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 .
DETAILED ACTION Summary
This is a Final Office action based on the 17/423868 application filed on 11/18/2024.
Claims 1-2, 9-10, 22-23, 26-32 are pending and have been fully considered.
Claims 3-8, 11-21, 24-25, and 33-57 are cancelled.
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 ofmatter, or any new and useful improvement thereof, may obtain a patent therefor, subject to theconditions and requirements of this title.
The claimed invention of Claims 1-2, 9-10, 22-23, 26-32 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:
Is the claim directed to a statutory category of invention?
Yes, the claim are drawn towards a statutory category method.
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 (use of a predictive model/math to calculate allograft status if stable or if instead afflicted by AR or BKVN infection).
Analysis of other factors to determine if the claim qualifies as eligible and ismore than the natural correlation/abstract idea.
There are no features instantly claimed in independent Claim 1 which result in significantly more than the judicial exceptions.
The detection as claimed can through as no specific method of detection is claimed is routine and conventional in the art. As claimed, it also is merely a data pull for inputting to the predictive model. Both things that are routine and conventional and data pulls do not add “practical application,” nor do they make the claims significantly more than the claimed judicial exceptions.,
Nothing in the claim is drawn towards any specificity of measurement, detection, or treatment that results in a practical application of the claimed judicial exceptions.
See MPEP 2106.04(a) & (b) for the natural correlation and abstract ideas, and also Parker v. Flook, 437 U. S. 584, 590.
Nothing in any of the dependent claims 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.
See MPEP 2106.05(d)- what is well understood, routine and conventional.
Claim Rejections - 35 USC §103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 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.
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 which are also instantly claimed by applicant).
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; 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 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,.
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 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)). 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 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.
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 at the time of the 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.
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
Applicant's arguments filed 11/18/2024 have been fully considered but they are not persuasive.
With respect to the 101 rejection, applicant argues that the instantly claimed panel of biomarkers is a discovery by applicant that can be applied to differentiate AR and BKVN infection in allograft patients. With respect to this- though the examiner is not doubting applicant’s assertion that applicant may have “discovered,” this panel per say, this in itself unfortunately does not make the instant claims patent eligible, as instantly claimed the claims are drawn towards nothing more than the recited judicial exceptions as shown in the above 101 rejection, Though the examiner is not sure applicant has disclosure for more in claiming of their predictive model, adding more details and specific to the claims could help matters if applicant does have more detailed disclosure. Claiming of general detecting/detection and general distinguishing/modeling does not help these claims, as what the claims are left at currently are the judicial exceptions.
With respect to the 103 rejections, applicant makes no substantive arguments other than to say that the applicant doesn’t think the references teach what is claimed. The examiner disagrees.
All claims remain rejected.
Conclusion
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 extension fee 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 date of this final action.
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.
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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.
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/REBECCA M FRITCHMAN/Primary Examiner, Art Unit 1758