CTNF 17/423,868 CTNF 85192 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION Summary This is a Non-Final Office action based on the 17/423868 application election filed on 04/20/2026 and RCE filed on 06/23/2025. Claims 1-2, 9-10, 22-23, 26-32, & 58-65 are pending. Claims 3-8, 11-21, 24-25, and 33-57 are cancelled. Claims 58-65 were newly added. Claims 1-2, 9-10, 22-23, 26-32, & 58-65 were restricted and Claims 1-2, 9-10, 22-23, 26-32 were elected and have been fully considered. Claims 58-65 are withdrawn. Continued Examination Under 37 CFR 1.114 07-42-04 AIA A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/23/2025 has been entered. Election/Restrictions 08-25-01 AIA Applicant’s election without traverse of Claims 1-2, 9-10, 22-23, 26-32 in the reply filed on 04/20/2026 is acknowledged. 08-06 AIA Claim s 58-65 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention , there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 04/20/2026 . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition ofmatter, or any new and useful improvement thereof, may obtain a patent therefor, subject to theconditions and requirements of this title. Claims 1-2, 9-10, 22-23, 26-32 are rejected under 35 U.S.C. 101 because they are directed to non-statutory subject matter. The invention of instant claims 1-2, 9-10, 22- 23, 26-32 are drawn towards a method of determination of hormone levels based on genetic variations. Through 101, inquiry analysis: Step 1: Is the claim directed to a statutory category of invention? Yes, the claim are drawn towards a statutory category method. Step 2A,1: Does the claim involve a Judicial Exception? Yes. The claims involve the judicial exceptions of a natural correlation (level of a panel of naturally occurring biomarkers with acute rejection (AR) or BKVN infection). There is also an abstract idea claimed, including “classifying,” in the preamble and the use of a supervised machine learning model to output the classification to calculate allograft status if stable or if instead afflicted by AR or BKVN infection). The use of the model and the claimed classifying, as instantly claimed is done by simple comparison to biopsy matched urine samples and simple normalization. This includes the claimed steps (d)-(h) for the claimed classification and machine learning model/modeling. These are mathematical concepts and processes or mental processes (reporting) which though are claimed as being performed on a processor (computer), as claimed it is a general-purpose computer, since these steps as processing steps could be performed by a human mind. See MPEP 2106.04 (a)(2) III.C.: “ Claim That Requires a Computer May Still Recite a Mental Process.” “1. Performing a mental process on a generic computer.” “2. Performing a mental process in a computer environment.” “3. Using a computer as a tool to perform a mental process.” Instant Claim 1 reads similarly to USPTO subject matter eligibility example 47, Claim 2, which was found ineligible. Step 2A, 2 Do the claims practically apply the claimed judicial exceptions? There are no features instantly claimed in independent Claim 1 which practically apply the judicial exceptions. The detection as claimed can be through no specific means and is done to perform data-gathering to use the judicial exceptions. Therefore, it is mere data gathering and insignificant extra-solution activity, and does not practically apply. The same applies to the claimed collecting of a sample from a patient and adding an isotopically labelled internal standard mixture to the sample. It is not claimed if the internal standard binds to anything or derivatizes the sample at all. This is all done for data gathering to use the judicial exception and therefore is insignificant extra solution activity. See MPEP 2106.05 (g). Further- “reporting the classification….in an electronic record,” which is the last step of the instant Claim is not practical application in light of USPTO subject matter eligibility (SME) law and examples. See USPTO SME example 47, Claim 2, which has a similar last step---and was found ineligible. Step 2A, 2 Does anything in the claims add significantly more to the judicial exceptions? There are no features instantly claimed in independent Claim 1 which add significantly more to the judicial exceptions. The detection as claimed can be through no specific means. The same applies to the claimed collecting of a sample from a patient and adding an isotopically labelled internal standard mixture to the sample. It is not claimed if the internal standard binds to anything or derivatizes the sample at all. All of these things are well understood, routine and conventional (WURC) in the art and therefore not significantly more. See MPEP 2016.05 (d). This is evidenced by KEOWN in US 20110189680 in view of SALOMON in US 20180371546. KEOWN teaches a method of distinguishing a stable kidney allograft from a kidney allograft afflicted by an alloimmune injury (A method of determining the acute allograft rejection status of a subject, abstract; Indicators of allograft rejection may include... tissue injury, paragraph 0006) and further of determining if acute rejection is occurring(since KEOWN teaches of looking for acute rejection this reads on what the instant claim is looking for since acute rejection and BKVN infection are claimed as “or”) (abstract, paragraph 0007,0023, 0026 among others) comprising: (a) obtaining a sample from a subject that received a kidney allograft (The present invention relates to methods of diagnosing rejection of a kidney allograft using genomic expression profiling or proteomic expression profiling of one or more biological samples obtained from a subject, paragraph 0022); (b) detecting a panel of metabolites in the sample (For example, a metabolite profile is a dataset of the presence, absence, relative level or abundance of metabolic markers, paragraph 0090; Nucleic acid profiling may also be used in combination with metabolite (“metabolomics”) or proteomic profiling.... KEOWN further teaches wherein the panel of metabolites includes at least one amino acid, at least one amino acid derivative, at least one carbohydrate, and at least one organic compound (Other non-limiting examples of small molecule metabolites are listed in Table 3, paragraph 0219; Table 3 discloses glycine & taurine and asparagine which are also instantly claimed by applicant, and applicant claims “at least three,” which this includes). KEOWN further teaches wherein the panel of metabolites is a 9-metabolite panel (A “profile” is a set of one or more markers and their presence, absence, relative level or abundance (relative to one or more controls). For example, a metabolite profile is a dataset of the presence, absence, relative level or abundance of metabolic markers, paragraph 0090. KEOWN teaches of isotopically altering the samples (paragraph 0044, 0188). --- the examiner notes that even though applicant claims “detecting a panel of metabolites in the sample, wherein the panel of metabolites comprises….” Does not mean that the metabolites are actually present in the sample, but that they are screened for and the method of KEOWN can be considered screening for the claimed metabolites , especially for AR assessment and it is not required that the BKNV assessment also occurs as claimed; and (c) distinguishing if the kidney allograft is stable or is afflicted by the alloimmune injury (In accordance with another aspect of the invention, there is provided a method of assessing, monitoring or diagnosing kidney allograft rejection in a subject, the method comprising: a) determining the expression profile of at least one or more nucleic acid markers presented in Table 2 in a biological sample from the subject; b) comparing the expression profile of the at least one or more markers to a non-rejector profile; and c) determining whether the expression level of the at least one or more markers is up-regulated (increased) or down-regulated (decreased) relative to the control profile, wherein up-regulation or down-regulation of the at least one or more markers is indicative of the rejection status, paragraph 0060; “Markers”, “biological markers” or “biomarkers” may be used interchangeably and refer generally to detectable (and in some cases quantifiable) molecules or compounds in a biological sample... In some usages, these terms may reference the level or quantity of... metabolites, in a subject's biological sample, paragraph 0088). KEOWN does not specifically call out distinguishing if the kidney allograft is stable or is afflicted by inputting data from the detection of the panel of metabolites into a machine learning predictive model, wherein the output of the model is indicative of allograft status. Though KEOWN does not need to teach of BK virus (BKVN infection) as it is claimed as “or”—KEOWN doesn’t teach of BKVN. KEOWN also does not call out detecting glutaric acid, adipic acid, inulobiose, threose, sulfuric acid, myinositol, 5-aminovaleric acid lactame, or N-methylalanine--- but it is not required as claimed to detect all of the claimed metabolites. KEOWN also doesn’t call out normalization with respect to creatine. SALOMON is used to remedy this and teaches of using predictive modeling to determine kidney allograft status in a subject (Disclosed herein are methods of detecting, predicting or monitoring a status or outcome of a transplant in a transplant recipient (abstract) including allograft rejection (paragraph 0166) which means alloimmune injury occurred. The methods, kits, and systems disclosed herein may comprise one or more algorithms or uses thereof... In some cases, the gene expression levels are inputted to a trained algorithm for classifying the sample as one of the conditions comprising AR, ADNR, or TX(acute rejection, acute dysfunction with no rejection, chronic allograft nephropathy, or transplant excellent) (paragraph 0128-0129); In some instances, the methods are used to diagnose or detect AR, ADNR, IFTA(interstitial fibrosis and tubular atrophy), CAN(chronic allograft nephropathy), TX, SCAR(subclinical rejection acute rejection), or other disorders in a transplant recipient with an accuracy, error rate, sensitivity, positive predictive value, or negative predictive value provided herein, (paragraph 0169); The algorithm may provide a record of its output/generate/report things including a classification of a sample and/or a confidence level. In some instances, the output of the algorithm can be the possibility of the subject of having a condition, such as AR, ADNR, or TX, (paragraph 0130)). The algorithm is “trained algorithm,” so this makes in a machine learning algorithm (paragraph 0013). It is trained on multidimensional classifiers among other things (paragraph 0080, 0088, 0097, 0118, 0119, 0139, 0138-0140) and training on biopsy matched, but using urine samples (paragraph 0017, 0061, 0241) and utilized models/modeling (paragraph 0230, 0242, 0263, 0272-0276). SALOMEN even further teaches of needing to take creatine level into consideration (paragraph 0042), and of needing to take creatine level into consideration with respect to diagnosis of the other conditions since they present differently dependent on the level of creatine (paragraph 0048, 0166-0167, 0181, 0239-0241). SALOMEN also teaches of normalization vectors for the classifier algorithms (paragraph 0248, 0096, 0131). This can be considered normalization with respect to creatine through broadest reasonable interpretation (BRI). SALOMON further teach of these conditions being due to immune system rejection/response (paragraphs 0044-0046). SALOMON also teaches of detecting inositol (Table 3, 11, 12) and phosphate (Table 1). SALOMON teach of detecting and sorting BK nephropathy (BKVN=BK virus nephropathy) (paragraph 0268, Table 5). BERG is used to remedy this and teaches of a method for diagnosis of kidney disease (abstract), which includes using isotopic standards as internal standards that can be amino acid internal standards as instantly claimed (paragraph 0112, 0110). All of this shows the claimed sampling, and detection is WURC. Nothing in any of the dependent claims 2, 9-10, 22-23 or 26-32 change the matters above. Claims 2, 9-10, 22-23, & 26-27 make further specifications on the claimed judicial exception including what the sample is, what biomarkers are detected, how specific or sensitive the biomarkers allow for prediction of the natural correlation, and further specify the disease or condition the biomarkers are predictive of. All of this is part of the claimed judicial exception. Claims 28-31, specify what techniques are used for detection of the biomarkers. All of the claimed detection methods here however are routine and conventional in the art and merely function as a data pull here. Therefore- these do not do anything to turn the judicial exceptions into a practical application. Claim 32 specifies that the predictive model is a “supervised learning model that has been trained on biopsy cohort of samples.” Supervised learning models and training on a cohort of matched samples are routine and conventional in the art and therefore do not turn the claims into a practical application of the judicial exceptions. Claim Rejections - 35 USC §103 07-20-aia AIA 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. 07-23-aia AIA 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 non-obviousness. 07-20-02-aia AIA 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 1-2, 9-10, 22-23, 26-29, & 31-32 are rejected under 35 U.S.C. 103 as being obvious over KEOWN in US 20110189680 in view of SALOMON in US 20180371546 and further in view of ADAMS in US 8367359 and further in view of BERG in US 20140228296. With respect to Claim 1, KEOWN teaches a method of distinguishing a stable kidney allograft from a kidney allograft afflicted by an alloimmune injury (A method of determining the acute allograft rejection status of a subject, abstract; Indicators of allograft rejection may include... tissue injury, paragraph 0006) and further of determining if acute rejection is occurring(since KEOWN teaches of looking for acute rejection this reads on what the instant claim is looking for since acute rejection and BKVN infection are claimed as “or”) (abstract, paragraph 0007,0023, 0026 among others) comprising: (a) obtaining a sample from a subject that received a kidney allograft (The present invention relates to methods of diagnosing rejection of a kidney allograft using genomic expression profiling or proteomic expression profiling of one or more biological samples obtained from a subject, paragraph 0022); (b) detecting a panel of metabolites in the sample (For example, a metabolite profile is a dataset of the presence, absence, relative level or abundance of metabolic markers, paragraph 0090; Nucleic acid profiling may also be used in combination with metabolite (“metabolomics”) or proteomic profiling.... KEOWN further teaches wherein the panel of metabolites includes at least one amino acid, at least one amino acid derivative, at least one carbohydrate, and at least one organic compound (Other non-limiting examples of small molecule metabolites are listed in Table 3, paragraph 0219; Table 3 discloses glycine & taurine and asparagine which are also instantly claimed by applicant, and applicant claims “at least three,” which this includes). KEOWN further teaches wherein the panel of metabolites is a 9-metabolite panel (A “profile” is a set of one or more markers and their presence, absence, relative level or abundance (relative to one or more controls). For example, a metabolite profile is a dataset of the presence, absence, relative level or abundance of metabolic markers, paragraph 0090. --- the examiner notes that even though applicant claims “detecting a panel of metabolites in the sample, wherein the panel of metabolites comprises….” Does not mean that the metabolites are actually present in the sample, but that they are screened for and the method of KEOWN can be considered screening for the claimed metabolites , especially for AR assessment and it is not required that the BKNV assessment also occurs as claimed; and (c) distinguishing if the kidney allograft is stable or is afflicted by the alloimmune injury (In accordance with another aspect of the invention, there is provided a method of assessing, monitoring or diagnosing kidney allograft rejection in a subject, the method comprising: a) determining the expression profile of at least one or more nucleic acid markers presented in Table 2 in a biological sample from the subject; b) comparing the expression profile of the at least one or more markers to a non-rejector profile; and c) determining whether the expression level of the at least one or more markers is up-regulated (increased) or down-regulated (decreased) relative to the control profile, wherein up-regulation or down-regulation of the at least one or more markers is indicative of the rejection status, paragraph 0060; “Markers”, “biological markers” or “biomarkers” may be used interchangeably and refer generally to detectable (and in some cases quantifiable) molecules or compounds in a biological sample... In some usages, these terms may reference the level or quantity of... metabolites, in a subject's biological sample, paragraph 0088). KEOWN does not specifically call out distinguishing if the kidney allograft is stable or is afflicted by inputting data from the detection of the panel of metabolites into a machine learning predictive model, wherein the output of the model is indicative of allograft status. Though KEOWN does not need to teach of BK virus (BKVN infection) as it is claimed as “or”—KEOWN doesn’t teach of BKVN. KEOWN also does not call out detecting glutaric acid, adipic acid, inulobiose, threose, sulfuric acid, myinositol, 5-aminovaleric acid lactame, or N-methylalanine--- but it is not required as claimed to detect all of the claimed metabolites. KEOWN also doesn’t call out normalization with respect to creatine. SALOMON is used to remedy this and teaches of using predictive modeling to determine kidney allograft status in a subject (Disclosed herein are methods of detecting, predicting or monitoring a status or outcome of a transplant in a transplant recipient (abstract) including allograft rejection (paragraph 0166) which means alloimmune injury occurred. The methods, kits, and systems disclosed herein may comprise one or more algorithms or uses thereof... In some cases, the gene expression levels are inputted to a trained algorithm for classifying the sample as one of the conditions comprising AR, ADNR, or TX(acute rejection, acute dysfunction with no rejection, chronic allograft nephropathy, or transplant excellent) (paragraph 0128-0129); In some instances, the methods are used to diagnose or detect AR, ADNR, IFTA(interstitial fibrosis and tubular atrophy), CAN(chronic allograft nephropathy), TX, SCAR(subclinical rejection acute rejection), or other disorders in a transplant recipient with an accuracy, error rate, sensitivity, positive predictive value, or negative predictive value provided herein, (paragraph 0169); The algorithm may provide a record of its output/generate/report things including a classification of a sample and/or a confidence level. In some instances, the output of the algorithm can be the possibility of the subject of having a condition, such as AR, ADNR, or TX, (paragraph 0130)). The algorithm is “trained algorithm,” so this makes in a machine learning algorithm (paragraph 0013). It is trained on multidimensional classifiers among other things (paragraph 0080, 0088, 0097, 0118, 0119, 0139, 0138-0140) and training on biopsy matched, but using urine samples (paragraph 0017, 0061, 0241) and utilized models/modeling (paragraph 0230, 0242, 0263, 0272-0276). SALOMEN even further teaches of needing to take creatine level into consideration (paragraph 0042), and of needing to take creatine level into consideration with respect to diagnosis of the other conditions since they present differently dependent on the level of creatine (paragraph 0048, 0166-0167, 0181, 0239-0241). SALOMEN also teaches of normalization vectors for the classifier algorithms (paragraph 0248, 0096, 0131). This can be considered normalization with respect to creatine through broadest reasonable interpretation (BRI). SALOMON further teach of these conditions being due to immune system rejection/response (paragraphs 0044-0046). SALOMON also teaches of detecting inositol (Table 3, 11, 12) and phosphate (Table 1). SALOMON teach of detecting and sorting BK nephropathy (BKVN=BK virus nephropathy) (paragraph 0268, Table 5). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to detect allograft injury/allograft rejection from injury as is done in SALOMON in the method of KEOWN due to the advantage of using metabolites and predictive modeling methods to determine the kidney allograft status of a subject and due to the need in the art for a better method for detecting and tracking transplant rejection and dysfunction/injury (SALOMON, paragraphs 0003-0004). KEOWN and SALOMON do not call out glutaric acid, adipic acid, inulobiose, threose, 5-aminovaleric acid lactam- though again, detecting all of the claimed biomarkers if not required as instantly claimed. ADAMS is used to remedy this. ADAMS teaches measuring N-methylalanine and inulobiose (Another embodiment of the invention is the combination of newly identified small molecule metabolites with known metabolites to mark metabolic perturbation, column 1, lines 26-28; The small molecules among the metabolites may be primary metabolites which are required for normal... organ function, column 3, lines 26-28; Table 4 discloses inulobiose and N-methylatanine). ADAMS further teach of detecting inositol, isothreonic, adipic, benzlalcohol, and sorbitol (Tables 1, 3, & 4). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to modify KEOWN and SALOMON with the teaching of ADAMS for the purpose of screening for biomarkers essential for organ function due to the advantage these biomarkers has for indicating metabolic diseases which is relevant for organ transplant operations (Column 1, lines 35-42). KEOWN, SALOMON and ADAMS do not distinctly call out detection of the biomarkers glutaric acid, threose, sulfuric acid, 5-aminovaleric acid lactame, in combination with the other claimed biomarkers however it would have been obvious to one of ordinary skill in the art, when detecting for organ donation or graft status or rejection to run a full panel/marker check to see if all biomarkers regularly measured in the body (such as amino acids, variants, sugars, and fatty acids) are normal or if there is any changes in these values indicating adverse reaction. KEOWN, SALOMON and ADAMS do not teach of using an isotopic internal standard. BERG is used to remedy this and teaches of a method for diagnosis of kidney disease (abstract), which includes using isotopic standards as internal standards that can be amino acid internal standards as instantly claimed (paragraph 0112, 0110). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to use the isotopic internal standards as is done in BERG in the methods of KEOWN, SALOMON, and ADAMS due to the advantage isotopic internal standards have for assay optimization and specimen processing(BERG, paragraph 0083). With respect to Claim 2, KEOWN further teaches wherein the sample is a urine sample (For example, the biological sample may be... urine, (paragraph 0106). With respect to Claims 9-10, 22-23, 26-27, KEOWN teaches of detecting acute rejection specificity (paragraph 0024, 0152, 0194, 0262) and sensitivity (paragraph 0190, 0349, 0252, 0262-0265 & table 6) for 11 classifiers (0249) and also related the method to nephropathy(paragraph 0004, Table 4), and of determination in comparison to stable allografts(paragraph0255, 0258, 0266), but does not explicitly teach wherein the 11-metabolite panel has a sensitivity greater than 90% for detecting the acute rejection. SALOMON however teach of the method and classifier having a sensitivity and specificity and predictive value to 80&, 90%, 95%, and 99% (paragraph 0011, 0076,0098, 0182-0186) and further teach that the method can detect chronic allograft nephropathy (paragraph 0042,0046, 0103, 0122-0124, 0126, 0164, 0268). SALOMON teach of detecting a sorting with respect to BK nephropathy (BKVN=BK virus nephropathy) (paragraph 0268, Table 5). It would have been obvious to use an accurate predictive model to ensure a panel test for detecting allograft injury sufficiently sensitive to provide accurate detection of allograft injury to administer treatment to reduce or prevent further injury. With respect to Claim 28, KEOWN further discloses wherein the panel of metabolites is detected by a mass spectroscopy analysis (Some examples of techniques and methods that may be used (either singly or in combination) to obtain a metabolite profile of a subject include, but are not limited to,... mass spectroscopy, paragraph 0221). With respect to Claim 29, KEOWN further discloses wherein the mass spectroscopy analysis is a gas chromatography mass spectrometry (GC-MS) analysis (Some examples of techniques and methods that may be used (either singly or in combination) to obtain a metabolite profile of a subject include, but are not limited to,... gas chromatography in combination with mass spectroscopy (GC-MS), (paragraph 0221)). With respect to Claim 31, KEOWN further discloses wherein the mass spectroscopy analysis is a liquid chromatography - mass spectrometry (HPLC-MS) analysis (Some examples of techniques and methods that may be used (either singly or in combination) to obtain a metabolite profile of a subject include, but are not limited to,... mass spectroscopy,... high performance liquid chromatography,( paragraph 0221)). With respect to Claim 32, KEOWN teach of using a control cohort (matched cohorts) (paragraph 0232-0234, 0249, 0258) and further of using a classifier/training set (paragraph 0251-0252) to create predictive/statistical models (paragraph 0248) and of using biopsies (paragraph 0005, 0075, 0083-0085, 0234). If it’s unclear KEOWN teaches of predictive models, SALOMON does (paragraph 0303, 0311, 0320-0323, 0329) and of training the algorithms for matched cohorts (reads on supervised learning) (paragraph 0013, 0038-0039,0080,0088, 0097,0098). Claim 30 is rejected under 35 U.S.C. 103 as being obvious over KEOWN in US 20110189680 in view of SALOMON in US 20180371546 in view of ADAMS in US 8367359 and further in view of AFEYAN in US 20050170372. With respect to Claim 30, KEOWN teach of using electrophoresis (paragraph 0100). SALOMON teach of using electrophoresis (paragraph 0089, 0092). KEOWN and SALOMON and ADAMS do not teach of the use of capillary electrophoresis. AFEYAN is used to remedy this. AFEYAN teaches using CE-MS to detect metabolites in a biological sample (More specifically, the methods and systems can analyze and integrate data at the biomolecular component type level, i.e., the... metabolite level, (paragraph 0007); A measurement technique includes, among others,... capillary electrophoresis-mass spectrometry, (paragraph 0015)). It would have been obvious to one of ordinary skill in the art at the time of the invention to modify KEOWN with the teaching of AFEYAN for the purpose of using known techniques shown to be effective for detecting and quantifying metabolites from a biological sample for determining a disease status and due to the need in the art for better systems and methods for profiling and detecting of diseases and the advantages capillary electrophoresis has shown for this(AFEYAN, paragraph 0004-0005, 0015). Response to Arguments 07-37 AIA Applicant's arguments filed 06/23/2025 have been fully considered but they are not persuasive. With respect to the 101 rejection, the rejection is maintained. It is noted that this rejection was maintained after consultation with the examiner’s SPE. Please see USPTO subject matter examples 47, and 29 if applicant is looking to try to overcome the 101 rejection. As currently written, the examiner thinks the instant claims are most closely similar to USPTO SME example 47, Claim 2 which was found ineligible. In example 47, Claim 3 was found eligible, so applicant could consider amending the claims to more closely follow that example. Also, see Example 29--- for claims which were found eligible versus ineligible for diagnostic type claims. With respect to the 103 rejections, the examiner notes that significant amendments were made 06/23/2025, so it is shown how the prior art reads on these amendments in the rejection above. It is further noted, that a new piece of prior art was added to the rejection above, however if applicant overcomes the 101 rejection, it is fairly likely that the instant prior art rejection will be easier to move past as well. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin , 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). It is noted that the examiner only into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, therefore the claimed construction/ such a reconstruction is proper . 07-37-13 AIA In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller , 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Specifically, applicant argues that KEOWN and ADAMS and SALOMON do not “select exactly,” the claimed biomarkers. The examiner disagrees, as KEOWN does in fact teach of three of the claimed biomarkers, which is what is required. Further—detecting of all of the biomarkers in all of the sets of biomarkers as instantly claimed is not required, but instead it is only required to meet the detection limits for one of the sets of biomarkers as claimed since it is only required that one classification or diagnosis be made or reported out. All claims remain rejected. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA M FRITCHMAN whose telephone number is (303)297-4344. The examiner can normally be reached 9:30-4:30 MT Monday-Friday. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Maris Kessel can be reached on 571-270-7698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /REBECCA M FRITCHMAN/Primary Examiner, Art Unit 1758 Application/Control Number: 17/423,868 Page 2 Art Unit: 1758 Application/Control Number: 17/423,868 Page 3 Art Unit: 1758 Application/Control Number: 17/423,868 Page 4 Art Unit: 1758 Application/Control Number: 17/423,868 Page 5 Art Unit: 1758 Application/Control Number: 17/423,868 Page 6 Art Unit: 1758 Application/Control Number: 17/423,868 Page 7 Art Unit: 1758 Application/Control Number: 17/423,868 Page 8 Art Unit: 1758 Application/Control Number: 17/423,868 Page 9 Art Unit: 1758 Application/Control Number: 17/423,868 Page 10 Art Unit: 1758 Application/Control Number: 17/423,868 Page 11 Art Unit: 1758 Application/Control Number: 17/423,868 Page 12 Art Unit: 1758 Application/Control Number: 17/423,868 Page 13 Art Unit: 1758 Application/Control Number: 17/423,868 Page 14 Art Unit: 1758 Application/Control Number: 17/423,868 Page 15 Art Unit: 1758 Application/Control Number: 17/423,868 Page 16 Art Unit: 1758 Application/Control Number: 17/423,868 Page 17 Art Unit: 1758 Application/Control Number: 17/423,868 Page 18 Art Unit: 1758 Application/Control Number: 17/423,868 Page 19 Art Unit: 1758 Application/Control Number: 17/423,868 Page 20 Art Unit: 1758 Application/Control Number: 17/423,868 Page 21 Art Unit: 1758 Application/Control Number: 17/423,868 Page 22 Art Unit: 1758 Application/Control Number: 17/423,868 Page 23 Art Unit: 1758
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