DETAILED ACTION
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/7/2026 has been entered.
Applicant's response, filed 7 September 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Claims Status
Claims 2, 4-5, 7, 13, and 19-20 are pending.
Claims 3, 6, and 14-18 are cancelled.
Claims 2, 4-5, 7, 13, and 19-20 are rejected.
Claim Rejections - 35 USC § 103
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 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.
Claims 2, 4-5, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Arauna et al. (Free Radical Biology and Medicine (2020) 64-71; previously cited), Kitamura et al. (Environmental health and preventive medicine (2012) 164-168; previously cited), Tarekegn et al. (JMIR Medical Informatics (2019) 1-16; previously cited), Meynial-Denis et al. (Nutrition Reviews (2016) 225-236; previously cited), Kettunen et al. (Circulation: Genomic and Precision Medicine (2018) 1-7; previously cited), Riddle et al. (Frontiers of Bioscience (2016) 326-350; newly cited), Calvani et al. (Journal of Cachexia, Sarcopenia and Muscle (2015) 278-286; previously cited), Corona et al. (Journal of Cellular Physiology (2013) 898-902; previously cited), Ruopp et al. (Biomedical Journal (2008) 419-430; newly cited), Fiuzat et al. (JAMA Cariology (2020) 757–764; previously cited), and Jeffery et al. (Maturitas (2012) 21-25; previously cited).
Claim 2 is directed to a method of treating frailty in which levels of GDF-15 and additional biomarkers of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine, are used to determine an overall biosignature score, p using a logistic function and a multiple regression equation for the calculation of various features effects.
Arauna et al. teaches in the abstract “The determination of biomarkers of platelet dysfunction, oxidative stress and cell senescence/mitochondrial dysfunction may contribute to frailty diagnosis, and approaches aimed at regulating platelet function in frail older adults could contribute to its prevention and treatment”. On page 66, column 1, paragraph 4 Arauna et al states “The blood level of GDF15 might reflect mitochondrial function in patients, and thus could be a marker for mitochondrial dysfunction. Plasma GDF-15 human protein levels were determined by ELISA with a commercially available kit (R&D Systems, USA)” reading on the measuring of GDF-15 levels within a biological sample and in Figure 4 clearly outlines the “level of plasmatic marker of mitochondrial dysfunction GDF-15 according to the state of frailty”. Arauna et al. further teaches on page 66, column 1, paragraph 4 “Plasma GDF-15 human protein levels were determined by ELISA with a commercially available kit”, reading on the use of a GDF-15 analyzer.
Arauna et al. does not specifically teach a method of treatment for frailty nor does it specifically teach the use of additional biomarkers from the list or the use of logistic model for obtaining a biosignature score.
Ruopp et al. teaches in the abstract “We adapt non-parametric (empirical) and semi-parametric (ROC-GLM [generalized linear model]) methods and propose parametric methods (maximum likelihood (ML)) to estimate J and the optimal cut-point (c*) for a biomarker affected by a LOD”, and on page 2, paragraph 2 “As an extension, this paper focuses on the Youden Index (J), another main summary statistic of the ROC curve used in the interpretation and evaluation of a biomarker, which defines the maximum potential effectiveness of a biomarker. J can be formally defined as J = maxc {Se (c) + Sp (c) − 1}. The cut-point that achieves this maximum is referred to as the optimal cutpoint (c*) because it is the cut-point that optimizes the biomarker’s differentiating ability when equal weight is given to sensitivity and specificity”, it would be obvious that a parameter which is bounded by 0 and 1, and taught to be optimized in the art, would be optimized for decision processes, reading on wherein Z is 0.15 to 0.56.
Kitamura et al. teaches on page 164, column 2, paragraph 4 “Serum albumin levels and activities of daily living (ADL) are major predictors of mortality in frail elderly people” and on page 167 column 1, paragraph 2 “In the present study, we confirmed the close relationship between serum albumin and AL levels by demonstrating a significant correlation between their two year changes” and in column 2, paragraph 2 of the same page “the present cohort study has demonstrated that a decrease in the serum albumin level is associated with a decrease in ADL levels”. These read on the incorporation of albumin as an additional biomarker for frailty.
Kitamura et al. does not teach the use of a multiple regression or logistic function for the quantification of various features effects to predict frailty and need for treatment.
Tarekegn et al. teaches on page 2, column 2, paragraph 1 “the study examines the existing machine learning techniques (artificial neural networks, genetic programming, support vector machines, logistic regression, decision trees and random forests) to predict frailty according to the different adverse health outcomes” and on page 4, column 2, paragraph 2 “LR, a specific type of multivariate regression, is the most common and well-established binary classifier. LR is used to model only a dichotomous variable, which usually represents the presence or absence of an outcome or event based on a set of predictor variables. It predicts an event of occurrence by fitting a dataset into a logit function. In this study, like other machine learning models, LR has been used to distinguish frail and non-frail subjects”.
Arauna et al., Tarekegn et al. and Kitamura et al. do not teach the use of all specified biomarkers, albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine.
Meynial-Denis et al. teaches on page 228, column 1, paragraph 4 “Aging is also characterized by protein wasting, and glutamine, which is the most abundant amino acid in the blood, may be a hallmark of catabolic states. Indeed, a low concentration of glutamine in plasma reflects reduced stores in muscle, and this reduced availability of glutamine in the catabolic state seems to correlate with increased morbidity and mortality”, and on page 233, column 1, paragraph 3 “The effect of glutamine supplementation was compared with the effect of alanine and glycine supplementation. Only glutamine supplementation was able to significantly decrease glutamine synthetase activity in the skeletal muscle of very old rats (27 months), whereas supplementation with other amino acids decreased upregulated glutamine synthetase activity in adult rats”.
Kettunen et al. teaches on page 1, paragraph 1 “GlycA predicts risk of diverse outcomes, including cardiovascular disease, type 2 diabetes mellitus, and all-cause mortality”.
Corona et al. teaches on page 900, column 1, paragraph 3 “The perturbation of serum glycerolphospholipids occurring in Frail and Unfit patients was evident for both the glycerol-phosphatidylcholine (PCs) and the lysophosphatidylcholine (LysoPC) lipid subclasses”.
Riddle et al. teaches on page 328, column 1, paragraph 1 “The degradation of branched-chain amino acids provides products for the synthesis of amino acids such as glutamine in the muscle. Additionally, the release of amino acids (e.g., alanine and glutamine) from the skeletal muscle provides necessary products for synthesis of other amino acids and molecules, such as glucose, by other organs”, on page 333, column 1, paragraph 2 “In addition, Nguyen et al showed that supplementation of older HIV-infected men with this same N-acetylcysteine + glycine supplement for 14 days increased red blood cell glutathione level, reduced markers of oxidative stress, increased muscle strength, and improved mitochondrial function and insulin sensitivity”, and in the abstract “Life expectancy in the U.S. and globally continues to increase. Despite increased life expectancy quality of life is not enhanced, and older adults often experience chronic age-related disease and functional disability, including frailty. Additionally, changes in body composition such as the involuntary loss of skeletal muscle mass (i.e. sarcopenia) and subsequent increases in adipose tissue can augment disease and disability in this population. Furthermore, increased oxidative stress and decreased antioxidant concentrations may also lead to metabolic dysfunction in older adults. Specific amino acids, including leucine, cysteine and its derivative taurine, and arginine can play various roles in healthy aging, especially in regards to skeletal muscle health…Evidence suggests that supplemental doses of each of these amino acids may improve the aging phenotype”.
Calvani et al. teaches on page 280, column 1, paragraph 2 “Considerable research efforts have therefore been diverted towards the development and validation of blood-borne biomarkers for PF and sarcopenia. The most popular circulating markers are those related to the inflammatory response, clinical parameters, hormones, products of oxidative damage, or antioxidants”, reading on the biomarkers consisting of albumin, glutamine, GlycA, phosphoglycerides, glycine, and alanine, as all of these are either anti-inflammatory biomarkers or are markers of inflammation.
Jeffery et al teaches on page 22, columns 1-2 under Frailty and chronic disease that “Cardiovascular pathologies are common in frail older people” and on page 23, column 1, paragraph 1“while mobility problems are particularly linked to cerebrovascular disease, the most prevalent frailty criterion in patients with cardiovascular disease is muscle weakness” and in paragraph 3 “the association between frailty and hypertension is complex”.
Fiuzat et al teaches on page 758, column 1, paragraph 1 “Concentrations of amino-terminal pro-Btype natriuretic peptide (NT-proBNP) were chosen because they are strongly associated with outcomes in patients with HF and may be lowered using standard therapies inHF-rEF, such as β-blockers, angiotensin-converting enzyme inhibitors (ACEis), angiotensin II receptor blockers (ARBs), and mineralocorticoid receptor antagonists (MRAs). Smaller trials examining the use of NT-proBNP concentrations to “guide” GDMT have suggested that the approach leads to more assiduous application of GDMT along with better outcomes compared with usual care”, which in view of Jeffery et al reads on treating the subject by administering an effective amount of a therapeutic drug, wherein the therapeutic drug comprises sacubitril/valsartan, dapagliflozin, empagliflozin, beta blocker, renin-angiotensin system inhibitor, mineralocorticoid receptor antagonist, ivabradine, digoxin, inotropes, or inodilator.
It would have been obvious at the time of invention to modify the teachings of Arauna et al for determining biomarkers to contribute to proper diagnosis of frailty with the teachings Kitamura et al for the inclusion of albumin as a biomarker for frailty and Tarekegn et al for using a logistic function and multiple regression to obtain a score for determining frailty, with specific weights being dependent upon training data for the regression. Additionally it would have been obvious at the time of invention to modify the teachings of Arauna et al., Kitamura et al., and Tarekegn et al. with Corona et al., Kettunen et al. and Meynial-Denis et al. for the specific biomarker composition as Calvani et al., being a review of biomarkers that currently exist for frailty, suggests the broad usage of biomarkers associated with inflammation, hormones, products of oxidative damage and antioxidants, the same biomarkers used in Corona et al., Kettunen et al., and Meynial-Denis et al.. And, it would have been obvious at the time of invention to modify the teachings of Arauna et al for measuring levels of GDF-15 with the teachings of Fiuzat et al for the measuring of NT-proBNP and use of GDMT based upon the teachings of Jeffery et al that frailty and cardiovascular disease are linked. One would have had a reasonable expectation of success given that on page 22, column 1, paragraph 4 of Jeffery it’s pointed out that “recent work suggests that frailty and chronic disease are closely intertwined” and on the following page that “the most prevalent frailty criterion in patients with cardiovascular disease is muscle weakness”. This in coordination with Fiuzat et al page 1, column 1, paragraph 1 “use of NT-proBNP concentrations to “guide” GDMT have suggested that the approach leads to more assiduous application of GDMT along with better outcomes compared with usual care” and “the use of guideline-directed medical therapy (GDMT) at target doses reduces morbidity and mortality in heart failure” would make obvious the coordination of the biomarker levels and GDMT in the treatment of frailty. One would have had a reasonable expectation of success given that the methods of measuring levels of GDF-15 and albumin in a sample (and really any known biomarker) and general treatment for frailty are well understood and routine within the art, the use of logistic functions and multiple regressions for determining a binary outcome are also well-understood and routine within the art, and the association of numerous biomarkers with frailty or all-cause mortality has already been established in previous art. Additionally, Tarekegn et al specifically utilizes their multiple logistic regression model to successfully predict frailty. Therefore, it would be obvious to one with ordinary skill in the art to incorporate the teachings of each and to be successful.
Claim 4 is directed to the method of claim 2 but further specifies that Z is .259.
Ruopp et al. teaches in the abstract “We adapt non-parametric (empirical) and semi-parametric (ROC-GLM [generalized linear model]) methods and propose parametric methods (maximum likelihood (ML)) to estimate J and the optimal cut-point (c*) for a biomarker affected by a LOD”, and on page 2, paragraph 2 “As an extension, this paper focuses on the Youden Index (J), another main summary statistic of the ROC curve used in the interpretation and evaluation of a biomarker, which defines the maximum potential effectiveness of a biomarker. J can be formally defined as J = maxc {Se (c) + Sp (c) − 1}. The cut-point that achieves this maximum is referred to as the optimal cutpoint (c*) because it is the cut-point that optimizes the biomarker’s differentiating ability when equal weight is given to sensitivity and specificity”, it would be obvious that a parameter which is bounded by 0 and 1, and taught to be optimized in the art, would be optimized for decision processes, reading on wherein Z is 0.259.
Claim 5 is directed to the method of claim 2 but further specifies that the maximum value of Z is .553.
Ruopp et al. teaches in the abstract “We adapt non-parametric (empirical) and semi-parametric (ROC-GLM [generalized linear model]) methods and propose parametric methods (maximum likelihood (ML)) to estimate J and the optimal cut-point (c*) for a biomarker affected by a LOD”, and on page 2, paragraph 2 “As an extension, this paper focuses on the Youden Index (J), another main summary statistic of the ROC curve used in the interpretation and evaluation of a biomarker, which defines the maximum potential effectiveness of a biomarker. J can be formally defined as J = maxc {Se (c) + Sp (c) − 1}. The cut-point that achieves this maximum is referred to as the optimal cutpoint (c*) because it is the cut-point that optimizes the biomarker’s differentiating ability when equal weight is given to sensitivity and specificity”, it would be obvious that a parameter which is bounded by 0 and 1, and taught to be optimized in the art, would be optimized for decision processes, reading on wherein the maximum value of Z is 0.553.
Claim 7 is directed to the method of claim 2 but further specifies the treatment as being one of those specified.
Jeffery et al teaches on page 22, columns 1-2 under Frailty and chronic disease that “Cardiovascular pathologies are common in frail older people” and on page 23, column 1, paragraph 1“while mobility problems are particularly linked to cerebrovascular disease, the most prevalent frailty criterion in patients with cardiovascular disease is muscle weakness” and in paragraph 3 “the association between frailty and hypertension is complex”.
Fiuzat et al teaches on page 758, column 1, paragraph 1 “Concentrations of amino-terminal pro-Btype natriuretic peptide (NT-proBNP) were chosen because they are strongly associated with outcomes in patients with HF and may be lowered using standard therapies in HF-rEF, such as β-blockers, angiotensin-converting enzyme inhibitors (ACEis), angiotensin II receptor blockers (ARBs), and mineralocorticoid receptor antagonists (MRAs). Smaller trials examining the use of NT-proBNP concentrations to “guide” GDMT have suggested that the approach leads to more assiduous application of GDMT along with better outcomes compared with usual care”, Metoprolol, Cavedilol, and Bisoprolol are beta-blockers while Enalapril and Lisinopril are ACEis, which in view of Jeffery et al. reads on wherein the therapeutic drug comprises metoprolol, carvedilol, bisoprolol, enalapril, lisinopril, dobutamine, milrinone, or levosimendan.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Arauna et al. (Free Radical Biology and Medicine (2020) 64-71; previously cited), Kitamura et al. (Environmental health and preventive medicine (2012) 164-168; previously cited), Tarekegn et al. (JMIR Medical Informatics (2019) 1-16; previously cited), Meynial-Denis et al. (Nutrition Reviews (2016) 225-236; previously cited), Kettunen et al. (Circulation: Genomic and Precision Medicine (2018) 1-7; previously cited), Riddle et al. (Frontiers of Bioscience (2016) 326-350; newly cited), Calvani et al. (Journal of Cachexia, Sarcopenia and Muscle (2015) 278-286; previously cited), Corona et al. (Journal of Cellular Physiology (2013) 898-902; previously cited), Ruopp et al. (Biomedical Journal (2008) 419-430; newly cited), Fiuzat et al. (JAMA Cariology (2020) 757–764; previously cited), and Jeffery et al. (Maturitas (2012) 21-25; previously cited) as applied to claims 2, 4-5, and 7 above, and further in view of Beckonert et al. (Nature protocols (2007) 2692-2703; newly cited).
Claim 13 is directed to the method of claim 2 but further specifies the use of a biological sample from a subject with an assay kit to measure the specified biomarkers using H-NMR metabolic profiling.
Arauna et al., Kitamura et al., Tarekegn et al., Meynial-Denis et al., Kettunen et al., Calvani et al., Corona et al., Ruopp et al., Fiuzat et al., and Jeffery et al. teach the method of claim 2 as previously described.
Arauna et al. teaches on page 65, column 2, paragraph 1 “Blood was drawn by phlebotomy (needle 21G × 1½″) in 3.2% citrate (9:1 v/v) and in EDTA for each participant”.
Beckonert et al. teaches on page 2694 “Absolute concentrations can be obtained if the sample contains an added internal standard of known concentration, or if a standard addition of the analyte of interest is added to the sample, or if the concentration of a substance is known by independent means (e.g., glucose in plasma can be quantified by a conventional biochemical assay)”, and in the abstract “Metabolic profiling, metabolomic and metabonomic studies mainly involve the multicomponent analysis of biological fluids, tissue and cell extracts using NMR spectroscopy and/or mass spectrometry (MS). We summarize the main NMR spectroscopic applications in modern metabolic research, and provide detailed protocols for biofluid (urine, serum/plasma) and tissue sample collection and preparation, including the extraction of polar and lipophilic metabolites from tissues. 1H NMR spectroscopic techniques such as standard 1D spectroscopy, relaxation-edited, diffusion-edited and 2D J-resolved pulse sequences are widely used at the analysis stage to monitor different groups of metabolites and are described here. They are often followed by more detailed statistical analysis or additional 2D NMR analysis for biomarker discovery”, reading on wherein a) the biological sample is a blood serum or plasma sample from a subject and is measured using an assay kit on an immunoassay analyzer; and b) the levels of one or more biomarkers selected from the group consisting of albumin, glutamine,GlycA, phosphoglycerides, glycine, and alanine are measured using 1H-NMR metabolomic profiling.
It would have been obvious at the time of first filing to have modified the teachings of Arauna et al., Kitamura et al., Tarekegn et al., Meynial-Denis et al., Kettunen et al., Calvani et al., Corona et al., Ruopp et al., Fiuzat et al., and Jeffery et al. for the method of claim 2, with the teachings of Beckonert et al. for the use of assays on biological samples and H-NMR metabolic profiling for the analytes described previously as the latter teaches in the abstract “The standard acquisition time per sample is 4–5 min for a simple 1D spectrum, and both preparation and analysis can be automated to allow application to high-throughput screening for clinical diagnostic and toxicological studies, as well as molecular phenotyping and functional genomics”. One would have had a reasonable expectation of success given that the method described here is merely a precursor to the statistical analysis described in the claim much as was previously described by Beckonert et al. in the abstract. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Arauna et al. (Free Radical Biology and Medicine (2020) 64-71; previously cited), Kitamura et al. (Environmental health and preventive medicine (2012) 164-168; previously cited), Tarekegn et al. (JMIR Medical Informatics (2019) 1-16; previously cited), Meynial-Denis et al. (Nutrition Reviews (2016) 225-236; previously cited), Kettunen et al. (Circulation: Genomic and Precision Medicine (2018) 1-7; previously cited), Riddle et al. (Frontiers of Bioscience (2016) 326-350; newly cited), Calvani et al. (Journal of Cachexia, Sarcopenia and Muscle (2015) 278-286; previously cited), Corona et al. (Journal of Cellular Physiology (2013) 898-902; previously cited), Ruopp et al. (Biomedical Journal (2008) 419-430; newly cited), Fiuzat et al. (JAMA Cariology (2020) 757–764; previously cited), and Jeffery et al. (Maturitas (2012) 21-25; previously cited) as applied to claims 2, 4-5, and 7 above, and further in view of Cavusoglu et al. (International Journal of Cardiology (2008) e188-e191; newly cited).
Claim 19 is directed to the method claim 2 but further specifies the use of inodilators for therapy.
Arauna et al., Kitamura et al., Tarekegn et al., Meynial-Denis et al., Kettunen et al., Calvani et al., Corona et al., Ruopp et al., Fiuzat et al., and Jeffery et al. teach the method of claim 2 as previously described.
Arauna et al., Kitamura et al., Tarekegn et al., Meynial-Denis et al., Kettunen et al., Calvani et al., Corona et al., Ruopp et al., Fiuzat et al., and Jeffery et al. do not teach the use of inodilators for therapy.
Cavusoglu et al. teaches in the abstract “Levosimendan and dobutamine are comparable inotropic drugs as regards their hemodynamic effects and clinical outcomes in decompensated heart failure (HF). The aim of this study was to compare the effects of levosimendan and dobutamine treatment on NTproBNP levels in patients with decompensated HF…Both levosimendan and dobutamine treatments result in significant reduction of NT-proBNP levels at the end of the 24-h infusion. However, compared to dobutamine, levosimendan has better and prolonged effects on NT-proBNP levels in decompensated HF”, reading on wherein the therapeutic drug comprises inodilator.
It would have been obvious at the time of first filing to have modified the teachings of Arauna et al., Kitamura et al., Tarekegn et al., Meynial-Denis et al., Kettunen et al., Calvani et al., Corona et al., Ruopp et al., Fiuzat et al., and Jeffery et al. for the method of claim 2, with the teachings of Cavusoglu et al. for the use of Levosimendan as treatment for frailty given the latter explains within the abstract “Both levosimendan and dobutamine treatments result in significant reduction of NT-proBNP levels at the end of the 24-h infusion. However, compared to dobutamine, levosimendan has better and prolonged effects on NT-proBNP levels in decompensated HF”, and Jeffery et al. teaches on page 22, columns 1-2 under Frailty and chronic disease that “Cardiovascular pathologies are common in frail older people” and on page 23, column 1, paragraph 1“while mobility problems are particularly linked to cerebrovascular disease, the most prevalent frailty criterion in patients with cardiovascular disease is muscle weakness” and in paragraph 3 “the association between frailty and hypertension is complex”. One would have had a reasonable expectation of success given that it is merely deciding on a specific treatment, and said treatment is well known and associated with the phenotype. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 20 is directed to the method of claim 2 but further specifies the therapeutic drug as levosimendan.
Arauna et al., Kitamura et al., Tarekegn et al., Meynial-Denis et al., Kettunen et al., Calvani et al., Corona et al., Ruopp et al., Fiuzat et al., and Jeffery et al. teach the method of claim 2 as previously described.
Arauna et al., Kitamura et al., Tarekegn et al., Meynial-Denis et al., Kettunen et al., Calvani et al., Corona et al., Ruopp et al., Fiuzat et al., and Jeffery et al. do not teach the use of levosimendan.
Cavusoglu et al. teaches in the abstract “Levosimendan and dobutamine are comparable inotropic drugs as regards their hemodynamic effects and clinical outcomes in decompensated heart failure (HF). The aim of this study was to compare the effects of levosimendan and dobutamine treatment on NTproBNP levels in patients with decompensated HF…Both levosimendan and dobutamine treatments result in significant reduction of NT-proBNP levels at the end of the 24-h infusion. However, compared to dobutamine, levosimendan has better and prolonged effects on NT-proBNP levels in decompensated HF”, reading on wherein the therapeutic drug comprises levosimendan.
Response to Applicant Arguments
Applicant's arguments filed 9/7/2026 have been fully considered but they are not persuasive.
Applicant asserts on page 2 of the Remarks filed 9/7/2025 that the examiner has not made a prima facie case of obviousness.
In regards applicant claim that Arauna et al. does not teach GDF-15 as a clinically established frailty biomarker, Arauna et al. teaches “The determination of biomarkers of platelet dysfunction, oxidative stress and cell senescence/mitochondrial dysfunction may contribute to frailty diagnosis, and approaches aimed at regulating platelet function in frail older adults could contribute to its prevention and treatment”, “The blood level of GDF15 might reflect mitochondrial function in patients, and thus could be a marker for mitochondrial dysfunction. Plasma GDF-15 human protein levels were determined by ELISA with a commercially available kit (R&D Systems, USA)”, and “level of plasmatic marker of mitochondrial dysfunction GDF-15 according to the state of frailty” clearly linking GDF-15 and clinical frailty.
In regards applicant claim that Ruopp et al. does not teach using Youden Index to derive/validate the specific biomarker combination, Ruopp et al. teaches “As an extension, this paper focuses on the Youden Index (J), another main summary statistic of the ROC curve used in the interpretation and evaluation of a biomarker, which defines the maximum potential effectiveness of a biomarker…”, and the specific combination of biomarkers is given by the combination of cited art, NOT an individual art.
In regards applicant claim that Ruopp et al. does not specifically teach the range of the Z value, examiner specifically stated “it would be obvious that a parameter which is bounded by 0 and 1, and taught to be optimized in the art, would be optimized for decision processes”, the argument for which is not impermissible hindsight, rather knowledge that the Z value measures standard deviations an acceptable p is for being determined frail. This is necessarily bound by 0 and 1 and so has a limited number of possible threshold values determined in part by the weights of the features included in the model, which is determined through model fitting. This is definitionally the process of optimization.
In regards applicant claim that the cited references do not teach the biomarker combination, specifically that Riddle et al. teaches the amino acids in the context of amino acid metabolism and healthy aging muscle, Riddle et al. teaches “Despite increased life expectancy quality of life is not enhanced, and older adults often experience chronic age-related disease and functional disability, including frailty. Additionally, changes in body composition such as the involuntary loss of skeletal muscle mass (i.e. sarcopenia) and subsequent increases in adipose tissue can augment disease and disability in this population. Furthermore, increased oxidative stress and decreased antioxidant concentrations may also lead to metabolic dysfunction in older adults. Specific amino acids, including leucine, cysteine and its derivative taurine, and arginine can play various roles in healthy aging, especially in regards to skeletal muscle health…Evidence suggests that supplemental doses of each of these amino acids may improve the aging phenotype”, here the amino acid metabolism and healthy aging muscle are part of frailty not abstracted from.
In regards applicant claim that Corona et al. teaches a number of observed markers, so there is no reason to select glycerol-phospholipids, Corona et al. teaches “The perturbation of serum glycerolphospholipids occurring in Frail and Unfit patients was evident for both the glycerol-phosphatidylcholine (PCs) and the lysophosphatidylcholine (LysoPC) lipid subclasses”. The selection of a specific feature is given by its evidence for association with the disease, not its selection over other markers that also are associated with the disease.
In regards applicant claim that Kitamura et al. does not disclose a teaching or suggestion to combine serum albumin or glutamine with the other 5 specific markers, Kitamura et al. teaches “Serum albumin levels and activities of daily living (ADL) are major predictors of mortality in frail elderly people”, this is a suggestion to use serum albumin levels as a biomarker for frailty.
In regards applicant claim that examiner is using impermissible hindsight in regards Calvani et al. teaching biomarkers for frailty, Calvani et al. teaches “Considerable research efforts have therefore been diverted towards the development and validation of blood-borne biomarkers for PF and sarcopenia. The most popular circulating markers are those related to the inflammatory response, clinical parameters, hormones, products of oxidative damage, or antioxidants”, here PF stands for physical frailty, i.e. they linked the biomarkers to frailty. Furthermore the cited portion that supposed teaches away from the use of said biomarkers within Calvani et al. only directs back to their use, “Current available biomarkers for PF and sarcopenia are only able to capture single aspects of the conditions and are weakly associated with clinically meaningful outcomes. The adoption of multidimensional/multivariate approaches could help cope with the complex phenotypical and pathophysiological nature of PF and sarcopenia and allow (i) capturing the different domains of the syndromes, (ii) obtaining information about the underlying pathophysiology, and (iii) identifying novel biological targets for preventive or therapeutic interventions”, i.e. the markers are not as strong as in other cases with different outcomes and biomarkers so we should build models that use multiple markers that capture more aspects of the disease, exactly the model applicant claims.
In regards applicant claim that Tarekegn et al. teaches LR only as a general ML approach for administrative health databases of clinical and socioeconomic variables, Tarekegn et al. teaches “the study examines the existing machine learning techniques (artificial neural networks, genetic programming, support vector machines, logistic regression, decision trees and random forests) to predict frailty according to the different adverse health outcomes”, and the title of the paper is Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches. This is exactly directed at the use of logistic regression for predicting frailty, and furthermore applicant even admits this in their use of the phrase clinical variables, i.e. biomarkers.
In regards applicant claim that Jeffery et al. discloses ACE inhibitors only in the broader context of cardiovascular disease management, Jeffery et al. teaches “Cardiovascular pathologies are common in frail older people”, “while mobility problems are particularly linked to cerebrovascular disease, the most prevalent frailty criterion in patients with cardiovascular disease is muscle weakness”, “the association between frailty and hypertension is complex”, and “Here, we explore the associations between frailty and co-morbidity and evaluate whether the management of chronic disease may impact frailty development or progression. We consider how the management of hypertension may have an important role in the prevention of frailty, mediated by reduction of cerebrovascular disease, but why aggressive management of hypertension may have negative consequences for those who are already frail. We also summarise the evidence linking immunosenescence, inflammation and endocrine changes to frailty and investigate whether targeted drug therapy has the potential to influence frailty pathophysiology”. This is not a mere broader context of cardiovascular disease management, but rather inoperably linking the co-morbidities and suggesting how treatments can overlap or help in one context and hurt in another.
In regards applicant claim that Fiuzat et al. does not teach a GDF-15 guided method for treating frailty or combination of features, applicant is correct. But examiner never used Fiuzat et al. in such contexts but rather quoted Fiuzat et al. teaching “Concentrations of amino-terminal pro-Btype natriuretic peptide (NT-proBNP) were chosen because they are strongly associated with outcomes in patients with HF and may be lowered using standard therapies inHF-rEF, such as β-blockers, angiotensin-converting enzyme inhibitors (ACEis), angiotensin II receptor blockers (ARBs), and mineralocorticoid receptor antagonists (MRAs). Smaller trials examining the use of NT-proBNP concentrations to “guide” GDMT have suggested that the approach leads to more assiduous application of GDMT along with better outcomes compared with usual care” and stated that in view of the teachings of Jeffery et al., which linked multiple co-morbidities with frailty, including cardiovascular disease, immunosenescence, inflammation, and endocrine changes with frailty, which is sup[ported by the following teaching from Jeffery et al. “Antagonising negative compensatory mechanisms in the neurohormonal renin–angiotensin–aldosterone system and sympathetic nervous system is the fundamental treatment methodology through all phases of cardiovascular disease. Angiotensin converting enzyme inhibitors also improve skeletal muscle function and have been proposed as a therapy for sarcopenia itself”, suggesting treatments for cardiovascular disease have beneficial impacts on other conditions associated with frailty sarcopenia which was discussed in Calvani et al. with the biomarkers discussed previously.
In regards applicant claim that Beckonert et al. does not teach apply the H-NMR methodology to frailty prediction, Beckonert et al. teaches “Metabolic profiling, metabolomic and metabonomic studies mainly involve the multicomponent analysis of biological fluids, tissue and cell extracts using NMR spectroscopy and/or mass spectrometry (MS)”, of which the biomarkers are part of metabolic profiling, therefore it would be obvious that said techniques would be used for the profiling of the stated biomarkers, particularly serum levels such as serum albumin.
In regards applicant claim that Cavusoglu et al. does not teach the therapeutic use of other drugs recited in claims 19 and 20 for treatment of frailty, examiner quoted Cavusoglu et al. as saying “Levosimendan and dobutamine are comparable inotropic drugs as regards their hemodynamic effects and clinical outcomes in decompensated heart failure (HF). The aim of this study was to compare the effects of levosimendan and dobutamine treatment on NTproBNP levels in patients with decompensated HF…Both levosimendan and dobutamine treatments result in significant reduction of NT-proBNP levels at the end of the 24-h infusion. However, compared to dobutamine, levosimendan has better and prolonged effects on NT-proBNP levels in decompensated HF”, here again as in Fiuzat et al., in view of the teachings of Jeffery et al., which linked multiple co-morbidities with frailty, including cardiovascular disease, immunosenescence, inflammation, and endocrine changes with frailty, which is sup[ported by the following teaching from Jeffery et al. “Antagonising negative compensatory mechanisms in the neurohormonal renin–angiotensin–aldosterone system and sympathetic nervous system is the fundamental treatment methodology through all phases of cardiovascular disease. Angiotensin converting enzyme inhibitors also improve skeletal muscle function and have been proposed as a therapy for sarcopenia itself”, suggesting treatments for cardiovascular disease have beneficial impacts on other conditions associated with frailty
In regards applicant claim that there is no motivation to combine examiner has articulated the motivation to combine as each of the cited references either directly linked the biomarkers and methods to frailty prediction, or taught connections between various co-morbidities and frailty with suggestions on overlapping biomarkers and treatments, even suggesting similar multivariate models as applicant’s to compensate for weaker biomarkers. As such these are merely simple substitutions of biomarkers within linear models with optimized weights, all of which have known/expected outcomes, the only variance being the level of accuracy, which is in no small part driven by the data used for fitting the model.
In regards applicant claim that there are unexpected results in terms of high predictive performance, this is a difference in degree not kind, i.e. this is an argument related to the raw statistical success of a machine learning model, driven by the data used and optimizations made, not whether a person of ordinary skill in the art would predict. This model is merely obvious to try as all biomarkers have been shown to be associated in some way with predicting frailty and similar machine learning models used to predict frailty, the high accuracy of applicant’s model only serves to confirm that the expected path worked successfully, not that the choice of the path is retroactively unexpected.
Conclusion
No claims are allowed.
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/K.N.A./ Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
