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 Status
Claims 1, 16-41, and 43-46 are pending.
Claims 16-17, 19-29, and 31-32 are examined on the merits.
Claims 1, 33-36, 37-38,39-41, and 43-46 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or liking claim.
Claims 2-15, 42, and 46 were previously canceled.
Claims 18 and 30 are canceled.
Priority
Applicant's claim for the benefit of a prior-filed application, U.S. Provisional App. No. 62/968 ,941 filed 01/31/2020 is acknowledged. In this action, all claims are examined as though they had an effective filing date of 01/31/2020. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Withdrawn Rejections/Objections
Rejections and/or objections not reiterated from previous office actions are hereby
withdrawn in view of the amendments filed 05/07/2026.
All rejections of claims 18 and 30 are hereby withdrawn; their cancelation moots the
rejections.
The objections to claims 16 and 23 are withdrawn in the office action filed 02/10/2026 has been withdrawn in view of amendments received 05/07/2026 (pg. 8) specifically by replacing “a output” with “an output” in claim 16, and replacing “used a machine” with “using a machine”.
The 35 U.S.C. 112(b) rejections to claims 32 are withdrawn in the office action filed 02/10/2026 has been withdrawn in view of amendments received 05/07/2026 (pg. 8) specifically by pointing to specification and further explaining the “therapeutically effective amount”.
The following rejections and/or objections are either maintained or newly applied. They constitute the complete set presently being applied to the instant application.
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 16-17, 19-29, and 31-32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim(s) 16-17, 19-29, and 31-32 being representative) is directed to a method. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claims 16-17, 19-29, and 31-32 recite the following steps which fall under the mathematical concepts, mental processes, and/or certain methods of organizing human activity groupings of abstract ideas:
Claim 16 recites determining if the output probability is above or below a threshold for a classification; the limitation determining if a probability is above or below a threshold is considered a mathematical relationship, and as such, falls into mathematical concepts groupings of abstract ideas. Said limitation can also be performed in human mind (mental process), since human mind is capable of determining a mathematical relationship, for example, by using pen and paper.
Claim 16 further recites identifying a classification for the sample based on whether the output probability is above or below a threshold for a classification; the limitation “identifying”, given the plain meaning of “identifying” includes observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.), and as such falls into mental processes of abstract ideas, since human mind is capable of identifying a classification based on the result of a mathematical analysis.
Claim 16 further recites diagnosing the patient with the disease or condition based on the classification; the limitation of diagnosing based on the classification is considered a mental process, since the plain meaning of “diagnosing” encompasses observation, evaluation, judgment, and opinion (See MPEP 2106.04(a)(2), subsection III.) performable by human mind (mental process), since human mind is capable of diagnosing based on the result of an analysis.
Claim 23 recites that the model was trained using a mathematical algorithm (mathematical calculation/mathematical concepts).
Claim 26 recites comparing the quantification at the first time point with the quantification at the second time point (mathematical relationship/mathematical concepts).
Claim 27 recites comparing the quantification at the fourth time point with the quantification at the third time point (mathematical relationship/mathematical concepts).
Claim 28 recites monitoring the health status of a patient (mental process).
Claim 31 recites diagnosing a patient with a disease or condition based on the classification (mental process of diagnosing/identifying).
Claims 17-22, 24-25, and 29 provide further information.
Additionally, claims 16-17, 19-29, and 31-32 recite a correlation between glycopeptides in a patient’s sample and classification of disease, and as such, falls into judicial exception of Laws of nature and natural phenomena. See MPEP 2106(b) I.
The identified claims recite a law of nature, a natural phenomenon (product of nature) and/or fall into one of the groups of abstract ideas of mathematical concepts, mental processes, and/or certain methods of organizing human activity for the reasons set forth above. See MPEP 2106.04 (a)(2) III and MPEP 2106.04 (b) I. Therefore, claims are directed to one or more judicial exception(s) and require further analysis in Prong Two. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
The additional elements of claims 16-17, 19-29, and 31-32 include the following.
Claim 16 recites quantifying by multiple-reaction-monitoring (MRM) one or more glycopeptides in a sample; and inputting the quantification into a trained model to generate an output probability; treating the patient with a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy, and combinations thereof
Claim 26 recites quantifying by MRM a first glycopeptide in a sample at a first time point; quantifying by MRM a second glycopeptide in a sample at a second time point.
Claim 27 recites quantifying by MRM a third glycopeptide in a sample at a third time point; quantifying by MRM a fourth glycopeptide in a sample at a fourth time point.
Claim 32 recites treating the patient with a therapeutically effective amount of a therapeutic agent.
The additional elements of inputting and outputting/generating an output amount to necessary data gathering and outputting. The courts have found the limitations that amount to necessary data gathering and outputting are insignificant extra-solution activity that do not integrate a recited judicial exception into a practical application in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and O/P Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (see MPEP 2106.05(g)).
Furthermore, the additional elements of quantifying by multiple-reaction-monitoring (MRM) transitions associated one or more glycopeptides in a sample only serves to collect the information for use by the abstract idea.
Furthermore, the limitation treating the patient with a therapeutically effective amount of a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy, and combinations thereof is merely an intended use of the claimed invention or a field of use limitation, and it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. In addition, this treating step is not particular, and is instead merely instructions to "apply" the exception in a generic way. Thus, the administration step does not integrate the mental analysis step into a practical application. See MPEP 2106.04(d)(2).
Therefore, the additionally recited elements amount to insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application. Thus, claims 16-17, 19-29, and 31-32 are directed to an abstract idea. [Step 2A, Prong 2: NO]
MPEP 2106.04(d). I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
In Step 2A, Prong 1 above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In Step 2B below, any remaining steps and/or elements are therefore in addition to the identified JE(s). Any such additional steps and additional elements are further discussed in Step 2B.
Here in Step 2A, Prong 2, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
At this point in examination, it is not yet the case that any of the Step 2A, Prong 2 considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of 1. an improvement, 2. treatment, 3. a particular machine or 4. a transformation is clear in the record.
For example, regarding the first consideration at MPEP 2106.04(d)(2), the record, including for example the specification, does not yet clearly disclose an explanation of a particular treatment or prophylaxis for a disease or medical condition. The claims do not yet clearly result in such particular treatment or prophylaxis.
In conclusion regarding Prong 2, claims 16-17, 19-29, and 31-32 are directed to an abstract idea.
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. An inventive concept cannot be furnished by an abstract idea itself. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception.
The additional elements of claims 16-17, 19-29, and 31-32 include the following.
Claim 16 recites quantifying by multiple-reaction-monitoring (MRM) one or more glycopeptides in a sample; and inputting the quantification into a trained model to generate an output probability; treating the patient with a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy, and combinations thereof
Claim 26 recites quantifying by MRM a first glycopeptide in a sample at a first time point; quantifying by MRM a second glycopeptide in a sample at a second time point.
Claim 27 recites quantifying by MRM a third glycopeptide in a sample at a third time point; quantifying by MRM a fourth glycopeptide in a sample at a fourth time point.
Claim 32 recites treating the patient with a therapeutically effective amount of a therapeutic agent.
The additional elements of inputting and outputting/generating an output amount to necessary data gathering and outputting. The courts have found the limitations that amount to necessary data gathering and outputting are insignificant extra-solution activity that do not amount to significantly more (see MPEP 2106.05(g)).
Furthermore, the additional elements of quantifying by multiple-reaction-monitoring (MRM) one or more glycopeptides in a sample only amount to conventional methods and systems for performing mass spectrometry.
This position is supported by Kontostathi (Applications of multiple reaction monitoring targeted proteomics assays in human plasma, published online: 17 May 2019, Expert Review of Molecular Diagnostics, VOL. 19, NO. 6, 499–515; as cited on the attached Form 892). Kontostathi reviews multiple (or selected) reaction monitoring-mass spectrometry (MRM/SRM) methods that can be used for relative and absolute quantification of proteomic biomarker, targeting the analysis of lower abundance or modified (mainly glycosylated but also phosphorylated) proteins (abstract, pg. 500, col. 2, first para.).
Further, Kailemia (Recent Advances in the Mass Spectrometry Methods for Glycomics and Cancer, Anal Chem. 2018 January 02; 90(1): 208–224; as cited on the attached Form 892). Kailemia reviews … and teaches that multiple reaction monitoring is used for targeted analysis of glycoproteome (pg. 8, para. 4).
Further, Miyamoto (Multiple Reaction Monitoring for the Quantitation of Serum Protein Glycosylation Profiles: Application to Ovarian Cancer, Journal of Proteome Research, December 5, 2017, pages: 222-233; as previously cited on the Form 892 dated 02/10/2026) a targeted multiple reaction monitoring (MRM) based method for the protein- and site-specific quantitation involving serum proteins immunoglobulins A, G and M, alpha 1-antitrypsin, transferrin, alpha-2-macroglobulin, haptoglobin, alpha-1-acid glycoprotein and complement C3 (abstract).
Furthermore, the limitation treating the patient with a therapeutically effective amount of a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy, and combinations thereof is merely an intended use of the claimed invention or a field of use limitation, and it cannot amount to significantly more. In addition, this treating step is not particular, and is instead merely instructions to "apply" the exception in a generic way. Thus, the administration step does amount to significantly more. See MPEP 2106.05 A.
Therefore, the additional element is not sufficient to amount to significantly more than the judicial exception.
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments
Applicant's arguments filed 05/07/2026 have been fully considered but they are not persuasive. Applicant states:
claim 16 further includes the active elements of diagnosing the patient with the disease or condition based on the classification and treating the patient with a therapeutic agent selected from the recited group. Therefore, the present claims recite additional elements which would integrate the alleged judicial exception into a practical application (see USPTO October 2019 Update: Subject Matter Eligibility at III.C., pages 13-14). Claims 17 and 19-32 depend on claim 16 and therefore recite a practical solution.
As stated above, the limitation treating the patient with a therapeutically effective amount of a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy, and combinations thereof is interpreted as an intended use of the claimed invention or a field of use limitation, and it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration.
The treatment step of claim 16 is not yet clearly informed by the judicial exception and, relatedly, does not yet recite sufficient specificity regarding the treatment (the required treatment is limited only to broad classes of treatment covering most if not all possible treatments, in particular the recited "targeted therapy" reads on almost any treatment).
As such, the Rejection of claims under U.S.C. 101 is maintained.
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.
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 16-17, 19-24, 26-29, and 31-32 are rejected under 35 U.S.C. 103 as being unpatentable over Hood et al. (US8586006B2; as previously cited on the Form 892 dated 02/10/2026) in view of Miyamoto (Multiple Reaction Monitoring for the Quantitation of Serum Protein Glycosylation Profiles: Application to Ovarian Cancer, Journal of Proteome Research, December 5, 2017, pages: 222-233; as previously cited on the Form 892 dated 02/10/2026).
Regarding claims 16 and 17, Hood discloses methods for identifying and using organ-specific proteins and transcripts and a method for defining a biological state of a subject comprising (a) measuring the level of at least two organ-specific proteins selected from any one of the organ-specific protein sets provided in the Tables herein in a blood sample from the subject, where the level of the at least two organ-specific proteins is measured using mass spectrometry (col. 3, last para.). Hood further discloses that the organ-specific proteins that are being measured are glycosylated (col. 118, last para.); reading on limitations of a method for identifying a classification for a sample, the method comprising quantifying by mass spectroscopy (MS) one or more glycopeptides in a sample. Hood further discloses the glycopeptides of (Tables 34 A&B).
Hood further discloses that the sample is from a subject affiliated with a disease affecting the organ from which the organ-specific proteins are derived is above or below a predetermined normal range (col. 3, para. 4-6); reading on limitations of wherein the sample is a biological sample from a patient or individual having a disease or condition.
Hood further discloses that the statistical methods such as likelihood models, artificial intelligence, including artificial neural networks, machine learning, data mining, and boosting algorithms, and Bayesian analysis using prior probability distributions can be used to define statistical changes in the level of a particular protein measured between a normal control sample of blood and a blood sample (col. 112, para. 2); reading on limitations of inputting the quantification into a trained model to generate a output probability.
Hood further discloses comparing the amount of disease-specific protein detected to a predetermined normal control amount for each respective disease-specific protein; wherein a statistically significant altered level in said prostate-specific proteins indicates a perturbation in the normal biological state (claim 1, Example 2, col. 171-173). Hood further discloses determining if the output probability is above or below a threshold (col. 164, para.2); reading on limitations of determining if the output probability is above or below a threshold for a classification; and identifying a classification for the sample based on whether the output probability is above or below a threshold for a classification.
Hood further discloses diagnosis of specific diseases using the method (col. 175, para. 1); reading on limitations of diagnosing the patient with the disease or condition based on the classification.
Hood further discloses that by identifying organ-specific protein markers and measuring the level of these proteins in normal blood, the status of health or disease may be monitored through the correlation of the levels of proteins in this organ specific fingerprint at the earliest stages of disease and lead to early diagnosis and treatment (col. 82, para. 1). Hood further discloses that the application of disclosed organ-specific markers can lead to targeted treatments (col. 82, last para.). Hood further discloses that the invention also pertains to immunoconjugates comprising an antibody conjugated to a cytotoxic agent such as a chemotherapeutic agent, a growth inhibitory agent, a toxin (e.g., an enzymatically active toxin of bacterial, fungal, plant, or animal origin, or fragments thereof), or a radioactive iso tope (i.e., a radioconjugate) (col. 149, para. 1) reading on limitations of treating the patient with a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy, and combinations thereof.
Further regrading claims 16 and 17, Hood discloses the glycopeptides of Tables 34 A&B and quantifying by using Mass Spectrometry (MS) techniques such as ESI-MS/MS and LC-MS/MS (col. 118, para. 1).
Miyamoto discloses developing a targeted multiple reaction monitoring (MRM)-based method for the protein- and site-specific quantitation involving serum proteins immunoglobulins A, G and M, alpha-1-antitrypsin, transferrin, alpha-2-macroglobulin, haptoglobin, alpha-1-acid glycoprotein and complement C3 based on tryptic digestion of serum glycoproteins, followed by immediate reverse phase UPLC-QQQ-MS analysis of glycopeptides (abstract). Miyamoto further lists a number of glycopeptides monitored, disclosing “YLGNATAIFFLPDEGK” (Table 3). Miyamoto further discloses identifying the presence of cancer in the human, where the cancer is ovarian cancer, by obtaining samples from ovarian cancer patients. Miyamoto further discloses performing differential analysis and assesses he predictive potential of glycopeptides for ovarian cancer (pg. 230. Col. 1-2).
Regarding claim 19, Hood discloses that sample is blood sample from a subject afflicted with a disease affecting the organ from which the organ-specific proteins are derived is above or below a predetermined normal range. Hood further discloses that a disease refers to an immune disorder, such as autoimmune diseases, cancer (col. 163), or fibrosis (col. 162, para. 4); reading on limitation of the patient has cancer, an autoimmune disease, or fibrosis.
Regarding claims 20 and 22, Hood discloses that the patient has ovarian cancer (col. 164, para. 1).
Regarding claim 21, Hood discloses that the individual has an aging condition, for example, diabetes (col. 163, para. 1).
Regarding claim 23, Hood discloses that the trained model was trained used a machine learning algorithm selected from the group consisting of a deep learning algorithm, a neural network algorithm, an artificial neural network algorithm, a supervised machine learning algorithm, a linear discriminant analysis algorithm, a quadratic discriminant analysis algorithm, a support vector machine algorithm, a linear basis function kernel support vector algorithm, a radial basis function kernel support vector algorithm, a random forest algorithm, a genetic algorithm, a nearest neighbor algorithm, k-nearest neighbors, a naive Bayes classifier algorithm, a logistic regression algorithm, or a combination thereof (col. 112, para. 3).
Regarding claim 24, Hood discloses defining a biological state of a subject (col. 4, para. 1). Hood further discloses that the comparison of the normal levels of organ-specific proteins to the levels of these proteins found in a sample of patient blood or bodily fluid or other biological sample, such as a biopsy can be used to define normal health, detect the early stages of disease, monitor treatment, prognosticate disease, measure drug responses, titrate administered drug doses, evaluate efficacy, stratify patients according to disease type (e.g., prostate cancer may well have four or more major types) and define therapeutic targets when therapeutic intervention is most effective (col. 82, last para.); reading on limitations of the classification is a disease classification or a disease severity classification.
Regarding claims 26-28 Hood discloses standard screening test where one or more organ-specific proteins in a blood sample and any statistically significant deviation from a normal serum organ-specific blood fingerprint would indicate that disease-related perturbation was present (col. 131, para. 4). Hood discloses that in order to monitor the progression of a disease, or monitor responses to therapy, one or more organ-specific blood fingerprints are detected/measured as described herein using any of the methods as described herein at one time point and detected/measured again at subsequent time points, thereby monitoring disease progression or responses to therapy (col. 132, para. 3).
Regarding claim 29, Hood discloses that after isolating glycopolypeptides from a sample and cleaving the glycopolypeptide into fragments, the glycopeptide fragments released from the solid support and the released glycopeptide fragments are identified and/or quantified using mass spectrometry (col. 119, L. 6-25); reading on limitations of quantifying by MS a glycopeptide from whence the amino acid sequence selected from the group consisting of SEQ ID NOs: 1 - 76 was fragmented.
Regarding claim 31, Hood discloses that perturbation of the normal biological state is identified by measuring levels of organ-specific proteins from a patient and comparing the measured levels against the predetermined normal levels; therefore, providing panels for detecting and measuring the level of organ-specific proteins in blood that can be used in a variety of diagnostic settings (col. 122, para. 1). Miyamoto discloses that the classified glycoproteins are diagnostic markers for the detection of OC in blood (pg. 231, col. 1, para. 1).
Regarding claim 32, Hood discloses therapeutically treating a mammal having a cancerous cells or disease containing cells or tissues comprising cells that express an organ-specific polypeptide, wherein the method comprises administering to the mammal a therapeutically effective amount of an antibody, an oligopeptide or a small organic molecule that binds to the organ-specific polypeptide, thereby resulting in the effective therapeutic treatment of the tumor (col. 138, para. 3); reading on limitations of treating the patient with a therapeutically effective amount of a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy, and combinations thereof.
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard to Hood and Miyamoto, Examiner concludes that the combination of Hood and Miyamoto represents the use of known techniques to improve similar methods. Both Hood and Miyamoto are directed identifying glycopeptide biomarkers and classification of diseases. Hood only disclosed quantifying by mass spectroscopy (MS) one or more glycopeptides in a sample (Tables 34 A&B). In the same field of research, Miyamoto provided quantifying using the known technique of MRM-MS and the specific glycopeptide sequence, for example, SEQ ID 1: YLGNATAIFFLPDEGK. Combining the disease classification of Hood with quantification of specific glycopeptide using MRM-MS technology of Miyamoto would have allowed for more accurate cancer diagnosis since MRM offers superior sensitivity, selectivity, and reproducibility in biological samples targeting specific molecules. One ordinary skilled in the art before the effective filing data of the claimed invention would have had a reasonable expectation of success at using MRM technology to quantify glycoproeins and using the result in the classification of Hood and this combination would have been expected to have provided a more accurate diagnosis of disease, for example, ovarian cancer. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Claims 25 is rejected under 35 U.S.C. 103 as being unpatentable over Hood et al. (US8586006B2) in view of Miyamoto (Multiple Reaction Monitoring for the Quantitation of Serum Protein Glycosylation Profiles: Application to Ovarian Cancer, Journal of Proteome Research, December 5, 2017, pages: 222-233), as applies to claims 16-17, 19-24, 26-29, and 31-32 above, and further in view of Vogelstein et al. (US12195803B2; as previously cited on the Form 892 dated 02/10/2026).
Claim 25 depends on claim 24 and claim 16. Limitations of claims 1 and 24 have been taught in the above rejections.
Regarding claim 25, Hood discloses that statistically significant altered level in one or more of the organ-specific proteins indicates a perturbation in the normal biological state (col. 5, last para.)
Hood discloses that any of a variety of statistical methods known in the art and described herein, can be used to evaluate organ-specificity and, as discussed further herein, define statistical changes in the level of a particular protein measured between a normal control sample of blood and a blood sample that is changed from normal.
Miyamoto discloses high specificity (true negatives) and sensitivity (true positives) thresholds and AUC score of higher than 0.80 indicating clinically useful diagnostic discriminations with low standard deviations (Supplementary Table S1).
Vogelstein discloses methods and materials for identifying a subject as having cancer (e.g., a localized cancer) are provided in which the presence of member(s) of two or more classes of biomarkers are detected (abstract). Vogelstein further discloses that the method for identifying the presence of cancer in a human comprises detecting a level of protein biomarkers derived from a plasma sample obtained from the human; comparing the detected levels of each of the at least four protein biomarkers to reference levels of each of the protein biomarkers; and identifying the presence of cancer in the human, where the cancer is ovarian or endometrial cancer (claim 1; col. 21, first para.). Vogelstein further discloses that the biomarker is A1AT (Col. 78, para. 1) that is detected using mass spectrometry (col. 361, L.31).
Vogelstein further discloses identifying the presence of cancer in a subject based on one or more protein biomarkers (e.g., protein concentrations in whole blood or plasma) (col.70, para. 1). Various methods can be used to determine whether the subject has cancer and/or the likelihood that the subject has cancer. These methods involve various types of statistical techniques and methods described herein, including, e.g., a regression model, a logistic regression model, a neural network, a clustering model, principal component analysis, correlated component analysis, nearest neighbor classifier analysis, linear discriminant analysis, quadratic discriminant analysis, a support vector machine, a decision tree, Random Forest, a genetic algorithm, classifier optimization using bagging, classifier optimization using boosting, classifier optimization using the Random Subspace Method, a projection pursuit, and genetic programming and weighted voting, etc (col. 161, last para.; col. 166, para. 2 and last para.; col. 167).
Vogelstein further discloses a likelihood score that a subject has cancer (col. 161-162, col. 163, para. 2).
Vogelstein further discloses the performance of the method with the test's average performance (61%) at >99% specificity. Error bars represent 95% confidence intervals for sensitivity and specificity at this particular point. The median performance among the 8 cancer types assessed was 70%, as noted in the main text. (B) Sensitivity of CancerSEEK by stage. Error bars represent standard errors of the median. (C) Sensitivity of CancerSEEK by tumor type. Error bars represent 95% confidence intervals. Proportion detected for ovarian cancer is more than 97%. Proportion of patients correctly classified is100% (col. 22, para. 5, col. 23. FIG. 4, 8-9).
Applying the KSR standard to Hood, Miyamoto and Vogelstein, Examiner concludes that the combination of Hood, Miyamoto and Vogelstein represents the use of known techniques to improve similar methods. Hood, Miyamoto and Vogelstein are directed identifying glycopeptide biomarkers and classification of diseases. Hood and Miyamoto disclosed quantifying by multiple reaction monitoring mass spectroscopy (MRM-MS) one or more glycopeptides such as YLGNATAIFFLPDEGK and classifying the sample with clinically useful diagnostic discrimination with high specificity and sensitivity and high AUC. In the same field of research, Vogelstein provided the specific statistical results of confidence level of more than 80%. One ordinary skilled in the art before the effective filing data of the claimed invention would have had a reasonable expectation of success at using statistical analysis of Vogelstein to the method of Hood and Miyamoto. This combination would have been expected to have provided a more accurate diagnosis of disease, for example, ovarian cancer. Therefore, the invention would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention, absent evidence to the contrary.
Response to Arguments
Applicant's arguments filed 05/07/2026 have been fully considered but they are not persuasive. Applicant states:
Hood fails to disclose or otherwise suggest the glycopeptides consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1 - 76, and "quantifying by multiple-reaction-monitoring (MRM) transitions associated with one or more glycopeptides in a sample", as recited in present claim 16. Applicant submits that the present claims are non-obvious, the cited references, alone or in combination, do not disclose or suggest the method in claims 1-32. In view of the amendments and arguments above, Applicant respectfully requests reconsideration and withdrawal of the rejection under 35 U.S.C. § 103.
The amendments to claims necessitated a new round of art rejections. As such, combination of Hood and Miyomoto and Vodelstein teach all the limitations of the claimed invention.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 16—17, 19-24, 26-29, and 31-32 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 13, 20, 22-24, 28, and 31-33, 53, and 64 of U.S. Application No. in view of Hood et al. (US8586006B2), and further in view Miyamoto et al. (Multiple Reaction Monitoring for the Quantitation of Serum Protein Glycosylation Profiles: Application to Ovarian Cancer, J. Proteome Res. 2018, 17, 222−233, December 5, 2017).
The claims are directed to identifying glycopeptides and classifying disease.
Instant Application
‘971 Application
Claim
Limitations
Claim
Limitations
16, 18, 19, 20, 21, 22, 24, 28, 29, 30, 31
A method for identifying a classification for a sample, the method comprising quantifying by multiple reaction monitoring (MRM) one or more glycopeptides in a sample wherein the glycopeptides each, individually in each instance, comprises a glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1 - 76, and combinations thereof; and inputting the quantification into a trained model to generate a output probability; determining if the output probability is above or below a threshold for a classification; diagnosing the patient with the disease or condition based on the classification and treating the patient with a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy, and combinations thereof.; and identifying a classification for the sample based on whether the output probability is above or below a threshold for a classification; the sample is a biological sample from a patient or individual having a disease or condition; claim 19: the patient has cancer, an autoimmune disease, or fibrosis; claim 20 : wherein the patient has ovarian cancer; claim 21: the individual has an aging condition claim 22: the disease or condition is ovarian cancer. ; claim 24: the classification is a disease classification or a disease severity classification; claim 28: monitoring the health status of a patient; claim 29: quantifying by MS a glycopeptide from whence the amino acid sequence selected from the group consisting of SEQ ID NOs: 1 - 76 was fragmented; diagnosing a patient with a disease or condition based on the classification; claim 31: diagnosing the patient as having ovarian cancer based on the classification
13, 31, 33, 53, 64
A method for identifying a classification for a biological sample from a patient or individual having a disease or condition and monitoring the health status of the patient, the method comprising quantifying by multiple reaction monitoring mass spectroscopy (MRM-MS) one or more glycopeptides in a sample using a Triple Quadrupole Mass Spectrometer (QQQ) and/or qTOF mass spectrometer; wherein the one or more glycopeptides comprises at least one glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1 -262 and wherein the glycopeptide comprises glycan 5402 and at least one of glycans 3200, 3210, 3300, 3310, 3320, 3400, 3410, 3420, 3500, 3510, 3520, 3600, 3610, … treating the individual with a therapeutically effective amount of a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy,and combinations thereof.; wherein the method further comprises detecting the glycosylation site residue where the glycan bonds to the glycopeptide, or detecting a glycosylation site on a glycopeptide; and inputting the quantification into a trained model to generate an output probability, wherein the trained model compares the quantification to types, absolute amounts, and relative amounts of glycopeptides in at least one previously classified sample, or to a reference value obtained from a population of individuals whose disease state is known; determining if the output probability is above or below 0.30 for a classification; and identifying a classification for the sample based on whether the output probability is above or below 0.30 wherein the classification is having ovarian cancer or not having ovarian cancer and the classification is used to monitor the onset and progression of disease in the patient. Claim 31 and 53: diagnosing the individual as having ovarian cancer based on the classification; Claim 33: digesting and/or fragmenting one or more glycoproteins
17
claim 17: the glycopeptides each, individually in each instance, comprises a glycopeptide: consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1, 6, 7, 8, 10, 15, 21, 22, 23, 42, 47, 48, 51, 56, 57, 58, 62, 74, and 76, and combinations thereof; consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 1, 3,4,5, 8, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 27, 30, 32, 34, 43, 45, 51, 54, 55, 65, 68, 71, 73, 74, 75, and combinations thereof; or consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 2, 6, 7, 9, 10, 15, 21, 26, 28, 29, 31, 33, 35, 36, 37, 38, 39, 40, 41, 42, 44, 46, 47, 48, 49, 50, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 69, 70, 72, 73, 74, 76, and combinations thereof;
28
wherein the one or more glycopeptides comprise at least one glycopeptide consisting essentially of an amino acid sequence selected from the group consisting of SEQ ID NOs: 4, 5, 9, 12, 22, 24, 28, 32, 34, 35, 36, 37, 38, 53, 61, 65, 69, 82, 99, 104, 114, 115, 126, 128, 136, 146, 147, 150, 154, 177, 184, 190, and 194.
23
the trained model was trained used a machine learning algorithm selected from the group consisting of a deep learning algorithm, a neural network algorithm, an artificial neural network algorithm, a supervised machine learning algorithm, a linear discriminant analysis algorithm, a quadratic discriminant analysis algorithm, a support vector machine algorithm, a linear basis function kernel support vector algorithm, a radial basis function kernel support vector algorithm, a random forest algorithm, a genetic algorithm, a nearest neighbor algorithm, k-nearest neighbors, a naive Bayes classifier algorithm, a logistic regression algorithm, or a combination thereof.
20
the trained model was trained using a machine learning algorithm selected from the group consisting of a deep learning algorithm, a neural network algorithm, an artificial neural network algorithm, a supervised machine learning algorithm, a linear discriminant analysis algorithm, a quadratic discriminant analysis algorithm, a support vector machine algorithm, a linear basis function kernel support vector algorithm, a radial basis function kernel support vector algorithm, a random forest algorithm, a genetic algorithm, a nearest neighbor algorithm, k-nearest neighbors, a naive Bayes classifier algorithm, a logistic regression algorithm, or a combination thereof.
25
the classification is identified with greater than 80 % confidence, greater than 85 % confidence, greater than 90 % confidence, greater than 95 % confidence, greater than 99 % confidence, or greater than 99.9999 % confidence.
22
the classification is identified with greater than 80% confidence.
26
quantifying by MS a first glycopeptide in a sample at a first time point; quantifying by MS a second glycopeptide in a sample at a second time point; and comparing the quantification at the first time point with the quantification at the second time point.
23
quantifying by MRM-MS a first glycopeptide in a sample at a first time point; quantifying by MRM-MS a second glycopeptide in a sample at a second time point; and comparing the quantification at the first time point with the quantification at the second time point.
27
quantifying by MS a third glycopeptide in a sample at a third time point; quantifying by MS a fourth glycopeptide in a sample at a fourth time point; and comparing the quantification at the fourth time point with the quantification at the third time point.
24
quantifying by MRM-MS a third glycopeptide in a sample at a third time point;quantifying by MRM-MS a fourth glycopeptide in a sample at a fourth time point; and comparing the quantification at the fourth time point with the quantification at the third time point.
32
treating the patient with a therapeutically effective amount of a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy, and combinations thereof.
32
treating the individual with a therapeutically effective amount of a therapeutic agent selected from the group consisting of a chemotherapeutic, an immunotherapy, a hormone therapy, a targeted therapy, and combinations thereof.
Reference provisional Application No. 17/433,971 is different from instant application since it recites quantifying by multiple reaction monitoring mass spectroscopy (MRM-MS) one or more glycopeptides in a sample using a Triple Quadrupole Mass Spectrometer (QQQ) and/or qTOF mass spectrometer rather than quantifying by mass spectroscopy (MS) one or more glycopeptides in a sample as recited in instant claims.
Hood discloses methods for identifying and using organ-specific proteins and transcripts and a method for defining a biological state of a subject comprising (a) measuring the level of at least two organ-specific proteins selected from any one of the organ-specific protein sets provided in the Tables herein in a blood sample from the subject, where the level of the at least two organ-specific proteins is measured using mass spectrometry (col. 3, last para.). Hood further discloses that the organ-specific proteins that are being measured are glycosylated (col. 118, last para.); reading on limitations of a method for identifying a classification for a sample, the method comprising quantifying by mass spectroscopy (MS) one or more glycopeptides in a sample. Hood further discloses the glycopeptides of (Tables 34 A&B). Hood further discloses that the statistical methods such as likelihood models, artificial intelligence, including artificial neural networks, machine learning, data mining, and boosting algorithms, and Bayesian analysis using prior probability distributions can be used to define statistical changes in the level of a particular protein measured between a normal control sample of blood and a blood sample (col. 112, para.). Hood further discloses comparing the amount of disease-specific protein detected to a predetermined normal control amount for each respective disease-specific protein; wherein a statistically significant altered level in said prostate-specific proteins indicates a perturbation in the normal biological state (claim 1, Example 2, col. 171-173). Hood further discloses determining if the output probability is above or below a threshold (col. 164, para.2). Hood further discloses a method of identifying one or more genetic markers for a disease.
Miyamoto teaches a multiple reaction monitoring (MRM)- based method for the protein and site-specific quantitation of serum glycoproteins for the identification and classification of ovarian cancer in subjects (abstract). The subjects are preoperative (page 223, column 2, para 1). The method comprises enzymatic digestion/fragmentation and analysis of glycopeptides using MRM-MS on triple quadruploe (QQQ) spectrometers for the accurate quantitation of proteins from biological specimens (page 223, column 1, para 2); page 227, column 2, para 1). Eighteen transitions were monitored by QQQ for protein and protein subclass quantitation (table 2). The glycoprotein A1AT which is comprised of the peptide sequence AVLTIDEK was identified as a glycoprotein within the biological sample from patient with ovarian cancer (table 2). Said peptide sequence is 100% identical to SEQ ID NO: 4 of the instant application claims 13, 53, 64 (appendix A). Said glycoprotein is comprised of glycan 5402, which is H5N4S2and glycan 6503, which is also represented as H6N5S3 (page 223, column 2, para 3). Said glycans are attached to site N107 of the glycoprotein (page 223, column 2, para 3). Miyamoto teaches that the glycan is attached to the N107 site in the glycoprotein, for example, the site where the glycan bonds to the glycoprotein (page 223, column 2, para 3). Miyamoto further teaches sites of glycosylation were identified on the glycoprotein A1AT, for example, detecting a glycosylation site on a glycoprotein (table 3). The Global Protein Machine was used to analyze peptide profiles and identify unique tryptic peptides (page 223, column 2, para 3; page 225, column 1, para 1). Data was input into an in-house built software tool GPFinder (page 226, column 2, para 1). Miyamoto further teaches a method wherein statistical analysis is utilized as a predictive model for determining the probability of a subject having ovarian cancer, based on diagnostic biomarkers (page 230, column 2, para 2). A training set consisting of 40 cases and 40 controls was analyzed, and differential analyses were performed to identify aberrant glycopeptide levels (abstract). The p-value for subjects with A1AT glycoprotein is 0.00001 (Table S3) (a p-value of 0.00001 to be an output probability as it is the measure of the strength of confidence. As such, a p-value of 0.00001 to be below 0.3 and an indication of ovarian cancer as Miyamoto teaches patients with ovarian cancer, have higher concentrations of A1AT glycoprotein with a p-value of 0.0001 (figure 3). A p-value of 0.0001 is greater than 80% confidence as it is known by those of ordinary skill in the art that a p-value of 0.0001 corresponds to a confidence level of 1 - 0.0001, which equals 99.99% confidence. Miyamoto teaches a method wherein 18 samples were quantitated by MRM-MS at different time points and compared based on their peptide sequence, precursorion, production, collision energy and retention time (table 2). Examiner is interpreting the retention times in table 2 to be different time points, based on the glycoprotein. One would have had a reasonable expectation of success in using the quantification method of Miyamoto because they are drawn to the related field of biomarker identification and disease diagnosis, and one ordinary skilled in the art could have substitutes one known element for another and the substitution of known element for another would have yielded predictable results.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/G.S./
Examiner, Art Unit 1686
/G. STEVEN VANNI/
Primary patents examiner, Art Unit 1686
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