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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/22/2023 and 07/02/2024 were filed before the mailing date of the first office action. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 and 9-20 are rejected under 35 U.S.C. 101. Claims 1-7 and 9-11 are directed to a method, and claims 12-20 are directed to system; therefore, claims 1-7 and 9-20 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). However, claims 1-7 and 9-20 fall within the judicial exception of an abstract idea, specifically the abstract ideas of “Mental Processes” (including observation, evaluation, and opinion) and “Mathematical Concepts (including mathematical calculations and relationships)”.
Claim 1:
Claim 1 is directed to a method; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claim 1 recites the following abstract ideas:
predicting a methane emissions event associated with the asset, based on the current asset data (mental step directed to observation, evaluation – a person could predict a methane emissions event in their mind associated with observed current asset data).
Claim 1 recites the following additional elements:
obtaining current asset data for an asset, the current asset data comprising process data; using a machine learning model; and reporting the predicted methane emissions event in a user visualization. Obtaining current data and reporting a predicted methane emissions event are interpreted as transmitting and receiving data over a network and aspects of the technological environment in which the abstract ideas are performed. As no particular machine learning model nor technical details associated with using the machine learning model are claimed, the machine learning model is interpreted as a generic computer component merely used to implement the claimed abstract ideas. These elements do not integrate the abstract ideas into a practical application or amount to significantly more than the abstract ideas (see MPEP 2106.05(d)(II), MPEP 2106.05(f), and MPEP 2106.05(h)).
Claim 12 is a system claim and its limitation is included in claim 1. The only difference is that claim 12 requires a system, which is interpreted as generic computer components merely used to apply the abstract ideas recited in claim 1 (see MPEP 2106.05(f)). Therefore, claim 12 is rejected for the same reasons as claim 1.
The independent claims are not patent eligible.
Dependent claims 2-7, 9-11, and 13-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea, as they recite further embellishment of the judicial exception.
Claim 2 recites wherein the asset comprises at least one petrochemical asset. This limitation is interpreted as a further description of the technological environment in which the claimed abstract ideas are performed, and does not integrate the abstract ideas into a practical application or amount to significantly more than the abstract ideas (see MPEP 2106.05(h)).
Claim 3 recites 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. This limitation is interpreted as a further description of the technological environment in which the claimed abstract ideas are performed, and does not integrate the abstract ideas into a practical application or amount to significantly more than the abstract ideas (see MPEP 2106.05(h)).
Claim 4 recites wherein the prediction of the methane emissions event comprises a classification performed between multiple categories of methane emissions events of different magnitude. Wherein prediction comprises a classification is interpreted as a mental step directed to observation, evaluation – a person could classify predicted methane emissions events in their mind into multiple categories of different magnitudes.
Claim 5 recites wherein the prediction of the 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 methane emissions event. Wherein the prediction is selected from a time, a location, or a quantification is interpreted as a mental step directed to observation, evaluation – a person could predict methane emissions events related to a timing, a location, or a quantification in their mind.
Claim 6 recites predicting, using the machine learning model, a mitigation action for the methane emissions event. Wherein prediction comprises a mitigation action is interpreted as a mental step directed to observation, evaluation – a person could predict a mitigation action for a methane emissions event in their mind.
Claim 7 recites wherein the mitigation action comprises adjusting a setting of a valve associated with the asset. Wherein a predicted mitigation action comprises adjusting the setting of a valve is interpreted as a mental step directed to observation, evaluation – a person could predict a mitigation action associated with adjusting a setting for a valve in their mind.
Claim 9 recites obtaining, for the asset, archived asset data comprising process data and methane sensor data; and training the machine learning model to predict methane emissions events based on the archived asset data used as training data. Obtaining archived asset data is interpreted as an additional element directed to receiving data over a network and retrieving information from memory. As no particular machine learning model nor technical details associated with using the machine learning model are claimed, the machine learning model is interpreted as a generic computer component and training this model is interpreted as generic computer activity associated with merely implementing a mental step directed to predicting methane emissions events. These elements do not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas (see MPEP 2106.05(d)(II) and MPEP 2106.05(f)).
Claim 10 recites preprocessing the archived asset data, comprising at least one selected from a group consisting of removing outliers and removing false positives. Preprocessing data by removing outliers and removing false positives are both interpreted as mental steps directed to observation, evaluation – a person could preprocess observed data by removing outliers in their mind and removing false positives in their mind, potentially assisted by pen and paper (see MPEP 2106.04(a)(2)(III)).
Claim 11 recites standardizing the archived asset data for sensor-agnostic operation of the machine learning model. Standardizing archived data is interpreted as a mental step directed to observation, evaluation – a person could standardize observed archived asset data in their mind, potentially assisted by pen and paper (see MPEP 2106.04(a)(2)(III)).
Claim 13 recites 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. Use of a digital twin is interpreted as well-understood, routine, conventional activity in the art in light of US 20210208576 A1 (Muenzel), paragraph [0002] of which recites “Digital Twins of products and production systems (factories) are a well-known concept and used increasingly”. This additional element does not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas (see MPEP 2106.05(d)).
Claim 14 recites 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. This limitation is interpreted as a further description of the technological environment in which the claimed abstract ideas are performed, and does not integrate the abstract ideas into a practical application or amount to significantly more than the abstract ideas (see MPEP 2106.05(h)).
Claim 15 recites wherein the physical environment comprises sensors that obtain the current asset data for the asset in the physical environment. Obtaining current asset data from sensors in a physical environment is interpreted as receiving data over a network, which does not integrate the abstract ideas into a practical application or amount to significantly more than the abstract ideas (see MPEP 2106.05(d)(II)).
Claim 16 recites 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. Obtaining current asset data from sensors such as at least a fenceline sensor is interpreted as receiving data over a network, which does not integrate the abstract ideas into a practical application or amount to significantly more than the abstract ideas (see MPEP 2106.05(d)(II)).
Claim 17 recites 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. The edge computing platform is interpreted as generic computer component reflecting the technological environment in which the claimed abstract ideas are implemented. Receiving current asset data from sensors and forwarding asset data to a digital twin are interpreted as receiving and transmitting data over a network. These additional elements do not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas (see MPEP 2106.05(d) and MPEP 2106.05(h)).
Claim 18 recites wherein the computing environment comprises a cloud computing platform, and wherein the digital twin is executed on the cloud computing platform. The cloud computing platform is interpreted as generic computer component reflecting the technological environment in which the claimed abstract ideas are implemented, and does not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas (see MPEP 2106.05(d) and MPEP 2106.05(h)).
Claim 19 recites 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. Use of a SCADA system is interpreted as well-known, conventional activity in the art in light of US 20190204137 A1 (Little et al), paragraph [0004] of which recites “Remote-monitoring systems, such as SCADA (Supervisory Control and Data Acquisition) systems are also well-known and utilized in many industries and operations, including oil and gas, facilities management, and power generation and transport. These systems generally collect analog or digital data from various devices, sensors, and processes and transmit the information to remote locations for analysis or manipulation”. Obtaining process data and forwarding process data to a digital twin are interpreted as receiving and transmitting data over a network. These additional elements do not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas (see MPEP 2106.05(d)(II)).
Claim 20 recites wherein the user visualization in the dashboard is configurable to enable monitoring of the methane emissions on a global, regional, side-wide, and asset-specific level. A dashboard user visualization to enable monitoring of emissions is interpreted as an additional element directed to displaying, or transmitting data over a network, which does not integrate the claimed abstract ideas into a practical application or amount to significantly more than the claimed abstract ideas (see MPEP 2106.05(d)(II)).
Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. Examiner notes that claim 8 recites performing the mitigation action such that an actual occurrence of the predicted methane emissions event is avoided, which is interpreted as integrating the abstract idea of predicting the mitigation action into a practical application to avoid a predicted methane emissions event.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 and 9-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Scott et al (US 20220091026 A1, herein Scott).
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 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” (i.e., predicting a methane emissions event using a machine learning model));
and reporting the predicted 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)).
Regarding claim 2, Scott teaches the method of claim 1, wherein the asset comprises at least one petrochemical asset (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, Scott 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 (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”. 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, Scott 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 (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” (i.e., multiple categories of methane events at different magnitudes can be determined)).
Regarding claim 5, Scott teaches the method of claim 1, wherein the prediction of the 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 methane emissions event (para. [0018] recites “With regard to localization, the system and methods can produce the required spatial resolution by using intensive modeling”. 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., events related to at least the spatial aspects, or location, of a methane emissions related event can be predicted)).
Regarding claim 9, Scott 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 (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)”. 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 methane emissions events based on the archived asset data used as training data (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 emissions-related events based on accumulated training data)).
Regarding claim 10, Scott 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 (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, Scott 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 (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 (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, Scott 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 (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”. 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, Scott 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 (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”. 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, Scott 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 (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., obtaining environment data from sensors)).
Regarding claim 16, Scott 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 (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, Scott 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 (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”. 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, Scott 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 (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)”. 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, Scott 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 (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”. 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)).
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 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Scott et al (US 20220091026 A1, herein Scott) in view of Dudar (US 20200370497 A1, herein Dudar).
Regarding claim 6, Scott teaches the method of claim 1.
However, while Scott teaches using a machine learning model to predict methane emissions events, which can be related to simulating a stuck valve (see at least paragraphs [0201] and [0250]), Scott does not explicitly teach a mitigation action for an emissions event.
Dudar teaches a mitigation action for an emissions event (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., taking a mitigating action when an emissions event is occurring)).
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 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 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)).
Regarding claim 8, the combination of Scott and Dudar teaches the method of claim 6, further comprising: performing the mitigation action such that an actual occurrence of the predicted methane emissions event is avoided (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 when an emissions event is occurring to avoid the occurrence of the event)).
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 Du et al (US 20180196023 A1, herein Du)
Regarding claim 20, Scott 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]), Scott 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. 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.
US 20240386445 A1 (Mustapha et al) teaches a method for utilizing machine learning to monitor and optimize methane emissions development plans for a given field site.
US 20240060785 A1 (Oltramari et al) teaches a method for engine emission calibration based on determined characteristics and machine-learned rule derivations.
US 11727519 B1 (Foiles et al) teaches a method for air quality monitoring and optimization utilizing an on-site SCADA system.
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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/L.M.F./ Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147