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, 9, 12 & 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Knappett et al., Rising arsenic concentrations from dewatering a geothermally influenced aquifer in central Mexico, Water Research, volume 185, published October 15, 2020 (hereafter Knappett et al.) in view of Guerillot (US 2014/0343909).
In regard to claim 1, Knappett et al. discloses a computer-implemented method of machine-learning a plurality of predictive basin-wise models (Figure 8: “machine learning (RF) model”; abstract: “basin-wide sampling”) each configured for predicting a concentration of an element at a given location in an aquifer of a respective basin (section 2.5: “The performance of all models were assessed by their ability to predict As concentrations using the root-mean-squared-error (RMSE) and R2. The goal was to develop a parsimonious model predicting As concentrations across the basin as a function of temperature and chemical parameters to elucidate geochemical and hydrological processes driving As concentrations.”), the method comprising, for each basin and with respect to a predetermined set of one or more geochemical variables: providing a dataset comprising, for respective aquifer locations of the basin, training samples each including a measurement of one or more geochemical variables of the predetermined set (section 1: “Geothermally influenced ground waters in volcanic rock and sediments commonly contain high dissolved As concentrations along with high temperature, pH and concentrations of fluoride (F), silica (Si), sodium (Na), lithium (Li), and boron (B)”; section 2.3: “The concentrations of major ions were compared between 1999 and 2016 in our previous study (Knappett et al., 2018), but here we introduce the results from an expanded dataset from 137 wells and includes trace elements which were not previously available. Samples taken from the 137 production wells in 2016 were analyzed for major cations (Na+, K+, Ca2+, Mg2+, Li+) and anions (Cl−, Br−, NO3−, SO42−, F−) using an Ion Chromatograph”), and a respective ground truth value representing a concentration of the element at the respective aquifer location (section 1: “As concentrations”); and learning the predictive basin-wise model based on the dataset (section 1: “The objectives of this study are to: 1) estimate changes in temperature and groundwater chemistry across the Independence Basin between 1999 and 2016; 2) estimate changes in As concentrations at different spatial scales; and 3) deduce the hydrogeochemical processes driving As concentrations using multivariate regression and machine learning.”).
However, Knappett et al. does not disclose: in regard to claim 1, that the aquifer is a “saline” aquifer.
Guerillot discloses: in regard to claim 1, a saline aquifer (see paragraph 0141).
It would have been obvious to one of ordinary skill in the art at the time the application was filed to have applied the teachings of Knappett et al. to the saline aquifer taught by Guerillot, the motivation being to enable storage of CO2 (see paragraph 0141).
In regard to claim 2, Knappett et al. discloses that the element is a metal (abstract: Arsenic).
In regard to claim 3, Knappett et al. discloses that the element is lithium (abstract: “As concentrations are closely associated with high pH and temperature, and high concentrations of fluoride (F), molybdenum (Mo), lithium (Li), sodium (Na) and silica (Si), but low calcium (Ca) and nitrate (NO3) concentrations.”).
In regard to claim 4, Knappett et al. discloses that the predetermined set of one or more geochemical variables comprises a concentration of any one or any combination of the following chemical elements: Cl, Ca, Na, B, Mg, Sr, and/or K (section 1: “Geothermally influenced ground waters in volcanic rock and sediments commonly contain high dissolved As concentrations along with high temperature, pH and concentrations of fluoride (F), silica (Si), sodium (Na), lithium (Li), and boron (B)”; section 2.3: “The concentrations of major ions were compared between 1999 and 2016 in our previous study (Knappett et al., 2018), but here we introduce the results from an expanded dataset from 137 wells and includes trace elements which were not previously available. Samples taken from the 137 production wells in 2016 were analyzed for major cations (Na+, K+, Ca2+, Mg2+, Li+) and anions (Cl−, Br−, NO3−, SO42−, F−) using an Ion Chromatograph”).
In regard to claim 5, Knappett et al. discloses that each predictive basin-wise model comprises an ensemble-learning model (Section 2.6: “Random Forest (RF) regression models, a classification and regression tree (CART) model, were trained to analyze and account for non-linear relationships”).
In regard to claim 6, Knappett et al. discloses that the ensemble-learning model is a tree-based model, for example an XG boost model or a Random Forest model (Section 2.6: “Random Forest (RF) regression models, a classification and regression tree (CART) model, were trained to analyze and account for non-linear relationships”).
Claim 9 recites limitations that are believed to be sufficiently similar and/or implied by the limitations of claim 1.
Claim 12 recites limitations that are believed to be the combined limitations of claims 1 and 9.
In regard to claim 18, Knappett et al. discloses that the device further comprises a processor coupled to the computer readable storage medium (these limitations are suggested by “machine learning algorithms” mentioned in section 2.6).
In regard to claim 19, Knappett et al. discloses that the element is a metal (abstract: Arsenic).
In regard to claim 20, Knappett et al. discloses that the element is lithium (abstract: “As concentrations are closely associated with high pH and temperature, and high concentrations of fluoride (F), molybdenum (Mo), lithium (Li), sodium (Na) and silica (Si), but low calcium (Ca) and nitrate (NO3) concentrations.”).
In regard to claim 21, Knappett et al. discloses that the predetermined set of one or more geochemical variables comprises a concentration of any one or any combination of the following chemical elements: Cl, Ca, Na, B, Mg, Sr, and/or K (section 1: “Geothermally influenced ground waters in volcanic rock and sediments commonly contain high dissolved As concentrations along with high temperature, pH and concentrations of fluoride (F), silica (Si), sodium (Na), lithium (Li), and boron (B)”; section 2.3: “The concentrations of major ions were compared between 1999 and 2016 in our previous study (Knappett et al., 2018), but here we introduce the results from an expanded dataset from 137 wells and includes trace elements which were not previously available. Samples taken from the 137 production wells in 2016 were analyzed for major cations (Na+, K+, Ca2+, Mg2+, Li+) and anions (Cl−, Br−, NO3−, SO42−, F−) using an Ion Chromatograph”).
Allowable Subject Matter
Claims 7, 8, 10, 11 and 22-24 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon (see attached PTO-892 form) is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Peter Vincent Agustin whose telephone number is (571) 272-7567. The examiner can normally be reached on Monday - Thursday 8:30 am - 6:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steven Lim can be reached on 571-270-1210. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Peter Vincent Agustin/
Primary Examiner, Art Unit 2688
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