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 .
DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/605,776 filed on 03/14/2024. This communication is the first action on the merits.
Drawings
The drawings were received on 03/14/2024. These drawings are acceptable.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A method of determining nitrous oxide emissions of a river based on land-river-atmosphere simulation, comprising the steps of:
(1) obtaining nitrogen emission from land in each region;
(2) dividing the nitrogen emission from land into a nitrogen emission prediction set and a nitrogen emission test set; dividing geographical variables in each region into geographical variable prediction set and geographical variable test set; dividing climate variables in each region into climate variable prediction set and climate variable test set; training a RF regression model by using nitrogen emission prediction set, geographical variable prediction set and climate variable prediction set to obtain a trained RF regression model, inputting the nitrogen emission test set, the geographical variable test set and the climate variable test set into the trained RF regression model, and outputting a river water quality concentration of each sub-basin in each region;
(3) obtaining river hydrological parameters of each sub-basin in each region, wherein the river hydrological parameters of each sub-basin comprises a water depth of the river, a flow velocity of the river, a water temperature of the river and a water surface area of the river;
(4) providing an air-water interface gas exchange model and inputting the hydrological parameters of each sub-basin and river water quality concentration of each sub-basin in each region; and processing concentration conversion to obtain a total river N20 emission in each sub-basin in each region.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Under Step 1 of the analysis, claim 1 belongs to a statutory category, namely it is a method claim.
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a Mental Process and a Mathematical Concept. This can be seen in the claim limitations of “dividing the nitrogen emission from land into a nitrogen emission prediction set and a nitrogen emission test set; dividing geographical variables in each region into geographical variable prediction set and geographical variable test set; dividing climate variables in each region into climate variable prediction set and climate variable test set; training a RF regression model by using nitrogen emission prediction set, geographical variable prediction set and climate variable prediction set to obtain a trained RF regression model, inputting the nitrogen emission test set, the geographical variable test set and the climate variable test set into the trained RF regression model, and outputting a river water quality concentration of each sub-basin in each region”, and “providing an air-water interface gas exchange model and inputting the hydrological parameters of each sub-basin and river water quality concentration of each sub-basin in each region; and processing concentration conversion to obtain a total river N20 emission in each sub-basin in each region” which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to determine a river water quality concentration and a total river emission for each sub-basin in each region and is capable of being performed mentally and/or with the aid of pen and paper. Additionally, the aforementioned limitations recite mathematical calculations, e.g. see Spec. [0012]-[0022] describing the use of mathematical formulas to calculate nitrogen emission from urban residential, urban stormwater runoff, rural residential, crop farming, and livestock farming sources.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claim 1, the claimed method recites additional elements including “a method of determining nitrous oxide emissions of a river based on land-river-atmosphere simulation”, “obtaining nitrogen emission from land in each region”, and “obtaining river hydrological parameters of each sub-basin in each region, wherein the river hydrological parameters of each sub-basin comprises a water depth of the river, a flow velocity of the river, a water temperature of the river and a water surface area of the river” however these elements are found to be data gathering and output steps, which are recited at a high level of generality, and thus merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”. Furthermore, the recited RF regression model and air-water interface gas exchange model merely instruct that the mathematical calculations and relationships be applied to determine river water quality concentration and total river emissions. Such limitations amount to mere instructions to apply the judicial exception and/or generally link the exception to the particular field of river emission determination and therefore do not integrate the abstract idea into a practical application. See MPEP 2106.05(f).
The generic data gathering, processing, and output steps, are recited at such a high level of generality that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, nothing is done with the resulting river water quality concentration or total river emission to effect any change in the river or other physical process. For example, the claim does not recite adjusting river flow, modifying water treatment operations, controlling pollutant discharge, or otherwise using the calculated N2O emission to alter the physical environment.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activit(ies) (claim 1). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 2-9 merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for claim 1. Specifically:
With respect to dependent claims 2 and 3 specifically, the claims further recite particular sources of nitrogen emissions and mathematical calculations for determining those emissions. These limitations merely further refine how the nitrogen emission data is calculated and analyzed and therefore expand upon the mathematical concepts recited in claim 1. The limitations do not improve computer functionality or another technology. Accordingly, these limitations fail to integrate the abstract idea into a practical applications or amount to significantly more. See MPEP 2106.05(g)(h).
With respect to dependent claims 4 and 5 specifically, the claims further recite additional data used in the RF regression model, including environmental investment data, population and economic data, geographical variables, and climate variables. These limitations merely specify additional types and sources of data used in the same abstract mathematical analysis. The claims do not recite any improvement to the RF regression model, computer functionality, or another technology. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(g)(h).
With respect to dependent claim 6 specifically, the claim further recites inputting climate data into a SWAT model to simulate and output river hydrological parameters. This limitation merely uses a model to process data and generate additional data values for use in the abstract analysis. The claim does not recite any improvement to the SWAT model or hydrological simulation technology. Accordingly, this limitation fails to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(f)(h).
With respect to dependent claims 7-9 specifically, the claims further recite mathematical equations for determining N2O emissions flux, total N2O emission, dissolved N2O concentration, gas transfer velocity, and related values. Claims 8 and 9 further recite equations based on wind speed, flow velocity, and water depth. These limitations merely further define the mathematical calculation used to determine N2O emissions and therefore expand upon the mathematical concepts recited in claim 1. Nothing is done with the calculated N2O emission to effect a change in the river or another physical process. Accordingly, these limitations fail to integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(g)(h).
Accordingly, for the reasons above and those discussed in relation to independent claim 1, dependent claims 2-9 are insufficient to integrate the recited abstract idea into a practical application or amount to significantly more.
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 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over CN 114936721 A, XIA et al. (hereinafter XIA), in view of CN 114662766 A, TAN et al. (hereinafter Tan), in further view of CN 109242203 A, Cheng et al (hereinafter Cheng).
Regarding Claim 1, Xia discloses A method of determining nitrous oxide emissions of a river based on land-river-atmosphere simulation (Xia, [Page 5] FIG. 1 shows a flow diagram of the method for determining the discharge of the nitrogen oxide in the river of the embodiment of the present invention, the determining method is used for determining the discharge amount of the discharged nitric oxide in the atmosphere of the target river flowing to the atmosphere), comprising the steps of:
(3) obtaining river hydrological parameters of each sub-basin in each region (Xia, [Page 2] the step of obtaining the first coefficient and the first concentration of the target river), wherein the river hydrological parameters of each sub-basin comprises a water depth of the river (Xia, [Page 2] the river water river of the water depth), a flow velocity of the river (Xia, [Page 2] obtaining the river water river of the flow speed), a water temperature of the river (Xia, [Page 2] the first coefficient is a water-gas exchange coefficient, can be determined according to the water temperature parameter of the target river) and a water surface area of the river (Xia, [Page 2] distance target river water surface preset height of the wind speed value and target river area atmosphere in the atmosphere);
(4) providing an air-water interface gas exchange model (Xia, [Page 7] the first concentration, the second concentration and the first coefficient input to the water-gas interface diffusion model, determining the nitrogen oxide emission flux) and inputting the hydrological parameters of each sub-basin (Xia, [Page 3] the second concentration and the first coefficient into the water-gas interface diffusion model, determining the nitrogen oxide emission flux; obtaining the area of the river water river; and determining the discharge amount of the nitrogen oxide according to the discharge flux and river water area of the nitrogen oxide) and river water quality concentration of each sub-basin in each region (Xia, [Page 7] determining the second concentration according to the discharge factor, the second concentration is used for indicating the target river water in the nitrogen oxide concentration [Page 7] the first concentration, the second concentration and the first coefficient input to the water-gas interface diffusion model, determining the nitrogen oxide emission flux) and processing concentration conversion to obtain a total river N20 emission in each sub-basin in each region (Xia, [Page 7] obtaining the target river of the river water [Page 7] determining the nitrogen oxide discharge amount according to the nitrogen oxide discharge flux and the river water area).
Xia does not disclose (1) obtaining nitrogen emission from land in each region;
(2) dividing the nitrogen emission from land into a nitrogen emission prediction set and a nitrogen emission test set; dividing geographical variables in each region into geographical variable prediction set and geographical variable test set; dividing climate variables in each region into climate variable prediction set and climate variable test set; training a RF regression model by using nitrogen emission prediction set, geographical variable prediction set and climate variable prediction set to obtain a trained RF regression model, inputting the nitrogen emission test set, the geographical variable test set and the climate variable test set into the trained RF regression model, and outputting a river water quality concentration of each sub-basin in each region;
However, Tan teaches obtaining nitrogen emission from land in each region (Tan, [Page 1] the spatial data acquire the target area and attribute data, wherein the spatial data comprises elevation data, land use data, soil distribution data and soil gradient data, the attribute data comprises weather attribute data and soil attribute data [Page 2] based on the elevation data, the watershed river construction of the target area, dividing the watershed river network into several sub-watershed, according to the land use data corresponding to each sub-watershed, the combination data of soil distribution and soil gradient data, the sub-drainage area is divided into several hydrological response units, construction hydrological model associated with the target area);
inputting the nitrogen emission test set, the geographical variable test set and the climate variable test set into the trained RF regression model (Tan, [Page 6] the total nitrogen discharge load data computing as follows: SN=orgNsurf + NO3surf + NO3lat, ly + NO3perc, ly in the formula, SN is the initial water non-point source total nitrogen annual discharge load data, orgNsurf is the organic nitrogen discharge load data, NO3surf is the surface runoff nitrate discharge load data, NO3lat, ly is the lateral flow nitrate discharge load data, NO3perc, ly is the underground nitrate discharge load data), and outputting a river water quality concentration of each sub-basin in each region (Tan, [Page 6] the optimizing device according to the nitrogen discharge load data corresponding to each sub-watershed and total nitrogen discharge load data computing the acquire water non-point source total nitrogen discharge load data corresponding to each sub-watershed);
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Xia in view of Tan teachings because Tan teaches determining nitrogen discharge loads for respective sub-watersheds using land use, soil, geographical, and weather data, while Xia teaches determining river N2O emissions using river hydrological parameters and a water-gas interface diffusion model. A person skilled in the art would have been motivated to integrate the land nitrogen discharge information of Tan into the river N2O emission determination method of Xia to account for nitrogen entering the river from the surrounding land and determine river N2O emissions for the respective sub-watersheds.
Xia in view of Tan does not disclose dividing the nitrogen emission from land into a nitrogen emission prediction set and a nitrogen emission test set; dividing geographical variables in each region into geographical variable prediction set and geographical variable test set; dividing climate variables in each region into climate variable prediction set and climate variable test set; training a RF regression model by using nitrogen emission prediction set, geographical variable prediction set and climate variable prediction set to obtain a trained RF regression model
However, Cheng teaches dividing the nitrogen emission from land into a nitrogen emission prediction set and a nitrogen emission test set (Cheng, [Page 3] the explanatory variables of the characteristic quality factor is important for the dependent variable (quality); node purity increment meaning is as follows: each classification tree in random forest is binary tree, which generates recursive splitting principle follows from top downwards, namely in order from the root node divides the training set in the binary tree, the root node contains all the training data, according to the node purity minimum principle, is split into a left node and a right node, a subset of them respectively comprise training data, according to the same rule node continues, until the branch stopping rule to stop growth); dividing geographical variables in each region into geographical variable prediction set and geographical variable test set (Cheng, [Page 2] randomly extracting the sample from the original training set, building several sub-training set, preferably by bagging algorithm with back without the weight of concentrated randomly extracted sample the original training, constructing multiple sub-training set); dividing climate variables in each region into climate variable prediction set and climate variable test set (Cheng, [Page 3] according to different domain characteristic sub-training set selected attribute, according to the splitting attribute generating the decision tree training set to train. The multiple decision trees built integrated construction of random forest); training a RF regression model by using nitrogen emission prediction set, geographical variable prediction set and climate variable prediction set to obtain a trained RF regression model (Cheng, [Page 5] the random forest model has been constructed, evaluation simulation precision, obtaining the characteristic data of the quality prediction river specific point into a random forest model, using decision tree classification, using the pre-building the data voting the way most obtained ticket of the classification result, the river under the condition specific time, specific point of total phosphorus concentration)
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Xia in view of Tan and Cheng teachings because Chen teaches training and evaluating a Random Forest model using watershed characteristic data, to predict river water quality, while Tan teaches nitrogen discharge, geographical, and weather data for respective sub-watersheds. A person skilled in the art would have been motivated to apply the Random Forest modeling technique of Cheng to the nitrogen, geographical, and climate data of Tan in order to predict river water quality concentration for the respective sub-watersheds, which may then be used in the river N2O emissions determination method of Xia.
Regarding Claim 6, Xia in view of Tan in further view of Cheng teaches the method of determining nitrous oxide emissions of a river based on land river-
atmosphere simulation according to claim 1, wherein in step (3), the step of obtaining
hydrological parameters of each sub-basin in each region of the river comprises the substeps of:
inputting climate data of each sub-basin in each region into a SW AT model for hydrological parameter simulation (Tan, [Page 3] the hydrological model is one of SWAT (Soil and Water Tool) model, the nitrogen pollution time-space distribution of the used for area is simulated, the hydrological model is composed of several hydrological response units), then outputting the river hydrological parameters of each sub-basin (Tan, [Page 2] inputting the weather attribute data and soil attribute data to the hydrological model, based on the hydrological response unit and corresponding hydrological data acquire acquire to the hydrological data corresponding to each sub-watershed output by the hydrological model), wherein the climate data comprises rainfall temperature data, wind speed data, relative humidity data and solar radiation data (Tan, [Page 3] the attribute data comprises weather attribute data and soil attribute data, the weather attribute data is a group of data reflecting weather, can from acquire weather station, comprising annual temperature data, daily rainfall amount, daily average steaming and scattering amount, daily highest lowest temperature data, daily relative humidity data, daily solar radiation data and daily average wind speed data).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Xia in view of Tan in further view of Cheng teachings because Tan teaches using a SWAT model with climate data including temperature, rainfall, relative humidity, solar radiation, and wind speed to obtain hydrological data for respective sub-watersheds, while Xia teaches using river hydrological parameters to determine river N2O emissions, and Cheng teaches predicting river water quality using a random forest model as discussed above. A person who has ordinary skill in the art would have been motivated to use the SWAT hydrological modeling technique of Tan with the river N2O emission determination method of Xia and the water quality prediction method of Cheng in order to obtain the hydrological parameters for each sub-watershed needed to determine river N2O emissions.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over CN 114936721 A, XIA et al. (hereinafter XIA), in view of CN 114662766 A, TAN et al. (hereinafter Tan), in further view of CN 109242203 A, Cheng et al (hereinafter Cheng), in further view of CN 114462698 A, Zuo et al. (hereinafter Zuo).
Regarding Claim 2, Xia in view of Tan in further view of Cheng discloses the method of determining nitrous oxide emissions of a river based on land river- atmosphere simulation according to claim 1, wherein nitrogen emissions on land (Tan, [Page 2] it can integrate the target area of water non-point source total nitrogen discharge load data and nitrogen oxide discharge load data for each kind of flow domain nitrogen emission control scheme for analysis, solves the problem according to the experience to optimize, the problem of limitation, The optimization of the nitrogen emission control scheme achieve emission domain, increasing of optimizing the accuracy and high efficiency of the flow domain nitrogen emission control scheme)
Xia in view of Tan in further view of Cheng does not disclose comprises urban residential anthropogenic nitrogen emission, industrial anthropogenic nitrogen emission, urban stormwater runoff non-point source nitrogen emission, rural residential nitrogen emission, crop farming nitrogen emission, and livestock farming nitrogen emission.
However, Zuo teaches comprises urban residential anthropogenic nitrogen emission, industrial anthropogenic nitrogen emission, urban stormwater runoff non-point source nitrogen emission, rural residential nitrogen emission, crop farming nitrogen emission, and livestock farming nitrogen emission (Zuo, [Page 2] the pollution source type of phosphorus discharge, respectively pollution discharge phosphorus load of each type; [Page 2] the phosphorus emission of the pollution source type comprises: urban residents, industrial, rural residents, crop planting, animal husbandry, urban rainwater).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Xia in view of Tan in further view of Cheng and Zuo teachings because Zuo teaches separating watershed pollution sources into urban resident, industry, rural residents, crop planting, livestock, and urban rainwater, while Tan teaches determining nitrogen discharge loads for respective sub-watersheds. A person having ordinary skill in the art would have been motivated to apply the pollution source categories of Zuo to the nitrogen discharge loads of Tan in order to separately determine the contribution of the different point and non-point sources to the total nitrogen load of the watershed.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over CN 114936721 A, XIA et al. (hereinafter XIA), in view of CN 114662766 A, TAN et al. (hereinafter Tan), in further view of CN 109242203 A, Cheng et al (hereinafter Cheng), in further view of CN 111435485 A, Qin. et al (hereinafter Qin).
Regarding Claim 4, Xia in view of Tan in further view of Cheng discloses the method of determining nitrous oxide emissions of a river based on land river-atmosphere simulation according to claim 1, wherein in step (2), the step of outputting a water quality concentration of each sub-basin in each region of the river comprises the sub-steps of:
dividing the nitrogen emissions from the land in each region into nitrogen emission prediction set and nitrogen emission test set (Tan, [Page 1] the spatial data acquire the target area and attribute data, wherein the spatial data comprises elevation data, land use data, soil distribution data and soil gradient data, the attribute data comprises weather attribute data and soil attribute data [Page 2] based on the elevation data, the watershed river construction of the target area, dividing the watershed river network into several sub-watershed, according to the land use data corresponding to each sub-watershed, the combination data of soil distribution and soil gradient data, the sub-drainage area is divided into several hydrological response units, construction hydrological model associated with the target area)
training the RF regression model by using the nitrogen emission prediction set, geographical variable prediction set, climate variable prediction set, environmental investment data prediction set (Cheng, [Page 3] the explanatory variables of the characteristic quality factor is important for the dependent variable (quality); node purity increment meaning is as follows: each classification tree in random forest is binary tree, which generates recursive splitting principle follows from top downwards, namely in order from the root node divides the training set in the binary tree, the root node contains all the training data, according to the node purity minimum principle, is split into a left node and a right node, a subset of them respectively comprise training data, according to the same rule node continues, until the branch stopping rule to stop growth), the prediction set of the social statistical data of population and economy to obtain the trained RF regression model; and then inputting the nitrogen emission test set, geographical variable test set, climate variable test set (Tan, the total nitrogen discharge load data computing as follows: SN=orgNsurf + NO3surf + NO3lat, ly + NO3perc, ly in the formula, SN is the initial water non-point source total nitrogen annual discharge load data, orgNsurf is the organic nitrogen discharge load data, NO3surf is the surface runoff nitrate discharge load data, NO3lat, ly is the lateral flow nitrate discharge load data, NO3perc, ly is the underground nitrate discharge load data), environmental investment data test set, the test set of the social statistical data of population and economy to obtain the trained RF regression model (Cheng, [Page 5] the random forest model has been constructed, evaluation simulation precision, obtaining the characteristic data of the quality prediction river specific point into a random forest model) and output the river water quality concentration of each sub-basin in each region (Xia, [Page 2] the step of obtaining the first coefficient and the first concentration of the target river)
Xia in view of Tan in further view of Cheng does not disclose dividing the environmental investment data in each region into an environmental investment data prediction set and an environmental investment data test set and dividing the social statistical data on population and economy in each region into a prediction set of the social statistical data on population and economy, and a test set of the social statistical data on population and economy;
However, Qin teaches dividing the environmental investment data in each region into an environmental investment data prediction set and an environmental investment data test set (Qin, [Page 3] the management (M) comprises policy, management investment parameter, the preferred index parameter of the invention is: Environmental Protection Investment Index, implementation of policy and regulations, and wetland management level), and dividing the social statistical data on population and economy in each region into a prediction set of the social statistical data on population and economy, and a test set of the social statistical data on population and economy (Qin, [Page 2] the driving force (D) comprises animal and plant, natural environment and so on, preferably human, and driving force (D) is preferably selected in the human society in the invention: The per capita GDP, the annual growth rate of GDP, the Engel coefficient, the population density and the natural growth rate of the population);
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Xia in view of Tan teachings because Qin teaches using environmental investment data and social statistical data including GDP and population data for evaluating ecological conditions, while Cheng teaches using watershed characteristic data to train a Random Forest model for predicting river water quality. A person having ordinary skill in the art would have been motivated to include the environmental investment and social statistical data of Qin as additional input variables in the Random Forest model of Cheng in order to account for human and economic factors affecting water quality and improve the prediction of river water quality concentration.
Examiner Notes
There is no prior art rejection over claims 3, 5, 7, 8, and 9. However, there is a 101 rejection over the claims for the reasons set forth above. Regarding claim 3, the closest prior art fails to teach the limitation
Regarding claim 5, the closest prior art fails to teach the limitation wherein the environmental investment data comprises a proportion of environmental pollution investment and a number of environmental regulations;
the social statistical data on population and economy comprises a gross national product, a number of mobile phone households and a length of graded highway kilometers;
the geographic variables comprise a soil bulk density, soil organic matter, soil conductivity, soil pH, soil type proportion, land use proportion, maximum patch index, edge density, landscape shape index, Shannon diversity index and median landscape perimeter-to-area ratio;
the climate variables comprises an average temperature, an accumulated temperature greater than l 0°C, an average rainfall, a humidity index and a normalized vegetation index.
Regarding claim 7, the closest prior art fails to teach the limitation wherein the total river N2O emission in each sub-basin refers to the equations recited in claim 7.
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Regarding claim 8, the closest prior art fails to teach the limitation wherein n is ½, W10 is a windspeed at 10m height and the equation recited in the claim:
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Regarding claim 9, the closest prior art fails to teach the limitation n is ½, W10 is a windspeed at 10m height, V and H are flow velocity and water depth respectively, and the equation recited in the claim:
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Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclose:
-CN 115424134 A, describing a method for predicting water pollution flux using remote sensing images and Random Forest models trained using water quality, water depth, and spectral band data to predict water quality and water depth of a water body.
-CN 115965496 A, describing an intelligent river-basin water environment management method that collects and standardizes watershed environmental data, analyzes and predicts water quality using hydrological, meteorological, pollution-source, geographic and socioeconomic data, and performs pollution analysis and source tracing.
-US 11681839 B2, describing systems and methods for modeling water quality using multispectral remote sensing data and an ensemble machine learning model that combines multiple empirical models to improve water quality prediction accuracy across different regions and time periods.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM NAGI SHOHATEE whose telephone number is (571)272-6612. The examiner can normally be reached 8am-5pm.
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/IBRAHIM NAGI SHOHATEE/
Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
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