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 June 22, 2026, claim(s) 1-4, 6-15, and 17-27 is/are pending in this application; of these claim(s) 1 and 12 is/are in independent form. Claim(s) 5 and 16 is/are cancelled.
Response to Amendment
The amendments to the claims, specification, and abstract are acknowledged and have been entered into the record.
Response to Arguments
Applicant’s arguments, see page 19 lines 12-16, filed June 22, 2026, with respect to the abstract of the specification have been fully considered and are persuasive. The objection to the abstract mailed March 19, 2026 has been withdrawn.
Applicant’s arguments, see page 19 lines 17-21, filed June 22, 2026, with respect to specification have been fully considered and are persuasive. The objection of specification mailed March 19, 2026 has been withdrawn.
Applicant’s arguments, see page 20 lines 1-8, filed June 22, 2026, with respect to claims 1-22 have been fully considered and are persuasive. The rejection of claims 1-22 mailed March 19, 2026 has been withdrawn.
Regarding 35 U.S.C. § 101: Applicant's arguments filed June 22, 2026 have been fully considered but they are not persuasive. Applicant argues that the regression analysis is an additional element, whereas the Examiner argues that regression analysis is mathematics, a type of judicial exception that is an abstract idea. Applicant argues that the inputs involving D- and L-amino acid levels measured in biological samples of arbitrary subjects and dependent variables that are glomerular filtration rates of subjects. However, the Examiner interprets these levels and digital information inputting into the regression analysis, rather than claim elements that involve a step of physical measurement or configuration for measurement.
Regarding 35 U.S.C. § 102: Applicant's arguments filed June 22, 2026 have been fully considered but they are not persuasive. Applicant argues WO-2017200025 (“Isaka”) does not anticipate “2 or more” variables in a multiple regression. The Examiner respectfully disagrees. The Examiner argues that Isaka Para [0048] teaches “at least one type of chiral amino acids”, of which “2 types” is at once envisaged. See MPEP § 2131.02 (III).
Applicant also argues that Isaka focuses on diabetes. However, the Examiner respectfully argues that Para [0025] involves “another aspect of the invention” that forms the basis of the rejection. Paragraph [0025] and elsewhere describe a “subject suffering from kidney disorder” as another embodiment from other diabetic embodiments. Therefore, the rejection under 35 U.S.C. § 102 is not withdrawn.
Regarding 35 U.S.C. § 103: Applicant again argues that Isaka focuses on diabetes. However, the Examiner respectfully argues that Para [0025] involves “another aspect of the invention” that forms the basis of the rejection. Paragraph [0025] and elsewhere describe a “subject suffering from kidney disorder” as another embodiment from other diabetic embodiments. Therefore, the rejection under 35 U.S.C. § 102 is not withdrawn.
Claim Interpretation
Regarding claims 4 and 15, the element “a correlation coefficient of R≥0.5, or R≥0.8” is interpreted as “a correlation coefficient of R≥0.5” by broadest reasonable interpretation.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“storage unit stores…” in claim 12 and described in the specification at Para [0050].
“analytical measurement unit…” in claim 12 and described in the specification at Para [0052].
“data processing unit inputs…” in claim 12 and described in the specification at Para [0053].
“output unit outputs…” in claim 12 and described in the specification at Para [0054].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Note that dependent claims 25 and 27 do not invoke 35 USC § 112(f) because the phrase “analytical measurement unit” is modified by sufficient structure in the dependent claim.
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 1-4, 6-15, 17-22, 24, 25, and 27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mathematical calculations without significantly more. The claim(s) recite(s) a mathematical calculation. This judicial exception is not integrated into a practical application because the use of the biological sample is necessary pre-solution activity to carry out the mathematical calculation. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of calculation using the biological sample is well-understood, routine, and conventional according the precedent. See MPEP § 2106.05(d), which cites an example of well-understood routine and conventional laboratory activities: “Determining the level of a biomarker in blood by any means, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017)”. Claim 12 includes additional structural because it invoke 35 U.S.C. § 112(f) and therefore includes limitations from the Specification at Paras [0050] and [0052]-[0054]. However, these elements describe general purpose computer necessary to apply the mathematics and measurement apparatus necessary for the well-understood, routine, and conventional laboratory activities.
Dependent claims 2, 4, 6-10, 13, 15, 17-21, and 24, are limitations to the mathematical calculation itself and are part of the judicial exception.
Dependent claim 3, 11, 14, 22, 25, and 27 limit the determination of the level of a biomarker but is recited at a high-level of generality and well-understood, routine, and conventional. For example, medical doctors routinely analyze blood, plasma, and serum for biomarkers in a patients suspected of having kidney disease.
1. A method for estimating kidney function of a subject being evaluated (the limitation of a “subject” in the preamble of the independent claim is not a positively recited step), wherein the method includes:
a step of estimating the kidney function of the subject being evaluated, based on the value of Y calculated from the levels of D- and L-amino acids in a biological sample from the subject being evaluated, using the following formula (I):
Y=a 1 ·X 1 +a 2 ·X 2 + . . . +a n ·X n +b (I)
where
a 1 to a n represent constants obtained by regression analysis (mathematics),
X 1 to X n represent variables for the D- and L-amino acid levels selected by the regression analysis (mathematics),
N is an integer of 2 or more (mathematics), and
b represents a constant obtained by the regression analysis (mathematics),
wherein formula (I) is predetermined by the regression analysis using D- and L-amino acid levels (a mathematical step) in biological samples of arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subjects as the response variables (see MPEP § 2106.05(d), which cites an example of well-understood routine and conventional laboratory activities: “Determining the level of a biomarker in blood by any means, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017)”).
2. The method according to claim 1, wherein the step of estimating the kidney function of the subject being evaluated is a step of estimating the kidney function of the subject being evaluated by estimating the glomerular filtration rate of the subject being evaluated, based on the Y value (limitations to the mathematical step).
3. The method according to claim 1, wherein the biological sample is blood, plasma or serum (well-understood, routine, and conventional because medical doctors routinely analyze blood, plasma, and serum for biomarkers in a patients suspected of having kidney disease).
4. The method according to claim 1, wherein formula (I) with a correlation is used with coefficient of R≥0.5, or R≥0.8 (limitations to the mathematical step).
6. The method according to claim 1, wherein
the explanatory variables further include the level of a factor selected from the group consisting of creatinine and cystatin C, and
X 1 to X n represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis (limitations to the mathematical step).
7. The method according to claim 1, wherein
the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels,
the response variables are the standardized values of the logarithm of the glomerular filtration rates, and
the standardized values for the logarithm of the D- and L-amino acid levels selected by the regression analysis are applied to X 1 to X n (limitations to the mathematical step).
8. The method according to claim 7, wherein
the explanatory variables further include the standardized level of the logarithm of the level of a factor selected from the group consisting of creatinine and cystatin C, and
X 1 to X n represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis, the standardized levels of the logarithm of the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis being applied to X 1 to X n (limitations to the mathematical step).
9. The method according to claim 1, wherein X 1 to X n include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine (limitations to the mathematical step).
10. The method according to claim 1, wherein the glomerular filtration rates of the arbitrary subjects are the glomerular filtration rates calculated by inulin clearance, creatinine clearance, 51Cr-EDTA clearance, 125I-sodium iotalamate clearance, 99mTc-DTPA clearance, sodium thiosulfate clearance, iohexol clearance, iodixanol clearance or iotalamate clearance (limitations to the mathematical step).
11. The method according to claim 1, wherein the subject being evaluated is a subject being evaluated who has been assessed to have suspected kidney disease by a conventional examination method (well-understood, routine, and conventional because medical doctors routinely analyze blood, plasma, and serum for biomarkers in a patients suspected of having kidney disease).
12. A system for estimating kidney function of a subject being evaluated (the limitation of a “subject” in the preamble of the independent claim is not a positively recited step), the system including a storage unit, an analytical measurement unit, a data processing unit and an output unit (invokes 35 U.S.C. § 112(f) and therefore includes limitations from the Specification at Paras [0050] and [0052]-[0054]. However, these elements describe general purpose computer necessary to apply the mathematics and measurement apparatus necessary for the well-understood, routine, and conventional laboratory activities), wherein:
the storage unit stores the following formula (II):
Y=a 1 ·X 1 +a 2 ·X 2 + . . . +a n ·X n +b (II)
where
a 1 to a n represent constants obtained by regression analysis (mathematics),
X 1 to X n represent variables for the D- and L-amino acid levels selected by the regression analysis,
n is an integer of 2 or more (mathematics), and
b represents a constant obtained by the regression analysis (mathematics),
wherein formula (II) is predetermined by the regression analysis using D- and L-amino acid levels (a mathematical step) in biological samples of arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subjects as the response variables (see MPEP § 2106.05(d), which cites an example of well-understood routine and conventional laboratory activities: “Determining the level of a biomarker in blood by any means, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017)”),
the analytical measurement unit quantifies the levels of D- and L-amino acids in a biological sample from the subject being evaluated (the biological sample is necessary pre-solution activity to carry out the mathematical calculation; calculation using the biological sample is well-understood, routine, and conventional according the precedent. See MPEP § 2106.05(d), which cites an example of well-understood routine and conventional laboratory activities: “Determining the level of a biomarker in blood by any means, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017)),
the data processing unit inputs the D- and L-amino acid levels in the biological sample of the subject being evaluated into formula (II) stored in the storage unit to calculate the value of Y and estimates the kidney function of the subject being evaluated, based on the value of Y (applying mathematics on a general purpose computer), and
the output unit outputs information regarding the estimated kidney function of the subject being evaluated (necessary post-solution activity for the mathematical calculation).
13. The system according to claim 12, wherein the data processing unit inputs the D- and L-amino acid levels in the biological sample of the subject being evaluated into formula (II) stored in the storage unit to calculate the value of Y, and estimates the glomerular filtration rate of the subject being evaluated, based on the value of Y, thereby estimating the kidney function of the subject being evaluated (limitations to the mathematical step).
14. The system according to claim 10, wherein the biological sample is blood, plasma or serum (well-understood, routine, and conventional because medical doctors routinely analyze blood, plasma, and serum for biomarkers in a patients suspected of having kidney disease).
15. The system according to claim 12, wherein formula (II) is used with a correlation coefficient of R≥0.5 or R≥0.8, (limitations to the mathematical step).
17. The system according to claim 12, wherein the explanatory variables further include the level of a factor selected from the group consisting of creatinine and cystatin C, and
X 1 to X n represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis (limitations to the mathematical step).
18. The system according to claim 12, wherein the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels,
the response variables are the standardized values of the logarithm of the glomerular filtration rates, and
the standardized values for the logarithm of the D- and L-amino acid levels selected by the regression analysis are applied to X 1 to X n (limitations to the mathematical step).
19. The system according to claim 18, wherein
the explanatory variables further include the standardized level of the logarithm of the level of a factor selected from the group consisting of creatinine and cystatin C, and
X 1 to X n represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis (mathematics), the standardized levels of the logarithm of the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis being applied to X 1 to X n (limitations to the mathematical step).
20. The system according to claims 12, wherein X 1 to X n include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine (limitations to the mathematical step).
21. The system according to claim 12, wherein the glomerular filtration rates of the arbitrary subjects are the glomerular filtration rates calculated by inulin clearance, creatinine clearance, 51Cr-EDTA clearance, 125I-sodium iotalamate clearance, 99mTc-DTPA clearance, sodium thiosulfate clearance, iohexol clearance, iodixanol clearance or iotalamate clearance (limitations to the mathematical step).
22. The system according to claims 12, wherein the subject being evaluated is a subject being evaluated who has been assessed to have suspected kidney disease by a conventional examination method (well-understood, routine, and conventional because medical doctors routinely analyze blood, plasma, and serum for biomarkers in a patients suspected of having kidney disease).
24. The method according to claim 1, further comprising obtaining the levels of D- and L-amino acids in a biological sample from the subject being evaluated (obtaining levels is interpreted as obtaining digital levels whose meaning is the quantity of D- and L- amino acids in a biological sample… ).
25. The system according to claim 12, wherein the analytical measurement unit quantifies the levels of D- and L-amino acids using an enzyme method, an immunological method, gas chromatography (GC), capillary electrophoresis (CE), high-performance liquid chromatography (HPLC), a chiral chromatography system, or a two-dimensional HPLC system (this limitation causes claim 12 to not invoke 112(f); nevertheless, the claimed invention is not used with a particular machine but instead a broad list of common machines that routinely used to carryout analytical measurements; the set of machines is essentially for determining the level of a biomarker in blood by any means, which is well-understood, routine, and conventional. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017).
27. The system according to claim 12, wherein the analytical measurement unit quantifies the levels of D- and L-amino acids using an enzyme method, an immunological method, gas chromatography (GC), capillary electrophoresis (CE), high-performance liquid chromatography (HPLC), a chiral chromatography system, or a two-dimensional HPLC system (this limitation causes claim 12 to not invoke 112(f); nevertheless, the claimed invention is not used with a particular machine but instead a broad list of common machines that routinely used to carryout analytical measurements; the set of machines is essentially for determining the level of a biomarker in blood by any means, which is well-understood, routine, and conventional. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017),
the biological sample is blood, plasma or serum (well-understood, routine, and conventional because medical doctors routinely analyze blood, plasma, and serum for biomarkers in a patients suspected of having kidney disease),
the subject being evaluated is a subject being evaluated who has been assessed to have suspected kidney disease by a conventional examination method (well-understood, routine, and conventional because medical doctors routinely assess for kidney disease),
the data processing unit inputs (interpreted as digital inputs) the D- and L-amino acid levels in the biological sample of the subject being evaluated into formula (II) stored in the storage unit to calculate the value of Y, and estimates the glomerular filtration rate of the subject being evaluated, based on the value of Y, thereby estimating the kidney function of the subject being evaluated (limitations to the mathematical step),
formula (II) is used with a correlation coefficient of R >= 0.5 or R >= 0.8 (limitations to the mathematical step),
the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels (limitations to the mathematical step),
the response variables are the standardized values of the logarithm of the glomerular filtration rates (limitations to the mathematical step),
the explanatory variables further include the standardized level of the logarithm of the level of a factor selected from the group consisting of creatinine and cystatin C (limitations to the mathematical step; the level of a factor is interpreted as a digital representation),
X1 to Xn represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis, the standardized levels of the logarithm of the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis being applied to X1 to Xn (limitations to the mathematical step), and
X1 to Xn include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine (limitations to the mathematical step).
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.
Claim(s) 1-6, 9-17, and 20-25 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WO-2017200025-A1 (“Isaka”). The translation relied upon to correspond to WO-2017200025-A1 is AU-2017266331-A1, a foreign Australian patent application.
As to claim 1, Isaka teaches a method for estimating kidney function of a subject being evaluated (Isaka Para [0025]: a subject suffering from kidney disorder), wherein the method includes:
a step of estimating the kidney function of the subject being evaluated (Isaka Para [0048]: an estimating the eGFR values), based on the value of Y calculated from the levels of D- and L-amino acids in a biological sample from the subject being evaluated (Isaka Para [0048]: based on chiral amino acid values, which the Examiner interprets as a pair of chiral amino acids that have a mutually chiral relationship), using the following formula (I):
Y=a 1 ·X 1 +a 2 ·X 2 + . . . +a n ·X n +b (I)
where
a 1 to a n represent constants obtained by regression analysis (Isaka Para [0048]: a regression curve; the Examiner interprets the equation to be a standard linear regression curve),
X 1 to X n represent variables for the D- and L-amino acid levels selected by the regression analysis (Isaka Para [0048]: inputs to the regression curve using chiral amino acids),
n is an integer of 2 or more (Isaka Para [0048] teaches “at least one type of chiral amino acids”, of which “2 types” is at once envisaged; see MPEP § 2131.02 (III); see Isaka Para [0033]: “the D-form and L-form of an amino acid can be measured simultaneously”), and
b represents a constant obtained by the regression analysis] (Isaka Para [0048]: the Examiner interprets that the use of a constant term in regression analysis is at once envisaged from the concept of a regression curve),
wherein formula (I) is predetermined by the regression analysis using D- and L-amino acid levels in biological samples of arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subjects as the response variables (Isaka Para [0048]: the regression formula is part of a system that has the chiral amino acids as inputs and the eGFR values as the output).
As to claim 2, Isaka teaches the method according to claim 1, wherein the step of estimating the kidney function of the subject being evaluated is a step of estimating the kidney function of the subject being evaluated by estimating the glomerular filtration rate of the subject being evaluated, based on the Y value (Isaka Para [0048]: estimating the kidney function described in Para [0025] is done by estimating the eGFR rate).
As to claim 3, Isaka teaches the method according to claim 1, wherein the biological sample is blood, plasma or serum (Isaka Para [0018]: blood, plasma, or serum samples).
As to claim 4, Isaka teaches the method according to claim 1, wherein formula (I) is used with a correlation coefficient of R≥0.5, or R≥0.8 (Figure 4-L illustrates a correlation coefficient which is visually > 0.5 given its statistical significance; the Examiner notes that regression R values increase monotonically as a function of the number of variables used in the model; that is, adding additional variables would not decrease the R value in the linear regression; hence, hence, it is at once envisaged that a regression using the independent variable of Figure 4-L would also have similar predictive power, even if the regression coefficient involves additional independent variables).
As to claim 6, Isaka teaches the method according to claim 1, wherein
the explanatory variables further include the level of a factor selected from the group consisting of creatinine and cystatin C (Isaka Para [0053]: a level of serum creatine was used to provide at least some explanatory value for optimization), and
X 1 to X n represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis (this element does not require creatine or cystatin because these element are recited in the alternative along with the D- and L-amino acids).
As to claim 9, Isaka teaches the method according to claim 1, wherein X 1 to X n include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine (Isaka Para [0028]: the determination of kidney disease may be based on the same amino acids D-serine, D-alanine, D-proline, or D-asparagine; note that the claim recites the terms “include” and “selected from”, hence the claimed set of amino acids is not a narrower improvement over the sets described in Isaka).
As to claim 10, Isaka teaches the method according to claim 1, wherein the glomerular filtration rates of the arbitrary subjects are the glomerular filtration rates calculated by inulin clearance (this element is claimed in the alternative and does not need to be mapped), creatinine clearance (Isaka Para [0053]: eGFR is computed from serum creatine), 51Cr-EDTA clearance, 125I-sodium iotalamate clearance, 99mTc-DTPA clearance, sodium thiosulfate clearance, iohexol clearance, iodixanol clearance or iotalamate clearance (these elements are claimed in the alternative and do not all need to be mapped).
As to claim 11, Isaka teaches the method according to claim 1, wherein the subject being evaluated is a subject being evaluated who has been assessed to have suspected kidney disease by a conventional examination method (Isaka Para [0025]: a subject suffering from kidney disorder as determined by either the glomerular filtration rate or the estimated glomerular filtration rate, or by serum creatine concentration).
As to claim 12, Isaka teaches a system for estimating kidney function of a subject being evaluated (Isaka Para [0025]: a subject suffering from kidney disorder), the system including a storage unit (Isaka Para [0040]: computerized storage), an analytical measurement unit (Isaka Para [0042]: analysis unit), a data processing unit (Isaka Para [0043]: data processing unit) and an output unit (Isaka Para [0044]: output unit), wherein:
the storage unit (Isaka Para [0040]: computerized storage) stores the following formula (II) (Isaka Para [0048]: a regression curve; the Examiner interprets the equation to be a standard linear regression curve):
Y=a 1 ·X 1 +a 2 ·X 2 + . . . +a n ·X n +b (II)
where
a 1 to a n represent constants obtained by regression analysis (Isaka Para [0048]: a regression curve),
X 1 to X n represent variables for the D- and L-amino acid levels selected by the regression analysis (Isaka Para [0048]: inputs to the regression curve using chiral amino acids),
n is an integer of 2 or more (Isaka Para [0048] teaches “at least one type of chiral amino acids”, of which “2 types” is at once envisaged; see MPEP § 2131.02 (III); see Isaka Para [0033]: “the D-form and L-form of an amino acid can be measured simultaneously”); and
b represents a constant obtained by the regression analysis] (Isaka Para [0048]: the Examiner interprets that the use of a constant term in regression analysis is at once envisaged from the concept of a regression curve),
wherein formula (II) is predetermined by the regression analysis using D- and L-amino acid levels in biological samples of arbitrary subjects as the explanatory variables and glomerular filtration rates of the arbitrary subjects as the response variables (Isaka Para [0048]: the regression formula is part of a system that has the chiral amino acids as inputs and the eGFR values as the output),
the analytical measurement unit (Isaka Para [0042]: analysis unit) quantifies the levels of D- and L-amino acids in a biological sample from the subject being evaluated (Isaka Para [0018]: blood, plasma, or serum samples),
the data processing unit (Isaka Para [0043]: data processing unit) inputs the D- and L-amino acid levels in the biological sample of the subject being evaluated into formula (II) stored in the storage unit to calculate the value of Y and estimates the kidney function of the subject being evaluated, based on the value of Y (Isaka Para [0048]: the regression formula is part of a system that has the chiral amino acids as inputs and the eGFR values as the output), and
the output unit outputs information regarding the estimated kidney function of the subject being evaluated (Isaka Para [0044]: output unit).
As to claim 13, Isaka teaches the system according to claim 12, wherein the data processing unit inputs the D- and L-amino acid levels in the biological sample of the subject being evaluated into formula (II) stored in the storage unit to calculate the value of Y, and estimates the glomerular filtration rate of the subject being evaluated, based on the value of Y, thereby estimating the kidney function of the subject being evaluated (Isaka Para [0048]: estimating the kidney function described in Para [0025] is done by estimating the eGFR rate).
As to claim 14, Isaka teaches the system according to claim 10, wherein the biological sample is blood, plasma or serum (Isaka Para [0018]: blood, plasma, or serum samples).
As to claim 15, Isaka teaches the system according to claim 12, wherein formula (II) is used with a correlation coefficient of R≥0.5 or R≥0.8 (Figure 4-L illustrates a correlation coefficient which is visually > 0.5 given its statistical significance; the Examiner notes that regression R values increase monotonically as a function of the number of variables used in the model; that is, adding additional variables would not decrease the R value in the linear regression; hence, hence, it is at once envisaged that a regression using the independent variable of Figure 4-L would also have similar predictive power, even if the regression coefficient involves additional independent variables).
As to claim 17, Isaka teaches the system according to claim 12, wherein the explanatory variables further include the level of a factor selected from the group consisting of creatinine and cystatin C (Isaka Para [0053]: a level of serum creatine was used to provide at least some explanatory value for optimization), and
X 1 to X n represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis (this element does not require creatine or cystatin because these element are recited in the alternative along with the D- and L-amino acids).
As to claim 20, Isaka teaches the system according to claims 12, wherein X 1 to X n include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine (Isaka Para [0028]: the determination of kidney disease may be based on the same amino acids D-serine, D-alanine, D-proline, or D-asparagine; note that the claim recites the terms “include” and “selected from”, hence the claimed set of amino acids is not a narrower improvement over the sets described in Isaka).
As to claim 21, Isaka teaches the system according to claim 12, wherein the glomerular filtration rates of the arbitrary subjects are the glomerular filtration rates calculated by inulin clearance (this element is claimed in the alternative and does not need to be mapped), creatinine clearance (Isaka Para [0053]: eGFR is computed from serum creatine), 51Cr-EDTA clearance, 125I-sodium iotalamate clearance, 99mTc-DTPA clearance, sodium thiosulfate clearance, iohexol clearance, iodixanol clearance or iotalamate clearance (these elements are claimed in the alternative and do not all need to be mapped).
As to claim 22, Isaka teaches the system according to claims 12, wherein the subject being evaluated is a subject being evaluated who has been assessed to have suspected kidney disease by a conventional examination method (Isaka Para [0025]: a subject suffering from kidney disorder as determined by either the glomerular filtration rate or the estimated glomerular filtration rate, or by serum creatine concentration).
As to claim 23, Isaka teaches the method according to claim 1, further comprising a step of administering to the subject being evaluated a treatment intervention based on the estimated kidney function, wherein the treatment intervention comprises administration of one or more drugs selected from the group consisting of diuretic drugs, calcium antagonists, angiotensin converting enzyme inhibitors, angiotensin receptor antagonists (Isaka Para [0054]: renin-angiotensin system inhibitors), sympatholytic drugs, SGLT2 inhibitors (Isaka Para [0035]: SGLT2 inhibitors), sulfonylurea drugs (Isaka Para [0035]: sulfonylurea drugs), thiazolidine drugs (Isaka Para [0035]: thiazolidine), biguanide drugs (Isaka Para [0035]: biguanides), alpha-glucosidase inhibitors (Isaka Para [0035]: glucosidase inhibitors), glinide drugs, insulin formulations, NRF2 activators, immunosuppressive agents, statins, fibrates, anemia treatments, erythropoietin formulations, HIF-1 inhibitors, iron agents, electrolyte regulators, calcium receptor agonists, phosphorus adsorbents, uremic toxin adsorbents, DPP4 inhibitors (Isaka Para [0035]: DPP4 inhibitors), EPA formulations, nicotinic acid derivatives, cholesterol transporter inhibitors and PCSK9 inhibitors (these elements are claimed in the alternative and do not all need to be mapped for the rejection).
As to claim 24, Isaka teaches the method according to claim 1, further comprising obtaining the levels of D- and L-amino acids in a biological sample from the subject being evaluated (Isaka Para [0033]: “the D-form and L-form of an amino acid can be measured simultaneously” using the physical extraction methods described in the paragraph).
As to claim 25, Isaka teaches the system according to claim 12, wherein the analytical measurement unit quantifies the levels of D- and L-amino acids using an enzyme method, an immunological method (Isaka Para [0033]: “Alternatively, D-amino acids can be assayed by an immunological method using monoclonal antibody capable of identifying optical isomers of amino acids, such as monoclonal antibody that specifically binds with D-leucine or D-aspartic acid and the like”), gas chromatography (GC), capillary electrophoresis (CE), high-performance liquid chromatography (HPLC), a chiral chromatography system (Isaka Para [0033]: a column-based chromatography system that separates optical isomers), or a two-dimensional HPLC system (these elements are claimed in the alternative and do not all need to be mapped).
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.
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.
Claim(s) 7, 18, 26, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO-2017200025-A1 (“Isaka”) over “Bruckner”, which refers to the following non-patent literature reference:
Brückner, Hans, and Michael Hausch. "Gas chromatographic characterization of free D-amino acids in the blood serum of patients with renal disorders and of healthy volunteers." Journal of Chromatography B: Biomedical Sciences and Applications 614.1 (1993): 7-17.
The translation relied upon for Isaka to correspond to WO-2017200025-A1 is AU-2017266331-A1, a foreign Australian patent application..
As to claim 7, Isaka teaches the method according to claim 1, which includes Isaka’s teachings regarding the response variables being glomerular filtration rates (Isaka Para [0048]: the regression formula includes the eGFR values as the output), but does not teach wherein
the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels,
the response variables are the standardized values of the logarithm of the glomerular filtration rates, and
the standardized values for the logarithm of the D- and L-amino acid levels selected by the regression analysis are applied to X 1 to X n.
Nevertheless, Bruckner teaches:
the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels (Bruckner: see the standardized values of amino acids in tables I-IV; the use of a logarithm is a common and obvious variant in regression analysis),
the response variables are the standardized values of the logarithm of the glomerular filtration rates (standardization is an obvious variation used for regression analysis, noted in Bruckner’s processing of standardized values in Tables I-IV; the Examiner infers that standardization of the dependent variables is as obvious as standardization of the independent variables for regression analysis), and
the standardized values for the logarithm of the D- and L-amino acid levels selected by the regression analysis are applied to X 1 to X n (Bruckner: Figures 5 and 6 involve selection of variables based on predictive capability).
Isaka and Bruckner are in the same field of regression analysis of amino acids. Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Isaka to include the teachings of Bruckner because Bruckner provides additional statistical evidence to support the significance of the findings of Isaka (See Bruckner Figures 5 and 6). There would be a reasonable expectation of success because Brucker’s r values would be improved, not diminished by the additional chiral variables used by Isaka, and vice-versa. This is because r values improve monotonically with increases in the number of independent variables used for prediction.
As to claim 18, Isaka teaches the system according to claim 12, and includes teachings regarding the response variables being glomerular filtration rates (Isaka Para [0048]: the regression formula includes the eGFR values as the output), but does not teach
wherein the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels,
the response variables are the standardized values of the logarithm of the glomerular filtration rates, and
the standardized values for the logarithm of the D- and L-amino acid levels selected by the regression analysis are applied to X 1 to X n.
Nevertheless, Bruckner teaches:
but does not teach wherein the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels (Bruckner: see the standardized values of amino acids in tables I-IV; the use of a logarithm is a common and obvious variant in regression analysis),
the response variables are the standardized values of the logarithm of the glomerular filtration rates, (standardization is an obvious variation used for regression analysis, noted in Bruckner’s processing of standardized values in Tables I-IV; the Examiner infers that standardization of the dependent variables is as obvious as standardization of the independent variables for regression analysis) and
the response variables are the standardized values of the logarithm of the glomerular filtration rates (Bruckner: see the creatine values used for regression analysis; the use of standardization a logarithm is a common and obvious variant in regression analysis; creatine is a proxy for glomerular filtration rate analysis), and
the standardized values for the logarithm of the D- and L-amino acid levels selected by the regression analysis are applied to X 1 to X n (Bruckner: Figures 5 and 6 involve selection of variables based on predictive capability).
Isaka and Bruckner are in the same field of regression analysis of amino acids. Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Isaka to include the teachings of Bruckner because Bruckner provides additional statistical evidence to support the significance of the findings of Isaka (See Bruckner Figures 5 and 6). There would be a reasonable expectation of success because Brucker’s r values would be improved, not diminished by the additional chiral variables used by Isaka, and vice-versa. This is because r values improve monotonically with increases in the number of independent variables used for prediction.
As to claim 26, Isaka teaches the method according to claim 1, further comprising:
obtaining the levels of D- and L-amino acids in the biological sample from the subject being evaluated (Isaka Para [0033]: “the D-form and L-form of an amino acid can be measured simultaneously” using the physical extraction methods described in the paragraph); and
administering to the subject being evaluated a treatment intervention based on the estimated kidney function, wherein the treatment intervention comprises administration of one or more drugs selected from the group consisting of diuretic drugs, calcium antagonists, angiotensin converting enzyme inhibitors, angiotensin receptor antagonists (Isaka Para [0054]: renin-angiotensin system inhibitors), sympatholytic drugs, SGLT2 inhibitors (Isaka Para [0035]: SGLT2 inhibitors), sulfonylurea drugs (Isaka Para [0035]: sulfonylurea drugs), thiazolidine drugs (Isaka Para [0035]: thiazolidine), biguanide drugs (Isaka Para [0035]: biguanides), biguanide drugs (Isaka Para [0035]: biguanides), alpha-glucosidase inhibitors (Isaka Para [0035]: glucosidase inhibitors), glinide drugs, insulin formulations, NRF2 activators, immunosuppressive agents, statins, fibrates, anemia treatments, erythropoietin formulations, HIF-1 inhibitors, iron agents, electrolyte regulators, calcium receptor agonists, phosphorus adsorbents, uremic toxin adsorbents, DPP4 inhibitors (Isaka Para [0035]: DPP4 inhibitors), EPA formulations, nicotinic acid derivatives, cholesterol transporter inhibitors and PCSK9 inhibitors (these elements are claimed in the alternative and do not all need to be mapped for the rejection),
wherein the biological sample is blood, plasma or serum (Isaka Para [0018]: blood, plasma, or serum samples),
the subject being evaluated is a subject being evaluated who has been assessed to have suspected kidney disease by a conventional examination method (Isaka Para [0025]: a subject suffering from kidney disorder as determined by either the glomerular filtration rate or the estimated glomerular filtration rate, or by serum creatine concentration),
the step of estimating the kidney function of the subject being evaluated is a step of estimating the kidney function of the subject being evaluated by estimating the glomerular filtration rate of the subject being evaluated, based on the value of Y (Isaka Para [0048]: estimating the kidney function described in Para [0025] is done by estimating the eGFR rate),
formula (I) is used with a correlation coefficient of R > 0.5 or R >0.8 (Figure 4-L illustrates a correlation coefficient which is visually > 0.5 given its statistical significance; the Examiner notes that regression R values increase monotonically as a function of the number of variables used in the model; that is, adding additional variables would not decrease the R value in the linear regression; hence, hence, it is at once envisaged that a regression using the independent variable of Figure 4-L would also have similar predictive power, even if the regression coefficient involves additional independent variables),
…
the explanatory variables further include the standardized level of the logarithm of the level of a factor selected from the group consisting of creatinine and cystatin C (Isaka Para [0053]: a level of serum creatine was used to provide at least some explanatory value for optimization),
However, Isaka does not teach:
the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels, the response variables are the standardized values of the logarithm of the glomerular filtration rates,…
X1 to Xn represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis, the standardized levels of the logarithm of the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis being applied to X1 to Xn, and
X1 to Xn include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine.
Nevertheless, Bruckner teaches:
the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels (Bruckner: see the standardized values of amino acids in tables I-IV; the use of a logarithm is a common and obvious variant in regression analysis), the response variables are the standardized values of the logarithm of the glomerular filtration rates (standardization is an obvious variation used for regression analysis, noted in Bruckner’s processing of standardized values in Tables I-IV; the Examiner infers that standardization of the dependent variables is as obvious as standardization of the independent variables for regression analysis), …
X1 to Xn represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis (this element does not require creatine or cystatin because these element are recited in the alternative along with the D- and L-amino acids), the standardized levels of the logarithm of the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis being applied to X1 to Xn (this element does not require creatine or cystatin because these element are recited in the alternative along with the D- and L-amino acids; Bruckner: Figures 5 and 6 involve selection of variables based on predictive capability), and
X1 to Xn include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine (Isaka Para [0028]: the determination of kidney disease may be based on the same amino acids D-serine, D-alanine, D-proline, or D-asparagine; note that the claim recites the terms “include” and “selected from”, hence the claimed set of amino acids is not a narrower improvement over the sets described in Isaka).
Isaka and Bruckner are in the same field of regression analysis of amino acids. Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Isaka to include the teachings of Bruckner because Bruckner provides additional statistical evidence to support the significance of the findings of Isaka (See Bruckner Figures 5 and 6). There would be a reasonable expectation of success because Brucker’s r values would be improved, not diminished by the additional chiral variables used by Isaka, and vice-versa. This is because r values improve monotonically with increases in the number of independent variables used for prediction.
As to claim 27, Isaka teaches the system according to claim 12, wherein the analytical measurement unit quantifies the levels of D- and L-amino acids using an enzyme method, an immunological method (Isaka Para [0033]: “Alternatively, D-amino acids can be assayed by an immunological method using monoclonal antibody capable of identifying optical isomers of amino acids, such as monoclonal antibody that specifically binds with D-leucine or D-aspartic acid and the like”), gas chromatography (GC), capillary electrophoresis (CE), high-performance liquid chromatography (HPLC), a chiral chromatography system (Isaka Para [0033]: a column-based chromatography system that separates optical isomers), or a two-dimensional HPLC system (these elements are claimed in the alternative and do not all need to be mapped),
the biological sample is blood, plasma or serum (Isaka Para [0018]: blood, plasma, or serum samples),
the subject being evaluated is a subject being evaluated who has been assessed to have suspected kidney disease by a conventional examination method (Isaka Para [0025]: a subject suffering from kidney disorder as determined by either the glomerular filtration rate or the estimated glomerular filtration rate, or by serum creatine concentration),
the data processing unit inputs the D- and L-amino acid levels in the biological sample of the subject being evaluated into formula (II) stored in the storage unit to calculate the value of Y, and estimates the glomerular filtration rate of the subject being evaluated, based on the value of Y (Isaka Para [0048]: estimating the kidney function described in Para [0025] is done by estimating the eGFR rate), thereby estimating the kidney function of the subject being evaluated,
formula (II) is used with a correlation coefficient of R >= 0.5 or R >= 0.8 (Figure 4-L illustrates a correlation coefficient which is visually > 0.5 given its statistical significance; the Examiner notes that regression R values increase monotonically as a function of the number of variables used in the model; that is, adding additional variables would not decrease the R value in the linear regression; hence, hence, it is at once envisaged that a regression using the independent variable of Figure 4-L would also have similar predictive power, even if the regression coefficient involves additional independent variables),
…
the explanatory variables further include the standardized level of the logarithm of the level of a factor selected from the group consisting of creatinine and cystatin C (Isaka Para [0053]: a level of serum creatine was used to provide at least some explanatory value for optimization),
However, Isaka does not teach:
the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels,
the response variables are the standardized values of the logarithm of the glomerular filtration rates,
…
X1 to Xn represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis, the standardized levels of the logarithm of the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis being applied to X1 to Xn, and
X1 to Xn include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine.
Nevertheless, Bruckner teaches:
the explanatory variables are the standardized values for the logarithm of the D- and L-amino acid levels (Bruckner: see the standardized values of amino acids in tables I-IV; the use of a logarithm is a common and obvious variant in regression analysis),
the response variables are the standardized values of the logarithm of the glomerular filtration rates (standardization is an obvious variation used for regression analysis, noted in Bruckner’s processing of standardized values in Tables I-IV; the Examiner infers that standardization of the dependent variables is as obvious as standardization of the independent variables for regression analysis),
…
X1 to Xn represent variables for the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis (this element does not require creatine or cystatin because these element are recited in the alternative along with the D- and L-amino acids), the standardized levels of the logarithm of the levels of D- and L-amino acids, creatinine or cystatin C selected by the regression analysis being applied to X1 to Xn (this element does not require creatine or cystatin because these element are recited in the alternative along with the D- and L-amino acids; Bruckner: Figures 5 and 6 involve selection of variables based on predictive capability), and
X1 to Xn include at least the variable of the level of a factor selected from the group consisting of D-serine, D-alanine, D-proline and D-asparagine (Isaka Para [0028]: the determination of kidney disease may be based on the same amino acids D-serine, D-alanine, D-proline, or D-asparagine; note that the claim recites the terms “include” and “selected from”, hence the claimed set of amino acids is not a narrower improvement over the sets described in Isaka).
Isaka and Bruckner are in the same field of regression analysis of amino acids. Moreover, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Isaka to include the teachings of Bruckner because Bruckner provides additional statistical evidence to support the significance of the findings of Isaka (See Bruckner Figures 5 and 6). There would be a reasonable expectation of success because Brucker’s r values would be improved, not diminished by the additional chiral variables used by Isaka, and vice-versa. This is because r values improve monotonically with increases in the number of independent variables used for prediction.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO-2017200025-A1: Prior art by similar inventive entity
US-20150079623-A1: amino acid analysis without a regression
US-20180003721-A1: regression using amino acids
US-20190271708-A1: double patenting consideration
US-10852308-B2: double patenting consideration
US-11099191-B2: double patenting consideration; Table 2 has Cox-regression analysis, but only using D-amino acids
US-20210373030-A1: double patenting consideration
US-20220170945-A1: using amino acids to predict renal function
Duranton, Flore, et al. "Plasma and urinary amino acid metabolomic profiling in patients with different levels of kidney function." Clinical Journal of the American Society of Nephrology 9.1 (2014): 37-45.
Waldhier, Magdalena C. Towards Chiralomics: Targeted and Untargeted Gas Chromatography-Mass Spectrometry based Enantioselective Metabolome Analysis. Diss. 2016.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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 24, 2026
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