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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/29/2026 has been entered.
Status of Claims
This action is in response to the amendments filed 07/29/2026. Claims 1, 14, 12, and 20 have been amended, claims 1-7 and 9-20 are currently pending.
Response to Arguments
Applicant’s arguments regarding the prior art rejection have been fully considered but are moot because of the new ground(s) of rejection. Applicant argues that the previously cited references do not teach “performing a preventative action such that an actual occurrence of the predicted future methane emissions event is avoided”. Examiner notes that the Dudar reference, while still relied upon to teach the mitigation event in claims 6-7, is no longer relied upon in the rejection of claim 1. The Hay reference has been brought in to more clearly teach a preventative action related to automatically controlling a flare temperature to destroy methane and avoid emissions. The prior art rejections have been updated to include the amended limitations and to clarify the reasoning given for the limitations that were not amended.
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 1-5 and 9-19 are rejected under 35 U.S.C. 103 as being unpatentable over Scott et al (US 20220091026 A1, herein Scott) in view of Hay et al (US 20230266543 A1, herein Hay).
Regarding claim 1, Scott teaches a method (para. [0010] recites “The air quality monitoring system and method may detect and report pollution from monitored site for a variety of reasons”), comprising: obtaining current asset data for an asset, the current asset data comprising process data (para. [0089] recites “FIG. 1 shows an example of an air quality monitoring system 110, which handles air quality data from different sources. As illustrated in FIG. 1, air quality monitoring system 110 may include an air quality data processing module 121, a plurality of air quality monitors 132-134, reference monitors 137 and environmental monitors 139”. Para. [0091] recites “Air quality data processing module 121 is configured to communicate with air quality monitors 132-134, reference monitors 137, and environmental monitors 139. For example, air quality data processing module 121 may receive data from these monitors, such as measurements. Air quality data processing module 121 may also transmit data to these monitors, such as providing calibration data” (i.e., obtaining process data));
predicting, using a machine learning model, a future methane emissions event associated with the asset, based on the current asset data (para. [0150] recites “The individual reference spectra at known concentrations of water vapor, carbon dioxide and methane may be recovered from investigations or from a public database”. Para. [0200] recites “One embodiment of step 836 uses statistical inference together with emission statistics 847 to identify the type of normal emission or leaks by distinguishing their intensity, frequency, and composition over time during a period of interest”. Para. [0201] recites “An alternative method to this qualification method embodiment may be to use artificial intelligence, machine learning, or neural networks. In this case, a training set is first created to identify the signatures of the emissions. The artificial intelligence method may learn over time by accumulating validation information from the type of emission through the site operator maintenance log 848. Over time, emission types may be more and more accurately qualified by a learning algorithm”. Para. [0290] recites “the use of data stream and statistical inference of the conditional probabilities of leaks is tremendous for the prediction of potential leaks and appropriate inspection schedules and methods. The proposed method weights these various factors to select the most appropriate inspection embodiment”. (i.e., predicting a methane emissions event using a machine learning model));
reporting the predicted future methane emissions event in a user visualization (para. [0317] recites “With reference to FIG. 20A and 20B, graphical representations 2000A, 2000B of results from (a) event detection and (b) background concentration are depicted” (i.e., visual representations of emissions-related events, such as the potential, or future events as described in paragraph [0290])).
However, while Scott teaches predicting future emissions events and preventative maintenance (see at least paragraphs [0201], [0250], and [0290]), Scott does not explicitly teach performing a preventative action such that an actual occurrence of the [predicted] future methane emissions is avoided.
Hay teaches performing a preventative action such that an actual occurrence of the [predicted] future methane emissions is avoided (para. [0032] recites “one or more flare monitors 12A may be used to monitor flaring of one or more flares 14 at the oil and gas worksite 10 in order to prevent methane emissions by combusting methane into carbon dioxide. Substantial methane emissions may occur if flares 14 are unlit or burn inefficiently. The flares 14 may be monitored by many various types of flare monitors 12A. As described in greater detail herein, the one or more flare monitors 12A may be configured to detect radiant or thermal heat emitted by the one or more flares 14 for the purpose of determining the flaring efficiency of the one or more flares 14”. Para. [0043] recites “the processor(s) 34 may be configured to automatically adjust parameters of the flare 14 based at least in part on the flaring efficiency that is determined by the processor(s) 34” (i.e., a preventative action to avoid a potential emissions event caused by potential improper flaring)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by utilizing the flare monitoring and automated emission prevention system from Hay to adjust the flare parameters when a monitoring system, such as the emission monitoring system from Scott detects an emissions event. Hay and Scott are both directed to the common field of air quality monitoring, and while Scott teaches the ability to simulate an emissions event related to a stuck valve in at least paragraph [0250], Scott does not explicitly teach a preventative action such as adjusting parameters of a flare to avoid emissions. One of ordinary skill in the art would recognize that it would be beneficial to utilize the automated preventative system from Hay to mitigate real world versions of Scott’s simulated event to prevent extraneous emissions.
Regarding claim 2, the combination of Scott and Hay teaches the method of claim 1, wherein the asset comprises at least one petrochemical asset (Scott para. [0010] recites “a monitored site may be an oil facility removing natural gas (and/or oil) from an underground reservoir utilizes equipment (e.g., pumpjacks, holding tanks, valves, pipes, etc.) that requires maintenance” (i.e., a monitored asset can be related to oil and gas, or petrochemical operations)).
Regarding claim 3, the combination of Scott and Hay teaches the method of claim 1, wherein the current asset data further comprises at least one selected from a group consisting of: environmental data, historical data associated with the asset, and methane sensor data (Scott para. [0199] recites “emissions are to be qualified in step 836. The qualification allows further refinements of the understanding of the emissions. Indeed, emissions can come from different elements within an equipment but more importantly, emissions from the same equipment may be separated into categories of expected emissions from normal operation and spurious emissions from leaks or abnormal operations. For example, in the upstream and midstream oil and gas industry, equipment such as compressors and pneumatic actuators may emit methane in normal operation”. Scott para. [0201] recites “An alternative method to this qualification method embodiment may be to use artificial intelligence, machine learning, or neural networks. In this case, a training set is first created to identify the signatures of the emissions. The artificial intelligence method may learn over time by accumulating validation information from the type of emission through the site operator maintenance log 848. Over time, emission types may be more and more accurately qualified by a learning algorithm” (i.e., asset data can be related to at least methane sensor data)).
Regarding claim 4, the combination of Scott and Hay teaches the method of claim 1, wherein the prediction of the methane emissions event comprises a classification performed between multiple categories of methane emissions events of different magnitude (Scott para. [0020] recites “The emission qualification with respect to type, for instance routine versus fugitive emission, is obtained by performing a statistical analysis of the detected emissions based on their source, magnitude, and frequency, compared to the source, magnitude, and frequency of routine emissions”. Scott para. [0290] recites “the use of data stream and statistical inference of the conditional probabilities of leaks is tremendous for the prediction of potential leaks and appropriate inspection schedules and methods. The proposed method weights these various factors to select the most appropriate inspection embodiment” (i.e., multiple categories of potential, or future methane events at different magnitudes can be determined)).
Regarding claim 5, the combination of Scott and Hay teaches the method of claim 1, wherein the prediction of the future methane emissions event comprises a prediction of at least one selected from a group consisting of a timing, a location, and a quantification of the future methane emissions event (Scott para. [0018] recites “With regard to localization, the system and methods can produce the required spatial resolution by using intensive modeling”. Scott para. [0201] recites “An alternative method to this qualification method embodiment may be to use artificial intelligence, machine learning, or neural networks. In this case, a training set is first created to identify the signatures of the emissions. The artificial intelligence method may learn over time by accumulating validation information from the type of emission through the site operator maintenance log 848. Over time, emission types may be more and more accurately qualified by a learning algorithm”. Scott para. [0290] recites “the use of data stream and statistical inference of the conditional probabilities of leaks is tremendous for the prediction of potential leaks and appropriate inspection schedules and methods. The proposed method weights these various factors to select the most appropriate inspection embodiment” (i.e., events related to at least the spatial aspects, or location, of a potential, or future methane emissions related event can be predicted)).
Regarding claim 9, the combination of Scott and Hay teaches the method of claim 1, further comprising, prior to performing the prediction: obtaining, for the asset, archived asset data comprising process data and methane sensor data (Scott para. [0026] recites “a method is proposed which fully characterizes the detection area of the sensor system based on not only the sensor characteristics (detection limit, compounds detectable, frequency of measurement), but also project characteristics (fraction of emissions to be detected, localization requirement, report frequency requirement), site characteristics (location of equipment in the field, terrain topology, terrain cover and roughness, historical weather patterns, area where the sensor can actually be deployed, and restricted deployment area), and/or prior network data (if a prior deployment configuration was in place)”. Scott para. [0150] recites “The individual reference spectra at known concentrations of water vapor, carbon dioxide and methane may be recovered from investigations or from a public database” (i.e., obtaining historical, or archived sensor data));
and training the machine learning model to predict future methane emissions events based on the archived asset data used as training data (Scott para. [0201] recites “An alternative method to this qualification method embodiment may be to use artificial intelligence, machine learning, or neural networks. In this case, a training set is first created to identify the signatures of the emissions. The artificial intelligence method may learn over time by accumulating validation information from the type of emission through the site operator maintenance log 848” (i.e., training a machine learning model to predict future emissions-related events based on accumulated training data)).
Regarding claim 10, the combination of Scott and Hay teaches the method of claim 9, further comprising, prior to training the machine learning model: preprocessing the archived asset data, comprising at least one selected from a group consisting of removing outliers and removing false positives (Scott para. [0189] recites “In step 832, the data of step 850 associated with the sensing of the target compounds is preprocessed. In an embodiment of step 832 for the specific case of spectroscopy sensor technologies, a raw spectrum is processed. The preprocessing includes denoising the data, peak alignment and bias shifting and computing an absorbance spectrum of sample t 853 from the transmission spectrum by using a spectral baseline 841 as a reference transmission. This step may involve sensor metadata for sensor-specific preprocessing. Regardless of the sensing technology embodiment used, step 832 may involve denoising, debiasing, or otherwise calibrating and enhancing the raw signal with preprocessing strategies that may involve sensor-specific information such that the preprocessed sensor signal may be analyzed” (i.e., preprocessing data by at least removing outliers with a denoising method)).
Regarding claim 11, the combination of Scott and Hay teaches the method of claim 9, further comprising, prior to training the machine learning model: standardizing the archived asset data for sensor-agnostic operation of the machine learning model (Scott para. [0155] recites “The local computing unit 717 runs the main firmware, which schedules and collects data from compound sensor 710 and weather sensor system 711, conditions the sensor signals into a rational format, performs data preprocessing, locally stores data, formats, and prepares messages, and generates diagnostic and metadata pertaining to the identification, time stamping and operational diagnostics of the sensor system and supporting circuitry” (i.e., formatting, or standardizing, the sensor asset data)).
Claim 12 is a system claim and its limitation is included in claim 1. The only difference is that claim 12 requires a system (Scott para. [0010] recites “The air quality monitoring system and method may detect and report pollution from monitored site for a variety of reasons”). Therefore, claim 12 is rejected for the same reasons as claim 1.
Regarding claim 13, the combination of Scott and Hay teaches the system of claim 12, wherein the machine learning model is a digital twin that establishes a virtual model that reflects characteristics of a physical environment related to the methane emissions event (Scott para. [0175] recites “a simulation of the emission transport using a digital twin of the site is performed. Such a digital twin can reconstruct an estimation of the actual flux responsible for the transport and consider the effect of terrain 747, obstacles 748, source geometry 743, 742, 749, as well as other parameters relevant for the turbulent advection/diffusion of the target emitted compounds”. Scott para. [0179] recites “In order to perform localization, quantification and qualification, centralized computing unit 727 may reference field metadata 737 collected by field operators such as, but not limited to, topological maps of the field deployment, images of site, the potential sources and equipment, equipment inventory and GPS coordinates of features of interest, for the purpose of creating a digital twin of the site for the purpose of atmospheric transport modeling and simulation” (i.e., creating a digital twin to virtually model the physical environment related to the emissions event)).
Regarding claim 14, the combination of Scott and Hay teaches the system of claim 13, wherein the asset is in the physical environment reflected by the virtual model, and wherein the asset is one selected from a group consisting of a vapor recovery unit, a compressor, storage tank, a power unit, a valve, a flange, and a seal (Scott para. [0010] recites “a monitored site may be an oil facility removing natural gas (and/or oil) from an underground reservoir utilizes equipment (e.g., pumpjacks, holding tanks, valves, pipes, etc.) that requires maintenance”. Scott para. [0250] recites “a stuck open valve may be voluntarily simulated to evaluate its emission profile if such an event did happen by accident” (i.e., a monitored physical asset can be at least a valve)).
Regarding claim 15, the combination of Scott and Hay teaches the system of claim 14, wherein the physical environment comprises sensors that obtain the current asset data for the asset in the physical environment (Scott para. [0089] recites “FIG. 1 shows an example of an air quality monitoring system 110, which handles air quality data from different sources. As illustrated in FIG. 1, air quality monitoring system 110 may include an air quality data processing module 121, a plurality of air quality monitors 132-134, reference monitors 137 and environmental monitors 139”. Scott para. [0285] recites “inspection methods such as operator-based, drone-based, plane-based, satellite-based or fence line monitoring may be used together with continuous monitoring from static sensors to provide a holistic approach to monitoring” (i.e., obtaining environment data from sensors)).
Regarding claim 16, the combination of Scott and Hay teaches the system of claim 15, wherein the sensors comprise at least one selected from a group consisting of a fenceline sensor, a thermal camera, a non-thermal camera, an optical gas imaging camera, a drone-based sensor, a robot-based sensor, a helicopter-based sensor, an airplane-based sensor, and a satellite-based sensor (Scott para. [0089] recites “FIG. 1 shows an example of an air quality monitoring system 110, which handles air quality data from different sources. As illustrated in FIG. 1, air quality monitoring system 110 may include an air quality data processing module 121, a plurality of air quality monitors 132-134, reference monitors 137 and environmental monitors 139”. Para. [0285] recites “inspection methods such as operator-based, drone-based, plane-based, satellite-based or fence line monitoring may be used together with continuous monitoring from static sensors to provide a holistic approach to monitoring” (i.e., sensors can include at least a fenceline sensor)).
Regarding claim 17, the combination of Scott and Hay teaches the system of claim 15, wherein the computing environment comprises an edge computing platform that receives the current asset data from the sensors, and forwards the current asset data to the digital twin (Scott para. [0166] recites “dynamic scheduling may be decided by a sensor system control unit using edge computing resources, or by query from the centralized computing unit 727 of FIG. 7B for scheduling decisions requiring human intervention or larger computing resources”. Scott para. [0179] recites “centralized computing unit 727 may reference field metadata 737 collected by field operators such as, but not limited to, topological maps of the field deployment, images of site, the potential sources and equipment, equipment inventory and GPS coordinates of features of interest, for the purpose of creating a digital twin of the site for the purpose of atmospheric transport modeling and simulation” (i.e., an edge computing platform can be used to communicate sensor data to a digital twin model)).
Regarding claim 18, the combination of Scott and Hay teaches the system of claim 13, wherein the computing environment comprises a cloud computing platform, and wherein the digital twin is executed on the cloud computing platform (Scott para. [0091] recites “Air quality data processing module 121 may be implemented in any appropriate physical or virtual computing platform (such as a networked server) and may operate and act through any suitable interface (such as a cloud computing platform)”. Scott para. [0179] recites “centralized computing unit 727 may reference field metadata 737 collected by field operators such as, but not limited to, topological maps of the field deployment, images of site, the potential sources and equipment, equipment inventory and GPS coordinates of features of interest, for the purpose of creating a digital twin of the site for the purpose of atmospheric transport modeling and simulation” (i.e., the cloud computing platform can execute a digital twin model)).
Regarding claim 19, the combination of Scott and Hay teaches the system of claim 13, wherein the computing environment comprises a supervisor control and data acquisition (SCADA) system that obtains the process data associated with the asset and forwards the process data to the digital twin (Scott para. [0155] recites “The communication protocol may be wired, such as a SCADA system or wireless, such as Bluetooth®, Wi-Fi, LoRa, cellular or satellite or any other radiofrequency, optical line of sight, or other wireless data-transmission protocol”. Scott para. [0179] recites “centralized computing unit 727 may reference field metadata 737 collected by field operators such as, but not limited to, topological maps of the field deployment, images of site, the potential sources and equipment, equipment inventory and GPS coordinates of features of interest, for the purpose of creating a digital twin of the site for the purpose of atmospheric transport modeling and simulation” (i.e., a SCADA system can be used as part of the system which communicates process data from the sensors to the digital twin model)).
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Scott et al (US 20220091026 A1, herein Scott) in view of Hay et al (US 20230266543 A1, herein Hay), in further view of Dudar et al (US 20200370497 A1, herein Dudar)
Regarding claim 6, the combination of Scott and Hay teaches the method of claim 1.
However, while Scott teaches preventative maintenance and predicting emissions events with a machine learning model (see at least paragraphs [0201], [0250], and [0290]), the combination of Scott and Hay does not explicitly teach predicting, [using the machine learning model], a mitigation action for the future methane emissions event.
Dudar teaches predicting, [using the machine learning model], a mitigation action for the future methane emissions event (para. [0133] recites “one or more of fuel tank pressure, temperature of fuel in the fuel tank, ambient temperature, prediction of upcoming fuel slosh events, etc., may be relied upon for inferring whether conditions are such that in response to fuel tank depressurization along the first flow path, a condition of degraded engine stability may occur”. Para. [0167] recites “the method may further comprise indicating the fuel tank isolation valve is stuck in the first open position in response to the time duration comprising a first time duration and indicating the fuel tank isolation valve is stuck in the second open position in response to the time duration comprising a second time duration”. Para. [0174] recites “taking mitigating action may further comprise in response to the fuel tank isolation valve being indicated to be stuck in the second open position, and further in response to an indication of a request for refueling or a vehicle-off condition, sealing the fuel system and the evaporative emissions system from atmosphere and fluidically coupling the fuel system and the evaporative emissions system to the engine” (i.e., using a machine learning model to predict a mitigation action for a potential, or future, emissions event)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by utilizing the emission control system from Dudar to implement mitigation actions when the monitoring system from Scott (as modified by Hay) detects an emissions event. Dudar and Scott are both directed to the common field of air quality monitoring, and while Scott teaches the ability to simulate an emissions event related to a stuck valve in at least paragraph [0250], Scott does not explicitly teach the ability to mitigate a real world version of this simulated event. One of ordinary skill in the art would recognize that it would be beneficial to utilize the mitigation operations from the system from Dudar to mitigate real world versions of Scott’s simulated event to prevent extraneous emissions.
Regarding claim 7, the combination of Scott, Hay, and Dudar teaches the method of claim 6, wherein the mitigation action comprises adjusting a setting of a valve associated with the asset (Scott para. [0250] recites “a stuck open valve may be voluntarily simulated to evaluate its emission profile if such an event did happen by accident”. Dudar para. [0167] recites “the method may further comprise indicating the fuel tank isolation valve is stuck in the first open position in response to the time duration comprising a first time duration and indicating the fuel tank isolation valve is stuck in the second open position in response to the time duration comprising a second time duration”. Dudar para. [0174] recites “taking mitigating action may further comprise in response to the fuel tank isolation valve being indicated to be stuck in the second open position, and further in response to an indication of a request for refueling or a vehicle-off condition, sealing the fuel system and the evaporative emissions system from atmosphere and fluidically coupling the fuel system and the evaporative emissions system to the engine” (i.e., taking a mitigating action associated with the setting of a valve when an emissions event is occurring)).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Scott et al (US 20220091026 A1, herein Scott) in view of Hay et al (US 20230266543 A1, herein Hay), in further view of Du et al (US 20180196023 A1, herein Du).
Regarding claim 20, the combination of Scott and Hay teaches the system of claim 12.
However, while Scott teaches using a user visualization to enable monitoring of methane emissions at least at an asset level (see at least paragraphs [0119] and [0449]), the combination of Scott and Hay does not explicitly teach wherein the user visualization in the dashboard is configurable to enable air quality monitoring on a global, regional, and side-wide level.
Du teaches wherein the user visualization in the dashboard is configurable to enable air quality monitoring on a global, regional, and side-wide level (para. [0013] recites “some embodiments provide a forecasting system that uses blended global and regional data to improve the accuracy of certain models (e.g., NAQF models). For instance, some embodiments of the present invention blend global (i.e., large-scale) weather forecasts and air quality forecasts with regional (i.e., small-scale) weather forecasts and air quality forecasts in a manner that in improves the effective representation of large-scale features”. Para. [0030] recites “A display (e.g., screen, a display monitor) 515 is connected to the system bus 502 by a display adapter 516, which may include a graphics controller to improve the performance of graphics intensive applications and a video controller” (i.e., displaying air quality monitoring on a global, regional, and small-scale, or site-wide level)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by adapting the visualization system from Du to display air quality monitoring on a variety of levels including the global and regional levels to include the more specific monitoring levels from Scott (as modified by Hay). Du and Scott both teach methods of monitoring and visualizing air quality metrics associated with emissions; accordingly, one of ordinary skill in the art would recognize the usefulness in monitoring emissions levels on a wider variety of areas.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“LSTM based encoder-decoder for short-term predictions of gas concentration using multi-sensor fusion” (Lyu et al) teaches a multi-step prediction method for gas concentration time series prediction based on the ARMA model, the CHAOS model and single sensor and multi-sensor Encoder-Decoder models.
“Next-Generation SCADA Architectures for Enhanced Field Automation and Real-Time Remote Control in Oil and Gas Fields” (Enemosah et al) teaches an overview of advances in the architecture, functionality, and operational value of next-generation SCADA systems for enhanced field automation and real-time remote control in oil and gas fields.
US 20200159203 A1 (Shetty et al) teaches a method for predicting leading indicators for predicting occurrence of an event at a target asset in order to proactively prevent an event from occurring.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEAH M FEITL whose telephone number is (571) 272-8350. The examiner can normally be reached on M-F 0900-1700 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached on (571) 270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L.M.F./ Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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