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 Objections
Claim 1 is objected to because of the following informalities: Claim 1 uses dashes, bullet points, and a list of letters (i.e., a), b), c)). Where a claim sets forth a plurality of elements or steps, each element or step of the claim should be separated by a line indentation, 37 CFR 1.75(i). There may be plural indentations to further segregate subcombinations or related steps. (MPEP 608.01(m). It is suggested to remove the dashed list and the bullet points and ensure that each step is separated by a line indentation . Additionally, it is suggested to separate the steps with a semicolon (note that there is no semicolon at the end of “a sequence for calibration”). Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, claim 1 recites the limitation "the spectral signature" in line 9. There is insufficient antecedent basis for this limitation in the claim. Claims 2-3 are rejected by virtue of their dependency on claim 1.
Regarding claim 1, claim 1 recites the limitation "the variables…the contaminants" in line 10. There is insufficient antecedent basis for this limitation in the claim. Claims 2-3 are rejected by virtue of their dependency on claim 1.
Regarding claim 1, claim 1 recites the limitation "the variables…the substrates" in line 12. There is insufficient antecedent basis for this limitation in the claim. Claims 2-3 are rejected by virtue of their dependency on claim 1.
Regarding claim 1, claim 1 recites “calibrating an item of field analysis equipment with respect to the first item of equipment, the item of field analysis equipment including a light source and a spectral sensor” in lines 14-16. It is unclear if “an item” and “the item” is the same or different from “the first item” established in lines 4-5. Does the “item” comprise the “first item”? The phrase “calibrating…with respect to the first item of equipment” is unclear. Claims 2-3 are rejected by virtue of their dependency on claim 1.
Regarding claim 1, claim 1 recites “a light source and a spectral sensor” in lines 15-16. It is unclear if the light source and spectral sensor of lines 15-16 are the same or different from the light source and at least one spectral sensor established in lines 4-5. Claims 2-3 are rejected by virtue of their dependency on claim 1.
Regarding claim 1, claim 1 recites the limitation "the characterization" in line 20. There is insufficient antecedent basis for this limitation in the claim. Claims 2-3 are rejected by virtue of their dependency on claim 1.
Regarding claim 1, claim 1 recites the limitation "the data" in line 21. There is insufficient antecedent basis for this limitation in the claim. Claims 2-3 are rejected by virtue of their dependency on claim 1.
Regarding claim 2, claim 2 recites “the analysis of a site” in lines 2-3. It is unclear if “the analysis” is referring to the “hyperspectral analysis” of claim 1, “spectral analysis” of claim 1, or “sequences for analyzing a soil sample of a geological site” of claim 1.
Regarding claim 3, claim 3 recites “the samples” in line 4. It is unclear if “the samples” is referring to the “soil sample” of the geological site of claim 1 or the “plurality of reference samples” of claim 1.
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 1-3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim 1 recite the limitations “estimating the characterization of the pollutants by processing the signature by a learning engine exploiting the data from the database established during the learning sequence”.
In accordance with MPEP 2106, the claims are found to recite statutory subject matter (Step 1: YES) and are analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A: Prong 1).
In the instant application, the limitations of “estimating the characterization of the pollutants” covers performance of a limitation in the mind, i.e. mental process. Other than “by a learning engine”, if the claim limitations, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components, then the claim limitations fall within the “Mental Processes” grouping of abstract ideas (MPEP 2106.05(f)). Accordingly, the claims recite abstract ideas (Step 2A: Prong 1: Yes).
This judicial exception is not integrated into a practical application because the claims do not recite any additional elements that reflects an improvement to technology or applies or uses the judicial exception in some other meaningful way (Step 2A, Prong 2: No). Regarding claim 1, after the limitation of “estimating the characterization”, there are no further actions performed that integrates the abstract ideas into a practical application. The claimed limitation does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims only recite the structures of a first item of equipment, an item of field analysis equipment, and learning engine to perform the limitations as claimed. The learning engine is recited at a high-level of generality (i.e., as generic computer) such that it amounts no more than mere instructions to apply the exception using a generic computer component; wherein a general purpose computer is not a particular machine (MPEP 2106.05(b)). Accordingly, the claimed limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, additional elements of a first item of equipment, an item of field analysis equipment, and learning database, are merely means to gather data that is utilized as input for the abstract idea and there is no indication of a practical application of the judicial exception associated with these components. The limitations using information and generating a recommendation equate to mere data input activity. Therefore, these limitations equate to insignificant extra-solution activity. Note that mere data gathering has been found to be insignificant extra-solution activity, and not a particular practical application (MPEP 2106.05(g)). Furthermore, the limitations of “characterize” (claim 2) and “physically and/or chemically analyzing” (claim 3), covers performance of a limitation in the mind, i.e. mental process. Thus, the claims are directed to an abstract idea (Step 2A, Prong 2: No).
The claims (claims 1-3) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment or well-understood, routine and conventional activities. As discussed above, the additional elements of the learning engine amounts to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(b)). Further, additional elements of: a first item of equipment, an item of field analysis equipment, and learning database are merely well-understood, routine, and conventional as evidenced in Scafutto et al. (SCAFUTTO ET AL., Quantitative Characterization of Crude Oils and Fuels in Mineral Substrates Using Reflectance Spectroscopy: Implications for remote Sensing, International Journal of Applied Earth Observation and Geoinformation, International Journal of Applied Earth Observation and Geoinformation Vol. 50, (2016), pp. 221-242; cited in the IDS filed 03/27/2023), Zou et al. (ZOU ET AL., Multisource Spectral-Integrated Estimation of Cadmium Concentrations in Soil Using a Direct Standardization and Spiking Algorithm, Science of the Total Environment, Vol. 701, (2020), 10 pages; cited in the IDS filed 03/27/2023), Pyo et al. (Pyo et al., “Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil”, Available online 06/18/2020, Science of the Total Environment 741, 140162), and Jiao et al. (CN 110658327A; see machine translation) (MPEP 2106.05(d)). Additionally, the claims comprise elements for data gathering, wherein mere data gathering has been found to be insignificant extra-solution activity, and not a particular practical application (MPEP 2106.05(g)). The additional elements of the claims and dependent claims do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No).
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 3 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Scafutto et al. (SCAFUTTO ET AL., Quantitative Characterization of Crude Oils and Fuels in Mineral Substrates Using Reflectance Spectroscopy: Implications for remote Sensing, International Journal of Applied Earth Observation and Geoinformation, International Journal of Applied Earth Observation and Geoinformation Vol. 50, (2016), pp. 221-242; cited in the IDS filed 03/27/2023).
Regarding claim 1, Scafutto teaches a method for analyzing soil contamination by pollutants (abstract teaches analysis of contaminants; page 222, right column, second paragraph, teaches this study characterizes contaminated soils) by way of hyperspectral analysis of reflection and/or photoluminescence (sections 2.1-2.2 teaches a high resolution spectrometer was used to take reflectance measurements), wherein the analysis is carried out using a first item of equipment by illuminating a sample using a light source and by at least one spectral sensor sensitive to a spectrum ranging from thermal infrared to ultraviolet (sections 2.1-2.2 teaches reflectance measurement was taken by a high resolution spectrometer that has a spectral range of 350-2500 nm, which includes a spectrum including infrared and ultraviolet wavelengths; since reflectance measurement was taken, it is implied that a light source is utilized in order for the sample to be illuminated for optical measurement), the method includes:
- a learning sequence comprising analyzing a plurality of reference samples, and recording in a learning database (section 3.1 teaches creating a reference spectral library from the samples, therefore reference samples were analyzed and recorded in a database, i.e. library):
a) the spectral signature of reflection acquired by spectral analysis (sections 2.1 and 3.1 and Fig. 4 teach a reference spectral library of spectral signatures were acquired from spectral analysis of reflectance);
b) known values of the variables representative of the contaminants present in each of the reference samples (sections 2.1 and 3.1 teach adding known concentrations of a specific contaminant to samples for spectral analysis to create a library for the samples; therefore, the known concentrations representative of the contaminants are implied to be recorded to be associated with the spectral library); and
c) known values of the variables representative of the substrates of each of the reference samples (Table 1, sections 2.1 and 3.1 discusses variations related to mineral grain size and composition are used, therefore, known values of variables representative of the soil samples, i.e. grain size and composition, are recorded as in Table 1, i.e. recorded in a database),
- a sequence for calibrating an item of field analysis equipment with respect to the first item of equipment, the item of field analysis equipment including a light source and a spectral sensor (sections 2.1-2.2 teaches reflectance measurement was taken by a high resolution spectrometer that has a spectral range of 350-2500 nm, which includes a spectrum including infrared and ultraviolet wavelengths; since reflectance measurement was taken, it is implied that a light source is utilized in order for the sample to be illuminated for optical measurement; section 2.1 teaches spectralon calibration was repeated during measurement, therefore the item of field analysis equipment, which includes the spectrometer, is calibrated),
- sequences for analyzing a soil sample of a geological site comprising acquiring the reflection and/or photoluminescence signature of the sample using the item of field equipment thus calibrated (section 2.1 teaches acquiring reflectance signatures of soil samples using a spectrometer that was calibrated); and
- estimating the characterization of the pollutants by processing the signature by a learning engine exploiting the data from the database established during the learning sequence (page 325, section 3.3, teaches samples were selected and applied to the prediction model to predict concentrations of PHC and ethanol contaminants; section 4.2 teaches estimation of contaminant concentrations in soils).
Regarding claim 3, Scafutto further teaches the method of claim 1, further comprising physically and/or chemically analyzing at least some of the samples (section 2.1 and Table 1 teaches characterization of the sample regarding grain size, therefore at least some of the samples are physically and/or chemically analyzed to determine the grain size of the samples).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Zou et al. (ZOU ET AL., Multisource Spectral-Integrated Estimation of Cadmium Concentrations in Soil Using a Direct Standardization and Spiking Algorithm, Science of the Total Environment, Vol. 701, (2020), 10 pages; cited in the IDS filed 03/27/2023) in view of Pyo et al. (Pyo et al., “Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil”, Available online 06/18/2020, Science of the Total Environment 741, 140162).
Regarding claim 1, Zou teaches a method for analyzing soil contamination by pollutants (abstract teaches analyzing soil samples for contamination of heavy metals, such as cadmium) by way of hyperspectral analysis of reflection and/or photoluminescence (abstract, section 4, first paragraph, and section 5 discuss hyperspectral investigation of heavy metal pollution based on reflectance), wherein the analysis is carried out using a first item of equipment (section 2.4, teaches analysis using a spectrometer and light source) by illuminating a sample using a light source (section 2.4, halogen lamp as the test light source) and by at least one spectral sensor (section 2.4, portable spectrometer) sensitive to a spectrum ranging from thermal infrared to ultraviolet (section 2.4 teaches portable spectrometer has a spectral range between 339 and 2500 nm, which includes a spectrum including infrared and ultraviolet wavelengths), the method includes:
- a learning sequence comprising analyzing a plurality of reference samples (Fig. 2 and sections 2.2, 2.3 and 2.5 teaches steps of collection of topsoil samples and clean subsoil samples, to form and analyze near standard soil samples, i.e. reference samples), and recording in a learning database (section 2.5 teaches standard soil samples were analyzed to determine their Cd concentration and reflectance; Fig. 1 shows a database of the near standard soil samples; therefore, it is implied that data from the reference samples are recorded in a database for further analysis):
a) the spectral signature of reflection acquired by spectral analysis (section 2.5 teaches spectral reflectance, i.e. spectral signature of reflection, of the standard soil samples were acquired);
b) known values of the variables representative of the contaminants present in each of the reference samples (table 1 and section 2.5 teaches Cd concentrations; table 2B teaches concentrations of other components such as Pb, As, Cu; therefore, the concentration of soil components of heavy metals, i.e. Cd, Pb, As, Cu, are interpreted as values of variables representative of the contaminants present in each of the standard soil samples); and
c) known values of the variables representative of the substrates of each of the reference samples (Fig. 2b and section 3.1 teaches values of the standard soil samples, including pH, Fe, and organic matter, i.e. values of variables representative of substrates of each standard soil samples),
- a sequence for calibrating an item of field analysis equipment with respect to the first item of equipment, the item of field analysis equipment including a light source and a spectral sensor (section 2.4 teaches light source and spectrometer, i.e. item of field analysis equipment; section 2.4 teaches calibration of the spectrometer, therefore the “item” is calibrated with respect to a first item of equipment),
- sequences for analyzing a soil sample of a geological site comprising acquiring the reflection and/or photoluminescence signature of the sample using the item of field equipment thus calibrated (sections 2.3-2.4 teaches methods for collecting and analyzing main soil samples, which includes measuring and acquiring spectral reflectance, i.e. reflection signature, from each soil sample using the calibrated spectrometer); and
- estimating the characterization of a pollutant by processing the signature by a learning engine exploiting the data from the database established during the learning sequence (Fig. 2 and section 2.2 teaches estimation of concentration of Cd, i.e. characterization of a pollutant, by models that use data obtained from the standard soil samples; sections 2.6 and 2.7 teaches algorithms or models, i.e. learning engine, to estimate Cd concentration using data from the standard soil samples).
While Zou teaches estimation of Cd (i.e. one pollutant; sections 2.6-2.7), Zou fails to teach estimating the characterization of the pollutants by processing the signature by a learning engine exploiting the data from the database established during the learning sequence.
Zou teaches hyperspectral analysis provides a potential way to detect heavy metals in soil (abstract) and detecting heavy metals in soil is crucial for human health and social stability (section 1, first paragraph). Zou teaches multiple heavy metals are likely to be co-present in soil (section 1, second paragraph). Zou teaches the current approach can also be easily used to explore the spectral response characteristics of other heavy metals in soil by producing the near standard soil samples of the heavy metal (page 9, left column, first paragraph). Zou teaches producing standard soil samples of multiple heavy metals will become a great research opportunity and development of machine learning methods for estimation of soil heavy metal concentrations (page 9, left column, second full paragraph).
Pyo teaches estimation of heavy metals, such as, Cu, and Pb concentrations, using deep neural networks with spectroscopy of soil (abstract; Fig. 1). Pyo teaches procedures including soil sampling, measuring soil reflectance, analysis by a spectrometer for heavy metals in soil samples, data processing using a data-driven model, and implementation of deep learning and machine learning to estimate heavy metal concentration (Fig. 1). Pyo teaches reflectance spectra of soil samples were obtained with a contact probe comprising a halogen bulb and spectroradiometer (section 2.1.2). Pyo teaches processed spectral data for each heavy metal sample were fed into deep learning models and machine learning models to generate As, Cu, and Pb concentrations; wherein data included samples for training and validation (section 2.3; Fig. 4). Pyo teaches deep learning models have potential to estimate heavy soil metals concentrations using spectroscopy data, specifically high-spectral-resolution soil images with reliable accuracy (section 3.5, last paragraph; section 4, last paragraph). Pyo teaches applicability of the deep learning model will be expended to soil remote sensing with multi- or hyper-spectral imagery to estimate soil heavy metals and generate heavy metal distribution maps (section 4, last paragraph).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Zou to incorporate the teachings of a desire for applying the current approach to explore multiple other heavy metal concentrations of Zou (page 9, left column, first paragraph - second full paragraph) and the teachings of applicability of deep learning models and machine learning to estimate multiple heavy metal concentrations in soil samples using high-spectral-resolution soil data of Pyo (abstract; Fig. 1; sections 3.5 and 4) to provide: estimating the characterization of the pollutants by processing the signature by a learning engine exploiting the data from the database established during the learning sequence. Doing so would have a reasonable expectation of successfully improving analysis and characterization of soil samples for multiple pollutants.
Regarding claim 3, Zou further teaches the method of claim 1, further comprising physically and/or chemically analyzing at least some of the samples (sections 2.4 and 2.5 teaches measuring weight of the soil samples, therefore at least some of the samples were physically analyzed; section 3.1 teaches values of other soil components such as soil pH and organic matter are included to reflect soil property, thus at least some of the samples were physical and/or chemically analyzed to obtain the pH and organic matter properties).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Zou in view of Pyo as applied to claim 1 above, and further in view of Jiao et al. (CN 110658327A; see machine translation).
Regarding claim 2, Zou further teaches the method of claim 1, wherein, during the analysis of a site, at least one sampling operation is carried out (section 2.3).
Modified Zou fails to teach: the method of claim 1, wherein, during the analysis of the site, at least one core sampling operation is carried out, and wherein the analysis of a plurality of samples distributed over the height of the core is carried out to characterize contaminants at various depths.
Jiao teaches calculation of sediment enrichment ratio of non-point source heavy metals in watershed based on sediment analysis for pollution prevention and control (page 1; paragraph [0002]). Jiao teaches collecting sediment cores and measuring heavy metal contents at different sediment depths (paragraph [0009]). Jiao teaches based on heavy metal deposition flux values at different depths, regression analysis is applied to establish a long-term quantitative relationship (paragraph [0022]). Jiao teaches measuring heavy metal content at different sediment depths of sediment cores (paragraph [0068])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of modified Zou to incorporate the teachings of collecting sediment cores and measuring heavy metal contents at different depths of the cores of Jiao (paragraphs [0009],[0022],[0068]) to provide: the method of claim 1, wherein, during the analysis of the site, at least one core sampling operation is carried out, and wherein the analysis of a plurality of samples distributed over the height of the core is carried out to characterize contaminants at various depths. Doing so would have a reasonable expectation of successfully improving characterization and analysis of a site of soil pollution, such as the content of heavy metals at varying depths as taught by Jiao.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Scafutto as applied to claim 1 above, and further in view of Jiao et al. (CN 110658327A; see machine translation).
Regarding claim 2, Scafutto fails to teach the method of claim 1, wherein, during the analysis of a site, at least one core sampling operation is carried out, and wherein the analysis of a plurality of samples distributed over the height of the core is carried out to characterize contaminants at various depths.
Jiao teaches calculation of sediment enrichment ratio of non-point source heavy metals in watershed based on sediment analysis for pollution prevention and control (page 1; paragraph [0002]). Jiao teaches collecting sediment cores and measuring heavy metal contents at different sediment depths (paragraph [0009]). Jiao teaches based on heavy metal deposition flux values at different depths, regression analysis is applied to establish a long-term quantitative relationship (paragraph [0022]). Jiao teaches measuring heavy metal content at different sediment depths of sediment cores (paragraph [0068])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of modified Scafutto to incorporate the teachings of collecting sediment cores and measuring heavy metal contents at different depths of the cores of Jiao (paragraphs [0009],[0022],[0068]) to provide: the method of claim 1, wherein, during the analysis of a site, at least one core sampling operation is carried out, and wherein the analysis of a plurality of samples distributed over the height of the core is carried out to characterize contaminants at various depths.. Doing so would have a reasonable expectation of successfully improving characterization and analysis of a site of soil pollution, such as the content of heavy metals at varying depths as taught by Jiao.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kitic et al. (US 20230255133 A1; effectively filed 07/10/2020) teaches a system for soil sampling (abstract). Kitic teaches a sensor to detect the presence of certain ions of nitrate, nitrogen, phosphorus, potassium, calcium, carbon, magnesium, iron, etc., then measures the electrical conductivity and acidity of the soil, moisture, particle size, etc. (paragraph [0036]). Kitic teaches appropriate calibration standards used to calibrate the sensor before the measurement series, thus achieving measurement accuracy and repeatability (paragraph [0036]). Kitic teaches conducting hyperspectral analysis based on which following parameters can be estimated: moisture, organic matter (organic carbon), particle size, iron oxide concentration, mineral content, dissolved salts, heavy metals and the like (paragraph [0040]).
Ben-Dor et al. (US 20140012504 A1) teaches an apparatus for assessing results of reflectance spectroscopy on soil sample to determine contaminants in the soil by constructing a model based on analysis of known samples (abstract). Ben-Dor teaches the model is constructed on samples of different kinds of soil without pollutants and with different levels of pollutants (abstract). Ben-Dor teaches Artificial contaminated samples are analyzed chemically and spectrally to form a database (paragraph [0041]).
Rossel et al. (US 20180188225 A1) teaches a soil condition analysis system to analyze a soil core (abstract). Rossel teaches a data processing and data analytics component configured to process the measurement data to generate soil property data representing corresponding soil properties of the elongate soil core as a function of depth, based on mathematical and statistical methods (abstract).
Lim et al. (KR 101780058 B1; see machine translation) teaches a method for monitoring heavy metals in soils using hyperspectral images from a hyperspectral sensor (paragraphs [0001]-[0002]). Lim teaches an artificial soil sample creation step that creates an artificial soil sample composed of heavy metals by type and concentration (paragraph [0011]) and producing a spectral library of heavy metals in soil (paragraph [0014]), which serves as an identification standard (paragraph [0015]). Lim teaches it is desirable to build a library of different types for each heavy metal (paragraph [0036]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY H NGUYEN whose telephone number is (571)272-2338. The examiner can normally be reached M-F 7:30A-5:00P.
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/HENRY H NGUYEN/ Primary Examiner, Art Unit 1758