DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Receipt is acknowledged of claim amendments with associated arguments/remarks, received March 17, 2026. Claims 1-18, 21-22 are pending with amendments to claims 4-5, 11, 15 and new claims 21-22. Claims 19-20 are cancelled.
Response to Arguments
Applicant’s arguments, see Remarks, pg 9, filed 03/17/2026, with respect to the objection of claim 11 has been fully considered and, in light of the associated amendment, is persuasive. Therefore, the objection has been withdrawn.
Applicant’s arguments, see Remarks, pg 9, filed 03/17/2026, with respect to the objection of claim 20 is moot based on the claim being canceled. Therefore, the objection has been withdrawn.
Applicant’s arguments, see Remarks, pg 9-11, filed 03/17/2026, with respect to the rejection of claims 1, 3, 6-9, 11-12, 13, 16-17 under 35 USC § 102(a)(1) has been fully considered but is not persuasive.
Applicant first argues to distinguish the claim limitation arguing that Gomez et al does not teach to identify objects because the identities of the of the objects is known (Remarks – 03/17/2026, pg 10) in the limitation "identifying, based on the analyzed feature data and by the at least one machine learning model, one or more objects from the hydrocarbon equipment in the environment, the identifying being based on the two or more different time instances of the satellite input images," of claim 1.
The applicant’s limitation is identifying one or more objects and is not to identifying the identity of the object. The specification recites, similar to the claim language “identifying, based on the analyzed feature data and by the at least one machine learning model, one or more objects from the hydrocarbon equipment in the environment” (Specification ¶ [0014], with similar language at ¶ [0019]-[0020], [0024], [0026]).
The applicant’s specification does not appear to explicitly support the applicant’s claim identifying the “identity” of the object as argued by applicant and the applicant has not cited to the specification for support for the limitation.
Regarding applicant’s argument the prior art does not teach time series data to determine status of the equipment, Gomez et al teaches the time series data is acquired (¶ [00209] "a computing system that can perform such a method in an automated manner as data are acquired over time (e.g., intervals of days, weeks, etc.)") and analyzed by the ML model to determine the status of the hydro equipment being lit or unlit (¶ [00207]-[00209]).
Respectfully, the applicant is not persuasive.
Applicant further argues to distinguish the claim limitation arguing that Gomez et al does not teach generating a prediction as claimed because the prior art performs a retrospective analysis, rather than a prospective forecast (Remarks – 03/17/2026, pg 10) in the limitation "generating, based on the analyzed feature data and the identified one or more objects, a prediction for an object among the one or more objects in the environment." of claim 1.
The applicant’s specification recites “The predictions can each indicate a characteristic, such as the operational status, for the one or more objects (such as equipment).” (Specification ¶ [0005], with similar language at ¶ [0006]-[0007]). Further support that the “prediction” is a current status rather than a future event, as argued by applicant is found in the specification at para. [0018], reciting “the prediction includes a status indicator for the piece of the hydrocarbon equipment, the status indicator representing a health status of the piece of the hydrocarbon equipment.”
The applicant’s specification does appear to explicitly discuss “prospective” forecasting as applicant argues and the applicant did not cite to the specification to offer support for the interpretation of the claim limitation. Applicant is encouraged to amend the limitation to distinguish such features and cite to the specification for support.
Respectfully, the applicant is not persuasive.
Applicant’s arguments, see Remarks, pg 11, filed 03/17/2026, with respect to the rejections of claim 4-5 under 35 USC has been fully considered and, based on the amendment that changed the scope of the claim, is persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made over Gomez et al (WO 2022/187341) in view of Singh et al (US 2021/0341920).
All arguments were addressed.
Claim Rejections - 35 USC § 102
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 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, 6-9, 11-12, 13, 16-17, 21-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gomez et al (WO 2022/187341, disclosed by applicant in IDS 06/04/2025).
Regarding Claim 1, Gomez et al teach a method for analyzing and correcting satellite images representing an environment that includes hydrocarbon equipment (method 1600 for receiving spatial and temporal satellite image data of hydrocarbon production sites for analyzing gas flaring equipment; Fig 16 and ¶ [00203], [00205], [00212]), the method comprising:
receiving, by a computer system, satellite input images representing the environment captured by one or more satellites at different time instances (the computing system receives satellite data of a region of interest that includes multiple hydrocarbon production sites and includes data collected over time, block 1610; Fig 16 and ¶ [00203]-[00205], [00209]);
receiving, by a communication network coupled to the computer system, environmental data that represents a state of the environment (the computing system may receive weather data for the region of interest to analyze atmospheric conditions of the multiple hydrocarbon production sites, block 1630; Fig 16 and ¶ [00203]-[00204], [00206]);
determining at least one feature for extracting from the satellite input images (from the satellite data, one or more flares are identified from the one or more of the multiple hydrocarbon production sites, block 1640; Fig 16 and ¶ [00203]-[00205]);
applying, based on determining the at least one feature and the state of the environment determined from the environmental data (from the satellite data and at least a portion of the additional (weather) data, one or more flares are identified from the one or more of the multiple hydrocarbon production sites, block 1640; Fig 16 and ¶ [00203]-[00205]), one or more image processing functions to adjust pixels of the satellite input images for extraction of the at least one feature (the satellite and additional relevant (weather) image data is analyzed at the pixel-level in the machine learning model 1200 to detect thermal channel data, indicative of a pixel representing a fire and characterized to represent a lit flare; Fig 12 and ¶ [00141]-[00144], [00156]-[00157]);
analyzing, by at least one machine learning model, feature data including the at least one feature that is extracted from two or more different time instances of the satellite input images (from the satellite data acquired over time (¶ [00205], [00209]), the trained machine learning model (Fig 12 and ¶ [00156]) identifies one or more flares at one or more of the multiple hydrocarbon production sites to determine if the gas flare is lit, block 1640; Fig 16 and ¶ [00207]-[00209]);
identifying, based on the analyzed feature data and by the at least one machine learning model, one or more objects from the hydrocarbon equipment in the environment, the identifying being based on the two or more different time instances of the satellite input images (from the analyzed data, the trained machine learning model (Fig 12 and ¶ [00156]) identifies one or more flares at one or more of the multiple hydrocarbon production sites as lit or unlit at each time point included in the analysis to identify change in lit status, block 1640; Fig 16 and ¶ [00205], [00207]-[00209]); and
generating, based on the analyzed feature data and the identified one or more objects, a prediction for an object among the one or more objects in the environment (from the satellite data acquired over time (¶ [00205], [00209]), the trained machine learning model classifies (predicts) the one or more flares at one or more of the multiple hydrocarbon production sites as intermittent or continuous lit, block 1640; Fig 16 and ¶ [00207]-[00209]).
Regarding Claim 3, Gomez et al teach the method of claim 1 (as described above), further comprising: determining, based on the environmental data, one or more environmental effects from the environment that affect a quality of the satellite input images (false positives may be identified from intermittent flares based on flare satellite imagery and environment data, such as cloud coverage satellite images; ¶ [00139], [00142], [00206]); and removing, the one or more environmental effects from the satellite input images (the false positive data may be filtered based on the cloud coverage satellite images; ¶ [00142]-[00143], [00206]).
Regarding Claim 6, Gomez et al teach the method of claim 1 (as described above), wherein the object is a piece of the hydrocarbon equipment (the gas flaring equipment of the one or more hydrocarbon production sites is identified; ¶ [00205], [00209]), and the prediction comprises a status indicator for the piece of the hydrocarbon equipment (the trained machine learning model classifies (predicts) the one or more flares of the gas flaring equipment at one or more of the multiple hydrocarbon production sites as intermittent or continuous lit, block 1640; Fig 16 and ¶ [00207]-[00209]), the status indicator representing a health status of the piece of the hydrocarbon equipment (the status (lit or unlit) of each of the one or more flares of the gas flaring equipment may include determining a reason for an unexpected unlit flare, such as reasons associated with weather, regulatory or operational status; ¶ [00215]).
Regarding Claim 7, Gomez et al teach the method of claim 6 (as described above), further comprising: providing the prediction comprising the status indicator for the piece of the hydrocarbon equipment in the environment to a system configured to monitor the environment (the reason for a status (lit or unlit) of each of the one or more flares of the gas flaring equipment may be determined, including reasons associated with weather, regulatory or operational status; ¶ [00215]).
Regarding Claim 8, Gomez et al teach the method of claim 1 (as described above), wherein identifying the one or more objects in the environment comprises determining a difference in (interpreted to only require one of the following differences based on the conjunction “or” before “(ii)”; see Superguide Corp. v. Direct TV Enterprises, Inc., 358 F.3d 870, 69 USPQ2d 1865 (Fed. Cir. 2004)) (i) size and structures, or (ii) positions, of pixels from the analyzed feature data for the satellite input images (the satellite image can be analyzed at each pixel by the trained ML model to determine characteristics of a plume (smoke related to a lit flare) and may be characterized based on size or shape; Fig 12 and ¶ [00162]-[00163]).
Regarding Claim 9, Gomez et al teach the method of claim 1 (as described above), wherein analyzing the feature data by the at least one machine learning model (one or more machine learning model 1200 is used for analyzing the satellite image data; Fig 12 and ¶ [00156], [00167]) comprises:
generating, by a convolutional neural network (CNN) of the at least one machine learning model, subsets of the satellite input images (a CNN may be utilized for pixel-wise segmentation; Fig 12 and ¶ [00157]), wherein a subset of the satellite input images share a plurality of topological features in the feature data determined by the convolutional neural network (the pixel-wise classification of the segmented regions is used to identify, map and classify different object types; Fig 12 and ¶ [00157]);
determining, by one or more recurrent neural networks (RNN) of the at least one machine learning model (a long short-term memory (LSTM) RNN may be used as the ML model 1200; Fig 12 and ¶ [00167]-[00168]), one or more patterns over the different time instances from the feature data for the subsets of the satellite input images (the satellite image data are input as time series data to the LSTM RNN to detect temporal patterns regarding the flare; Fig 12 and ¶ [00167]-[00168]);
identifying one or more objects in the environment based on the one or more patterns (the patterns are used to recognize the flare as lit or unlit based on the detected temporal pattern by the LSTM RNN; Fig 12 and ¶ [00167]-[00168]); and
generating the prediction based on the one or more patterns for the one or more objects in the environment from the feature data the LSTM RNN to detect temporal patterns regarding the flare; Fig 12 and ¶ [00167]-[00168]).
Regarding Claim 11, Gomez et al teach the method of claim 1 (as described above), wherein the one or more image processing functions comprises at least one of (i) denoising, (ii) filtering, (iii) contrast adjustment, (iv) position alignment, (v) downsampling, (vi) up-sampling, or (vii) edge enhancement, of the pixels in the satellite input images (false positives may be identified from intermittent flares based on flare satellite imagery and environment data, and the satellite images may be filtered based on atmospheric conditions indicated by the weather data; ¶ [00142], [00206]).
Regarding Claim 12, Gomez et al teach the method of claim 1 (as described above), wherein the environmental data (environmental data from environmental satellites; ¶ [00198]) comprises at least one of (i) infrared data, (ii) simulation data, (iii) weather conditions, (iv) ground truth measurements, (v) historical data, (vi) geological data, or (vii) climate data, of the environment (the computing system may receive weather data for the region of interest to analyze atmospheric conditions of the multiple hydrocarbon production sites, block 1630; Fig 16 and ¶ [00206], [00212]).
Regarding Claim 13, Gomez et al teach a system for analyzing and correcting satellite images representing an environment that includes hydrocarbon equipment (system 110 for well testing and equipment in environment 101 and/or marine environment 102; ¶ [0034]-[0036]), the system comprising: at least one processor (processor 112; Fig 1 and ¶ [0036]); and a memory storing instructions (memory 114 storing instructions 116; Fig 1 and ¶ [0036]) that, when executed by the at least one processor, cause the at least one processor to perform operations (processor 112 executes instructions 116 to examine operations in environment 101, 102; Fig 1 and ¶ [0036]) comprising: steps identical to claim 1 (as described above).
Regarding Claim 16, Gomez et al teach the system of claim 13 (as described above), wherein the limitations are claimed identical to claim 6 (as described above).
Regarding Claim 17, Gomez et al teach one or more non-transitory computer readable media storing instructions (memory 114 storing instructions 116; Fig 1 and ¶ [0036]) to analyze and correct satellite images representing an environment that includes hydrocarbon equipment (system 110 for well testing and equipment in environment 101 and/or marine environment 102; ¶ [0034]-[0036]), the instructions, when executed by at least one processor, configured to cause the at least one processor to perform operations (processor 112 executes instructions 116 to examine operations in environment 101, 102; Fig 1 and ¶ [0036]) comprising: steps identical to claim 1 (as described above).
Regarding Claim 21, Gomez et al teach the method of claim 1 (as described above), wherein the prediction for the object comprises a position in the environment at a time after the two or more different time instances (spatial-temporal imaging assessed by the ML model may be N clips over time (thereby accounting for time and position at a third time used in the time series data; ¶ [00179], [0209]-[00214]).
Regarding Claim 22, Gomez et al teach the method of claim 1 (as described above), wherein applying the one or more image processing functions comprises adjusting the pixels of the satellite input images to reduce distortion in the satellite input images, the distortion associated with the state of the environment represented by the environmental data (image processing can include filtering based on atmospheric conditions indicated by the weather data for the region of interest (reducing distortions causing false identifications) or correcting for spatial jittering caused by satellite geospatial positions (also to reduce distortions); ¶ [00142]-[00145], [00206]).
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 2, 14, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Gomez et al (WO 2022/187341 in view of Avidan et al (US 2020/0342242).
Regarding Claim 2, Gomez et al teach the method of claim 1 (as described above), including the at least one machine learning model is trained to generate predictions of objects from the hydrocarbon equipment in the environment (from the satellite data acquired over time (¶ [00205], [00209]), the trained machine learning model classifies (predicts) the one or more flares at one or more of the multiple hydrocarbon production sites as intermittent or continuous lit, block 1640; Fig 16 and ¶ [00207]-[00209]).
Gomez et al does not teach to generate predictions of objects from the hydrocarbon equipment in the environment, with the method comprising: generating, by a simulation configured to generate synthetic satellite images of the environment and using the environmental data, a training example comprising the generated synthetic satellite images; applying, by the at least one machine learning, the one or more image processing functions to the training example to generate a training set of adjusted images, wherein an adjusted image from the training set of adjusted images comprises pixels adjusted by the one or more image processing functions; comparing the training set of adjusted images from the generated synthetic satellite images of the environment to a set of adjusted images generated from the satellite input images of the environment; and updating, based on a comparison of the training set of adjusted images and the set of adjusted images, one or more parameters of the at least one machine learning model.
Avidan et al is analogous art pertinent to the technological problem addressed in this application and teaches generating, by a simulation configured to generate synthetic satellite images of the environment and using the environmental data (a synthetic data platform 107 is used to generate records for the geographic database 109, which includes synthetic image records 1009; Fig 1 and ¶ [0075], [0099]-[0100]), a training example comprising the generated synthetic satellite images (remote sensing satellite photography is used to generate records for the geographic database 109, which includes the generated synthetic image records 1009; Fig 1 and ¶ [0075], [0099]-[0100]);
applying, by the at least one machine learning (the machine learning system 103; Fig 1 and ¶ [0075]), the one or more image processing functions to the training example to generate a training set of adjusted images (the machine learning system 103 processes an input image to process the synthetic image data generated to detect objects and labeling the images; Fig 1 and ¶ [0075]-[0077]), wherein an adjusted image from the training set of adjusted images comprises pixels adjusted by the one or more image processing functions (the machine learning system 103 processes a portion of an input image in a given grid or receptive field and may perform edge detection of geographic features is detected (understood to be performed with pixel analysis) in the generation of synthetic image data; ¶ [0075]-[0076], [0086]);
comparing the training set of adjusted images from the generated synthetic satellite images of the environment to a set of adjusted images generated from the satellite input images of the environment (the mapped geographic features stored in the geographic database 109 are used to facilitate the generation of the synthetic image data used for machine learning; Fig 1 and ¶ [0076]); and
updating, based on a comparison of the training set of adjusted images and the set of adjusted images, one or more parameters of the at least one machine learning model (the machine learning model may be evaluated (updated) based on the labeled synthetic image data and labeled ground truth examples; Fig 1 and ¶ [0031]-[0032], [0037], [0081]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Gomez et al with Avidan et al including generating, by a simulation configured to generate synthetic satellite images of the environment and using the environmental data, a training example comprising the generated synthetic satellite images; applying, by the at least one machine learning, the one or more image processing functions to the training example to generate a training set of adjusted images, wherein an adjusted image from the training set of adjusted images comprises pixels adjusted by the one or more image processing functions; comparing the training set of adjusted images from the generated synthetic satellite images of the environment to a set of adjusted images generated from the satellite input images of the environment; and updating, based on a comparison of the training set of adjusted images and the set of adjusted images, one or more parameters of the at least one machine learning model. By generating synthetic image data and using such data to evaluate, validate and retrain a machine learning model, an ample amount of training data may be generated and used by the model, thereby improving the generalizability and prediction accuracy for rare events but necessary for training the model for identifying such events, as recognized by Avidan et al (¶ [0032]).
Regarding Claim 14, Gomez et al teach the system of claim 13 (as described above), wherein the limitations are claimed identical to claim 2 (as described above).
Regarding Claim 18, Gomez et al teach the one or more non-transitory computer readable media of claim 17 (as described above), wherein the limitations are claimed identical to claim 2 (as described above).
Claims 4, 5, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Gomez et al (WO 2022/187341) in view of Singh et al (US 2021/0341920).
Regarding Claim 4, Gomez et al teach the method of claim 1 (as described above).
Gomez et al does not teach wherein the object is an autonomous vehicle and the prediction for the object comprises a trajectory comprising a plurality of predicted positions for the autonomous vehicle.
Singh et al is analogous art pertinent to the technological problem addressed in this application and teaches the object is an autonomous vehicle (top-down images of autonomous vehicles are taken, step 302; Fig 3 and ¶ [0034]-[0035]) and the prediction for the object comprises a trajectory (a trajectory is predicted for the autonomous vehicle based on the time series data, step 304; Fig 3 and ¶ [0036]-[0038]) comprising a plurality of predicted positions for the autonomous vehicle (multiple reference paths may be mapped to reference centerline, step 306; Fig 3 and ¶ [0039]-[0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Gomez et al with Singh et al including wherein the object is an autonomous vehicle and the prediction for the object comprises a trajectory comprising a plurality of predicted positions for the autonomous vehicle. By using motion forecasting, trajectories of autonomous vehicles can be planned with multiple paths, thereby improving safety in the control of the autonomous vehicle by providing tangential coordinates and allow for the vehicle to respond to other objects in motion, as recognized by Singh et al (¶ [0046]).
Regarding Claim 5, Gomez et al in view of Bond et al teach the method of claim 4 (as described above), further comprising: providing the prediction comprising the trajectory (Singh et al, a trajectory is predicted for the autonomous vehicle based on the time series data, step 304 using the prediction and forecasting subsystem 123; Fig 1, 3 and ¶ [0027]-[0030], [0036]-[0038]) to at least one of (i) a system configured to retrieve autonomous vehicles, or (ii) a computing device configured to monitor retrieval of autonomous vehicles, in the environment (Singh et al, the vehicle trajectory data generated by the prediction and forecasting subsystem 123 is advanced to the motion planning subsystem 124 for motion planning and is then transmitted to the vehicle control system 113 to control (retrieve) the autonomous vehicle 101; Fig 1 and ¶ [0029]-[0032]).
Regarding Claim 15, Gomez et al teach the system of claim 13 (as described above).
Gomez et al does not teach wherein the object is an autonomous vehicle and the operations further comprise: providing the prediction a trajectory to at least one of (i) a system configured to retrieve autonomous vehicles, or (ii) a computing device configured to monitor retrieval of autonomous vehicles, in the environment.
Singh et al is analogous art pertinent to the technological problem addressed in this application and teaches the object is an autonomous vehicle (top-down images of autonomous vehicles are taken, step 302; Fig 1, 3 and ¶ [0027], [0034]-[0035]) and the operations further comprise: providing the prediction a trajectory (a trajectory is predicted for the autonomous vehicle based on the time series data, step 304 using the prediction and forecasting subsystem 123; Fig 1, 3 and ¶ [0027]-[0030], [0036]-[0038]) to at least one of (i) a system configured to retrieve autonomous vehicles, or (ii) a computing device configured to monitor retrieval of autonomous vehicles, in the environment (the vehicle trajectory data generated by the prediction and forecasting subsystem 123 is advanced to the motion planning subsystem 124 for motion planning and is then transmitted to the vehicle control system 113 to control (retrieve) the autonomous vehicle 101; Fig 1 and ¶ [0029]-[0032]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Gomez et al with Singh et al including wherein the object is an autonomous vehicle and the operations further comprise: providing the prediction a trajectory to at least one of (i) a system configured to retrieve autonomous vehicles, or (ii) a computing device configured to monitor retrieval of autonomous vehicles, in the environment. By using motion forecasting, trajectories of autonomous vehicles can be planned with multiple paths, thereby improving safety in the control of the autonomous vehicle by providing tangential coordinates and allow for the vehicle to respond to other objects in motion, as recognized by Singh et al (¶ [0046]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Gomez et al (WO 2022/187341) in view of Huang et al (Meteorological Satellite Images Prediction Based on Deep Multi-scales Extrapolation Fusion, cited in Non-Final Rejection – 12/19/2025).
Regarding Claim 10, Gomez et al teach the method of claim 1 (as described above).
Gomez et al does not teach downsampling the satellite input images at a first resolution to a plurality of image datasets at a plurality of resolutions, wherein each resolution in the plurality of resolutions is lower than the first resolution, generating a first set of predictions for the plurality of image datasets; and combining, by a conditional generative adversarial network of the at least one machine learning model, the first set of predictions to a second set of predictions generated from the input images at the first resolution, wherein the second set of predictions are generated at second resolution greater than the first resolution.
Huang et al is analogous art pertinent to the technological problem addressed in this application and teaches downsampling the satellite input images at a first resolution to a plurality of image datasets at a plurality of resolutions (the satellite images are scaled to several smaller sized by a down-sampling method; Fig 6-8 and 3.2 Method ¶ 1), wherein each resolution in the plurality of resolutions is lower than the first resolution, generating a first set of predictions for the plurality of image datasets (down-sampling of original satellite images results in several smaller scale sizes; Fig 6-8 and 3.2 Method ¶ 1); and
combining, by a conditional generative adversarial network of the at least one machine learning model (deep Multi-scales Extrapolation Fusion Generative Adversarial Network (GAN); Fig 6 and 3.2 Method ¶ 1), the first set of predictions to a second set of predictions generated from the input images at the first resolution (the CGAN model is used to generate the prediction results of the satellite images by fusing the multi-scale prediction results; Fig 6, 9 and 3.2 Method ¶ 5), wherein the second set of predictions are generated at second resolution greater than the first resolution (the prediction images are generated at different scales and the scales are combined to generate the fused prediction satellite image; Fig 8, 9 and 3.2 Method ¶ 7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Gomez et al with Huang et al including downsampling the satellite input images at a first resolution to a plurality of image datasets at a plurality of resolutions, wherein each resolution in the plurality of resolutions is lower than the first resolution, generating a first set of predictions for the plurality of image datasets; and combining, by a conditional generative adversarial network of the at least one machine learning model, the first set of predictions to a second set of predictions generated from the input images at the first resolution, wherein the second set of predictions are generated at second resolution greater than the first resolution. By performing downsampling of the satellite images and using a spatiotemporal sequence for prediction of the different resolutions, realistic prediction images of the environment are gained from the satellite images, thereby allowing for spatiotemporal sequence predictions over a large area of environmental surface area, as recognized by Huang et al (1. Introduction ¶ 4).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Salman et al (US 2022/0262104, cited in Non-Final Rejection – 12/19/2025) teach a method and system for using a machine learning model to identify and classify objects from satellite images and label the probabilistically identified object concerning to equipment, operations and features, such as damage to the equipment based on temporal analysis.
Schmidt et al (US 2021/0398289, cited in Non-Final Rejection – 12/19/2025) teach a system and method that utilizes machine learning to analyze geospatial data for well pad detection and determining a probability of damage to the well pad from an environmental event.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661
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