DETAILED ACTION
Claim Rejections - 35 USC § 101
Previous rejection is withdrawn in view of the Applicant’s amendment filed on 10/27/2025.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 8-11, 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kastner et al., US-PGPUB 2023/0362335 (hereinafter Kastner) in views of Tohidi et al., US-PGPUB 2020/0155881 (hereinafter Tohidi), Varon et al., “Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes,” Atmos. Meas. Tech., 11 (2018) (cited by the Applicant) (hereinafter Varon) and Hosseini et al., US-PGPUB 2022/0262116 (hereinafter Hosseini)
Regarding Claim 1, 8 and 15. Kastner discloses creating a dataset of plume concentration data by combining high-resolution synthetic plume data generated from a synthetic plume model (Paragraph [0336], one or more attributes that are indicative of an incident, smoke; Paragraph [0338], attributes associated with smoke or plume, plume growing quickly and significantly, its origin (image coordinate) density of the plume, thin and thick, indicative of the magnitudes. Paragraphs [0370]-[0373], images are captured based on location data included in notifications, where the modeling involves the sensors exceeding thresholds, providing alerts thereafter.) and modeled distribution of the actual plume data using weather data, the dataset including synthetic plumes, corresponding positions and magnitudes and the weather data (In Fig. 1B, the data from the third party is disclosed, and as disclosed in Paragraph [0342], the third party data is from the National Weather Service, where various indications, such as low to the ground, below the horizon line, and so on, are obviously from the modeling weather at various conditions);
Kastner further discloses partitioning the datasets of plume concentration data according to a preset proportion (Fig. 22; Paragraph [0332[, first image and plurality of images associated with first and second machine learning models)
training two machine learning models on at least one of the two separate datasets (Paragraph [0139], first and second machine learning models for training; Paragraph [0320]; Paragraph [0335]-[0336], training image include one or more attributes, where the attribute can be plume, as disclosed in Paragraph [0338]), wherein by training a first machine learning model to identify a presence of plumes (Paragraph [0332], first training model to detect incidents, where incident include smoke or plume; Paragraph [0334]; Paragraph [0345], confidence score) and training a second machine learning model to identify a source position and magnitude of the identified plumes (Paragraph [0347], second machine learning model; Paragraph [0350], second machine learning capturing the time and geolocation of the image capturing device, indicating the position of the plume, and that would also visually show the magnitude of the identified plume; Paragraph [0353], origin of the attribute, for example the origin of the smoke or plume, a density of the attribute or magnitude of the smoke; Paragraph [0361], confidence score); and applying the two machine learning models, to each pixel in sequence of a data patch of a new a set of actual plume concentration data to identify an actual plume (Paragraph [0004], image coordinate in pixel values, and wherein the machine learning is applied to each images in sequence that involves applying to each pixel in the image to arrive at the coordinates of the pixels associated with region of interest, such as the location of the plume, Paragraph [0187], Paragraphs [0332]-[0334], pixels in the image, and first ML; Paragraphs [0348]-[0350], second ML), responsive to receiving an output from the second machine learning model of a subpixel position of a plume source detected by the first machine learning model at a respective pixel, mathematically combining the respective pixel and the subpixel position to form a single coordinate (Paragraph [0116], when event (or pixel or pixels) detected by both the first and second machine learning models, just using one individual image with identical pixels or mathematically not using the same pixels; Paragraphs [0299]-[0300], coordinate for the incident; Paragraph [0004], image coordinate in pixel values) and, logging the single coordinate of the actual plume to identify position and of the identified plume (Paragraph [0042]; Fig. 22; Paragraph [0004], image coordinate in pixel values)
Kastner further discloses organizing the dataset to an array (Paragraph [0227], organize data using arrays) and resolution (Paragraph [0202], high-resolution images in transmission; Paragraph [0205], image can have any resolution in terms of pixels more than 500 megapixels, obviously including at satellite resolution)
Kastner does not disclose synthetic plume data having a higher spatial and temporal resolution than satellite data and responsive to the synthetic plume concentration data having the higher spatial and temporal resolution, down sampling the dataset from the high-resolution synthetic plume data to a lower resolution array of synthetic plume data at satellite resolution, and does not disclose partitioning the dataset of plume concentration data into two separate datasets according to a preset proportion as a training dataset and a validation dataset.
Tohidi discloses generating synthetic plume data having a higher spatial and temporal resolution than satellite data (Paragraph [0062]), satellite-downscaling machine-learning model (Figs. 14, 17, Paragraph [0098]) and analyzing the plume at the pixel level using the machine learning (Paragraphs [0109]-[0112])
Varon discloses synthetic plume data having a higher spatial and temporal resolution than satellite data and responsive to the synthetic plume concentration data having the higher spatial and temporal resolution (Fig. 2, 300 m resolution from LES), down sampling, by the one or more processors, the dataset to an array at satellite resolution (Section 5.1, Section 4, Figure 2, down sampling to 3000m resolution),
Hosseini discloses partitioning the array into two separate datasets according to a preset proportion as a training dataset and a validation dataset (Paragraph [0054]; Abstract)
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teachings of Varon, Tohidi and Hsseini in Kastner and down sampling the dataset from the high-resolution synthetic plume data to a lower resolution array of synthetic plume data at satellite resolution and partition the array into two separate datasets according to a preset proportion as a training dataset and a validation dataset, so that most of the information is in the source pixel and the mean wind across the pixel can be well defined, and perform accurate video frame analysis and classification to accurately identify the plume, its position and magnitude.
Regarding Claims 2, 9 and 16. Varon disclose using, by the one or more processors, synthetic plume model to compute two-dimensional concentration data for a plurality of wind and atmospheric conditions from a plurality of positions and at a plurality of magnitudes, and wherein the synthetic plume model is selected from the group consisting of: a superposition of gaussians (SOG) model, a puff model, and a computational fluid dynamics (CFD) model (Section 2.4; synthetic plumes generated by LES; section 3; Fig. 1; sections 5.1, 5.2)
Regarding Claim 3, 10 and 17. Varon disclose using, by the one or more processors, collected actual two-dimensional satellite concentration data at a given resolution from known emission source locations (Figs. 1-2)
Regarding Claims 4 and 11, Varon discloses down sampling the dataset to the array at the satellite resolution comprises arranging, by the one or more processors, the dataset as a first array of pixels with the plume at or near a center pixel of the first array, down sampling, by the one or more processors, the first array of pixels to a second array of pixels at the satellite resolution, wherein at least half of the second array of pixels contains the plume and a remainder containing no plume (Section 4, when pixel resolution is coarse, most of the information is in the source pixel, Fig. 2, 300m resolution to 3000m resolution)
5. Claims 5, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kastner et al., US-PGPUB 2023/0362335 (hereinafter Kastner) in views of Tohidi, US-PGPUB 2020/0155881, Varon, “Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes,” Atmos. Meas. Tech., 11 (2018) and alternately in view of Harter et al., US-PGPUB 2017/0134497 as applied to Claims 4, 11 and 15, and further in view of Larsen et al., “A deep learning approach to identify smoke plumes in satellite imagery in near real-time for health risk communication,” J Expo Sci Environ Epidemiol (2021) (cited by the Applicant) (hereinafter Larsen)
Regarding Claims 5 and 12. The modified Kastner does not disclose encoding, by the one or more processors, the source position of the plume in the second array of pixels as a single number corresponding to a pixel position relative to the center pixel; and aggregating, by the one or more processors, data of the second array of pixels and the source position in a single dataset.
Larsen discloses encoding, by the one or more processors, the source position of the plume in the second array of pixels as a single number corresponding to a pixel position relative to the center pixel; and aggregating, by the one or more processors, data of the second array of pixels and the source position in a single dataset (page 4, FCN Model Architecture and Training section; Introduction)
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Larsen in the modified Kastner and encode, by the one or more processors, the source position of the plume in the second array of pixels as a single number corresponding to a pixel position relative to the center pixel, and aggregate, by the one or more processors, data of the second array of pixels and the source position in a single dataset, so as to predict plume presence in high-resolution satellite imagery in near real time.
Regarding Claim 18. Varon discloses arranging the dataset as a first array of pixels with the plume at or near a center pixel of the first array, down sample the first array of pixels to a second array of pixels at the satellite resolution, wherein at least half of the second array of pixels contains the plume and a remainder containing no plume (Section 4, when pixel resolution is coarse, most of the information is in the source pixel, Fig. 2, 300m resolution to 3000m resolution),
The modified Kastner does not disclose encoding the source position of the plume in the second array of pixels as a single number corresponding to a pixel position relative to the center pixel, and aggregating data of the second array of pixels and the source position in a single dataset.
Larsen discloses encoding the source position of the plume in the second array of pixels as a single number corresponding to a pixel position relative to the center pixel, and aggregating data of the second array of pixels and the source position in a single dataset (page 4, FCN Model Architecture and Training section; Introduction)
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Larsen in the modified Kastner and encode the source position of the plume in the second array of pixels as a single number corresponding to a pixel position relative to the center pixel, and aggregate data of the second array of pixels and the source position in a single dataset, so as to predict plume presence in high-resolution satellite imagery in near real time.
Response to Arguments
Applicant’s arguments with respect to claims have been considered but are moot in view of new grounds of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HYUN D PARK whose telephone number is (571)270-7922. The examiner can normally be reached 11-4.
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/HYUN D PARK/Primary Examiner, Art Unit 2857