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 .
Specification
The abstract of the disclosure is objected to because it exceeds 150 words in length. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 1-26 are objected to because of the following informalities:
In claim 1, lines 5-7, “the number of library samples to retain in a decimation step, the number of calibration clusters to form, and the number of fundamental parameters to us” should be --a number of library samples to retain in a decimation step, a number of calibration clusters to form, and a number of fundamental parameters to us-- to avoid the issue of lack of antecedent basis.
In claim 1, line 8, “a decimation step” should be --the decimation step-- to avoid creating another antecedent basis.
In claim 1, line 18-19, “a target sample” should be --the target sample-- to avoid creating another antecedent basis.
In claim 1, lines 20, “where ISUx and ISUy sample-wise similarities” should be --where the ISUx and ISUy sample-wise similarities-- to avoid creating another antecedent basis.
In claim 1, line 21, “the degree of matrix matching” should be--a degree of matrix matching-- to avoid the issue of lack of antecedent basis.
In claim 2, line 1, “wherein ISU” should be --wherein the ISU-- to avoid creating another antecedent basis.
In claim 3, line 1, “wherein LAFR process parameters” should be --wherein the LAFR process parameters-- to avoid creating another antecedent basis.
In claim 4, line 3, “relates the analyte amount to the measured spectral responses” should be --relates an analyte amount to measured spectral responses-- to avoid the issue of lack of antecedent basis.
In claim 5, “the analyte amounts present in each target sample” should be -- the analyte amount present in each of a plurality of target samples-- to avoid the issue of lack of antecedent basis; and to avoid being too broad in scope without clear boundaries.
In claim 7, line 5, “define” should be --defining-- to correct a grammatical error. Similar problems are found in the other step and sub-steps.
In claim 7, line 7, “for each HPPC” should be --for each of the HPPC’s-- to avoid being too broad in scope without clear boundaries.
in claim 7, line 11, “the target” should be --the target sample-- to avoid the issue of lack of antecedent basis.
In claim 7, line 15, “each HPPC” should be --the HPPC-- because it is in the loop “for each HPPC” already.
in claim 9, lines 2-3, “the number of samples to remain” should be --a number of samples to remain-- to avoid the issue of lack of antecedent basis.
In claim 10, lines 2-3, “the number of outliers to remove each iteration” should be --a number of outliers to remove for each of the HPPC’s-- to avoid the issue of lack of antecedent basis; and for better clarity.
In claim 11, line 2, “the number of calibration sets to form” should be --a number of calibration sets to form-- to avoid the issue of lack of antecedent basis.
In claim 12, line 2, “the ISUx and ISUy sample-wise similarities” should be --ISUx and ISUy sample-wise similarities-- to avoid the issue of lack of antecedent basis.
In claim 13, line 1-2, “wherein ISUx comprises matrix-match spectra, and ISUy” should be --wherein the ISUx comprises matrix-match spectra, and the ISUy-- to avoid creating another antecedent basis.
In claim 13, lines 3-4, “the known analyte reference samples” should be --known analyte reference samples-- to avoid the issue of lack of antecedent basis.
In claim 14, line 1, “wherein ISUx and ISUy” should be --wherein the ISUx and ISUy-- to avoid creating another antecedent basis.
In claim 14, line 4, “each sample prediction amount” should be --a sample prediction amount-- to avoid being too broad in scope without clear boundaries.
In claim 14, line 5, “the calibration set” should be --a calibration set-- to avoid the issue of lack of antecedent basis.
In claim 15, line 1, “the quantitative analysis” should be --a quantitative analysis-- to avoid the issue of lack of antecedent basis.
In claim 15, lines 10-11, “a target sample” should be --the target sample-- to avoid creating another antecedent basis.
In claim 15, lines 11-12, “the analyte amount highly similar to the unknown analyte amount” should be --an analyte amount highly similar to an unknown analyte amount-- to avoid the issue of lack of antecedent basis.
In claim 16, line 6, “the LAFR process parameters” should be --LAFR process parameters-- to avoid the issue of lack of antecedent basis.
In claim 16, line 12, “the degree of matrix matching” should be --a degree of matrix matching-- to avoid the issue of lack of antecedent basis.
In claim 17, line 3. There should be a period “.” in the end.
In claim 18, line 2-4, “each sample prediction amount” should be --a sample prediction amount-- to avoid being too broad in scope without clear boundaries.
In claim 19, line2 1-2, “each linear training set is matched to each target sample” should be --each of the linear training sets is matched to a target sample-- to avoid being too broad in scope without clear boundaries.
In claim 20, line 3, “the concentration” should be --a concentration-- to avoid the issue of lack of antecedent basis.
In claim 23, line 2, “the field” should be --a field-- to avoid the issue of lack of antecedent basis.
In claim 25, line 2, “the field” should be --a field-- to avoid the issue of lack of antecedent basis.
In claim 26, line 2, “the field” should be --a field-- to avoid the issue of lack of antecedent basis.
The other claim(s) not discussed above are objected to for inheriting the issue(s) from their linking claim(s).
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 1-14, 16-18, 23-25 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, it recites “most N spectrally similar to target sample” in lines 8-9. N is not defined by the claim and it is not clear whether it means “N most spectrally similar samples to the target sample” or else. For examination purpose, --N most spectrally similar samples to the target sample, N being a natural number-- is assumed.
Further regarding claim 1, it recites “the target prediction sample” in line 16. There is no antecedent basis for the limitation. It is unclear whether it is meant to be “a target prediction sample” or else. For examination purpose, --the target sample-- is assumed.
Regarding claim 7, it recites “N most spectrally similar to target sample” in line 8. N is not defined by the claim and it is not clear whether it means “N most spectrally similar samples to the target sample” or else. For examination purpose, --N most spectrally similar samples to the target sample, N being a natural number-- is assumed.
Further regarding claim 7, it recites “select best N sets” in line 16. It is unclear whether the N in line 16 is the same as the N in line 8, or else. For examination purpose, --select M best sets, M being a natural number-- is assumed.
Further regarding claim 7, it recites “select best K samples from the N Selected CalSets” in lines 18-19. K and N are not defined by the claim. For examination purpose, --select K best samples from the M selected CalSets, K being a natural number-- is assumed.
Regarding claim 12, it recites “wherein the matrix matching” in line 1. There are multiple antecedent bases for “the matrix matching.” For examination purpose, --wherein using matrix matching to select K best samples from the M Selected CalSets--.
Further regarding claim 12, it recites “the target prediction sample, where ISUx and ISUy sample” in lines 3-4. There is no antecedent basis for “the target prediction sample” and it is unclear what “where ISUx and ISUy sample” mean. For examination purpose, --“the target sample-- is assumed.
Regarding claim 16, it recites “most N spectrally similar to target sample” in lines 3-4. N is not defined by the claim and it is not clear whether it means “N most spectrally similar samples to the target sample” or else. For examination purpose, --N most spectrally similar samples to the target sample, N being a natural number-- is assumed.
Further regarding claim 16, it recites “the target prediction sample” in lines 10-11. There is no antecedent basis for the limitation. It is unclear whether it is meant to be “a target prediction sample” or else. For examination purpose, --the target sample-- is assumed.
The other claim(s) not discussed above are rejected for inheriting the issue(s) from their linking claim(s).
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.
MPEP 2106 outlines a two-part analysis for Subject Matter Eligibility as shown in the chart below.
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Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter.
Step 2, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception.
Step 2A is a two-prong inquiry, as shown in the chart below.
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Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. If the claim does not recite a judicial exception (a law of nature, natural phenomenon, or abstract idea), then the claim cannot be directed to a judicial exception (Step 2A: NO), and thus the claim is eligible at Pathway B without further analysis. Abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes.
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B.
Claims 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes). The claim recites in preamble: “searching a large library of field sample spectra (Library X, y) using a generalized local adaptive fusion regression (LAFR) process for quantitative analysis of molecular based spectroscopic data (xnew) from a target sample of analytes” and the steps beginning from “defining LAFR process parameters, including the number of library samples to retain in a decimation step, the number of calibration clusters to form, and the number of fundamental parameters to use” to the end of the claim.
All the above indicated limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper).
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites no additional elements. Accordingly, no additional elements are sufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception, as discussed with respect to Step 2A Prong Two above. The claim does not amount to significantly more than the abstract idea.
Regarding claim 7, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes). The claim recites in preamble: “searching a large library of spectral measurement (Library X, y) using a generalized local adaptive fusion regression (LAFR) process for quantitative analysis of molecular based spectroscopic data (xnew) from a target sample of analytes” and the steps beginning from “define process parameters and obtain all possible hyperparameter combinations (HPPC's) thereof” to the end of the claim.
All the above indicated limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper).
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites no additional elements. Accordingly, no additional elements are sufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception, as discussed with respect to Step 2A Prong Two above. The claim does not amount to significantly more than the abstract idea.
Regarding claim 15, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes). The claim recites in preamble: “predicting the quantitative analysis of a target sample” and the steps beginning from “searching through a library of spectral samples with corresponding analyte values (Library X, y) using a generalized local adaptive fusion regression (LAFR) process for identifying subsets of samples with similar matrix effects” to the end of the claim.
All the above indicated limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper).
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites no additional elements. Accordingly, no additional elements are sufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception, as discussed with respect to Step 2A Prong Two above. The claim does not amount to significantly more than the abstract idea.
Dependent claims 2-6, 8-14, and 16-26 when analyzed as a whole respectively are held to be patent ineligible under 35 U.S.C. 101 because they either extend (or add more details to) the abstract idea or the additional recited limitation(s) (if any) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as discussed below: there is no additional element(s) in the dependent claims that sufficiently integrates the abstract idea into a practical application of, or makes the claims significantly more than, the judicial exception (abstract idea). The additional element(s) (if any) are mere instructions to apply an except, field of use, and/or insignificant extra-solution activities (applied to Step 2A_Prong Two and Step 2B; see MPEP 2016.05(f)-(h)) and/or well-understood, routine, or conventional (applied to Step 2B; see MPEP 2106.05(d)) to facilitate the application of the abstract idea. Note that claims 23-26 recites a handheld device, or a smartphone as a platform for performing the claimed methods. However, they are invoked for its computer functionalities (see MPEP 2106.05(f)), or for its portability as a field of use (see MPEP 2106.05(h)). They are not sufficient to make the claims a practical application of, or significantly more than, the abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 15, 16, 19, 20, and 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chang et al. ("A New Local Modelling Approach Based on Predicted Errors for Near-Infrared Spectral Analysis" Journal of Analytical Methods in Chemistry
Volume 2016; herein after “Chang”).
Regarding claim 15, Chang teaches methodology for predicting the quantitative analysis of a target sample (i.e., “analysis of complex samples or chemical process”; see p. 1, Introduction, ¶ 1), comprising:
searching through a library of spectral samples with corresponding analyte values (Library X, y) (i.e., “near-infrared spectroscopy”) using a generalized local adaptive fusion regression (LAFR) process (i.e., “local errors regression method”) for identifying subsets of samples with similar matrix effects (i.e., “a calibration subset is selected by a new similarity criterion which takes the full information of spectra, chemical property, and predicted errors”; see Abstract);
forming linear training sets (i.e., “After the selection of calibration subset, the partial least squares regression is applied to build calibration model”; see Abstract; note that the calibration subset is linear training sets because the partial least squares regression is a linear regression; “simple linear approximation technique… finding some samples whose 𝑋 and 𝑌 meet linear relationship”; see p. 2, col. 1, ¶ 3) defined by the LAFR process identified subsets of samples (i.e., “a calibration subset is selected by a new similarity criterion which takes the full information of spectra, chemical property, and predicted errors”; see Abstract);
forming a final local training set from the linear training sets (i.e., “a calibration subset is selected by a new similarity criterion which takes the full information of spectra, chemical property, and predicted errors”; see Abstract note that the selected subset is a final local training set);
forming a prediction model with the final local training set (i.e., “After the selection of calibration subset, the partial least squares regression is applied to build calibration model”; see Abstract); and
using the prediction model to predict the quantitative analysis of a target sample (i.e., “multivariate calibration methods that relate property (𝑌) and spectra (𝑋) have been extensively used in the quantitative analysis of NIR spectroscopy”; see p.1, Introduction, ¶ 1),
where the final local training set is composed of samples with the analyte amount highly similar to the unknown analyte amount in the target sample to be predicted (i.e., “The local errors regression utilizing SLPP and similar errors strategy in finding the optimized calibration subset for each query sample is described”; see p. 2, col. 1, ¶ 3).
Regarding claim 16, Chang further teaches:
where the LAFR process further includes:
applying a decimation step to the Library to reduce the library to most N spectrally similar to target sample, and to perform an outlier check to remove reduced library components for which the target sample is an outlier (i.e., “Step 1. Compute the predicted properties of prediction set
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and predicted properties of calibration set
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with global PLS regression. Step 2. For each query sample, select the most relevant sample from calibration set with SLPP”; see p. 3, col. 2, sect. (2));
forming linear calibration sets defined by the LAFR process parameters (i.e., “Step 3. Predefine parameters… Step 4. For each query sample
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, calibration subset is selected based on predicted error similarity criteria”; see p. 3, col. 2, sect. (2)),
performing an outlier check to remove linear training sets for which the target sample is an outlier (see id. note that the selection of subset is a removal of outliers); and
using ISUx and ISUy sample-wise similarities to mine the remaining linear training sets for the final local training set explicitly matrix-matched to the target prediction sample, wherein the ISUx and ISUy sample-wise similarities comprise indicators of system uniqueness (ISU) (i.e.,
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) to assess the degree of matrix matching between reference samples and the target sample (i.e., “the 𝑗th sample in calibration set is selected if
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, where
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is the property of the 𝑗th sample in calibration set”; see p. 4, col. 1, ¶ 1).
Regarding claim 19, Chang further teaches:
wherein each linear training set is matched to each target sample by both spectra (i.e., X) and analyte amount (i.e., Y; “This step ensures that the relationships between X and Y are highly linear in both calibration subset and query sample”; see p. 2, col. 2, ¶ 3).
Regarding claim 20, Chang further teaches:
wherein matching parameters for spectroscopic samples include at least one of:
analyte values comprising the concentration of constituent of interest,
spectra comprising response fingerprints when externally stimulated, and
matrix effects comprising relationships between spectrum and analyte (i.e., “using similarity of prediction errors which indicates how linear the spectra and property are to the samples for prediction”; see p. 2, sect. 2.1, ¶ 1).
Regarding claim 22, Chang further teaches:
wherein ISUy comprises a holistic characterization of implicit y sample-wise differences between target and calibration sample matrix effects (i.e., “the 𝑗th sample in calibration set is selected if
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, where
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is the property of the 𝑗th sample in calibration set”; see p. 4, col. 1, ¶ 1), where ISUy values depend on sample X values (note that y is sample property corresponding to spectra x, therefore, the property-wise difference values above also depends on spectra; see p. 1, Introduction).
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 1-3, 17, 18, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Chang.
Regarding claim 1, Chang teaches methodology for searching a large library (i.e., “a calibration subset is selected by a new similarity criterion which takes the full information of spectra, chemical property, and predicted errors”) of field sample spectra (Library X, y) (i.e., “near-infrared spectroscopy”) using a generalized local adaptive fusion regression (LAFR) process (i.e., “local errors regression method”; see Abstract) for quantitative analysis of molecular based spectroscopic data (xnew) (i.e., “near-infrared spectroscopy”) from a target sample of analytes (i.e., “analysis of complex samples or chemical process”; see p. 1, Introduction, ¶ 1), comprising:
defining LAFR process parameters, including (i.e., “K nearest neighbors”; see p. 2, col. 2, step (1)), and the number of fundamental parameters (i.e., “N-dimensional calibration spectral data”; see p. 2, col. 2, ¶ 3) to use;
applying a decimation step to the library to reduce the library to most N spectrally similar to target sample, and to perform an outlier check to remove reduced library components for which the target sample is an outlier (i.e., “Step 1. Compute the predicted properties of prediction set
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and predicted properties of calibration set
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with global PLS regression. Step 2. For each query sample, select the most relevant sample from calibration set with SLPP”; see p. 3, col. 2, sect. (2));
forming linear calibration sets defined by the LAFR process parameters (i.e., “Step 3. Predefine parameters… Step 4. For each query sample
y
i
q
, calibration subset is selected based on predicted error similarity criteria”; see p. 3, col. 2, sect. (2));
performing an outlier check to remove linear calibration sets for which the target sample is an outlier (see id. note that the selection of subset is a removal of outliers);
using ISUx and ISUy sample-wise similarities to mine the library of field sample spectra with reference amounts for a local training set explicitly matrix-matched to the target prediction sample (i.e., “the 𝑗th sample in calibration set is selected if
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