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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Ashtekar et al. (Ashtekar) (US 11,861,625 B2).
Regarding claim 1, Ashtekar discloses a method (e.g., methods and systems for carbon footprint determination, monitoring, and verification for agricultural parcels based on implementation of regenerative management practices, paragraph 3) comprising:
determining a carbon calculation for a land area based on carbon emissions (e.g., preferably, truth-based data includes data obtained directly from growers and may include “as applied” data corresponding to fertilizer application and field trial results, namely, the measurements taken by farming partners who plant and harvest crops under a wide range of specified scenarios. These field trial results are employed by the CO2E sequestration server 130 to test and improve the accuracy of crop simulations that may be performed to generate baseline and regenerative practices carbon footprints and to translate these footprints into agriculturally meaningful metrics and valuations for similar parcels, paragraph 32);
determining a carbon calculation for the land area based on public-data sources (e.g., Public data comprises a wide variety of sources such as, but not limited to, county records, United States Department of Agriculture reports; parcel geographic coordinates data and topography; soil types and layering (e.g., Soil Survey Geographic Database (SSURGO); historical crop planting, harvesting, and yield data; soil type indexes (e.g., Corn Stability Rating 2 (CSR2); historical and forecast weather data; and satellite and aerial image data taken across agriculturally meaningful spectral bands (e.g., LANDSAT, SENTINEL) that may be processed by the CO2E sequestration server 130 to understand crop types, rotations, baseline management practices (e.g., planting dates, tillage types and dates, fertilization types and dates, irrigation types and dates, harvesting dates), and stages of growth at any given time, paragraph 33);
processing the aggregation of carbon calculations from carbon emissions, carbon sequestration and public data sources (e.g., now referring to FIG. 3, a flow diagram 300 is presented featuring system level flow for generation of carbon footprint potentials along with regenerative potential metrics associated with agricultural parcels within a prescribed growing region, such as might be performed by the CO2E sequestration server 130 of FIG. 1, and such as might be stored in the exemplary parcel database records 200 of FIG. 2. Flow begins at block 304 where databases 302 are accessed and data therefrom is automatically cleansed of error and formatted for analysis and simulation, paragraph 55, figure 3); and
determining a single carbon footprint calculation for the land area based on the processed aggregation of carbon calculations from carbon emissions, carbon sequestration and public data sources (e.g., at block 306, data that has been cleansed and formatted in block 304 is analyzed for each parcel to generate inferences regarding a number of attributes that include, but are not limited to, crop types, crop rotations and cover cropping, key management practices (e.g., planting dates; tillage types and dates; fertilization types, amounts, and dates; irrigation amounts and dates; buffer zones, drainage control, harvesting dates), and stages of crop growth at any given time. These inferences may be generated by the CO2E management processor 154 and the remote sense processor 156 of FIG. 1, and are provided to the data selection block 308. The CO2E management processor 154 is configured to make the above inferences because it is programmed with grower management practices that are common to different geographical areas. Accordingly, the CO2E management processor 154 applies this information to a particular field location in order to build a scenario of “typical farming” on that field, paragraph 60, figure 3).
Ashtekar, in one embodiment, does not specifically disclose determining a carbon calculation for carbon sequestration of the land area based on a correlation of NDVI and AGB.
Ashtekar, in another embodiment, discloses determining a carbon calculation for carbon sequestration of the land area based on a correlation of NDVI and AGB (e.g., At block 508, relevant spectral bands for a given observation are combined to generate composite vegetative indices for subparts of the parcels according to well-known techniques. Preferably, the Landsat Surface Reflectance-derived Enhanced Vegetation Index (EVI) (which is similar to AGB) is employed to determine crop type and maturity. Another embodiment contemplates use of the normalized difference vegetation index (NDVI) for purposes of determining crop type and maturity…, paragraph 81);
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to have modified Ashtekar, in one embodiment, to include determining a carbon calculation for carbon sequestration of the land area based on a correlation of NDVI and AGB as taught by Ashtekar in another embodiment. It would have been obvious to one of ordinary skill in the art at the time of the invention to have modified Ashtekar in one embodiment by the teaching of Ashtekar in another embodiment to use for particular application.
Regarding claim 2, Ashtekar discloses wherein:
determining the carbon calculation for the land area based on carbon emissions uses object- detection data based on historical and real-time satellite images of the land area (e.g., In one embodiment, a method for monitoring implementation and maintenance of regenerative management practices in agricultural parcels is provided, the method including: determining a regenerative carbon footprint value for a parcel, where the determining comprises a difference of a regenerative carbon footprint and a baseline carbon footprint, where the baseline carbon footprint is derived by calculating greenhouse gas emissions based on simulating crop growth under current management practices, and where the regenerative carbon footprint is derived by calculating greenhouse gas emissions based on simulating crop growth under one or more regenerative management practices, paragraph 11).
Regarding claim 3, Ashtekar discloses wherein:
determining the carbon calculation for the land area based on carbon sequestration uses NDVI and AGB data based on historical and real-time satellite images of the land area (e.g., at block 508, relevant spectral bands for a given observation are combined to generate composite vegetative indices for subparts of the parcels according to well-known techniques. Preferably, the Landsat Surface Reflectance-derived Enhanced Vegetation Index (EVI) is employed to determine crop type and maturity. Another embodiment contemplates use of the normalized difference vegetation index (NDVI) for purposes of determining crop type and maturity…, paragraph 81).
Regarding claim 4, Ashtekar discloses wherein:
determining the carbon calculation for the land area based on carbon emissions uses machine-learning modeling for breadth of object recognition and increasing accuracy of object recognition (e.g., the remote sense processor 156 in conjunction with the CO2E detection processor 155 may additionally employ machine learning and computer vision techniques, described in further detail below, to infer implementation and maintenance of one or more regenerative management practices, paragraphs 41; In this embodiment, the CO2E determination processor 155 may employ a convolutional neural network to identify irrigated fields using the wavelength-based index. In addition, the CO2E determination processor 155 is configured to detect the colors of irrigated and non-irrigated fields and process the differences in color to infer the amount of irrigation water that has been applied over time. In one embodiment, the CO2E determination processor 155 comprises linear model based on ground truth data accessed from the truth database 121, where the linear model is configured to infer irrigation types, dates, and amounts as a function of related color differences in the images, paragraph 104).
Regarding claim 5, Ashtekar discloses wherein:
determining the carbon calculation for the land area based on carbon sequestration uses machine-learning modeling for breadth of pattern matching and increasing accuracy of pattern recognition (e.g., The remote sense processor 156 may process satellite/aerial images, and may merge selected images to determine vegetative indices, to estimate missing image data, and to determine both baseline management practices and the amount of CO2E that may be sequestered under one or more best management practices when compared to the baseline management practices for the parcel. In addition, the remote sense processor 156 in conjunction with the CO2E detection processor 155 may additionally employ machine learning and computer vision techniques, described in further detail below, to infer implementation and maintenance of one or more regenerative management practices, paragraph 41).
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.
Claim 6 is rejected under 35 U.S.C. 102(a1) as being anticipated by Ashtekar et al. (Ashtekar) (US 11,861,625 B2).
Regarding claim 6, Ashtekar discloses a system supporting object-based emissions and AGB sequestration (e.g., Referring to FIG. 1, a block diagram is presented illustrating an agricultural parcel carbon footprint system 100 according to the present invention. The system 100 may include a carbon dioxide equivalent (CO2E) sequestration server 130 that is coupled to one or more client devices 101-103 through the internet cloud 110, paragraph 30) comprising:
at least one satellite operative to provide images of a land area based on the land area's coordinates (e.g., satellite and aerial image data taken across agriculturally meaningful spectral bands (e.g., LANDSAT, SENTINEL) that may be processed by the CO2E sequestration server 130 to understand crop types, rotations, baseline management practices (e.g., planting dates, tillage types and dates, fertilization types and dates, irrigation types and dates, harvesting dates), and stages of growth at any given time, paragraph 33);
a public source of emission and sequestration carbon footprint data (e.g., Public Database 122, figure 1, a carbon dioxide equivalent (CO2E) sequestration server 130 that is coupled to one or more client devices 101-103 through the internet cloud 110, paragraph 30);
a processing subsystem (e.g., The system 100 may include a carbon dioxide equivalent (CO2E) sequestration server 130 that is coupled to one or more client devices 101-103 through the internet cloud 110, paragraph 30) comprising:
an input-output subsystem (e.g., The CO2E sequestration server 130 is coupled to a truth database 121, a public database 122, a commercial database 123, and a scientific database 124, paragraph 31);
a processor (e.g., processors 152-156, figure 1);
at least one program (e.g., a computer-readable storage medium storing program instructions that, when executed by a computer, cause the computer to perform a method for monitoring implementation and maintenance of regenerative management practices in agricultural parcels, paragraph 12); and
a machine-learning model subsystem (e.g., the remote sense processor 156 in conjunction with the CO2E detection processor 155 may additionally employ machine learning and computer vision techniques, described in further detail below, to infer implementation and maintenance of one or more regenerative management practices, paragraph 41).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUANG N VO whose telephone number is (571)270-1121. The examiner can normally be reached Monday-Friday, 7AM-4PM, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad K Ghayour can be reached at 571-272-3021. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QUANG N VO/Primary Examiner, Art Unit 2683