one historical operational state of a power system asset including at least one of a component/asset failure event or a non-nominal operational state (see [0127-0131] “…In practice, identifying past occurrences of the given failure may involve the data science system 404 identifying the type of operating data, such as abnormal-condition data, that indicates the given failure… After identifying the type of operating data that indicates the given failure, the data science system 404 may identify the past occurrences of the given failure in a number of manners. For instance, the data science system 404 may locate, from historical operating data stored in the databases 406, abnormal-condition data corresponding to the indicators associated with the given failure… At block 506, the data science system 404 may identify a respective set of operating data that is associated with each identified past occurrence of the given failure…”; also, see [0139] “Continuing in the particular example of defining a failure model based on a response variable, the data science system 404 may train the failure model with the historical operating data identified at block 506 and the generated response variable. Based on this training process, the data science system 404 may then define the failure model that receives as inputs various sensor data…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Spalt’s invention to include training a model with historical data, wherein the historical data associated with at least one historical operational state of the power system asset including at least one of a component failure event or a non-nominal operational state as taught by McElhinney in order to predict and output anomalous operational states indicative of predicted future failure or non-nominal operations of the asset (see [0117] “…Based on the defined set of failures, the data science system 404 may take steps to define a model for predicting a likelihood of any of the failures occurring within a given timeframe in the future (e.g., the next two weeks).”; also, see [0118-0119]).
As per claim 2, Spalt-McElhinney teaches the system of claim 1, Spalt further teaches wherein the sensor includes at least one of
voltage/current meters, current transformers, potential transformers, or transducers for determining voltage, current, load, or temperature;
cameras for determining visual characteristics; and
microphones for determining acoustic characteristics (see [0045] “…potential transformer…”; also, [0054] “Metering devices 118a-118n can process and sample the voltage signals such that the sampled voltage signals are sampled as a time series (e.g., uniform time series free of spectral aliases or non-uniform time series); also, see [0068] voltage and current measurements; [0070]); and
As per claim 3, Spalt-McElhinney teaches the system of claim 1, Spalt further teaches wherein the power system model is a trained power system model trained using a training dataset including historical data of the power system (see [0010] “The method can include the data processing system training the machine learning model using a historical input matrix generated from historical signals and historical statistical metrics. The data processing system can apply one or more weights to a dimension of the historical input matrix to adjust an impact values of the dimension have on the machine learning model”; also, see [0019], [0040], and [0101]).
As per claim 5, Spalt-McElhinney teaches the system of claim 1, Spalt further teaches the processor further configured to receive real-time data from at least one real-time data source and apply the power system model to the received real-time data to determine at least one other operational state of the component (see [0042] “The data processing system can receive or ingest real-time data from utility grid devices and prior commands issued to these devices. The data processing system can predict the change in the utility grid conditions for a given set of device changes”, the change in the utility grid conditions for a given set of device changes refers to at least one other operational state of the component; also, see [0075] and [0111], [0125] and [0126] “…. A data processing system can receive or identify signals via a real-time data feed or stream. Signals can refer to or include utility grid data, such as characteristics of electricity, device settings, device operation modes, or weather conditions. Signals can be obtained from various sources, such as from different devices at different locations on the utility grid, or an external server or other data sources.”; also, see [0133] “predict an output value”; also, see [0110] islanding is at least one other operational state of the component that is predicted based on the real time data; also, see [0117] “For example, the report or notification can indicate that a device on the utility grid 100 is nearing failure or has failed”, thus, another state is a device nearly failing or has failed; also, see [0138] “…including real-time signals and other control commands, and continue predicting the behavior of the utility grid…”).
As per claim 7, Spalt-McElhinney teaches the system of claim 5, Spalt further teaches the control circuitry further configured to effect a change in the at least one of the component and another component of the power system in response to the at least one other operational state (see [0042] “The data processing system can receive or ingest real-time data from utility grid devices and prior commands issued to these devices. The data processing system can predict the change in the utility grid conditions for a given set of device changes”, the change in the utility grid conditions for a given set of device changes refers to at least one other operational state of the component; also, see [0075] and [0111] “…can determine the anomalous behavior may be the result of a faulty device on the utility grid 100, or other fault, failure or attack on the utility grid…, [0125] and [0126] “…. A data processing system can receive or identify signals via a real-time data feed or stream. Signals can refer to or include utility grid data, such as characteristics of electricity, device settings, device operation modes, or weather conditions. Signals can be obtained from various sources, such as from different devices at different locations on the utility grid, or an external server or other data sources.”; also, see [0133] “predict an output value”; also, see [0110] islanding is at least one other operational state of the component that is predicted based on the real time data; also, see [0117] “For example, the report or notification can indicate that a device on the utility grid 100 is nearing failure or has failed”, thus, another state is a device nearly failing or has failed; also, see [0138] “…including real-time signals and other control commands, and continue predicting the behavior of the utility grid…”; see [0106] and [0112] “The data processing system 202 (e.g., via grid controller 218) can generate one or more commands to control one or more of the components to achieve the desired outcome for the utility grid 100. The data processing system 202 can compare the value for the signal of the utility grid 100 predicted by the circuit optimizer 216 via the machine learning model with a threshold, and generate the command to control the component on the utility grid 100 based on the comparison (e.g., adjust tap setting to increase or decrease voltage level).”; also, see [0113]. Thus, the grid components are controlled/adjusted based on several states detected including islanding, change in the utility grid conditions for a given set of device changes, the anomalous behavior as a result of a faulty device on the utility grid 100, or other fault, failure or attack on the utility grid, near failure or failing or failed device (see [0117]).
As per claim 8, Spalt-McElhinney teaches the system of claim 1, Spalt further teaches wherein the control circuitry includes circuit breakers (see [0069] circuit breaker), switchgear (see [0069] “…switch, breakers, fuse…” these are examples of switchgear), reclosers (see [0069] recloser), disconnects (see [0069] “…switch, breakers, fuse…” these are examples of disconnect devices; also, see [0117]), interrupters (see [0069] “…switch, breakers, fuse…” these are examples of interrupters), tap changers (see [0015” tap changer”; also, see [0045], [0050], and [0055]), circuit switchers (see [0069] “…switch…”), and switches (see [0069] “…switch…”).
As per claim 9, Spalt teaches a method (see [0002], [0005] and [0125]) comprising:
receiving, from a sensor designed to determine at least one performance characteristic of a first component of a plurality of components of a power system, the at least one performance characteristic (see Fig. 1 sensing modules 118a..n or 120a sense grid characteristics and transmit the measured grid characteristics; also, see [0045] “monitoring devices 118a-118n that can be coupled through optional potential transformers 120a-120n to secondary utilization circuits 116. The monitoring or metering devices 118a-118n can detect (e.g., continuously, periodically, based on a time interval, responsive to an event or trigger) measurements and continuous voltage signals of electricity supplied to one or more electrical devices 119 connected to circuit 112 or 116 from a power source 101 coupled to bus 102”; also, see [0053],[0054]);
applying, using a processor (see Fig. 2 processing system 202, including a processing module optimizer 216 and model 214 and controller 218; and see [0014] “The system can include one or more processors. The one or more processors can identify a first plurality of signals detected by one or more devices located at one or more portions of a utility grid”; also, see [0059-0060] and [0061]), a power system model to the at least one characteristic to determine at least one operational state, (see Fig. 2 model 224 and 214; also, see [0078] “…using the received utility grid data to generate an input matrix for input into the machine learning model generator 214 or the machine learning model…”; also, see [0104] and [0105] “. For example, the circuit optimizer 216 can be configured to facilitate Volt/VAR optimization and voltage management; system-state prediction,…; fault, location, isolation, and system restoration; anti-islanding detection; also, see [0107] and [0108] “The data processing system 202 can determine when faults may occur on the utility grid 100, such as due to excess power usage during peak energy consumption (e.g., brownouts due to increased air conditioning use, or increased electric vehicle charging). The data processing system 202 can detect that such a fault is imminent based on detecting a trend in the input matrix using the machine learning model, and pre-emptively adjust signals of the utility grid 100 by generating commands to devices on the utility grid 100”; also, see [0111] “…If the real-time measured do not match the predicted signals, then the data processing system 202 can determine that the behavior of the utility grid 100 may be anomalous. The data processing system 202 can determine the anomalous behavior may be the result of a faulty device on the utility grid 100, or other fault, failure or attack on the utility grid 100…”; also, see [0109] and [0110] “…The data processing system 202, using the machine learning model and an input matrix, can detect when such islanding may occur based on predicting output values for signals such as frequency and voltage… whether to classify the predicted output variables or signals as an instance of islanding…”; also, see [0103], [0105], [0117], [0122]; the operational state of a generator or inverter such as its frequency and voltage or islanding is determined and outputted by the processor by using the model), the power system model corresponding to the power system trained, using a training dataset comprising historical data associated with at least one historical operational state of the power system (see [0010] “The method can include the data processing system training the machine learning model using a historical input matrix generated from historical signals and historical statistical metrics. The data processing system can apply one or more weights to a dimension of the historical input matrix to adjust an impact values of the dimension have on the machine learning model”; also, see [0019], [0040], [0094] and [0101]), to detect anomalous operational states (see [0105] “0105] … For example, the circuit optimizer 216 can be configured to facilitate Volt/VAR optimization and voltage management; system-state prediction,…; fault, location, isolation, and system restoration; anti-islanding detection; also, see [0106] “…The circuit optimizer 216 can also account for general signals, such as time of day, weather, or transmission resources. Using the machine learning model, the circuit optimizer 216 can determine whether the predicted output for a signal falls within a desired range…”; also, see [0108] and [0111] “The data processing system 202 can use the machine learning model to verify behavior of the utility grid 100…If the real-time measured do not match the predicted signals, then the data processing system 202 can determine that the behavior of the utility grid 100 may be anomalous. The data processing system 202 can determine the anomalous behavior may be the result of a faulty device on the utility grid 100, or other fault, failure or attack on the utility grid 100”) and outputting a prediction indicative of a predicted future non-nominal operation for a respective component (see [0012] and see [0102] and [0106] “…The circuit optimizer 216 can also account for general signals, such as time of day, weather, or transmission resources. Using the machine learning model, the circuit optimizer 216 can determine whether the predicted output for a signal falls within a desired range….”; also, see [0110] “The data processing system 202 can perform anti-islanding detection, which can refer to a condition in which a distributed generator (e.g., a gas power generator or solar inverter device on the utility grid 100) continues to power a location even though electrical grid power is no longer present…. The data processing system 202, using the machine learning model and an input matrix, can detect when such islanding may occur based on predicting output values for signals such as frequency and voltage… For example, the data processing system 202, using the machine learning model, can include an output signal in the output matrix that corresponds to a classification of islanding (e.g., binary classification indicating whether islanding is present, or not present, or a likelihood that islanding is present, or a confidence score associated with the binary classification). The data processing system 202 can predict islanding based on predicted output signals, or an output matrix generated via the machine learning model can include an islanding classification signal or variable”; also, see [0041] “The data processing system, using the input matrix and machine learning model generator, can predict a corresponding output matrix. The model can predict a new output for a future time period, or predict an output value for a variable for which data does not exist in a current time period”; also, see [0108]); and
directing, a control circuitry (see Fig. 2 grid controller 218 connected to control circuitry to effect changes in the grid; see [0072] and [0106]) operatively connected to at least one of the first component and a second component of the plurality of components, to adjust an operational parameter of the respective component and another component of the power system based on the prediction to mitigate a detected or predicted non-nominal operation (see [0073] “Commands 226 data structure can include control commands to control devices on the utility grid 100, alerts, notifications, reports, instructions, or other actions that can be performed by the data processing system 202 responsive to, or based on, utility grid data 222. Examples of commands 226 can include a command to increase or decrease a tap setting of a voltage regulating transformer 106a on the primary circuit 112, or change a setpoint on the primary circuit 112 or secondary circuit 116. Additional actions or determinations can include determining a topology of a utility grid 100 (e.g., main branches, secondary branches, customer sites or other connections or couplings of the utility grid 10…”; also, see [0106] and [0111]-[0112] “The data processing system 202 (e.g., via grid controller 218) can generate one or more commands to control one or more of the components to achieve the desired outcome for the utility grid 100. The data processing system 202 can compare the value for the signal of the utility grid 100 predicted by the circuit optimizer 216 via the machine learning model with a threshold, and generate the command to control the component on the utility grid 100 based on the comparison (e.g., adjust tap setting to increase or decrease voltage level).”; also, see [0113] and [0115]; also, see [0073] “…Examples of commands 226 can include a command to increase or decrease a tap setting of a voltage regulating transformer 106a on the primary circuit 112, or change a setpoint on the primary circuit 112 or secondary circuit 116…”; also, see [0106] “…Thus, the circuit optimizer 216, using the model, can determine a signal at the substation that facilitates VVO, and provide the value to the grid controller 218 to generate one or more commands to instruct one or more devices on the utility grid 100 to realize the predicted value to facilitate VVO”; also, see [0107], also, see page 19 claim 11; also, see [0133-0135]), by modifying a control setpoint of a voltage regulating transformer or by activating a protection relay to prevent component failure (see [0112] “The data processing system 202 can compare the value for the signal of the utility grid 100 predicted by the circuit optimizer 216 via the machine learning model with a threshold, and generate the command to control the component on the utility grid 100 based on the comparison (e.g., adjust tap setting to increase or decrease voltage level).”), or by either modifying a control setpoint of the voltage regulating transformer to maintain operation within nominal parameters or by disconnecting a protection device to isolate the component predicted to fail (see [0112] “The data processing system 202 can compare the value for the signal of the utility grid 100 predicted by the circuit optimizer 216 via the machine learning model with a threshold, and generate the command to control the component on the utility grid 100 based on the comparison (e.g., adjust tap setting to increase or decrease voltage level).”; also, see [0117] “…can automatically disable the failed or failing device using a control command. Types of control commands can include, for example, activating or deactivating a device, changing a parameter of the device, setting a parameter of the device, adjusting operation of the device, disabling or turning off a device…”; also, see [0111-0112] and [0133-0138]).
While Spalt teaches training model with historical data associated with an operational state (see [0010], [0019], [0040], [0094] and [0101]), Spalt does not explicitly teach the historical data associated with at least one historical operational state of the power system including at least one of a component failure event or a non-nominal operational state.
However, McElhinney teaches a system and method comprising a model trained with historical data, wherein the historical data associated with at least one historical operational state of a power system asset including at least one of a component/asset failure event or a non-nominal operational state (see [0127-0131] “…In practice, identifying past occurrences of the given failure may involve the data science system 404 identifying the type of operating data, such as abnormal-condition data, that indicates the given failure… After identifying the type of operating data that indicates the given failure, the data science system 404 may identify the past occurrences of the given failure in a number of manners. For instance, the data science system 404 may locate, from historical operating data stored in the databases 406, abnormal-condition data corresponding to the indicators associated with the given failure… At block 506, the data science system 404 may identify a respective set of operating data that is associated with each identified past occurrence of the given failure…”; also, see [0139] “Continuing in the particular example of defining a failure model based on a response variable, the data science system 404 may train the failure model with the historical operating data identified at block 506 and the generated response variable. Based on this training process, the data science system 404 may then define the failure model that receives as inputs various sensor data…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Spalt’s invention to include training a model with historical data, wherein the historical data associated with at least one historical operational state of the power system asset including at least one of a component failure event or a non-nominal operational state as taught by McElhinney in order to predict and output anomalous operational states indicative of predicted future failure or non-nominal operations of the asset (see [0117] “…Based on the defined set of failures, the data science system 404 may take steps to define a model for predicting a likelihood of any of the failures occurring within a given timeframe in the future (e.g., the next two weeks).”; also, see [0118-0119]).
As to claim 10, this claim is the method claim corresponding to the system claim 2 and is rejected for the same reasons mutatis mutandis.
As to claim 11, this claim is the method claim corresponding to the system claim 8 and is rejected for the same reasons mutatis mutandis.
As per claim 13, Spalt- McElhinney teaches the method of claim 9, further comprising: Spalt further teaches receiving, from a real-time data source, real-time data associated with at least one of the first component, the second component, and a third component of the plurality of components (see first component 101/power sources, second components 104, third components/loads 119,…more components include vehicles (0006), voltage regulators, inverters, circuit meters, and so on
[0042] “The data processing system can receive or ingest real-time data from utility grid devices and prior commands issued to these devices. The data processing system can predict the change in the utility grid conditions for a given set of device changes”, the change in the utility grid conditions for a given set of device changes refers to at least one other operational state of the component; also, see [0075] and [0111], [0125] and [0126] “…. A data processing system can receive or identify signals via a real-time data feed or stream. Signals can refer to or include utility grid data, such as characteristics of electricity, device settings, device operation modes, or weather conditions. Signals can be obtained from various sources, such as from different devices at different locations on the utility grid, or an external server or other data sources.”; also, see [0133] “predict an output value”; also, see [0110] islanding is at least one other operational state of the component that is predicted based on the real time data; also, see [0117] “For example, the report or notification can indicate that a device on the utility grid 100 is nearing failure or has failed”, thus, another state is a device nearly failing or has failed; also, see [0138] “…including real-time signals and other control commands, and continue predicting the behavior of the utility grid…”);
applying, using the processor, the power system model to the received real-time data to determine at least one other operational state (see [0042] “The data processing system can receive or ingest real-time data from utility grid devices and prior commands issued to these devices. The data processing system can predict the change in the utility grid conditions for a given set of device changes”, the change in the utility grid conditions for a given set of device changes refers to at least one other operational state of the component; also, see [0075] and [0111], [0125] and [0126] “…. A data processing system can receive or identify signals via a real-time data feed or stream. Signals can refer to or include utility grid data, such as characteristics of electricity, device settings, device operation modes, or weather conditions. Signals can be obtained from various sources, such as from different devices at different locations on the utility grid, or an external server or other data sources.”; also, see [0133] “predict an output value”; also, see [0110] islanding is at least one other operational state of the component that is predicted based on the real time data; also, see [0117] “For example, the report or notification can indicate that a device on the utility grid 100 is nearing failure or has failed”, thus, another state is a device nearly failing or has failed; also, see [0138] “…including real-time signals and other control commands, and continue predicting the behavior of the utility grid…”);
and directing the control circuitry to effect a change in the at least one of the first, second, and third components in response to the determined at least one other operational state (see [0106] and [0112] “The data processing system 202 (e.g., via grid controller 218) can generate one or more commands to control one or more of the components to achieve the desired outcome for the utility grid 100. The data processing system 202 can compare the value for the signal of the utility grid 100 predicted by the circuit optimizer 216 via the machine learning model with a threshold, and generate the command to control the component on the utility grid 100 based on the comparison (e.g., adjust tap setting to increase or decrease voltage level).”; also, see [0113]).
As to claim 16, this claim is the non-transitory computer readable storage medium claim corresponding to the system claim 1 and/or method claim 9 and is rejected for the same reasons mutatis mutandis (see Spalt [0141] “The processes, systems and methods described herein can be implemented by the computing system 500 in response to the processor 510 executing an arrangement of instructions contained in main memory 515. Such instructions can be read into main memory 515 from another computer-readable medium, such as the storage device 525. Execution of the arrangement of instructions contained in main memory 515 causes the computing system 500 to perform the illustrative processes described herein).
As to claim 18, this claim is the non-transitory computer readable storage medium claim corresponding to the method claim 13 and is rejected for the same reasons mutatis mutandis.
As to claim 20, this claim is the non-transitory computer readable storage medium claim corresponding to the method claim 15 and is rejected for the same reasons mutatis mutandis.
Claims 4, 12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Spalt et al (US 20210373518) in view of McElhinney et al (US 20160155315) as applied to claims 1, 9, 16, respectively above, and further in view of Koval et al (US 11734704).
As per claim 4, Spalt-McElhinney teaches the system of claim 1, while Spalt teaches collecting voltage/current measurements and temperature measurements of one or more components of a poser system for determining a condition/state (see claim 2), Spalt does not explicitly teach wherein the at least one operational state is an overload, an underload, a short circuit, and overheating.
However, Koval teaches a system comprising determining at least one operational state of component of a power system (see the Abstract “The present disclosure is directed to a machine learning system for use with a power distribution system. The machine learning system includes a data library, a machine learning module, and an action module. The data library stores a plurality of data samples, where at least a portion of the data samples are associated with one or more intelligent electronic devices (IEDs). The machine learning module processes data samples from the data library using at least one machine learning algorithm and outputs at least one recommendation and/or prediction based on the data samples received.”; also, see Col 54 lines 3-42 predicting a fault using a machine learning algorithm and performing an action correction), wherein the at least one operational state is an overload, an underload, a short circuit, and overheating (see Col 3 lines 57-60 “…the at least one type of fault includes at least one of transients, interruption of supply voltage or load current, undervoltage, overvoltage, waveform distortion, and/or voltage fluctuations”; also, see Col 62 lines 40-58 “…short circuit…”; also, see Col 63 lines 8-15 “…short circuit…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Spalt’s combination as taught above to include the determining at least one operational state of component of a power system, wherein the at least one operational state is an overload, an underload, a short circuit, and overheating as taught by Koval in order to perform at least one action based on a recommendation and/or prediction, wherein the action includes outputting at least one of a communication signal and/or at least one control signal to at least one client or at least one of the one or more IEDs to mitigate the operational state (see Col 2 lines 32-49 “…perform at least one action based on the recommendation and/or prediction, wherein the action includes outputting at least one of a communication signal and/or at least one control signal to at least one client or at least one of the one or more IEDs…”; also, see Col 3 lines 1-10; also, see Col 61 lines 23-41).
As to claim 12, this claim is the method claim corresponding to the system claim 4 and is rejected for the same reasons mutatis mutandis.
As per claim 17, Spalt-McElhinney teaches the non-transitory computer-readable storage medium of claim 16, Spalt further teaches wherein the sensing module includes at least one of
voltage/current meters, current transformers, potential transformers, or transducers for determining voltage, current, load, or temperature;
cameras for determining visual characteristics; and
microphones for determining acoustic characteristics (see [0045] “…potential transformer…”; also, [0054] “Metering devices 118a-118n can process and sample the voltage signals such that the sampled voltage signals are sampled as a time series (e.g., uniform time series free of spectral aliases or non-uniform time series); also, see [0068] voltage and current measurements; [0070]);
wherein the control module includes circuit breakers (see [0069] circuit breaker), switchgear (see [0069] “…switch, breakers, fuse…” these are examples of switchgear), reclosers (see [0069] recloser), disconnects (see [0069] “…switch, breakers, fuse…” these are examples of disconnect devices; also, see [0117]), interrupters (see [0069] “…switch, breakers, fuse…” these are examples of interrupters), tap changers (see [0015” tap changer”; also, see [0045], [0050], and [0055]), circuit switchers (see [0069] “…switch…”), and switches (see [0069] “…switch…”); and
while Spalt teaches collecting voltage/current measurements and temperature measurements of one or more components of a poser system for determining a condition/state (see claim 2), Spalt does not explicitly teach wherein the at least one operational state is an overload, an underload, a short circuit, and overheating.
However, Koval teaches a system comprising determining at least one operational state of component of a power system (see the Abstract “The present disclosure is directed to a machine learning system for use with a power distribution system. The machine learning system includes a data library, a machine learning module, and an action module. The data library stores a plurality of data samples, where at least a portion of the data samples are associated with one or more intelligent electronic devices (IEDs). The machine learning module processes data samples from the data library using at least one machine learning algorithm and outputs at least one recommendation and/or prediction based on the data samples received.”; also, see Col 54 lines 3-42 predicting a fault using a machine learning algorithm and performing an action correction), wherein the at least one operational state is an overload, an underload, a short circuit, and overheating (see Col 3 lines 57-60 “…the at least one type of fault includes at least one of transients, interruption of supply voltage or load current, undervoltage, overvoltage, waveform distortion, and/or voltage fluctuations”; also, see Col 62 lines 40-58 “…short circuit…”; also, see Col 63 lines 8-15 “…short circuit…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Spalt’s combination as taught above to include the determining at least one operational state of component of a power system, wherein the at least one operational state is an overload, an underload, a short circuit, and overheating as taught by Koval in order to perform at least one action based on a recommendation and/or prediction, wherein the action includes outputting at least one of a communication signal and/or at least one control signal to at least one client or at least one of the one or more IEDs to mitigate the operational state (see Col 2 lines 32-49 “…perform at least one action based on the recommendation and/or prediction, wherein the action includes outputting at least one of a communication signal and/or at least one control signal to at least one client or at least one of the one or more IEDs…”; also, see Col 3 lines 1-10; also, see Col 61 lines 23-41).
Claims 6, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Spalt et al (US 20210373518) in view of McElhinney et al (US 20160155315) as applied to claims 5. 13, and 18, respectively above, and further in view of Prasanna et al (US 20220036199), Priyadarsini et al (US 20210382470) and Mestha et al (US 20180260561).
As per claim 6, Spalt- McElhinney teaches the system of claim 5, Spalt further teaches wherein the at least one real-time data source includes;
weather and environmental data (see [0015] “The second plurality of signals can include at least one of current weather, forecasted weather…”; also, see [0071]);
However, Spalt does not explicitly teach wherein the at least one real-time data source includes at least one of real-time images, video, and sounds;
anecdotal/observational human reports;
and social media data (while the claim requires only of “one real-time data source”, the following references have been provided to teach the limitations).
However, Prasanna teaches a system for identifying states/anomalies of a component of a power system (see the Abstract) comprising at least one real-time data source for providing real time data, wherein the at least one real-time data source includes at least one of real-time images, video, and sounds(see [0054] “Field sensors can also include video data, e.g., from cameras. Field sensors can also include RF noise detectors that may detect cracked insulators”); weather and environmental data (see [0054] “…Field sensors can additionally measure environment and/or weather sensors that can measure temperature, relative humidity, and wind speed and direction…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Spalt’s combination as taught above to include at least one real-time data source for providing real time data, wherein the at least one real-time data source includes at least one of real-time images, video, and sounds; weather and environmental data as taught by Prasanna in order to detect one other operational state of a component of a power system (see [0048] and [0084] “…to determine the presence of an anomaly corresponding to foliage impingement, abnormal power flow loading, infrastructure failure, and predictive failure…”).
However, Spalt- McElhinney-Prasanna still does not explicitly teach wherein the at least one real-time data source includes at least one of anecdotal/observational human reports; and social media data.
However, Priyadarsini teaches a system for predicting state of a plant comprising a least one real-time data source for providing real time data, wherein the at least one real-time data source includes at least one of anecdotal/observational human reports
(see Fig. 2 incident reports 246; also, see Fig. 3 320; also, see [0019] “…and field logbooks and incident reports (246)…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Spalt’s combination as taught above to include wherein the at least one real-time data source includes at least one of anecdotal/observational human reports and other data as taught by Priyadarsini in order to detect the state of a system plant using machine learning (see [0019] “…An embodiment processes and integrates multiple data sources to learn about the plant states and plant modes. FIG. 2 illustrates the primary data sources that are used to first train a model to determine a plant state …The primary data sources include sensor data (process parameter values) from plant process histories; …and engineering configuration data field logbooks (e.g., incident reports) from field operators.… log file data are integrated from a plant 210, an operation/control room 220, and field data 230. …field logbooks and incident reports (246). These data are first processed in the machine learning/training phase 250. This learning/training generates data mining and analysis modules/models 260; also, see [0031] and see claim 18 in page 9).
However, Spalt- McElhinney-Prasanna-Priyadarsini still does not explicitly teach wherein the at least one real-time data source includes social media data.
However, Mestha teaches a system for determining a state of a component of an electrical component (see Fig. 1, Fig. 3 and Fig. 8 using a model to detect the state of one or more electrical components in a grid 332) comprising a least one real-time data source for providing real time data, wherein the at least one real-time data source includes social media data (see [0021] “According to some embodiments, the data source nodes 130 provide “heterogeneous” data. That is, the data may represent information from widely diverse areas, such as social media data, w… weather data (e.g., temperature data, National Oceanic and Atmospheric Administration (“NOAA”) information, etc.), IT inputs, market pricing, etc.”; also, see [0047], [0055], [0075] “…social media, weather data,…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Spalt’s combination as taught above to include a least one real-time data source for providing real time data, wherein the at least one real-time data source includes social media data as taught by Mestha in order to determine the state of component of a power system using machine learning (see [0022] and [0030] “At S240, the system may automatically transmit an abnormal alert signal (e.g., a notification message, etc.) based on results of the comparisons performed at S230. The abnormal state might be associated with, for example, an actuator attack, a controller attack, a data source node attack, a plant state attack, spoofing, physical damage, unit availability, a unit trip, a loss of unit life, asset damage requiring at least one new part, a stealthy attack not detectable by alarms, a load alternating attack, a topology change attack, a stability compromise attack, and/or a frequency compromise attack. According to some embodiments, one or more response actions may be performed when an abnormal alert signal is transmitted…”; also, see [0032]) and allow an operator to restore power grid to normal operations and/or avoid damages to the system (see [0025] and [0030], [0032].
As to claim 14, this claim is the method claim corresponding to the system claim 6 and is rejected for the same reasons mutatis mutandis.
As to claim 19, this claim is the non-transitory computer readable storage medium claim corresponding to the system claim 6 and is rejected for the same reasons mutatis mutandis.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Spalt et al (US 20210373518) in view of McElhinney et al (US 20160155315) as applied to claim 9 above, and further in view of Smiley et al (US 20140365271).
As per claim 15, Spalt- McElhinney teaches the method of claim 9, Spalt further teaches wherein the power system model is a trained power system model (see [0072] “…The model 224 can include a voltage power flow model generated, constructed or trained by a machine learning model generator 214…”; also, see Fig. 2 214 is a machine learning model; also, see [0101] “…Once trained, the model can be used to predict new outputs using a new input matrix.”), the method further comprising:
retrieving, from a database, a training dataset including historical data associated with at least one historical operational state of the power system (see [0101] “The machine learning model generator 214 can be configured to generate and train the initial model based on initial training data sets that may be weighted. The machine learning model generator 214 can use a historical input matrix (e.g., generated from historical signals and historical statistical metrics determined from the historical signals) to train the machine learning model. Once trained, the model can be used to predict new outputs using a new input matrix. );
applying, using the processor, the power system model to the historical data (see [0010], [0019], [0094] and [0101]);
determining, using the processor, a predicted operational state of the power system based on the historical data (see [0101] “…Once trained, the model can be used to predict new outputs using a new input matrix...”; also, see [0103]);
and updating, using the processor, the power system model (see [0034] “These models can be updated whenever new input data is available, which can produce a dynamically accurate prediction during state changes in the circuit…”; also, see [0043] and [0116])
However, Spalt does not explicitly teach updating the power system model based on a deviation between the predicted operational state and the at least one historical operational state to create the trained power system model.
However, Smiley teaches a system and non-transitory computer readable storage medium (see [0131]) comprising teaches updating the power system model based on a deviation between a predicted operational state and at least one historical operational state to create the trained power system model (see [0027] “..industrial asset is used herein to describe a piece of equipment, element thereof, and/or a group of equipment logically and/or physically assembled together to form a production unit. Examples of such industrial assets may include a transformer, bushing, circuit breaker, substation (e.g., comprising one or more transformers and/or one or more circuit breakers), transmission tower, power generator, etc.…”; also, [0002] “to analyze data pertaining to an industrial asset to generate a health profile that describes past, present, or expected future conditions of the industrial asset and/or likely cause(s) of the conditions”; the health profile suggests at least one historical operational state or past conditions and also predicted operational states; see [0007] “…to update the model based upon a comparison of the health profile with data generated during the prediction period and indicative of the industrial asset”; also, see Figs. 3-5 show predicted operational state during predicted period 310 and at least one historical operational state during assessment period 308 of assets are determined; see [0031]-[0033]; also, see Fig. 5 and see [0065] “The assessment period 308 describes a time window of data samplings used to generate the health profile and the prediction period 310 describes a forecast window of interest. Thus, based upon the data acquired during the assessment period 308, one or more predictions or forecasts can be made about a state or health of the industrial asset during the prediction period 310…”; health profile; also, see Fig. 8 and [0088]-[0089] “At 806 in the example method 800, the data generated during the prediction period and/or other acquired data is compared to a health profile of the industrial asset to identify discrepancies, if any, between the information in the health profile and actual events that occurred during the prediction period… If a maintenance report generated during the prediction period indicates that the insulation was degraded but operating reports do not indicate that the loading on the transformer was reduced after the identification of the degraded insulation (e.g., despite redundant transformers that a portion of the load could have been moved to), the likelihood that overloading was the cause of the degraded insulation may be reduced (e.g., unless during the analysis it can be determined that the operators were likely to have been either unaware of the degradation or unaware that load reduction might be a useful mitigation technique to slow the deterioration of insulation). As still another example, data generated during the prediction period and yielded from an enterprise system may be analyzed to determine whether one or more predicted impacts of a condition were felt…”; also, see [0126]; also, see page 16 claim 10).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Spalt’s combination as taught above to include updating the power system model based on a deviation between the predicted operational state and the at least one historical operational state to create the trained power system model.
as taught by Smiley in order to facilitate addressing operational and/or physical changes to an industrial asset (see [0084])) and improve the confidence of prediction of the model prediction (see [0096]).
Conclusion
The prior art made of record and not relied upon, as cited in PTO form 892, is considered pertinent to applicant's disclosure.
Currently cited references:
Yan et al (US 20200292608) teaches “training can be done with both normal and abnormal data set, if the models provide values for other quantities not used in the monitoring nodes”, 0053 he training data may include historical data in an historical data store 210 which may be provided to offline feature extraction module 205”. Thus, Yan teaches the training of a model to diagnose one or more electrical assets using historical data set associated with non-nominal states data.
Nikovski et al (US 20180164794) teaches training of a model to using historical data set associated with non-nominal states operational states/abnormal states (See 0011-0013 and [0065-0067], see page 13 claim 1) for predicting on-nominal states operational states/abnormal state of a machine.
Bertoni et al (US 11488083) teaches training of a model to using historical data set associated with non-nominal states operational states/abnormal states for predicting on-nominal states operational states/abnormal state of a machine (see claim 1).
Yan et al (US 20200322366) teaches training of a model to using historical data set associated with non-nominal states operational states/abnormal states for predicting on-nominal states operational states/abnormal state of a machine (see 0034).
Poornaki et al (US 20200210824) teaches training of a model to using historical data set associated with non-nominal states operational states/abnormal states for predicting on-nominal states operational states/abnormal state of a machine (see 0008-0009, 0111).
Gundel et al (US 20210373063) teaches training of a model to using historical data set associated with non-nominal states operational states/abnormal states for predicting on-nominal states operational states/abnormal state of a machine (see 0129).
Herzog et al (US 10635095) teaches “historical operating data for assets in fleet 106 that has been labeled with the failure data for a given failure type in order to create a respective supervised predictive model that configured to predict occurrences of the given failure type at an asset in fleet 106”.
Previously cited references:
Visweswariah et al (US 20210203157, see 0009-00010, 0017, 0085, and 0284), McElhinney et al (US 201601553150, see 036 and 0139) teaches training a model with historical data associated with an operational state including a component failure event or non-nominal operations state.
Examiner respectfully requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
When responding to this Office Action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. Applicant must also show how the amendments avoid or differentiate from such references or objections. See 37 CFR 1.111 (c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLVIN LOPEZ ALVAREZ whose telephone number is (571) 270-7686 and fax (571) 270-8686. The examiner can normally be reached Monday thru Friday from 9:00 A.M. to 6:00 P.M.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Robert Fennema, can be reached at (571) 272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/O. L./
Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117
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