DETAILED ACTION
This communication is in response to the application filed 9/15/23 in which claims 1-5 were presented for examination.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
A high-resolution standardized precipitation evapotranspiration index (SPEI) dataset development method based on a random forest regression model, comprising:
step 1, acquiring daily meteorological station information of a target area in a study period through a national meteorological science data center and removing erroneous observations using Python programming language technology to obtain daily meteorological information, and then converting the daily meteorological information into monthly meteorological information;
step 2, based on the monthly meteorological information obtained in the step 1, calculating monthly potential evapotranspiration (PET) information at a station according to the FAO Penman-Monteith formula;
step 3, calculating differences of precipitation and potential evapotranspiration according to precipitation information obtained in the step 1 and the potential evapotranspiration information obtained in the step 2, and constructing time series of cumulative differences of precipitation and potential evapotranspiration at multiple time scales;
step 4, calculating SPEIs at different time scales of the station according to information of the time series of cumulative differences of precipitation and potential evapotranspiration at the different time scales obtained in the step 3;
step 5, acquiring global precipitation measurement (GPM) precipitation data, moderate-resolution imaging spectroradiometer (MODIS) land surface temperature data, ERA5-Land shortwave radiation data, and shuttle radar topography mission (SRTM) digital elevation data, based on a Google earth engine (GEE) cloud platform; and performing cloud removal processing on the MODIS land surface temperature data;
step 6, removing seasonality of the precipitation data, the land surface temperature data, and shortwave radiation data obtained in the step 5 and then converting into monthly data, and then resampling spatial resolutions of the precipitation data, the land surface temperature data, the shortwave radiation data and the elevation data to 1 kilometer (km) through a bicubic interpolation algorithm;
step 7, forming sample points by information of the SPEIs at the different time scales obtained in the step 4 and data values at the station of the precipitation data, the land surface temperature data, the shortwave radiation data and the elevation data processed by the step 6;
step 8, constructing the random forest regression model according to the sample points obtained in the step 7; and
step 9, inputting the precipitation data, the land surface temperature data, the shortwave radiation data and the elevation data obtained in the step 6 into the random forest regression model constructed in the step 8 for prediction, to thereby obtain a SPEI dataset with a spatial resolution of 1 km for the target area in the study period.
Step 1: YES. The claim is directed to a method and, therefore, falls into a statutory category.
Step 2A Prong 1: YES.
Step 1 describes removing erroneous observations to obtain daily meteorological information, and then converting the daily information into monthly information. The removal of erroneous observations may be performed mentally by evaluation, opinion, and judgment and, therefore, falls under the Mental Processes grouping of abstract ideas. The conversion of daily information into monthly information may also be performed mentally.
Steps 2-4 describe calculating PET information at a station using the FAO Penman-Monteith formula, calculating differences of precipitation and evaporation, and calculating SPEIs at different time scales. These are mathematical calculations and, therefore, fall under the Mathematical Concepts grouping of abstract ideas.
Step 6 describes removing seasonality of the precipitation data, the land surface temperature data, and shortwave radiation data and converting into monthly data. This step may be performed mentally by a user by observation, evaluation, and judgment and, therefore, falls under the Mental Processes grouping of abstract ideas. Step 6 further describes converting the data into monthly data and resampling spatial resolutions of the precipitation data, the land surface temperature data, the shortwave radiation data and elevation data to 1km through a bicubic interpolation algorithm. Converting data into monthly data and spatial resampling may be performed mentally (or with the aid of pen and paper) by a user and, therefore, falls under the Mental Processes grouping of abstract ideas.
Step 7 describes forming sample points by information of the SPEIs at the different time scales and data values of the precipitation data, land surface temperature data, the shortwave radiation data, and the elevation data. “Forming” under a broadest reasonable interpretation encompasses the manual creation of the samples and, therefore, falls under the Mental Processes grouping of abstract ideas.
Step 2A Prong 2/Step 2B: NO.
Step 1 describes acquiring meteorological data. Data inputting is insignificant extra-solution activity and, therefore, does not integrate the judicial exception into a practical application. The type or source of data also does not cause the data inputting step to integrate the exception into a practical application. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.”
Step 1 further describes removing erroneous observations using Python programming language technology. Recitation of generic computer components at a high level of abstraction is considered mere instruction to “apply” the judicial exception and, therefore, does not integrate the exception into a practical application or provide an inventive concept.
Step 5 describes acquiring GPM data, MODIS data, ERA5-Land data, and SRTM data. Data inputting is insignificant extra-solution activity and, therefore, does not integrate the judicial exception into a practical application. The type or source of data also does not cause the data inputting step to integrate the exception into a practical application. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.”
Step 5 further describes that the data is obtained based on a Google Earth Engine cloud platform. Recitation of generic computer components at a high level of abstraction is considered mere instruction to “apply” the judicial exception and, therefore, does not integrate the exception into a practical application or provide an inventive concept. Finally, step 5 describes performing cloud removal processing on the MODIS data, which is a mere instruction to apply the exception.
Step 8 describes constructing a random forest regression model according to the sample points without describing how the model is constructed and, therefore, is a mere instruction to apply the exception.
Step 9 describes inputting the precipitation data, land surface temperature data, shortwave radiation data, and elevation data into the model and obtain a prediction. Data inputting is considered insignificant extra-solution activity as is data outputting. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.”
Accordingly, claim 1 is ineligible.
Claim 2
Step 2A Prong 1: YES. Claim 2 describes the formula used to calculate the PET information and, therefore, falls under the Mathematical Concepts grouping of abstract ideas.
Accordingly, claim 2 is ineligible.
Claim 3
Step 2A Prong 1: YES. Claim 3 describes the formula used to calculate the cumulative difference of precipitation and evapotranspiration and, therefore, falls under the Mathematical Concepts grouping of abstract ideas.
Accordingly, claim 3 is ineligible.
Claim 4
Step 2A Prong 1: YES. Claim 4 describes the formulate used to calculate the SPEI and, therefore, falls under the Mathematical Concepts grouping of abstract ideas.
Accordingly, claim 4 is ineligible.
Claim 5
Step 2A Prong 2/Step 2B: NO. Claim 5 describes using a quality band cloud removal algorithm to remove clouds, cloud shadows, cirrus clouds, and ice and snow cover from satellite imagery. Describing the outcome or solution at a high level without describing how the removal algorithm works amount to a mere instruction to “apply” the judicial exception and, therefore, does not integrate the exception into a practical application or provide an inventive concept.
Accordingly, claim 5 is ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Pyarali, Karim, et al. "Development and application of high resolution SPEI drought dataset for Central Asia." Scientific data 9.1 (2022): 172 (Pyarali p. 3 (“In this study, following the work of Peng et al.15, a high-resolution drought index dataset containing SPEI values for Central Asia was prepared for 48 different time scales using the method proposed by Vicente-Serrano et al.17”), Pyarali p. 3 (“The input data used was CHIRPS precipitation dataset, which has a monthly temporal resolution and a 5 km spatial resolution, and the GLEAM evaporation data, which was downscaled from 25 km resolution to 5 km, using bilinear interpolation, and has a monthly temporal resolution.”), Pyarali p. 6 (“The precipitation data from stations around the globe are collected and converted into anomalies using the mean of every individual station. These anomalies are then interpolated over the 0.5° × 0.5° grid using ADW method and then converted back into actual precipitation using climatologies. Potential evaporation is one of the derived variables in CRU data, it is estimated using the Penman-Monteith equation44, PM, which is a method approved by Food and Agriculture Organization (FAO). The method uses the gridded vapor pressure, mean temperature, cloud cover and static average wind field observations. The application of PM in context of CRU data is explained in paper authored by M. Ekström et al.45”), Pyarali p. 3 (“The input data used was CHIRPS precipitation dataset, which has a monthly temporal resolution and a 5 km spatial resolution, and the GLEAM evaporation data, which was downscaled from 25 km resolution to 5 km, using bilinear interpolation, and has a monthly temporal resolution.
To compute SPEI we require water deficit values (D), which are calculated by subtracting Ep from precipitation (P) values using the following equation:
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Please note that different Ep methods could result in different SPEI estimations26–28.”) (Ep method includes using the Penman-Monteith equation as discussed above), Pyarali p. 3:
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.”).
Jha et al., How to remove cloudy pixel from MODIS NDVI (MOD13Q1) (Jul. 2016) teaches using Google Earth Engine’s API to remove cloudy pixel data from MODIS NDVI data by checking whether a pixel is covered by snow or cloud based on the QA band data.
Varghese, James. (2017). Re: How can I remove cloud from landsat satellite image ?1 (“Identification and removal of cloud pixels are easier with the 'Quality Assessment (QA)' pixel_qa band that can be ordered for free for a particular Landsat scene from USGS. The QA band is generated using the 'CFMask' algorithm. But bare in mind that Landsat 4-5 TM, 7 ETM+, did not have a 'cirrus' band (1.363 - 1.384 µm) unlike Landsat 8. Therefore identification of optically thin cirrus clouds through previous Landsat sensors is a difficult task. With Landsat 8, cirrus clouds, optically thick clouds, shadows, snow/ice, water, terrain occlusion, fill values and clear terrain pixels can be identified with the 'QA' band but there are also caveats and limitations with this algorithm like any other and is continuously being improved. Once cloud contaminated pixels are identified, you can create an image mask to remove these pixels for further image analysis.”)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID KHAN whose telephone number is (571)270-0419. The examiner can normally be reached M-F, 9-5 est.
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/SHAHID K KHAN/ Primary Examiner, Art Unit 2146
1 Retrieved from: https://www.researchgate.net/post/How-can-I-remove-cloud-from-landsat-satellite-image/5a24ddcf217e201d524e4f73/citation/download.
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