DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 are rejected under 35 U.S.C. 101 because:
The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) “ comparing the quantitative value(s) of the at least one biomarker to a control sample or to a control value; wherein an increase or a decrease in the quantitative value(s) of the at least one biomarker, when compared to the control sample or to the control value, is/are indicative of the subject having an increased risk of developing a mental disorder.” This judicial exception is not integrated into a practical application because these limitations recite abstract ideas (i.e. invoke a judicial exception) and this judicial exception is not integrated into a practical application because the above cited limitations are both directed to an abstract idea, which could be performed by a mental step or with the use of a black box computer.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additionally recited steps solely recite “determining in a biological sample obtained from the subject a quantitative value of at least one biomarker of the following in the biological sample” and that “ the quantitative value of glycoprotein acetyls quantifies a nuclear magnetic resonance spectroscopy signal that represents the abundance of circulating glycated proteins, and wherein the mental disorder is anxiety disorder.” The determining step and quantification are being viewed as simply mere data gathering steps, wherein quantification is done by NMR (which is well known technology, as will be detailed below in the prior art rejections).
Furthermore, it is noted that data gathering to be used in the abstract idea is insignificant extrasolution activity, and not a particular practical application. See MPEP 2106.05(g).
In addition, the examiner notes that office policy (per the July 2015 Interim Eligibility Guidance with regards to USC 101 rejections) states that abstracts ideas may be ideas themselves and that one specific example of an abstract idea is the idea of comparing new and stored information (such comparing the measured levels at least one signature protein) and using rules to identify options (such as whether or not the subject should be diagnosed with a mycobacterium species infection).
In addition, the examiner notes that following the procedure outlined in Mayo
Collab. Svcs. v. Prometheus Labs (SCOTUS) 101 USPQ2d 1961, 132 S. Ct. 1289 (2012) that claim 1 (and therefore their dependent claims as well) would be ineligible since in step 1 it is noted that the claims are directed towards one of the statutory categories (i.e. a method of determining whether a subject is at risk of developing a mental disorder)), while in step 2a it is noted that the claim is directed to a judicial exception (an abstract idea, as described above as the comparison step) and in step 2b it is noted that the claims do not recite additional elements that amount to significantly more than the judicial exception since the additional measuring step is simply a mere data gathering step.
Therefore claims 1-14 are ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
For example, in claim 1 it is unclear whether any of the biomarkers (besides glycoprotein acetyls) are types or consist of glycoprotein acetyls.
In addition, claim 1 recites that “the mental disorder is anxiety disorder” and it is unclear what level of anxiety is encompassed by the mental disorder.
In addition, in claim 10 it is unclear whether each and every biomarker in the list must be found in both the biological sample and the control sample and what the control values are for each biomarker.
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.
Claim(s) 1, 2, 5, 6 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Fitzgerald et al ( WO 2021/089771) in view of Nicholson et al (WO 2011010104).
Regarding Claim 1, Fitzgerald et al teaches a method for determining whether a subject is at risk of developing a mental disorder; wherein the method comprises determining in a biological sample obtained from the subject a quantitative value of at least one biomarker (see page 4, line 5 – page 5, line 25, page 9, lines 13-25, page 11, lines 4-17 and page 13, lines 1-15), wherein the at least one biomarker may include GFAP, NSE, midkine, folate, vitamin BI.sub.2, iron, homocysteine, HDL cholesterol, LDL cholesterol, total cholesterol, C-reactive protein (CRP), ferritin, insulin, leptin, adiponectin, cystatin C, PAI-1 , tissue plasminogen activator (tPA), PAI-1 /tPA complex, resistin, interleukin 2 (IL-2), interleukin 4 (IL-4), interleukin 6 (IL- 6), IL-8, interleukin 10 (IL-10), VEGF, interferon gamma (I FN y), TNFa, interleukin 1 alpha (I L-1 a), interleukin 1 beta (I L-1 b), MCP-1 , EGF, D-dimer, NGAL, Soluble tumour necrosis factor receptor-1 (sTNFRI), BDNF and heart-type fatty acid binding protein (H-FABP)in an ex vivo sample isolated from the patient, b) establishing the significance of the level of biomarkers (see page 4, lines 16-27) and that the mental disorder diagnosed is preferably anxiety (see page 4, lines 28-29).
Furthermore, Fitzgerald et al teaches comparing the quantitative value(s) of the at least one biomarker to a control sample or to a control value; wherein an increase or a decrease in the quantitative value(s) of the at least one biomarker, when compared to the control sample or to the control value, is/are indicative of the subject having an increased risk of developing a mental disorder (see page 11, lines 4-17 and page 13, lines 1-15).
Furthermore, Fitzgerald et al teaches that the biomarkers are quantified via ELISA-based assays (see page 16, lines 14-22).
However, Fitzgerald et al does not teach that the biomarker is or comprises glycoprotein acetyls and that the quantification is performed using NMR, and that the nuclear magnetic resonance spectroscopy signal represents the abundance of circulating glycated proteins.
However, in the analogous art of determining whether an individual has an increased risk of developing ASD (which is a mental disorder), Nicholson et al teaches that one of the biomarkers measured is N-acetyl glycoprotein (see abstract), and that the quantification if performed using NMR, since NMR identifies clear metabolic differences between autistic and normal children, since NMR is reliable and sensitive technology (see page 2, lines 3-8) and that alternatively the biomarker may be measured using ELISA assays (see page 5, lines 1-3). Furthermore, Nicholson et al teaches that it is appreciated that to confirm NMR and mass spectrometry assignments, spectra may also be compared to known reference standards of the particular metabolites in question (see page 6, lines 6-17). Further, Nicholson et al teaches that NMR is used as it’s a high resolution spectroscopic platform (see page 16, lines 11-15). Accordingly, it would have been obvious to one of ordinary skill in the art to utilize glycogen acetyl as the biomarker since using N-acetyl glycoprotein would enable one of ordinary skill in the art to accurately detect a mental disorder (such as ASD) and in addition it would have been obvious to one of ordinary skill in the art to utilize NMR instead of ELISA to quantify the biomarker for the benefit of providing a more sensitive, reliable and high resolution spectroscopic platform for quantification.
Regarding Claim 2, Fitzgerald et al discloses determining in the biological sample quantitative values of a plurality (such as at least two) biomarkers (see page 9, lines 26-27). In addition, Fitzgerald et al also discloses that 10 biomarkers (a plurality) of biomarkers were utilized when the disorder being studied was anxiety (see page 14, lines 14-24).
In addition, Nicholson et al also teaches that a plurality of biomarkers are used (see page 4, lines 4-9, which recites that “any combination of two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen” of biomarkers may be utilized).
Regarding Claim 5, Fitzgerald et al teaches that the mental disorder comprises anxiety (see page 10, lines 6-7). Furthermore, Nicholson et al teaches that the disorder it diagnoses (ASD), commonly comprises/includes anxiety (see abstract).
Regarding Claim 6, the combination of Fitzgerald et al and Nicholson et al teaches that the quantitative value of the at least one biomarker is/are measured using nuclear magnetic resonance spectroscopy (see page 2, lines 3-8 and page 16, lines 11-15 of Nicholson et al).
Regarding Claims 11-13, Fitzgerald et al discloses determining in the biological sample quantitative values of a plurality (such as at least two) biomarkers (see page 9, lines 26-27). In addition, Fitzgerald et al also discloses that 10 biomarkers (a plurality) of biomarkers were utilized when the disorder being studied was anxiety (see page 14, lines 14-24).
In addition, Nicholson et al also teaches that a plurality of biomarkers are used (see page 4, lines 4-9, which recites that “any combination of two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen” of biomarkers may be utilized).
Claim(s) 3, 7-9 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Fitzgerald et al and Nicholson et al as applied to claim 1 above, and further in view of Pritchard et al (WO 2020242976 A1).
Regarding Claim 3, the combination of Fitzgerald et al and Nicholson et al teaches determining in the biological sample a quantitative value of glycoprotein acetyls (see page 4, lines 18-35 of Nicholson et al ) and teaches comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s); wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to the control sample or to the control value, is/are indicative of the subject having an increased risk of developing the mental disorder (see page 11, lines 4-17 and page 13, lines 1-15 of Fitzgerald et al and page 6, lines 6-17 of Nicholson et al).
However, the combination of Fitzgerald et al and Nicholson et al does not explicitly disclose also quantitively measuring albumin as one of the biomarkers.
However, in the analogous art of methods and systems for predicting the risk of a subject developing a polygenic disease, Pritchard et al teaches quantitatively measuring a clinical biomarker such as albumin and/or glycoprotein acetyls (see [0014] and [0018]) to determine a polygenic risk score (see [0018]). It would have been obvious to one of ordinary skill in the art to also utilize albumin as one of measured biomarkers (as taught by Pritchard et al) for the benefit of allowing one to determine a polygenic risk score and aid in the diagnosis of various disorders not mentioned in the previous combination.
Regarding Claim 7, the combination of Fitzgerald et al and Nicholson et al does not disclose that the method of claim 1 further comprises determining whether the subject is at risk of developing a mental disorder using a risk score, hazard ratio, odds ratio, and/or predicted absolute risk or relative risk calculated on the basis of the quantitative value(s) of the at least one biomarker or of the plurality of the biomarkers.
However, in the analogous art of methods and systems for predicting the risk of a subject developing a polygenic disease, Pritchard et al teaches a method of predicting the risk of an individual developing a polygenic disease or medically relevant trait is provided, the method comprising: a) providing a database comprising correlation data for associations between genetic variants and the disease or medically relevant trait based on genome-wide testing of a population for genetic variants associated with the disease or the medically relevant trait; b) genotyping the individual to determine if the individual has one or more of the genetic variants associated with the disease or the medically relevant phenotypic trait; c) calculating at least one polygenic risk score based on the genetic variants detected in the individual by genotyping, wherein the polygenic risk score (PRS) indicates the risk of the individual developing the disease or the medically relevant trait (see [0004]). In addition, Pritchard et al teaches a computer implemented method for predicting the risk of an individual developing a disease or medically relevant phenotypic trait is provided, the computer performing steps comprising: a) receiving genome sequencing data for an individual; b) identifying variant alleles present in the individual from the genome sequencing data, wherein the individual has a plurality of variant alleles selected from Tables 5-10 and 13; c) calculating at least one polygenic risk score using a database, as described herein, based on the variant alleles present in the individual, wherein the polygenic risk score (PRS) indicates the risk of the individual developing the disease or the medically relevant trait; and d) displaying information regarding the risk of the individual developing the disease or the medically relevant trait.In certain embodiments, the computer implemented method further comprises: a) generating a predictive model using one or more algorithms, wherein the predictive model is based on at least one PRS for a genetic association with a size effect on a clinical biomarker measurement and at least one PRS for a genetic association with the disease or the medically relevant trait; and b) calculating a combined risk score from the predictive model, wherein the combined risk score better predicts the risk of the individual developing the disease or the medically relevant trait than each separate PRS (see [0027]-[0028]). Accordingly, it would have been obvious to one of ordinary skill in the art to utilize a risk score (as taught by Pritchard et al) when measuring biomarkers for the benefit of effectively and more accurately indicating the risk of an individual developing a disease and enabling this determination to be effectively displayed.
Regarding Claims 8-9 and 14, the combination of Fitzgerald et al, Nicholson et al and Pritchard et al teaches that wherein the risk score, hazard ratio, odds ratio, and/or predicted relative risk and/or absolute risk is calculated on the basis of at least one further measure, and wherein the further measure comprises a characteristic of the subject such as age, height, weight, body mass index, race or ethnic group, smoking, and/or family history of mental and/or behavioral disorders of the subject (see [0016]-[0017], [0269] and claim 28 of Pritchard et al).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Fitzgerald et al and Nicholson et al as applied to claim 1 above, and further in view of Wolf et al (WO 2021186047).
Regarding Claim 4, the combination of Fitzgerald et al and Nicholson et al teaches determining in the biological sample a quantitative value of glycoprotein acetyls (see page 4, lines 18-35 of Nicholson et al ) and teaches comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s); wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to the control sample or to the control value, is/are indicative of the subject having an increased risk of developing the mental disorder (see page 11, lines 4-17 and page 13, lines 1-15 of Fitzgerald et al and page 6, lines 6-17 of Nicholson et al).
However, the combination of Fitzgerald et al and Nicholson et al does not explicitly disclose at least at least one fatty acid measure(s) of the following: ratio of docosahexaenoic acid to total fatty acids, docosahexaenoic acid, ratio of linoleic acid to total fatty acids, linoleic acid, ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids, ratio of omega-6 fatty acids to total fatty acids, omega-6 fatty acids, ratio of saturated fatty acids to total fatty acids, saturated fatty acids, fatty acid degree of unsaturation.
However, in the analogous art of blood marker analyses with biomarkers, Wolf et al teaches that stronger correlations were found between the microbiome and an inflammatory surrogate (glycoprotein acetyls, GlycA, FIG. 16A), as well as various emerging lipid measures linked to host health, such as HDL and VLDL particle size (HDL-D and VLDL-D, p=0.3 and 0.28 respectively), the lipid content of lipoprotein subfractions (including XL-HDL-L and L-HDL-L, p=0.39 and 0.37 respectively), and circulating polyunsaturated fatty acids (PUFA) fatty acid (omega-6 [FAo)6/FA] and PUFA [PUFA/FA] to total fatty acid ratios, p=0.31 for both) (see [0320]). Accordingly, it would have been obvious to one of ordinary skill in the art to utilize a fatty acid measure/ratio (as taught by Wolf et al) since such ratios show stronger correlations and therefore more effectively predict or diagnosis disorders.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Fitzgerald et al and Nicholson et al as applied to claim 1 above, and further in view of Wolf et al, Tasic et al (WO 2017100879), and KADDURAH-DAOUK et al (WO 2007050318 A2).
Regarding Claim 10, the combination of Fitzgerald et al and Nicholson et al teaches determining in the biological sample a quantitative value of glycoprotein acetyls (see page 4, lines 18-35 of Nicholson et al ) and teaches comparing the quantitative value(s) of the biomarkers to quantitative value(s) of the biomarkers in a control sample or to a control value(s); wherein an increase or a decrease in the quantitative value(s) of the biomarkers, when compared to the control sample or to the control value, is/are indicative of the subject having an increased risk of developing the mental disorder (see page 11, lines 4-17 and page 13, lines 1-15 of Fitzgerald et al and page 6, lines 6-17 of Nicholson et al).
In addition, the combination of Fitzgerald et al and Nicholson et al teaches measuring triglycerides in high-density lipoprotein (HDL), - triglycerides in low-density lipoprotein (LDL), - high-density lipoprotein (HDL) particle size, - low-density lipoprotein (LDL) particle size, - very-low-density lipoprotein (VLDL) particle size (see page 13, line 27 – page 14, line 2 and page 4, lines 16-29).
However, the combination of Fitzgerald et al and Nicholson et al does not teach that the following biomarkers are also measured:
- the ratio of monounsaturated fatty acids and/or of oleic acid to total fatty acids, - the ratio of omega-3 fatty acids to total fatty acids, - the ratio of omega-6 fatty acids to total fatty acids,
- the ratio of saturated fatty acids to total fatty acids,
- fatty acid degree of unsaturation,
- monounsaturated fatty acids and/or oleic acid, - omega-3 fatty acids, - omega-6 fatty acids, - saturated fatty acids, particle size.
However, in the analogous art of blood marker analyses with biomarkers, Wolf et al teaches that stronger correlations were found between the microbiome and an inflammatory surrogate (glycoprotein acetyls, GlycA, FIG. 16A), as well as various emerging lipid measures linked to host health, such as HDL and VLDL particle size (HDL-D and VLDL-D, p=0.3 and 0.28 respectively), the lipid content of lipoprotein subfractions (including XL-HDL-L and L-HDL-L, p=0.39 and 0.37 respectively), and circulating polyunsaturated fatty acids (PUFA) fatty acid (omega-6 [FAo)6/FA] and PUFA [PUFA/FA] to total fatty acid ratios, p=0.31 for both) (see [0320]). Accordingly, it would have been obvious to one of ordinary skill in the art to utilize a fatty acid measure/ratio (as taught by Wolf et al) since such ratios show stronger correlations and therefore more effectively predict or diagnosis disorders.
In addition, Fitzgerald et al, Nicholson et al and Wolf et al does not disclose that biomarkers measured include acetate, citrate, glutamine and histidine.
However, in the analogous art of biomarkers for serious mental diseases, Tasic et al teaches that acetate, citrate, glutamine and histidine are utilized as biomarkers for diagnosing/predicting mental disorders (see [0010]-[0011]). It would have been obvious to one of ordinary skill in the art to utilize acetate, citrate, glutamine and histidine as additional biomarkers since these biomarkers are amino acids and well known metabolites that allow effective metabolic profiling to diagnose serious mental disorders.
In addition, Fitzgerald et al, Nicholson et al, Wolf et al and Tasic et al does not disclose that biomarkers measured include linoleic acid and linoleic acid/fatty acid ratios or docosahexanoic acid and docosahexanoic acid/fatty acid ratios.
However, in the analogous art of lipidomics approaches for central nervous system disorders, KADDURAH-DAOUK et al teaches the use of linoleic acid and fatty acids (and therefore potentially their ratios) as biomarkers measured for diagnosing and/or predicting central nervous system disorders (see page 13, lines 12-36). In addition, KADDURAH-DAOUK et al teaches using docosahexanoic acid and docosahexanoic acid/fatty acid ratios, wherein fatty acid ratios serve to predict CNS (see pages 38-42).
It would have been obvious to one of ordinary skill in the art to utilize linoleic acid and linoleic acid/fatty acid ratios, as well as docosahexanoic acid and docosahexanoic acid/fatty acid ratios as additional biomarkers since these biomarkers are amino acids and well known metabolites that allow effective metabolic profiling to diagnose/predict central nervous system disorders by profiling lipid metabolites to characterize changes in lipid metabolism as they relate to CNS disorders; to predict the efficacy of treatments; monitoring progression of CNS and thus serving as an early predictor of worsening clinical condition and thereby facilitate early therapeutic intervention or alterations in treatment regimen.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER WECKER whose telephone number is (571)270-1109. The examiner can normally be reached 9:30AM - 6 PM EST M-F.
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/JENNIFER WECKER/ Primary Examiner, Art Unit 1797