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 .
Remarks
In response to communications sent January 30, 2023, claim(s) 1-8 are pending in this application; of these claims 1 are in independent form.
Priority
The Examiner could not determine the filing date of each individual claim because the priority document is not written in English.
Drawings
The drawing(s) filed on January 30, 2023 are accepted by the Examiner.
Information Disclosure Statement
The Information Disclosure Statement(s) is/are acknowledged and the references contained therein have been considered by the Examiner. This includes the Information Disclosure Statements(s) filed on: February 19, 2024.
Claim Objections
Claims 1-8 are objected to because of the following informalities: On page 1 line 5, the “~” symbol is used to indicate a range, such as “0~10 cm”. However, upon translation to English, a dash should be used: “0-10 cm”. Appropriate correction is required.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mathematics, which is an abstract idea and a judicial exception. Claim 4 recites a mental process, performable as a judgement in the human mind with the aid of a pen and paper, in combination with the mathematical calculation. This judicial exception (and combination of judicial exceptions) is not integrated into a practical application because the step obtaining data is the only step that is not a judicial exception. Furthermore, the step of obtaining data is pre-solution activity that does not limit all meaningful uses of the judicial exception. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the step of obtaining is well-understood, routine, and conventional. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) In this precedential case, a computer receives and sends information over a network.
1. A method for estimating abundance and distribution features of antibiotic resistance genes (ARGs) in surfacial sediments of lake and reservoir, comprising:
a) obtaining an annual input total of nitrogen and phosphorus pollutants of each tributary flowing into the lake and reservoir (obtaining data; necessary pre-solution activity to carry out all uses of the judicial exception);
b) obtaining abundance of each type of ARGs in the 0~10 cm sediment of a surface layer of the lake and reservoir in the next year (obtaining data; necessary pre-solution activity to carry out all uses of the judicial exception), and analyzing correlation between the abundance of ARGs and the annual input total of the surrounding nitrogen and phosphorus pollutants by using a geographical weighted regression model (mathematical calculations), so as to construct a linear regression equation between the abundance of each type of ARGs and the nitrogen and phosphorus discharge (mathematical calculations); and
c) calculating annual input total of nitrogen and phosphorus pollutants to be estimated for each geographical location of the lake and reservoir using inverse distance weighting interpolation analysis based on the annual input total of the nitrogen and phosphorus pollutants obtained in step a (mathematical calculation), and substituting the calculated annual input total of the nitrogen and phosphorus pollutants to be estimated into the linear regression equation constructed in step b to estimate the abundance of each type of ARGs corresponding to the geographical location of the lake and reservoir and analyze a distribution feature of the ARGs of the lake and reservoir (mathematical calculation).
2. The method of claim 1, wherein the step a includes:
estimating the annual input total of nitrogen and phosphorus pollutants of each tributary flowing into the lake and reservoir by using a pollutant annual input total estimation model based on a status of a social and economic production activity in a studied basin of the lake and reservoir (mathematical calculation); or
calculating the annual input total of nitrogen and phosphorus pollutants of each tributary flowing into the lake and reservoir by using water quality and hydrological monitoring data of the tributary flowing into the lake and reservoir (mathematical calculation).
3. The method of claim 2, further comprising: estimating, by the pollutant annual input total estimation model applying an output coefficient method, a total nitrogen (TN) and total phosphorus (TP) pollutant index load amount of poultry, rural and urban life, and aquaculture pollution, respectively, from a pollutant generation stage, a pollutant loss stage, and a pollutant inflow stage, and couple the estimated pollutant index load amount to a SWAT hydrological model to simulate the annual input total of the nitrogen and phosphorus pollutant of the tributary flowing into the lake and reservoir (mathematical calculation).
4. The method of claim 2, wherein when the pollutant annual input total estimation model coupled to a SWAT hydrological model applies a farmland management component of the SWAT hydrological model, the method further comprises: determining, by the farmland management component, farm production time, fertilization time, and fertilization amount to introduce agricultural planting patterns in the basin of the lake and reservoir including agricultural management measures and estimate farmland soil pollutant amount flowing into the lake and reservoir in combination with rainfall time and rainfall amount of the basin, wherein the agricultural management measures include planting, farming, irrigation, fertilization (mental process of a judgement, performable in the human mind with the aid of pen and paper).
5. The method of claim 2, wherein the pollutant annual input total estimation model uses an output coefficient method to estimate the annual input total of nitrogen and phosphorus pollutants of each tributary flowing into the lake and reservoir by a following equation:
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wherein, L is the amount of nutrient, which refers to the annual input total of the pollutants; E.sub.i is an output coefficient of the i-th nutrient source; A.sub.i is the area of the i-th class land use type or the number of the i-th class livestock or population; I.sub.i is the nutrient input from the i-th nutrient source, p.sub.1 is the nutrient input from the rainfall, and c is a nutrient concentration (g/m.sup.3) of the rainfall itself; R is annual rainfall (m.sup.3) in the basin; and Q is a rainfall runoff coefficient (mathematical calculation).
6. The method of claim 1, wherein the correlation between the abundance of ARGs and the annual input total of the nitrogen and phosphorus pollutants in the peripheral tributaries is analyzed in the step b using the geographical weighted regression model to construct a geospatial relationship between the distribution feature of each type of ARGs in the lake and reservoir and the pollution input, i.e., a linear regression equation between the abundance of each type of ARGs and the nitrogen and phosphorus discharge, wherein the geographical weighted regression model always performs regression analysis by a following equation starting from the Ordinary Least Square regression:
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wherein, Y.sub.i is a response variable, (u.sub.i,v.sub.i) represents coordinates of a spatial location i, β.sub.0(u.sub.i,v.sub.i) and β.sub.k(u.sub.i,v.sub.i) represent an intercept and (p.sub.2−1) slope parameters at the location i, respectively, X.sub.ik represents (p.sub.2−) prediction variables at the location i, p.sub.2 is a total number of parameters to be estimated, and ε.sub.i is an error term at the location I (mathematical calculation).
7. The method of claim 1, wherein, in the step b, the linear regression equation represented by a following equation is constructed using the abundance of each type of ARGs in the surfacial sediments of field-investigated lake and reservoir, and the constructed linear regression equation is inversely validated using the abundance of each type of ARGs in the surfacial sediments of field-investigated lake and reservoir:
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wherein, y.sub.ARGs is the abundance of the antibiotic resistance gene ARGs at a point to be measured, x.sub.TN is an annual total nitrogen input pollution load, x.sub.TP is an annual total phosphorus input pollution load, and a, b, and c are intercepts of the linear regression equation (mathematical calculation).
8. The method of claim 1, wherein the inverse distance weighting interpolation analysis is performed according to a following equation:
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wherein, {circumflex over (Z)}(s.sub.0) represents an interpolation result at s.sub.0, Z(s.sub.i) is an annual pollution load value obtained at s.sub.i, N is the number of tributaries of the lake and reservoir around which the interpolation is performed, λ.sub.i is a weight of each lake and reservoir tributary inlet used in a interpolation calculation process, d.sub.i0 is a distance between the interpolation point and each known lake and reservoir tributary inlet s.sub.i, P is a weighted power index, and the sum of a weight λ.sub.i of each lake and reservoir tributary to an interpolation result is 1 (mathematical calculation).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhou, Zhen-Chao, et al. "Antibiotic resistance genes in an urban river as impacted by bacterial community and physicochemical parameters." Environmental Science and Pollution Research 24.30 (2017): 23753-23762.
Analyzes phosphorus and nitrogen in surface-sediment, but does not use a geographically-weighted regression. Instead uses ANOVA and correlations.
Beattie, Rachelle E., et al. "Agricultural contamination impacts antibiotic resistance gene abundances in river bed sediment temporally." FEMS microbiology ecology 94.9 (2018): fiy131.
Analyzes phosphorus and nitrogen in surface-sediment, but does not use a geographically-weighted regression. Instead uses PERMANOVA
Wang, Jiawen, et al. "Supercarriers of antibiotic resistome in a world’s large river." Microbiome 10.1 (2022): 111.
Uses geographical distances for preprocessing: “The rate of distance-decay of ARG/host/HPB communities was calculated as the slope of the ordinary least-squares regression line fitted to the relationship between geographic distance and community similarity”
WO 2024156901 A1: pertinent because of regression analysis for water quality assessment
Pruden, Amy, Mazdak Arabi, and Heather N. Storteboom. "Correlation between upstream human activities and riverine antibiotic resistance genes." Environmental science & technology 46.21 (2012): 11541-11549. (Year: 2012)
Zhao, Bin, et al. "Occurrence of antibiotics and antibiotic resistance genes in the Fuxian Lake and antibiotic source analysis based on principal component analysis-multiple linear regression model." Chemosphere 262 (2021): 127741. (Year: 2021)
Zhang, Weihong, et al. "Nitrogen rather than phosphorus driving the biogeographic patterns of abundant bacterial taxa in a eutrophic plateau lake." Science of The Total Environment 806 (2022): 150947. (Year: 2022)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jesse P Frumkin whose telephone number is (571)270-1849. The examiner can normally be reached Monday - Friday, 10-5 ET.
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/JESSE P FRUMKIN/ Primary Examiner, Art Unit 1685 July 20, 2026
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