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 .
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.
Claims 1 – 3, 5 – 8, 10 – 12, 14, 15 and 19 – 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Schmidt US Patent Application Publication No. US-20210398289-A1 (hereinafter Schmidt).
Regarding claim 1, Schmidt discloses a method for determining gas emission parameters over a geospatial area (Schmidt in [0018] discloses, “One or more embodiments of the present invention address one or more of the above-described shortcomings by providing methods and systems that use spectral image data to detect a combination of terrain, oil extraction equipment, and gas emissions to identify well pads”), comprising: obtaining, by a computing node and from one or more overhead sensors (Schmidt in [0019] discloses, “The sensor 108 can be attached to a satellite, aircraft, balloon, unmanned aerial vehicle (UAV), or other aerial source”), one or more spectral signals over the geospatial area in three or more different spectral bands and at two or more different time-periods (Schmidt in [0020] discloses, “The data can include satellite imagery, spectral imagery, digital maps, or other data describing a parcel of land”. Schmidt in [0039] discloses, “The sensor 108 , for example, can include a remote imaging sensor such as a moderate-resolution imaging spectroradiometer (MODIS) or Visible Infrared Imaging Radiometer Suite (VIIRS), or a Light Detection and Ranging system (LIDAR) acquired at different wavelengths, that can capture spectral data. The sensor 108 can further include a gas imager that is sensitive to infrared spectral bands to detect chemical plumes that can be generated by oil and gas operations”. Lastly, Schmidt in [0046] discloses about time periods, “the well pad detection system determine a volume of the gas and a rate of emission based on time series data”); determining, by the computing node, one or more gas emission parameters over the geospatial area based on the one or more spectral signals using one or more trained deep-learning classification models (Schmidt in [0046] discloses; “The terrain specific machine learning model also analyzes the data to determine whether any machinery or equipment associated with a well pad are found in any of the bounded areas. At block 414 , the well pad detection system annotates the data as to whether a bounded area includes or does not include a well pad. If any gases are identified, the well pad detection system determine a volume of the gas and a rate of emission based on time series data”. Furthermore, Schmidt in [0020] discloses, “The backbone model is an image classification neural network used to extract features. The SSD head includes one or more convolutional layers added to the end of the backbone model neural network”), wherein each of the one or more trained deep-learning classification models is generated by: generating training data based on training samples representative of historical spectral signals from one or more geospatial areas (Schmidt in [0023] discloses, “The terrain neural network model 122 can further track changes to a terrain from a historical snapshot or image of the terrain”); wherein the training samples comprise one or more positive samples representative of a presence of gas emissions and zero or more negative samples representative of an absence of gas emissions (Schmidt in [0038] discloses, “The well pad detection unit 106 can further use the appropriate neural network model to determine whether a well pad is compliant with existing regulations ... This including detecting a presence and level of gas emissions after a shutdown order to determine whether the well pad shutdown in response to the order”); forming a set of training data batches, wherein each training data batch comprises a part of the training data; training a deep learning classification model based on the set of training data batches [0020]; “The data can be organized, for example, based on one or more of a location, a time of capture, a change detected based on a reference image, proximity to an environmental event, a level of oil production, a level of detected gases, or other appropriate criteria”. [0022]; “Training machine learning units typically require large data sets. The data augmentation unit 118 creates new data points/images by manipulating the original data”.
Summary of Citations (Schmidt)
Paragraph [0018]; “One or more embodiments of the present invention address one or more of the above-described shortcomings by providing methods and systems that use spectral image data to detect a combination of terrain, oil extraction equipment, and gas emissions to identify well pads”.
Paragraph [0019]; “The sensor 108 can be attached to a satellite, aircraft, balloon, unmanned aerial vehicle (UAV), or other aerial source”.
Paragraph [0020]; “The data can include satellite imagery, spectral imagery, digital maps, or other data describing a parcel of land”.
Paragraph [0020]; “The backbone model is an image classification neural network used to extract features. The SSD head includes one or more convolutional layers added to the end of the backbone model neural network”.
Paragraph [0020]; “The data can be organized, for example, based on one or more of a location, a time of capture, a change detected based on a reference image, proximity to an environmental event, a level of oil production, a level of detected gases, or other appropriate criteria”.
Paragraph [0022]; “Training machine learning units typically require large data sets. The data augmentation unit 118 creates new data points/images by manipulating the original data”.
Paragraph [0023]; “The terrain neural network model 122 can further track changes to a terrain from a historical snapshot or image of the terrain".
Paragraph [0038]; “The well pad detection unit 106 can further use the appropriate neural network model to determine whether a well pad is compliant with existing regulations, including any shutdown orders from a regulatory agency. The well pad detection unit 106 receives as data, the identity and composition of any emitted gases from a well pad and compares the values with threshold values established through regulation to determine whether the well pad is compliant with applicable emissions standards. This including detecting a presence and level of gas emissions after a shutdown order to determine whether the well pad shutdown in response to the order”.
Paragraph [0039]; “The sensor 108 , for example, can include a remote imaging sensor such as a moderate-resolution imaging spectroradiometer (MODIS) or Visible Infrared Imaging Radiometer Suite (VIIRS), or a Light Detection and Ranging system (LIDAR) acquired at different wavelengths, that can capture spectral data. The sensor 108 can further include a gas imager that is sensitive to infrared spectral bands to detect chemical plumes that can be generated by oil and gas operations. Spectral bands are the different ranges of electromagnetic signals along the electromagnetic spectrum. Examples of chemical plumes include methane, CH4 , that is a gas that escapes from a well pad, Sulphur Dioxide, SO2 , that is a natural byproduct of oil and gas, and Nitrogen Dioxide, NO2 , that is a gas released from the pumps and compressors of a well pad”.
Paragraph [0046]; “The terrain specific machine learning model also analyzes the data to determine whether any machinery or equipment associated with a well pad are found in any of the bounded areas. At block 414 , the well pad detection system annotates the data as to whether a bounded area includes or does not include a well pad. If any gases are identified, the well pad detection system determine a volume of the gas and a rate of emission based on time series data”.
Regarding claim 2, Schmidt discloses method of claim 1, wherein the one or more spectral signals comprises one or more of reflectance signals, absorbance signals, radiance signals, transmittance signals, a ratio of reflectance signals in different spectral bands, a ratio of radiance signals in different spectral bands, a ratio of absorbance signals in different spectral bands, a temporal variation, spatial variation, or spectral variation of one or more of reflectance signals, absorbance signals, radiance signals, transmittance signals, or any combination thereof (Claim 2 limitation is treated as disjunctive condition meaning is “either/or”. Schmidt in [0018] discloses about reflectance signal, “A gas has its own spectral characteristics and will reflect electromagnetic waves at a specific frequency and wavelength”. Furthermore, Schmidt in [0026] discloses about absorbance signal, “As sunlight strikes different vegetation, the vegetation will absorb certain wavelengths and reflect other wavelengths”).
Summary of Citations (Schmidt)
Paragraph [0018]; “A gas has its own spectral characteristics and will reflect electromagnetic waves at a specific frequency and wavelength”.
Paragraph [0026]; “As sunlight strikes different vegetation, the vegetation will absorb certain wavelengths and reflect other wavelengths. Vegetation cell structure has evolved to reflect near-infrared (NIR) wavelengths light, whereas the chlorophyll or the plant's pigment absorbs the visible wavelengths of light”.
Regarding claim 3, Schmidt discloses the method of claim 1, wherein the one or more spectral signals correspond to a time series of spectral signals or a temporal difference of spectral signals (Schmidt in [0020] discloses, “The data preprocessing unit 102 can receive the data from multiple sources ... The data can include satellite imagery, spectral imagery, digital maps, or other data describing a parcel of land. The data can be organized, for example, based on one or more of a location, a time of capture, a change detected based on a reference image”).
Summary of Citations (Schmidt)
Paragraph [0020]; “The data preprocessing unit 102 can receive the data from multiple sources ... The data can include satellite imagery, spectral imagery, digital maps, or other data describing a parcel of land. The data can be organized, for example, based on one or more of a location, a time of capture, a change detected based on a reference image”.
Regarding claim 5, Schmidt discloses the method of claim 1, wherein the one or more gas emission parameters are selected from at least one of reflectance, a radiance, an absorbance, a transmittance, a gas spatial distribution, a source location for gas emissions, a mass, a volume, a gas emission rate, a gas concentration, or a temporal or a spatial variation thereof (Schmidt in [0046] discloses, “If any gases are identified, the well pad detection system determine a volume of the gas and a rate of emission based on time series data. The well pad detection system then compares the identity of the gas and a volume of gas to any applicable emissions standards or shutdown orders”).
Summary of Citations (Schmidt)
Paragraph [0046]; “If any gases are identified, the well pad detection system determine a volume of the gas and a rate of emission based on time series data. The well pad detection system then compares the identity of the gas and a volume of gas to any applicable emissions standards or shutdown orders”.
Regarding claim 6, Schmidt discloses the method of claim 1, wherein the training samples are pre-processed to extract signal parameters or features, wherein pre-processing comprises applying at least one of a normalization, a cropping, a rotation, a noise addition, an embedding, a denoising, a filtering, a statistical ratio, a density estimation, a differentiation analysis, a translation of the spectral signal, or another linear or non-linear operation thereof (Schmidt in [0020] discloses, “The data preprocessing unit 102 includes a normalization unit 114 to normalize data from different sources (i.e., different sensor(s) 108 and database(s) 110 )”).
Summary of Citations (Schmidt)
Paragraph [0020]; “The data preprocessing unit 102 includes a normalization unit 114 to normalize data from different sources (i.e., different sensor(s) 108 and database(s) 110 )”.
Regarding claim 7, Schmidt discloses the method of claim 1, wherein the training data further comprises auxiliary data, and wherein the auxiliary data is selected from at least one of: data from the spectral signal obtained at a different time, data from a different spectral signal, topography data, weather data, wind data, cloud data, digital elevation model, thermal data, optical data, albedo data, SAR data, or InSAR data, bottom-of-atmosphere reflectance data, or a time series thereof (Schmidt in [0046] discloses about time periods, “the well pad detection system determine a volume of the gas and a rate of emission based on time series data”. Additionally, Schmidt in [0025] discloses, “The terrain can be topography that describes, for example, the elevation of the land, vegetation, and man-made construction (roads, buildings, infrastructure)”).
Summary of Citations (Schmidt)
Paragraph [0025]; “The terrain can be topography that describes, for example, the elevation of the land, vegetation, and man-made construction (roads, buildings, infrastructure)”.
Paragraph [0046]; “the well pad detection system determine a volume of the gas and a rate of emission based on time series data”.
Regarding claim 8, Schmidt discloses the method of claim 1, further comprising rendering the one or more gas emission parameters to a user via a graphic user interface (GUI), a message notification, or an alert, and wherein the alert or the message notification is generated based on a value of at least one of the one or more gas emission parameters (Schmidt in [0037] discloses, “In response to determining that a location includes an actual well pad, the well pad detection unit 106 can also alert a well pad operator in response to environmental event data”. Furthermore, Schmidt in [0047] discloses, “The well pad detection system can further remotely establish a communication to direct an entity to control any oil rig by either slowing the oil rig, speeding up the oil rig, or disabling the oil, based on the gas emission and any applicable emission standard”).
Summary of Citations (Schmidt)
Paragraph [0037]; “In response to determining that a location includes an actual well pad, the well pad detection unit 106 can also alert a well pad operator in response to environmental event data”.
Paragraph [0047]; “The well pad detection system can further remotely establish a communication to direct an entity to control any oil rig by either slowing the oil rig, speeding up the oil rig, or disabling the oil, based on the gas emission and any applicable emission standard”.
Regarding claim 10, Schmidt discloses the method of claim 1, wherein the one or more overhead sensors is mounted on an overhead device selected from at least one of a multi-spectral satellite or a hyperspectral satellite, a drone, a balloon, a plane, an unmanned aircraft, an unmanned aerial vehicle, a remotely piloted vehicle, an uncrewed aerial vehicle, an unmanned spaceship, or any other macro or micro air vehicles thereof (Schmidt in [0019] discloses, “The well pad detection system 100 is also operable to exchange data with at least one sensor 108 and at least one database 110 via a communication network 112 . The sensor 108 can be attached to a satellite, aircraft, balloon, unmanned aerial vehicle (UAV), or other aerial source”).
Summary of Citations (Schmidt)
Paragraph [0019]; “The well pad detection system 100 is also operable to exchange data with at least one sensor 108 and at least one database 110 via a communication network 112 . The sensor 108 can be attached to a satellite, aircraft, balloon, unmanned aerial vehicle (UAV), or other aerial source”.
Regarding claim 11, is a non-transitory computer readable storage medium claim
corresponds to method claim 1. Therefore, the rejection analysis of claim 1 is applied in claim
11.
Regarding claim 12, is a non-transitory computer readable storage medium claim
corresponds to method claim 3. Therefore, the rejection analysis of claim 3 is applied in claim
12.
Regarding claim 14, is a non-transitory computer readable storage medium claim
corresponds to method claim 5. Therefore, the rejection analysis of claim 5 is applied in claim
14.
Regarding claim 15, is a non-transitory computer readable storage medium claim
corresponds to method claim 8. Therefore, the rejection analysis of claim 8 is applied in claim
15.
Regarding claim 19, Schmidt discloses the method of claim 1, wherein the geospatial area comprises one or more of an oil and gas extraction site, an oil and gas well or well pad, a power plant, a wastewater plant, a landfill, a mine, an agriculture area or wetlands (Schmidt in [0002] discloses, “Once the drilling rig is finished drilling the well, an oil rig is placed to extract any oil from the ground. The oil extraction process requires specialized equipment, including the oil rig, protective barriers, and storage tanks”).
Summary of Citations (Schmidt)
Paragraph [0002]; “Once the drilling rig is finished drilling the well, an oil rig is placed to extract any oil from the ground. The oil extraction process requires specialized equipment, including the oil rig, protective barriers, and storage tanks”.
Regarding claim 20, Schmidt discloses the method of claim 1, wherein the geospatial area comprises one or more of an oil and gas storage, an oil and gas transport, or an oil and gas refining piece of equipment or infrastructure (Schmidt in [0044] discloses, “Storage tanks 212 are located on top of the well pad, and an extraction point 214 has been designated at the center of the well pad 204”).
Summary of Citations (Schmidt)
Paragraph [0044]; “Storage tanks 212 are located on top of the well pad, and an extraction point 214 has been designated at the center of the well pad 204”.
Regarding claim 21, Schmidt discloses the method of claim 1, wherein the geospatial area comprises a flare stack or a compressor (Schmidt in [0019] discloses, “Examples of chemical plumes include methane, CH4 , that is a gas that escapes from a well pad ... that is a gas released from the pumps and compressors of a well pad”).
Summary of Citations (Schmidt)
Paragraph [0019]; “Examples of chemical plumes include methane, CH4 , that is a gas that escapes from a well pad, Sulphur Dioxide, SO2 , that is a natural byproduct of oil and gas, and Nitrogen Dioxide, NO2 , that is a gas released from the pumps and compressors of a well pad”.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim 18 is rejected under 35 U.S.C 103 as being unpatentable over Schmidt in view of Thomas ‘Analysis of Model Mismatch Effects for a Model-Based Gas Source Localization Strategy Incorporating Advection Knowledge’ (hereinafter Thomas).
Regarding claim 18, Schmidt discloses the method of claim 1.
Schmidt doesn’t disclose about the following limitation as further recited in the claim.
Thomas discloses the positive samples comprise synthetic positive samples that include synthetic target gas emission parameters (Thomas in [Section – 3.1, Paragraph – 1] discloses, “The simulated values at the sampling locations are used as point-wise synthetic measurements”), wherein the synthetic target gas emission parameters correspond to gas emission parameters generated using a numerical model or a physical model (Thomas in [Section – 1.2, Paragraph – 4] discloses, “The advection-diffusion PDE models the dynamic change of the gas concentrations. It considers the diffusion of gas and transportation by means of airflow. Furthermore, it considers inflow of material or gas sources as an input”. Furthermore, Thomas in [Section – 2.1, Paragraph – 9] discloses, “numerical approximation methods are needed, such as Finite Volume Method (FVM) or Finite Element Method (FEM)”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Thomas into the system of Schmidt because it would improve the model accuracy by creating more training example.
Summary of Citations (Schmidt)
[Section – 1.2, Paragraph – 4]; “The advection-diffusion PDE models the dynamic change of the gas concentrations. It considers the diffusion of gas and transportation by means of airflow. Furthermore, it considers inflow of material or gas sources as an input”.
[Section – 2.1, Paragraph – 9]; “numerical approximation methods are needed, such as Finite Volume Method (FVM) or Finite Element Method (FEM)”.
[Section – 3.1, Paragraph – 1]; “The simulated values at the sampling locations are used as point-wise synthetic measurements”.
Allowable Subject Matter
Claims 4, 13, 16 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter.
Regarding claim 4, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Schmidt, Thomas and Scott (US-20220091026-A1) doesn’t disclose the limitation, the positive samples comprise synthetic positive samples generated by superimposing simulated gas emission to one or more of the positive samples or the negative samples. Although, the prior art discloses about data augmentation techniques for increasing the size of the training data set by manipulating the images by rotation, color alteration, cropping scalling but the reference doesn’t disclose about synthetic positive gas emission samples. The prior art also doesn’t disclose about generating such samples by overlaying or superimposing a simulated gas emission onto existing positive or negative samples. Lastly, Thomas and Scott discloses about creating synthetic gas spectra measurement but not superimposing simulated gas emission.
Regarding claim 13, claim 13 a non-transitory computer readable storage medium claim
corresponds to method claim 4. Therefore, claim 13 is allowable for the same reason indicated in claim 4.
Regarding claim 16, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Schmidt doesn’t disclose the limitation, the positive samples comprise synthetic positive samples generated by superimposing gas emission data generated by a machine learning model to one or more of the positive examples or the negative samples, or target synthetic gas emission parameters generated by a machine learning model. Although, the prior art discloses about data augmentation techniques for increasing the size of the training data set by manipulating the images by rotation, color alteration, cropping scalling but the reference doesn’t disclose about synthetic positive gas emission samples. The prior art also doesn’t disclose about the positive samples comprise synthetic positive samples generated by superimposing gas emission data generated by a machine learning model. Lastly, Thomas and Scott (US-20220091026-A1) discloses about creating synthetic gas spectra measurement but not superimposing simulated gas emission.
Regarding claim 17, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Schmidt doesn’t disclose the limitation, the positive samples comprise synthetic positive samples generated by superimposing gas emission data to samples based on natural gas emission, or synthetic positive samples generated from examples of natural gas emissions through linear or non-linear operations. Although, the prior art discloses about data augmentation techniques for increasing the size of the training data set by manipulating the images by rotation, color alteration, cropping. The prior art doesn’t disclose about the positive samples comprise synthetic positive samples generated by superimposing natural gas emission and synthetic positive samples generated from examples of natural gas emissions through linear or non-linear operations. Lastly, Thomas and Scott (US-20220091026-A1) discloses about creating synthetic gas spectra measurement but not superimposing simulated gas emission.
Conclusion
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/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
06/13/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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