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 .
Status of the Claims
Claims 1-12 are pending. Claims 1, 5, and 8 are amended. Claim 12 is newly added. Claims 1-12 are examined herein.
Priority
The present application, filed 09/27/2023, is a continuation of PCT/EP2022/058071, filed 03/28/2022, which claims foreign priority of HRP20210509A and HRP20210511A, filed 03/30/2021. Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Claim Objections
Claim 1 is objected to because of the following informalities: the phrase “wherein the numerical model is developed based a computational pipeline” is grammatically incorrect. The phrase should be amended to recite “wherein the numerical model is developed based on a computational pipeline.” Appropriate correction is required.
Claim 3 is objected to because of the following informalities: the phrase “according to claims 1,” should be amended to “claim 1” for grammatical correctness. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. This is a new matter rejection.
Regarding claim 1, the claim has been amended to recite that the numerical model is developed based a computational pipeline including L1-regularized (LASSO) regression with 10-fold cross-validation, batch correction (ComBat), logit transformation of glycan areas, and stepwise selection narrowing 24 glycan candidates down to 10 specific peaks, achieving R² = 0.551 on independent test samples. The originally filed specification does not reasonably convey possession of the claimed combination of R² = 0.551 and independent test samples. Rather, the specification expressly distinguishes data used during development and cross-validation of the model from a data set separated before development for final validation.
Specifically, the specification states that before the development of the machine learning model, 33% of the data was separated to be used as a data set that will be employed for the final validation of results. The rest of the 67% data was used for the development of the linear model (p. 48). Thus, the specification expressly identifies a portion of the data that was separated before the development of the machine learning model and reserved for the final validation of results, while the remaining data was used for the development of the linear model. The specification separately describes the cross-validation procedure and expressly states that the parameters for the model were established by the use of the class GridSearchCV with cross-validation with the ShuffleSplit class. In this case, the data were divided ten times randomly in the fifth, of which one group of data was used for the validation of parameters (p. 48). Critically, the specification reports the R² obtained in connection with that cross-validation procedure as follows: a model with said parameters had R²=0.551 on the test sample of the cross-validation performed by the previously described procedure (p. 49). The specification immediately and separately reports the result obtained for the test sample isolated before model development, stating that on the test sample (isolated before the model development) R²=0.547, the maximal error was 1.04 and the square of the mean error value was 0.09 (p. 49).
Accordingly, the originally filed disclosure expressly associates R²=0.551 with the test sample of the cross-validation performed by the previously described procedure, whereas the separately reserved test sample isolated before the model development is expressly reported as having R²=0.547. This distinction is further reinforced earlier in the specification, which states that 33% of the data was separated before development and was to be used for the final validation of results. The specification therefore provides express support for R²=0.551 in connection with the cross-validation test sample and separately provides express support for R²=0.547 on the test sample isolated before model development. The originally filed disclosure does not, however, describe the presently claimed combination of R² = 0.551 on independent test samples. The disclosure instead assigns the two numerical R² values to the respective testing procedures as set forth above.
The remaining portions of the model-development disclosure do not supply the missing combination. For example, the specification states that the normalization was performed by dividing the area of each particular chromatographic peak (glycan) by the total area of the respective chromatogram (p. 47), and further discloses that ten (10) chromatographic peaks (glycans): GPB2, GPB4, GPB8, GPB10, GPB12, GPB15, GPB16, GPB18, GPB22 and GPB23 were selected, for which the p-value was <0.001 (p. 48). These passages provide disclosure concerning data normalization, transformation, batch correction, selection of glycan peaks, and model development, but they do not alter the specification’s express assignment of R²=0.551 to the test sample of the cross-validation and R²=0.547 to the test sample (isolated before the model development). Therefore, the originally filed specification does not reasonably convey to one skilled in the relevant art that the inventor was in possession of the presently claimed combination achieving R² = 0.551 on independent test samples.
Claims 2–12 are also rejected under 35 U.S.C. 112(a) for the same reasons set forth above with respect to claim 1 because claims 2–12 depend directly or indirectly from claim 1 and therefore incorporate the unsupported limitation.
Claim 12 is additionally rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. Claim 12 newly recites initiating or adjusting hormone replacement therapy of the female subject based on the determined multiday average estradiol concentration. The originally filed specification does not reasonably convey possession of the newly claimed treatment step or, particularly, the claimed operative relationship in which the multiday-average estradiol concentration determined according to the disclosed IgG N-glycan method is used as the basis for initiating or adjusting hormone replacement therapy.
When identifying the technical problem addressed by the invention, the specification states that the present invention solves the technical problem of a reliable method for determining average multiday estradiol (E2) concentration and, secondary, whether the examined female subject has entered into the perimenopause or menopause phase (p. 4). The specification further states that the determination of the multiday average molar concentration of estradiol (E2), which is an important diagnostic parameter, is not possible by any known single diagnostic method applied to only one or more blood or urine analyses (p. 4). The specification then identifies how the disclosed invention solves that technical problem, stating that the present disclosure solves this technical problem by quantitative analysis of N-glycans bound to immunoglobulin G (IgG) on the basis of one or more blood analyses (p. 4). The Summary likewise expressly characterizes the disclosed invention, stating that the present invention discloses a method for the assessment of perimenopause and menopause status in female subjects by an analysis of N-glycans (I), bound to immunoglobulin G (IgG) (p. 4). The specification thereafter expressly identifies the results provided by the disclosed method. Specifically, the method according to the present invention enables the determination: (i) of an average multiday molar concentration of estradiol (E2) in the blood for a 3-month period, preferably a 2-month period, and most preferably a 1-month period; (ii) whether the examined female subject has passed through perimenopause and entered into menopause; or (iii) whether the examined female subject has entered into perimenopause (p. 6).
Thus, the disclosed results are expressly directed to determining multiday-average E2 concentration and determining perimenopause or menopause status. The specification does not identify initiation or adjustment of hormone replacement therapy as one of the actions resulting from the disclosed determination. The specification does expressly refer to hormone-replacement therapy in describing selection of the female subjects included in the study. Specifically, it states that before inclusion into the study, the health status of female subjects was partially known. Particularly whether the subject is ill with acute or chronic disease, does not use hormone-replacing therapy, and is not pregnant or in menopause. In this manner, only healthy subjects were selected and included in the study, to avoid the potential influence of said health factors on IgG glycosylation (p. 24). Thus, the disclosed study population was selected based, inter alia, on the female subjects does not use hormone-replacing therapy, and this exclusion was made to avoid the potential influence of said health factors on IgG glycosylation. The specification therefore does not describe initiating or adjusting hormone replacement therapy based on a multiday-average estradiol concentration determined by the disclosed IgG N-glycan method. Rather, the specification expressly describes the absence of hormone-replacement therapy as a characteristic used in selecting subjects for the study.
The experimental portion likewise characterizes the resulting E2 concentration as a diagnostic determination. Specifically, the specification states that the revealed process enables the determination of the multiday average molar concentration of estradiol (E2), which is an important diagnostic parameter, hardly accessible by any known single diagnostic method applied to only one blood analysis (p. 50). Finally, the Industrial Applicability section expressly characterizes the disclosed invention stating that the present invention discloses a diagnostic process for the determination of multiday estradiol (E2) concentration and whether an examined female subject has entered the perimenopause or menopause phase, based on IgG N-glycan analysis from one or more blood samples. In this manner, the industrial applicability of the present invention is obvious (p. 50).
The originally filed drawings likewise do not provide the missing therapeutic disclosure. Figure 1 depicts chromatographic glycan peaks; Figures 2–5 concern the studied female subjects and variation in standardized glycan determinations; and Figure 6 depicts glycan and hormone concentrations relative to menstrual-cycle phase. None of Figures 1–6 depicts an HRT treatment step or an operative relationship in which the determined multiday-average E2 concentration is used to initiate or adjust HRT. Accordingly, although the originally filed specification expressly refers to hormone-replacing therapy, that disclosure concerns selection of subjects who does not use hormone-replacing therapy to avoid the potential influence of said health factors on IgG glycosylation. The disclosure does not describe using the determined multiday-average estradiol concentration as a basis for initiating hormone replacement therapy or for adjusting an existing hormone replacement therapy. In contrast, the originally filed disclosure expressly describes determining average multiday estradiol (E2) concentration, characterizes E2 concentration as an important diagnostic parameter, and ultimately characterizes the disclosed invention as a diagnostic process for the determination of multiday estradiol (E2) concentration and whether an examined female subject has entered the perimenopause or menopause phase. Therefore, one skilled in the relevant art reviewing the application as originally filed would not reasonably recognize that Applicant was in possession of the subject matter newly recited in claim 12, namely initiating or adjusting hormone replacement therapy of the female subject based on the determined multiday average estradiol concentration. Claim 12 therefore additionally fails to comply with the written description requirement of 35 U.S.C. 112(a).
Maintained 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.
The rejection under 35 U.S.C. 101 is set forth below in view of Applicant’s amendments.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to judicial exceptions without reciting additional elements sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exceptions. In particular, claims 1–7 and 9 recite an abstract idea in the form of mathematical concepts; and claims 8, 10, and 11 additionally recite a law of nature in the form of a naturally occurring relationship between multiday-average estradiol concentration and perimenopausal/menopausal physiological status.
This rejection is made in accordance with Patent Subject Matter Eligibility as set forth in MPEP §2106. Analysis of subject-matter eligibility under 35 U.S.C. §101 requires consideration under these steps as followed:
Step 1 – Statutory Category (Refer to MPEP § 2106.03): Claims 1–11 are drawn to a process, which falls within a statutory category under 35 U.S.C. § 101.
Step 2A, Prong One – Recitation of a Judicial Exception (Refer to MPEP § 2106.04): Regarding claim 1, the claim recites an abstract idea falling within the mathematical-concepts grouping. Specifically, claim 1 requires quantitatively analyzing released IgG N-glycans and inserting the resulting quantitative data into a numerical model that gives numerical data indicative of the multiday-average estradiol concentration. As amended, claim 1 further expressly requires that the numerical model be developed using a computational pipeline including L1-regularized (LASSO) regression with 10-fold cross-validation, batch correction (ComBat), logit transformation of glycan areas, and stepwise selection narrowing 24 glycan candidates to 10 specific peaks, with the model achieving R² = 0.551 on independent test samples. These limitations expressly recite mathematical relationships and mathematical calculations performed upon quantitative glycan data. LASSO regression, cross-validation, logit transformation, statistical feature selection, and generation of a numerical predicted estradiol concentration constitute mathematical processing of measured data. Thus, unlike the characterization in the previous Office action, the principal judicial exception recited by amended claim 1 is an abstract idea in the form of mathematical concepts. Although claim 1 itself does not require determining perimenopausal or menopausal status, the rejection of claim 1 is principally based on the expressly claimed mathematical concepts rather than separately characterizing claim 1 as reciting the menopausal-status natural correlation.
Claims 2–6 depend directly or indirectly from claim 1 and therefore incorporate the mathematical concepts recited by claim 1. Their respective additional limitations do not remove those mathematical concepts from the claims. Claim 2 limits the female subject to 40–55 years old; claim 3 specifies chemical or enzymatic glycan release using PNGase F; claim 4 specifies UPLC, MALDI-TOF mass spectrometry, LC-MS, or capillary electrophoresis; claim 5 specifies fluorescent derivatization using an aromatic amine and reducing agent followed by UPLC analysis of the recited glycans; and claim 6 specifies an alternative fluorescent derivatization reagent. These limitations constitute additional subject-selection, sample-preparation, derivatization, or analytical limitations. They do not alter the fact that each claim ultimately employs the mathematical processing required by claim 1 to derive the numerical multiday-average estradiol concentration.
Claim 7 recites an additional and particularly explicit mathematical concept. Claim 7 requires that the logarithm of the multiday-average molar concentration of estradiol be calculated from a specifically recited numerical model and further specifies that GPB2, GPB4, GPB8, GPB10, GPB12, GPB15, GPB16, GPB18, GPB22, and GPB23 represent natural logarithms of corresponding relative chromatographic peak areas. Thus, claim 7 expressly recites a mathematical equation, mathematical relationships among glycan-derived variables, natural-logarithm transformations, and mathematical calculation of an estradiol concentration. These limitations fall squarely within the mathematical-concepts grouping of abstract ideas. Claim 7 therefore recites mathematical concepts independently of, and in addition to, those already incorporated from claim 1.
Claim 8 incorporates the mathematical concepts of claims 1 and 7 and additionally recites a law of nature/natural correlation. Specifically, claim 8 requires interpreting the calculated multiday-average E2 concentration such that a concentration from 7 to 80 corresponds to the female subject having passed through perimenopause and entered menopause, whereas a concentration larger than 80 to 800 corresponds to the female subject not yet having passed through perimenopause and therefore not having entered menopause. The relationship between a female subject’s estradiol concentration and her naturally occurring perimenopausal/menopausal physiological status is a naturally occurring biological relationship that exists independently of the claimed method. Claim 8 therefore recites both (1) the mathematical concepts used to calculate the multiday-average E2 concentration and (2) a law of nature in the form of the naturally occurring relationship between that E2 concentration and the subject’s perimenopausal/menopausal physiological status. Claim 8 also requires interpreting the calculated value according to predetermined numerical ranges to determine the corresponding physiological status. To the extent this limitation constitutes evaluation or judgment of the calculated information, it additionally reflects an abstract mental process because the comparison of a known numerical value with the expressly provided threshold ranges can practically be performed in the human mind.
Claim 9 incorporates claim 1’s mathematical concepts and further specifies that the numerical data generated by the selected model is indicative of multiday-average E2 concentration over a three-month period. The three-month limitation further defines the temporal scope of the information represented by the mathematical output, but does not alter the mathematical character of the claimed calculation or introduce a separate practical application. Accordingly, claim 9 recites the mathematical concepts inherited from claim 1.
Claims 10 and 11 incorporate the mathematical concepts of claim 1 and additionally recite the naturally occurring relationship between multiday-average estradiol concentration and a female subject’s perimenopausal/menopausal physiological status. Claim 10 requires determining whether the subject has passed through perimenopause and entered menopause based on the calculated multiday-average E2 concentration, while claim 11 requires determining whether the subject has entered perimenopause based on that concentration. Accordingly, claims 10 and 11 recite both the mathematical concepts used to derive the multiday-average E2 concentration and a law of nature/natural correlation between that concentration and the naturally occurring perimenopausal or menopausal physiological status of the female subject. The recited act of determining physiological status based on the calculated E2 information additionally constitutes an evaluation or judgment of information and therefore falls within the mental-process grouping to the extent the determination can practically be performed by a person using the calculated concentration and the disclosed relationship.
Accordingly, claims 1–11 recite judicial exceptions as identified above: claims 1–7 and 9 recite mathematical concepts; claims 8, 10, and 11 additionally recite the natural relationship between E2 concentration and perimenopausal/menopausal physiological status, with claims 8, 10, and 11 further involving evaluation of the resulting information.
Step 2A, Prong Two – Integration into a Practical Application (Refer to MPEP § 2106.04(d)): Regarding claim 1, the additional elements include isolating plasma from one or more blood samples, releasing N-glycans from IgG, and quantitatively analyzing the released glycans, optionally following fluorescent derivatization. These steps serve to obtain the glycan measurements that are subsequently subjected to the recited mathematical processing. They constitute data-gathering and sample-preparation steps antecedent to application of the mathematical model. The newly added computational-pipeline limitations likewise do not, by themselves, integrate the mathematical concepts into a practical application. The LASSO regression, 10-fold cross-validation, ComBat batch correction, logit transformation, stepwise selection, and stated R² value further define the mathematical/statistical procedures used to develop the numerical model. They therefore form part of the identified mathematical concepts rather than an additional technological application of those concepts. Nor does claim 1 recite an improvement to the operation of a computer, laboratory instrument, chromatography system, mass spectrometer, or other technology. The claim does not require a particular computer architecture or altered operation of the analytical instrumentation. Rather, the claimed mathematical pipeline processes glycan-derived quantitative data to generate numerical information indicative of multiday-average E2 concentration.
The mere generation of a more useful or clinically informative numerical result does not, standing alone, constitute a technological improvement where the claim remains directed to mathematical processing of collected biological data. The claimed result is information—the determined multiday-average E2 concentration—and claim 1 does not require using that information to cause a further physical or technological result. Thus, considered as a whole, claim 1 uses biological sample-processing techniques to obtain quantitative glycan data and applies mathematical/statistical processing to that data to generate a predicted biological parameter. The claim therefore does not integrate the mathematical concepts into a practical application.
The additional limitations of claims 2–6 and 9 likewise do not integrate the mathematical concepts into a practical application. Claim 2 merely limits the subject population by age. Claims 3–6 further specify glycan-release, analytical, derivatization, or detection techniques used to generate the biological data. Claim 9 merely specifies that the numerical model output represents a three-month period. These limitations do not improve the functioning of the recited analytical equipment or another technology, do not effect a particular treatment, and do not otherwise impose a meaningful technological limitation on the mathematical processing. Rather, the physical laboratory limitations obtain the data to which the mathematical model is applied, while the age and temporal limitations define the context or informational scope of the calculation. Accordingly, claims 2–6 and 9 do not integrate the identified mathematical concepts into a practical application.
Claim 7’s additional numerical equation and natural-logarithm calculations are themselves part of the mathematical exception. The claim merely applies the specified equation to glycan-derived variables to calculate E2 concentration and does not recite any technological implementation or improvement arising from the equation. Accordingly, merely specifying the mathematical formula with greater precision does not integrate the mathematical concept into a practical application. Claim 8 uses predetermined numerical E2 ranges to interpret whether the subject has or has not passed through perimenopause and entered menopause. This merely applies the mathematical output and naturally occurring physiological relationship to generate diagnostic information concerning the subject’s physiological status. Claim 8 does not require treatment, prophylaxis, or another action that changes the subject’s physiological state based on the determination. Nor does the interpretation step improve the underlying laboratory or computational technology. Rather, the claim terminates with the diagnostic interpretation itself. Accordingly, claim 8 does not integrate the recited judicial exceptions into a practical application.
Similarly, claims 10 and 11 terminate with determining whether the female subject has passed through perimenopause and entered menopause, or whether the subject has entered perimenopause, based on the calculated multiday-average E2 concentration. These limitations apply the mathematical result and naturally occurring physiological relationship only to reach a diagnostic conclusion. Neither claim requires treatment, prophylaxis, modification of a therapeutic regime, or another technological action resulting from the determination. Accordingly, claims 10 and 11 do not integrate the judicial exceptions into a practical application. Therefore, claims 1–11 are directed to the identified judicial exceptions and proceed to Step 2B.
Step 2B, Inventive Concept (Refer to MPEP § 2106.05): Under Step 2B, the claims are considered to determine whether any additional element, individually or in combination, amounts to significantly more than the identified judicial exceptions. Regarding claim 1, the additional elements outside the mathematical concepts include obtaining/isolation of plasma from a blood sample, releasing N-glycans from IgG, and quantitatively analyzing the released glycans, including fluorescent derivatization where used. As set forth in the prior Office action, Ercan et al. demonstrates enzymatic release of glycans from serum or plasma samples, including release of glycans from IgG, fluorescent labeling following glycan release, and quantitative glycan analysis. Thus, these laboratory operations represent techniques previously known in the field for obtaining and quantitatively analyzing IgG glycan data. The newly recited LASSO regression, cross-validation, ComBat batch correction, logit transformation, stepwise feature selection, and resulting model-performance value do not constitute additional elements outside the exception for purposes of supplying an inventive concept because these limitations themselves define the mathematical/statistical concepts identified under Prong One. When claim 1 is considered as an ordered combination, the physical laboratory steps obtain biological data and the recited mathematical pipeline processes those data to generate the claimed numerical E2 information. The ordered combination therefore amounts to the use of data-gathering laboratory steps in conjunction with the identified mathematical concepts and does not add an inventive concept distinct from the exception itself.
Regarding claims 2–6 and 9, the additional limitations include subject-age selection, PNGase F glycan release, specified analytical techniques, fluorescent derivatization reagents, UPLC analysis, and a specified three-month informational period. As explained in the previous Office action, such glycan-release, derivatization, and analytical procedures were known laboratory techniques used for IgG glycan profiling. The limitations of claims 2–6 and 9 merely further specify the biological sample, data-gathering techniques, or informational context in which the mathematical concepts are employed. Considered individually and as an ordered combination with claim 1, they do not add an inventive concept distinct from the mathematical processing itself. Regarding claim 7, the expressly recited logarithmic equation and transformations remain mathematical concepts and therefore cannot themselves constitute the additional inventive concept required at Step 2B. Krištić et al., as previously relied upon, further evidences the known use of glycan-derived variables in predictive mathematical modeling of biological parameters. Accordingly, claim 7 does not recite significantly more than the judicial exception.
Regarding claims 8, 10, and 11, the additional limitations interpret or evaluate the mathematically determined E2 concentration to reach conclusions regarding the female subject’s naturally occurring perimenopausal or menopausal physiological status. These limitations constitute the judicial exception itself and/or the expression of its diagnostic result, rather than an inventive concept separate from the identified exceptions. No additional treatment, technological improvement, or other limitation is recited in claims 8, 10, or 11 that transforms the mathematical calculations and natural physiological relationship into significantly more. Accordingly, when the limitations are considered individually and as ordered combinations, claims 1–11 do not recite additional elements that integrate the identified judicial exceptions into a practical application or amount to significantly more than those exceptions. Claims 1–11 are therefore rejected under 35 U.S.C. § 101 as being directed to patent-ineligible subject matter.
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.
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.
The rejection under 35 U.S.C. 103 is set forth below in view of Applicant’s amendments.
Claims 1–4 are rejected under 35 U.S.C. 103 as being unpatentable over Ercan et al. (Estrogens Regulate Glycosylation of IgG in Women and Men. JCI Insight. Vol. 2, No. 4, February 2017 – Non-Final dated 04/10/2026) in view of Krištić et al. (Glycans Are a Novel Biomarker of Chronological and Biological Ages. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences. Vol. 69, No. 7, July 2014 – Non-Final dated 04/10/2026), Stricker et al. (Establishment of Detailed Reference Values for Luteinizing Hormone, Follicle Stimulating Hormone, Estradiol, and Progesterone during Different Phases of the Menstrual Cycle on the Abbott ARCHITECT® Analyzer. Clinical Chemistry and Laboratory Medicine (CCLM). Vol. 44, No. 7, 2006 – Non-Final dated 04/10/2026), Uh et al. (Choosing Proper Normalization Is Essential for Discovery of Sparse Glycan Biomarkers. Molecular Omics. Vol. 16, No. 3, March 10, 2020), Liu et al. (Ischemic Stroke Is Associated with the Pro-Inflammatory Potential of N-Glycosylated Immunoglobulin G. Journal of Neuroinflammation. Vol. 15, No. 1, April 2018), and Haakensen et al. (Serum N-Glycan Analysis in Breast Cancer Patients – Relation to Tumour Biology and Clinical Outcome. Molecular Oncology. Vol. 10, No. 1, January 2016).
Regarding claim 1, for a method for determining multiday average concentration of estradiol in a female subject by an analysis of N-glycans (I), bound to immunoglobulin G, Ercan teaches analysis of IgG glycosylation in women and expressly identifies estradiol as the primary circulating form of estrogen and reports that estradiol exhibits correlations between spot hormone levels and IgG glycans, particularly with respect to differential galactosylation (p. 2). Ercan further experimentally establishes the biological relationship between estrogen and IgG glycans, concluding that estrogens drive IgG galactosylation and implicating estrogen deficiency in the glycan changes accompanying menopause (p. 4, Fig. 3). Ercan therefore provides more than a mere association between two independently known analytes: Ercan identifies IgG glycosylation as an estrogen-responsive biological phenotype and demonstrates experimentally that changing estrogenic exposure changes the IgG-glycan phenotype.
Regarding a) isolation of plasma from one or more blood samples that have been collected from the female subject under examination, Ercan teaches analysis of samples obtained from female subjects and teaches that glycans were cleaved enzymatically from 5 μl of serum or plasma and that ORCADES samples were analyzed from IgG purified by plate-bound protein G (Methods, p. 7). Ercan thus teaches obtaining blood-derived plasma and analyzing the IgG-associated glycans contained therein. Regarding b) release of said glycans from IgG, Ercan expressly teaches that glycans were cleaved enzymatically from the samples and that the glycan signal analyzed was derived predominantly from IgG Fc glycans (Methods, p. 7). Regarding c) quantitative analysis of thus released glycans in the free form or derivatized by fluorescent derivatization, Ercan teaches that the released glycans were conjugated to the fluorochrome 2-aminobenzamide, and quantitated chromatographically and further teaches UPLC analysis of purified IgG glycans (Methods, p. 7). Thus, Ercan teaches fluorescent derivatization followed by quantitative chromatographic analysis of glycans. Regarding d) where the results from step c) are inserted in a numerical model suitable for the quantitative analysis used, where the numerical model is result of statistical data analysis that determined variation of quantitative IgG glycans content in the blood plasma of a female cohort that was not in the menstruation phase or any other known medical condition associated with sex hormones fluctuations, Ercan quantitatively evaluates variation in IgG glycoform abundance and applies statistical analysis to female cohorts, including postmenopausal women and hormonally manipulated premenopausal women. Ercan reports that menopause was associated with changes in IgG glycans and that experimental estradiol treatment altered those glycans. In particular, Ercan teaches G0 IgG increases in association with menopause and statistically analyzes the age/menopause-associated glycan changes (pp. 2–4, Figs. 2–3).
Regarding the reason the teachings of Ercan would have led one of ordinary skill to further quantitative modeling, Ercan expressly recognizes both the biological signal and the measurement problem: levels of these hormones not only differentiate men and women but also vary widely within an individual across the life span (p. 2). At the same time, Ercan demonstrates that altering estrogen exposure produces corresponding changes in IgG glycosylation. Thus, Ercan itself provides a reason to investigate whether quantitative IgG-glycan measurements can provide a more representative numerical indicator of estrogen exposure rather than relying solely upon an individual spot hormone measurement.
However, Ercan does not expressly teach that the quantitative IgG-glycan results are inserted into a numerical model that gives a numerical value indicative of multiday-average E2 molar concentration, nor does Ercan expressly teach the presently recited computational pipeline comprising LASSO regression with 10-fold cross-validation, ComBat batch correction, logit transformation, stepwise selection from 24 candidates to 10 peaks, or the recited R² performance on independent test samples.
Krištić teaches the missing concept of using quantitative IgG N-glycan measurements as inputs to a numerical predictive model of a physiological characteristic. Krištić teaches that IgG was isolated from plasma, that N-linked glycans were released, labeled with 2-aminobenzamide (2-AB), and analyzed by ultra performance liquid chromatography (UPLC), and that statistically significant associations were observed for 21 out of 24 directly measured glycan structures (Results, p. 781). Krištić further reports that the strongest female changes occurred between the ages of 45 and 55 when the majority of women enter menopause (p. 781). This disclosure is particularly pertinent to the female hormonal context of Ercan and the presently claimed method. Krištić then explains that, because the measured glycans were strongly associated with a physiological characteristic, the investigators attempted to build a predictive model and used backward regression to obtain the best minimal model (pp. 781–782). The model was trained and validated using separate subsets, with two thirds (1,357 samples) of Orkney cohort as training set and one third (678 samples) as validation set (p. 788). Krištić further teaches that the model explained 58% of variation in chronological age, with 95% of replicas giving R² between 54% and 61%, and thereafter tested the trained model in additional populations (pp. 782–784). Thus, Krištić demonstrates that quantitative IgG-glycan profiles were already capable of being transformed into a numerical prediction of a physiological characteristic and validated on data separate from the model-training data. Hence, Krištić provides evidence that the same class of quantitative IgG-glycan measurements could successfully support a validated numerical predictive model with substantial predictive performance.
Regarding where the numerical model gives a numerical data indicative of the multiday average estradiol molar concentration in the blood of the examined female subject, Stricker supplies the reason to characterize E2 over multiple days rather than rely upon an isolated measurement. Stricker expressly teaches that serum concentrations of reproductive hormones, including estradiol, vary significantly throughout the menstrual cycle, as well as from woman to woman, and that collection of multiple samples across many days is required to establish reference values with any degree of resolution (p. 883). Stricker accordingly collected serum on every day of the cycle and measured estradiol on each day (p. 883). Table 1B and Figure 1C demonstrate the substantial day-to-day change in estradiol concentration across the cycle (pp. 884-885). Stricker therefore provides a specific technical reason for seeking a temporally representative or multiday characterization of E2: individual E2 measurements are strongly dependent upon sampling day.
Regarding wherein the numerical model is developed based a computational pipeline including L1-regularized (LASSO) regression with 10-fold cross-validation, batch correction (ComBat) and 24 glycan candidates, Uh directly addresses predictive modeling of quantitative IgG-glycan data. Uh teaches that twenty four glycans bound to IgG are measured by UPLC and identifies the objective as efficient variable selection to identify glycan biomarkers with regard to accurate prediction as well as interpretability of the model chosen (p. 231). Uh further expressly states that its motivating example assesses prediction of age from immunoglobulin G (IgG) glycans and that Figure 1 shows twenty four glycans bound to IgG measured by ultra-performance liquid chromatography (UPLC) (p. 231, Fig. 1). Uh provides a direct reason for using LASSO rather than merely identifying it as an available algorithm. Uh explains that glycans are highly correlated and that to avoid overfitting and the multicollinearity problem, penalized regression can be considered (p. 233). In its actual IgG application, Uh applies LASSO considering high correlation shown among glycan variables, to avoid overfitting and to obtain an interpretable model (p. 238). Uh also teaches that 24 different glycan peaks are quantified using UPLC, performs batch correction to remove strong batch effects in the glycomics data using the ComBat function of sva package for R, and teaches that ten-fold cross-validation was applied to the lasso fits in two third of the dataset followed by prediction in the remaining one-third of the data (validation set) (p. 238). Accordingly, Uh supplies not merely the individual computational operations, but their technical purposes in quantitative IgG-glycan modeling: LASSO and cross-validation address correlated variables, overfitting and model selection, while ComBat addresses batch effects.
Regarding stepwise selection narrowing 24 glycan candidates down to 10 specific peaks, Liu further teaches feature selection directly in a 24-peak IgG-glycan dataset. Liu teaches that IgG N-glycans were analyzed by HILIC-UPLC into 24 IgG glycan peaks (GPs) and that each peak was expressed as a percentage of total integrated area (p. 3). Liu expressly identifies the technical problem requiring feature selection: there were internal associations among glycans, which could induce multicollinearity in the statistical models; consequently, ridge, lasso, and stepwise regression, was used to select glycans to make dimension reduction (p. 3). Liu demonstrates actual stepwise selection of a subset of IgG peaks from the larger glycan dataset (p. 5). Hence, Liu establishes that stepwise regression was a known technique applied to correlated IgG-glycan variables specifically to reduce the dimensionality of the glycan predictor set. The number of retained variables therefore follows from application of the selection criterion to the particular outcome/data being modeled rather than from a requirement that all 24 glycan variables be retained.
Regarding logit transformation of glycan areas, Haakensen directly teaches the claimed transformation for relative UPLC glycan-peak areas. Haakensen explains that the peaks of the Glycan UPLC-output represent the relative area for each glycan peak in the spectrum, that the data are compositional, and that the sum of all glycan peaks is constant (Statistical Analyses, p. 61). Haakensen then identifies the resulting statistical problem and solution: raw compositional data are not suitable for most statistical analysis and a logit transformation was performed to map data into real space (p. 61). Thus, Haakensen provides an express technical reason to apply the logit transformation to the same type of relative glycan-area data employed in quantitative glycan modeling.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ercan’s quantitative analysis of estrogen-responsive IgG glycosylation by using Krištić’s numerical predictive-model approach to model the IgG-glycan measurements, using a temporally representative E2 value as motivated by Stricker, and implementing the model using Uh’s 24-peak IgG dataset, LASSO/10-fold-cross-validation and ComBat processing, Liu’s stepwise dimensionality reduction, and Haakensen’s logit transformation of relative glycan peak areas. The motivation is supplied by the references themselves: Ercan establishes that estradiol is associated with and experimentally modulates IgG glycosylation while recognizing substantial within-individual sex-hormone variation; Stricker demonstrates why multiple days are needed to characterize the strongly time-varying E2 signal; Krištić demonstrates that quantitative IgG-glycan measurements can successfully be converted into and validated as a numerical predictor of a physiological characteristic; Uh identifies correlated glycan variables, overfitting and batch effects and expressly applies LASSO, 10-fold cross-validation and ComBat to a 24-peak IgG dataset to address those problems; Liu identifies multicollinearity among IgG glycans and applies stepwise selection for dimensionality reduction; and Haakensen identifies the statistical incompatibility of raw relative glycan areas and expressly applies logit transformation. One of ordinary skill would therefore have had a reasonable expectation of successfully obtaining a reduced predictive model because Krištić had already demonstrated validated quantitative prediction from IgG glycans, Uh demonstrated LASSO/CV model selection followed by validation on a held-out portion of the same 24-peak IgG platform, and Liu demonstrated successful selection of reduced subsets from 24 correlated IgG-glycan peaks. Applying these established data-processing techniques would predictably reduce overfitting, batch effects, multicollinearity and compositional-data artifacts while retaining those peaks contributing predictive information. The particular number of peaks retained and resulting R² value would be determined empirically when the known selection/validation procedures were applied to the E2-responsive glycan data; Krištić’s validated IgG-glycan model, which explained 58% of variation with R² values of 54% to 61%, further provides a reasonable expectation that an IgG-glycan predictive model could attain predictive performance encompassing the recited R² of 0.551.
Regarding claim 2, Ercan teaches that the study consisted of 58 healthy postmenopausal women (age 56 ± 4 years, age at menopause 48 ± 6 years) randomized to oral therapy with placebo (p. 6), thereby establishing that the menopausal transition occurs within a range spanning approximately the mid-40s to early-to-mid 50s.
Regarding claim 3, Krištić expressly teaches isolation of IgG from plasma followed by release of the IgG N-glycans: N-glycans were released by digestion with PNGase F (p. 787). Krištić further teaches that, after release, N-glycans were labeled with 2-AB fluorescent dye and subjected to quantitative glycan analysis (p. 787). Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ercan’s enzymatic release of glycans from the IgG-containing sample by employing Krištić’s PNGase F release procedure, because Krištić expressly demonstrates PNGase F digestion as an operative method for releasing N-glycans from human IgG prior to the same type of fluorescent quantitative glycan analysis relied upon in the combination. One of ordinary skill would have reasonably expected success because Krištić actually employs PNGase F on human IgG samples and successfully obtains released N-glycans for subsequent 2-AB labeling and chromatographic analysis.
Regarding claim 4, Ercan expressly teaches that samples from ORCADES donors were analyzed from purified IgG using ultra performance liquid chromatography (UPLC) (p. 7). Ercan further teaches that IgG-specific glycans were quantitated by UPLC in 261 women from the Orkney cohort (Fig. 2, p. 3), thereby explicitly disclosing UPLC as a quantitative analytical technique for glycan measurement.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ercan et al., Krištić et al., Stricker et al., Uh et al., Liu et al., and Haakensen et al., as applied to claim 1 above, and further in view of Pučić et al. (High Throughput Isolation and Glycosylation Analysis of IgG–Variability and Heritability of the IgG Glycome in Three Isolated Human Populations. Molecular & Cellular Proteomics. Vol. 10, No. 10, October 2011 – Non-Final dated 04/10/2026).
With respect to the teachings of Ercan et al., Krištić et al., Stricker et al., Uh et al., Liu et al., and Haakensen et al., see the discussion above, which applies equally here. These references differ from the instant claims in failing to collectively teach or specify that the released glycans are fluorescently derivatized using both an aromatic amine, a reducing agent for reductive amination, and that the analyzed glycans correspond to the specific set GPB1–GPB19 and GPB21–GPB24.
Regarding wherein the set of glycans I, released from IgG, is fluorescently derivatized in the step c) with a combination of: (a) an aromatic amine, and (b) a reducing agent for reductive amination, Krištić teaches fluorescent labeling of released IgG N-glycans with 2-aminobenzamide (2-AB) and teaches use of 2-picoline borane in dimethyl sulfoxide during the labeling procedure (p. 787). Thus, Krištić teaches the claimed fluorescent derivatization employing an aromatic amine together with a reducing agent.
Regarding the resulting mixture is analyzed by ultra-performance liquid chromatography for glycans GPB1-GPB19 and GPB21-GPB24 defined below, Pučić expressly teaches that fluorescently labeled N-glycans were separated by ultra performance liquid chromatography on a Waters Acquity UPLC instrument using fluorescence detection (p. 3). Pučić further teaches that The chromatograms obtained were all separated in the same manner into 24 peaks and the amount of glycans in each peak was expressed as % of total integrated area (p. 3). Pučić’s reported dataset includes GP1–GP24, including GP1–GP19 and GP21–GP24 (Table 1, p. 7).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ the fluorescent labeling chemistry taught by Krištić and the standardized chromatographic peak analysis taught by Pučić in the IgG-glycan method of claim 1. A skilled artisan would have been motivated to do so because Krištić’s labeling procedure provides fluorescently derivatized released IgG N-glycans suitable for quantitative chromatographic detection, while Pučić provides a reproducible UPLC framework for resolving and quantitatively identifying those fluorescently labeled human IgG glycans as discrete chromatographic peaks. A skilled artisan would have had a reasonable expectation of success because both references actually employ these techniques on released human IgG N-glycans in quantitative chromatographic workflows.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ercan et al., Krištić et al., Stricker et al., Uh et al., Liu et al., and Haakensen et al., as applied to claim 1 above, and further in view of Morris et al. (US 10,502,720 B2 – Non-Final dated 04/10/2026).
With respect to the teachings of Ercan et al., Krištić et al., Stricker et al., Uh et al., Liu et al., and Haakensen et al., see the discussion above, which applies equally here. These references differ from the instant claims in failing to collectively teach or specify that the set of glycans released from IgG is further fluorescently derivatized in the step c) with alternative reagent specifically identified as 5-dioxopyrrolidine-1-yl-[2N-(2-(N,N-diethylamino)ethyl)carbamoyl]-quinoline-6-yl-carbamate (RF).
Morris teaches rapid fluorescent labeling of released N-glycans using RapiFluor-MS and expressly identifies the reagent as 2,5-dioxopyrrolidin-1-yl (2-((2-(diethylamino)ethyl)carbamoyl)quinolin-6-yl) carbamate, identified as RapiFluor-MS or RFMS (Table 1, p. 18). Morris further explains the specific analytical advantage of the reagent: by utilizing the rapid tagging reagent, within a 5 minute reaction, N-glycans can be labeled, and the reagent contains an efficient quinoline fluorophore, and a highly basic tertiary amine for enhancing ionization (col. 4, p. 18). Morris also teaches that labeled N-glycans may be separated by hydrophilic interaction chromatography (HILIC) and detected by fluorescence (FLR) and mass spectrometry (MS) (col. 3, p. 18).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the fluorescent derivatization of released N-glycans in the method of claim 1 by employing the RapiFluor-MS reagent taught by Morris. A skilled artisan would have been motivated to make the modification because Morris teaches that within a 5 minute reaction, N-glycans can be labeled, and identifies the reagent as containing an efficient quinoline fluorophore and a highly basic tertiary amine for enhancing ionization. Morris therefore supplies a specific analytical reason for selecting the reagent for released N-glycan analysis rather than merely establishing that the reagent was known. A skilled artisan would have reasonably expected success because Morris expressly employs the reagent for labeling released N-glycans suitable for chromatographic and fluorescence/MS analysis.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ercan et al., Krištić et al., Stricker et al., Uh et al., Liu et al., Haakensen et al., and Pučić et al., as applied to claims 1 and 5 above, and further in view of Vanhooren et al. (Serum N-Glycan Profile Shift during Human Ageing. Experimental Gerontology. Vol. 15, No. 10, October 2010 – Non-Final dated 04/10/2026).
With respect to the teachings of Ercan et al., Krištić et al., Stricker et al., Uh et al., Liu et al., Haakensen et al., and Pučić et al., see the discussion above, which applies equally here. These references differ from the instant claims in failing to collectively teach or specify calculating a logarithm of a multiday average molar concentration of estradiol from a numerical model incorporating multiple glycan peak variables obtained from chromatographic analysis, as recited in claim 7.
Pučić teaches quantitative chromatographic glycan peaks including GP2, GP4, GP8, GP10, GP12, GP15, GP16, GP18, GP22 and GP23 and teaches that the amount of glycans in each peak was expressed as % of total integrated area (p. 3). Krištić further teaches incorporating quantitative IgG glycan-peak variables obtained by UPLC into a multivariable numerical predictive model. Krištić teaches that the IgG N-glycome is separated into chromatographic peaks corresponding to individual glycan structures (Fig. 1, p. 780), that the amount of glycans in each peak was expressed as percentage of total integrated area (p. 787), and that multiple glycan variables are incorporated into a predictive numerical model with coefficients assigned to individual glycan variables to calculate a predicted biological parameter (pp. 781–782). Krištić additionally teaches that, to obtain normally distributed variables for glycan structures, the glycan variables were log transformed prior to statistical modeling (p. 787). Thus, Krištić establishes the known use of quantitatively measured IgG glycan-peak variables, logarithmic transformation, and multivariable coefficient-based mathematical modeling to generate a predicted physiological value.
Vanhooren independently establishes that logarithmic mathematical combinations of quantitative glycan measurements were known for constructing a glycan-based physiological biomarker. Specifically, Vanhooren teaches the log of the ratio of two glycans (NGA2F and NA2F), named GlycoAgeTest and reports that the resulting glycan metric was useful for estimating the physiological age of a human individual (Abstract, p. 738; 741). Thus, Vanhooren reinforces the known use of logarithmically related quantitative glycan measurements to generate a numerical physiological biomarker.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the numerical glycan model of the combination applied to claims 1 and 5 by incorporating multiple selected quantitative IgG glycan-peak variables into a coefficient-based numerical model and expressing the selected quantitative glycan-peak measurements logarithmically when constructing the numerical biomarker model, because Krištić demonstrates that quantitative IgG glycan-peak variables can be log transformed and incorporated into a multivariable coefficient-based predictive model to generate a numerical physiological prediction, while Vanhooren demonstrates that logarithmically combining quantitative N-glycan measurements provides a physiologically informative glycan-derived numerical biomarker. Pučić further provides the quantitative relative chromatographic peak measurements corresponding to the GP variables recited in claim 7. A skilled artisan would therefore have been motivated to apply logarithmic transformation to the selected quantitative glycan-peak variables and incorporate those variables into the numerical predictive model because Krištić demonstrates that logarithmic transformation and multivariable coefficient-based modeling of quantitative IgG glycan variables permits prediction of a physiological characteristic, while Vanhooren demonstrates that logarithmically related glycan measurements can successfully generate a physiologically informative numerical biomarker. One of ordinary skill would have had a reasonable expectation of success because Krištić actually constructs and validates a predictive numerical model from quantitative IgG glycan variables, and Vanhooren actually obtains a physiologically informative biomarker from logarithmically related glycan measurements.
Claims 8, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Ercan et al., Krištić et al., Stricker et al., Uh et al., Liu et al., Haakensen et al., Pučić et al., and Vanhooren et al., as applied to claims 1, 5, and 7 above, and further in view of Coburn et al. (Comparability of Serum, Plasma, and Urinary Estrogen and Estrogen Metabolite Measurements by Sex and Menopausal Status. Cancer Causes & Control. Vol. 30, No. 1, January 2019 Non-Final dated 04/10/2026).
With respect to the teachings of Ercan et al., Krištić et al., Stricker et al., Pucić et al., and Vanhooren et al., see the discussion above, which applies equally here. These references differ from the instant claims in failing to collectively teach or specify interpreting multiday average estradiol concentrations using specific numerical ranges expressed in pmol/L to determine and classify menopausal status of a female subject, as recited in claims 8, 10, and 11.
Regarding claim 8, Coburn expressly compares estrogen concentrations according to menopausal status and reports materially lower estradiol concentrations in postmenopausal than premenopausal women. Coburn reports for postmenopausal women EDTA plasma unconjugated estradiol 13.6 (4.2–40.5) pmol/L and EDTA plasma combined unconjugated/conjugated Estradiol 31.4 (10.4–89.4) pmol/L, whereas premenopausal women exhibit EDTA plasma unconjugated estradiol 182 (27.1-1,072) pmol/L (Table 1, p. 78).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to interpret the E2 concentration generated by the claim 7 method according to menopausal status because Coburn expressly demonstrates substantially different circulating E2 distributions in postmenopausal and premenopausal women and therefore provides a reason to use the quantitatively determined E2 concentration as an indicator of whether a woman has entered menopause. A skilled artisan would have reasonably expected success because Coburn directly measures the separation in E2 concentration distributions according to known menopausal status. Coburn therefore supplies the motivation for interpreting a quantitatively determined E2 concentration according to menopausal status because it demonstrates substantially different circulating E2 distributions in premenopausal and postmenopausal women.
Regarding claim 10, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of claim 1 to use the determined E2 concentration to determine whether the female subject has entered menopause because Coburn establishes a pronounced menopausal-status-dependent difference in circulating E2 concentration. The modification would provide the clinically relevant interpretation of the numerical E2 result already generated by the method. A skilled artisan would have reasonably expected success because Coburn directly demonstrates lower measured E2 concentrations in women whose postmenopausal status was known.
Regarding claim 11, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the quantitative E2 information generated by the claim 1 method in determining whether the subject is undergoing the transition into menopause, because the references establish both estrogen-dependent biological changes associated with menopause and materially different E2 concentrations according to menopausal status. A skilled artisan would reasonably have expected the E2 determination to provide information relevant to that transition because circulating E2 is the very hormonal parameter shown by the art to change with menopausal status.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ercan et al., Krištić et al., Stricker et al., Uh et al., Liu et al., and Haakensen et al., as applied to claim 1 above, and further in view of Melo et al. (Effects in Post-Menopausal Women of Transdermal Estrogen Associated with Progestin upon the Removal from the Plasma of a Microemulsion That Resembles Low-Density Lipoprotein (LDL): Cardiovascular Disease. Maturitas. Vol. 50, No. 4, 2005 – Non-Final dated 04/10/2026).
With respect to the teachings of Ercan et al., Krištić et al., Stricker et al., Uh et al., Liu et al., and Haakensen et al., see the discussion above, which applies equally here. These references differ from the instant claims in failing to collectively teach or specify that the selected numerical model provides numerical data indicative of the multiday average estradiol concentration in the blood of the examined female subject for a defined 3-month period.
Melo teaches longitudinal evaluation of postmenopausal women over a three-month estrogen-treatment period. Specifically, the women were followed twice a week for 3 months by clinical, gynecological and laboratorial evaluations (p. 277), and transdermal estradiol was administered twice weekly during the treatment regimen. Melo further teaches that the concentration of estradiol in the plasma was determined in all patients during the hormonal replacement treatment (p. 277), and evaluates the subjects at the third month of treatment. Melo therefore establishes three months as an actual longitudinal period over which circulating E2 and estrogen-related physiological effects were clinically evaluated.
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to configure the multiday E2 assessment of the claim 1 combination to represent a three-month assessment period because Melo demonstrates repeated clinical follow-up of women undergoing estrogen exposure over three months and measurement of circulating estradiol during that treatment period. A skilled artisan seeking to implement the temporally representative E2 assessment motivated by Stricker would have had reason to select the three-month longitudinal interval taught by Melo because Melo demonstrates actual clinical assessment of women and circulating E2 over that duration. A reasonable expectation of success would have existed for evaluating the modeled E2 information over that interval because the claim 1 combination already provides the quantitative E2-modeling framework and Melo establishes the clinical feasibility of longitudinal E2 assessment over three months.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Ercan et al., Krištić et al., Stricker et al., Uh et al., Liu et al., and Haakensen et al., as applied to claim 1 above, and further in view of Sriprasert et al. (Determinants of Attained Estradiol Levels in Response to Oral Estradiol plus Progesterone Therapy. Climacteric. Vol. 24, No. 2, Nov. 2020).
Sriprasert teaches hormone therapy in postmenopausal women using oral estradiol and progesterone and evaluates attained E2 levels in response to different doses of E2 (Abstract, p. 1). Specifically, Sriprasert teaches that postmenopausal women received different daily doses of E2/P4, including 1/100 mg, 0.5/100 mg, 0.5/50 mg, and 0.25/50 mg, and that serum E2 levels were measured at baseline and during follow-up at months 1, 3, 6, 9, and 12 (pp. 1–2). Sriprasert further teaches that blood estradiol (E2) level has been shown to be significantly associated with HT treatment efficacy and that different clinical endpoints have different E2 response thresholds (p. 1). Sriprasert additionally teaches that E2 dose and serum E2 level are not equivalent measures, reporting only a moderate correlation between oral E2 dose and resulting serum E2 levels (p. 1). Consistent therewith, Sriprasert demonstrates a dose-dependent relationship between administered E2 and attained E2 concentration, reporting higher predicted E2 levels with the 1 mg dose, followed by the 0.5 mg and 0.25 mg doses (p. 3; Fig. 1, p. 4).
Importantly, Sriprasert expressly explains the clinical significance of evaluating E2 levels during HT, teaching that these attained levels relate to the possible treatment efficacy of HT, and further teaches that health-care providers prescribing HT need to consider time since menopause, current smoking, and current alcohol use and may adjust the treatment dose for each individual woman to maximize the treatment efficacy and minimize side effects (p. 5).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of the combination applied to claim 1 by using the determined E2 concentration when adjusting hormone replacement therapy for the female subject. One of ordinary skill would have been motivated to make this modification because Sriprasert teaches that attained E2 levels during HT relate to treatment efficacy, that administered E2 dose affects the attained serum E2 concentration, and that HT treatment dose may be adjusted for the individual woman to maximize treatment efficacy and minimize side effects. Thus, once the method of claim 1 provides an E2 concentration representative of the female subject, the skilled artisan would have had reason to use that physiologically relevant E2 information when individualizing the HT dose. One of ordinary skill would have had a reasonable expectation of success because Sriprasert demonstrates in women receiving HT that serum E2 levels can be repeatedly determined during therapy, that different administered E2 doses produce different attained E2 concentrations, and that attained E2 levels are clinically relevant to treatment efficacy.
Response to Arguments
Applicant’s arguments filed 07/10/2026 have been fully considered but they are not persuasive except as specifically indicated below.
Rejection Under 35 U.S.C. 112(b)
Applicant argues that, in view of the amendments to claims 1, 5, and 8, the rejection under 35 U.S.C. 112(b) is moot. The amendments resolve the indefiniteness previously identified in the Non-Final Office Action. Accordingly, the rejection of claims 1–11 under 35 U.S.C. 112(b) is withdrawn and is not maintained in this action.
Rejection Under 35 U.S.C. 112(a) — Written Description
Applicant identifies paragraphs [0017], [0018], [0019], and [0242]–[0248] as alleged support for amended claim 1 and paragraphs [0010]–[0017] as alleged support for newly added claim 12. These citations have been considered. However, identification of portions of the specification that generally concern the disclosed model or determination of multiday-average E2 does not establish written-description support for every newly claimed combination of limitations. Regarding amended claim 1, Applicant newly recites that the numerical model is developed using L1-regularized (LASSO) regression with 10-fold cross-validation, batch correction (ComBat), logit transformation of glycan areas, and stepwise selection narrowing 24 glycan candidates down to 10 specific peaks, achieving R² = 0.551 on independent test samples.
The cited portions of the originally filed specification do not reasonably convey possession of the presently claimed combination of R² = 0.551 and independent test samples. The issue is not whether the specification separately discloses an R² value of 0.551 or separately describes testing or validation. Rather, the issue is whether the originally filed disclosure associates the particular R² value of 0.551 with the independent test samples now recited. As explained in the rejection above, the specification expressly distinguishes the data used during model development and cross-validation from the dataset separated before development for final validation. The specification states that, before development of the machine-learning model, 33% of the data was separated to be used as a data set that will be employed for the final validation of results, while the remaining 67% was used for development of the linear model. The specification thereafter separately describes the GridSearchCV/ShuffleSplit cross-validation procedure in which the development data were divided ten times and reports R²=0.551 on the test sample of the cross-validation performed by the previously described procedure. The specification then separately reports the result for the test sample isolated before the model development, namely R²=0.547.
Accordingly, the originally filed disclosure expressly associates two different R² values with two different testing procedures. The 0.551 value is associated with the test sample arising from the disclosed cross-validation procedure, whereas the separately reserved sample isolated before model development is associated with R²=0.547. Applicant’s amendment combines the former numerical value with the latter independent-testing concept. Applicant’s citation to paragraphs [0242]–[0248] does not cure this deficiency. Those paragraphs establish disclosure of model development, normalization, transformation, batch correction, selection of glycan peaks, cross-validation, and testing, but they do not reasonably convey possession of the particular combination now claimed—R²=0.551 on independent test samples. The written-description inquiry concerns the claimed combination, not merely whether individual words or components of the amendment can separately be located somewhere in the specification. Thus, Applicant’s identification of paragraphs [0017]–[0019] and [0242]–[0248] does not overcome the rejection. Claims 2–12 depend directly or indirectly from claim 1 and therefore incorporate this unsupported limitation. The written-description rejection of claims 1–12 is maintained on this basis.
Applicant additionally asserts that support for new claim 12 can be found at least at paragraphs [0010]–[0017]. Claim 12 now requires initiating or adjusting hormone replacement therapy of the female subject based on the determined multiday average estradiol concentration. This assertion has been considered but does not overcome the written-description deficiency. The originally filed disclosure describes the invention as determining a multiday-average E2 concentration and, secondarily, determining whether the female subject has entered perimenopause or menopause. The disclosed outputs are diagnostic. The specification describes determination of average multiday E2 concentration over a three-month, preferably two-month, and most preferably one-month period, and determination of perimenopausal or menopausal status. It does not describe using the resulting value as an operative basis for initiating HRT or adjusting an existing HRT regimen.
Applicant argues in the Remarks that accurate determination of average multiday E2 concentration enables timely initiation of HRT, permits selection of an appropriate therapy and dose, and could serve as a treatment-response monitoring tool. Applicant further reasons that a woman at 10 pmol/L has a different hormonal status from a woman at 70 pmol/L. These statements explain a potential use of the disclosed diagnostic information, but they do not establish that this therapeutic use was actually disclosed in the application as originally filed. Written-description support must be found in the originally filed disclosure; it cannot be supplied by explaining in a later-filed response what an Applicant now considers could be done with the disclosed diagnostic result.
Indeed, the originally filed specification’s express reference to hormone-replacement therapy occurs in the context of excluding such subjects from the experimental population. As explained in the rejection above, the study selected subjects who did not use hormone-replacing therapy in order to avoid potential effects of such health factors on IgG glycosylation. Thus, that disclosure does not describe initiating therapy, selecting a therapy or dose, adjusting an existing dose, or monitoring therapeutic response based on the claimed multiday-average E2 determination. Likewise, the drawings do not supply the missing therapeutic disclosure. Figures 1–6 concern chromatographic glycan peaks, studied female subjects, standardized glycan determinations, and glycan/hormone concentrations relative to menstrual-cycle phase. They do not depict an HRT administration or adjustment step or a feedback relationship in which the determined multiday-average E2 concentration controls initiation or adjustment of HRT.
Accordingly, although the originally filed specification provides disclosure concerning the diagnostic determination of multiday-average E2 concentration and menopausal/perimenopausal status, it does not reasonably convey that Applicant was in possession of the newly claimed therapeutic use of that determination to initiate or adjust hormone replacement therapy. Claim 12 therefore additionally fails to comply with the written-description requirement.
Rejection Under 35 U.S.C. 101
Applicant argues that amended claim 1 is patent eligible under Step 2A, Prongs One and Two, and Step 2B. In particular, Applicant contends that the claim is directed to an improved laboratory technique for measuring average multiday E2 concentration; that IgG glycans provide a proxy for cumulative estrogen exposure; that the claim does not explicitly recite a mathematical concept; and that the newly added computational pipeline amounts to significantly more than any abstract idea. These arguments have been fully considered but are not persuasive as to claims 1–11.
Applicant first argues that amended claim 1 is not directed to an abstract idea because it is directed to determining multiday-average E2 concentration rather than merely observing a naturally occurring correlation between IgG glycosylation and estradiol concentration. The argument does not address the basis of the present rejection. The §101 analysis has been reconsidered in view of amended claim 1. The rejection is not maintained against claim 1 merely on the theory that claim 1 observes the natural relationship between IgG glycosylation and E2. Rather, amended claim 1 now expressly recites a mathematical-concepts exception. Specifically, amended claim 1 requires a numerical model developed using L1-regularized (LASSO) regression with 10-fold cross-validation, batch correction (ComBat), logit transformation of glycan areas, and stepwise selection narrowing 24 glycan candidates down to 10 specific peaks, and requires the model to achieve a specified R² value. These limitations expressly require statistical regression, mathematical transformation, feature selection, cross-validation, and calculation of a numerical predicted E2 value from quantitative glycan data. The present rejection therefore identifies mathematical concepts expressly recited by the amended claim.
Applicant’s assertion that amended claim 1 does not explicitly recite a mathematical concept is therefore not persuasive. LASSO regression is a mathematical/statistical regression procedure; logit transformation is expressly a mathematical transformation; cross-validation and stepwise feature selection are mathematical/statistical model-development operations; and the claimed R² is a numerical measure. Moreover, claim 7 goes further and expressly recites the numerical equation used to calculate the logarithm of the multiday-average E2 concentration and expressly defines the GPB factors as natural logarithms of relative chromatographic peak areas. Claims 8, 10, and 11 additionally recite application of the calculated E2 information to the naturally occurring physiological relationship between estradiol concentration and perimenopausal/menopausal status. Claim 8 expressly assigns predetermined E2 ranges to menopausal status, while claims 10 and 11 determine menopausal or perimenopausal status based on the calculated E2 concentration. Thus, Applicant’s argument directed principally to whether claim 1 merely observes a natural correlation does not overcome the present rejection because the amended claims expressly recite mathematical concepts, and claims 8, 10, and 11 additionally recite the identified natural relationship.
Applicant next argues that the claim as a whole integrates the alleged exception into a practical application because it provides a new and useful laboratory technique that produces a clinically meaningful multiday-average E2 concentration that was previously inaccessible by a conventional single blood test. The argument has been considered but is not persuasive. The physical steps of claim 1—isolating plasma, releasing N-glycans from IgG, and quantitatively analyzing the released glycans—produce the quantitative biological data subsequently processed by the numerical model. The claim does not recite an improvement in how plasma is isolated, how IgG glycans are released, how the analytical instrument operates, or how a computer itself functions. Instead, these laboratory operations gather the glycan data upon which the recited mathematical/statistical model operates. The newly added LASSO, cross-validation, ComBat, logit transformation, stepwise-selection, and R² limitations further specify the mathematical/statistical model itself; they do not constitute an additional technological application of that model. This is the basis expressly set forth in the present §101 rejection.
Applicant’s characterization of the output as a clinically meaningful number likewise does not alter this analysis. Claim 1 terminates with generation of numerical data indicative of multiday-average E2 concentration. It does not require that the calculated concentration cause a subsequent treatment, change the operation of laboratory equipment, control a technological process, or otherwise produce a physical or technological result beyond the generation of the information itself. The fact that the claimed model may provide information that is different from or more temporally representative than a single conventional E2 measurement does not, without more, convert the mathematical processing into an improvement in the functioning of a computer or laboratory technology. The asserted improvement is in the information produced—a predicted multiday-average E2 concentration—not an improvement to the operation of the underlying analytical instrument or computing technology. Claims 8, 10, and 11 likewise terminate with diagnostic interpretation or determination of physiological status. Claim 8 interprets E2 according to specified numerical ranges, while claims 10 and 11 determine menopausal/perimenopausal status based on the calculated E2 concentration. None requires treatment or another action changing the female subject’s physiological condition. The present rejection therefore properly distinguishes these claims from a claim that applies the information in a further therapeutic step.
Applicant further argues that the specific computational pipeline supplies significantly more because it includes LASSO regression, 10-fold cross-validation, ComBat batch correction, logit transformation, stepwise selection from 24 candidates to 10 peaks, and R²=0.551. This argument is not persuasive because the limitations relied upon by Applicant are themselves the mathematical/statistical operations forming the identified abstract idea. Greater specificity in the mathematical model does not, by itself, provide an additional element separate from the identified exception. The elements outside that mathematical processing comprise obtaining/isolation of plasma, release of N-glycans from IgG, and quantitative analysis of the released glycans, including fluorescent derivatization where applicable. As explained in the rejection, Ercan evidences that enzymatic glycan release from serum or plasma, IgG-associated glycan analysis, fluorescent labeling, and quantitative chromatographic glycan analysis were known laboratory procedures. Considered as an ordered combination, the physical steps obtain biological data and the mathematical pipeline processes those data to generate the predicted E2 information. Applicant therefore has not established that claims 1–11 integrate the identified judicial exceptions into a practical application or contain additional elements amounting to significantly more. Accordingly, the rejection of claims 1–11 under 35 U.S.C. 101 is maintained.
The §101 rejection is not extended to claim 12. Claim 12 expressly requires the additional physical therapeutic step of initiating or adjusting hormone replacement therapy of the female subject based on the determined multiday average estradiol concentration. Thus, although claim 12 remains subject to the other applicable statutory requirements, claim 12 is not rejected under §101 in this action.
Rejection Under 35 U.S.C. 103
Applicant argues that none of the cited references discloses, teaches, or suggests determining multiday-average E2 concentration using the specifically recited computational pipeline, including LASSO regression with 10-fold cross-validation, ComBat batch correction, logit transformation, stepwise selection from 24 glycan candidates to 10 peaks, and R²=0.551 on independent test samples. Applicant therefore concludes that the cited art would not have rendered claim 1 obvious. Applicant’s argument has been fully considered but is not persuasive. Applicant’s argument is directed substantially to the absence of all limitations in any single reference. The rejection, however, is based upon the combined teachings of Ercan, Krištić, Stricker, Uh, Liu, and Haakensen. Obviousness does not require one reference to expressly disclose the complete claimed method where the teachings of the references collectively establish the claimed subject matter and the record provides an articulated reason why a person of ordinary skill would have combined those teachings with a reasonable expectation of success.
The rejection therefore does not rest on the proposition that each computational technique was merely independently known. Ercan establishes an estrogen-responsive IgG-glycan phenotype and recognizes hormone variability; Stricker supplies the reason for temporal characterization of E2; Krištić demonstrates successful numerical prediction of a physiological characteristic from quantitative IgG glycans; Uh teaches LASSO, ten-fold cross-validation, ComBat, and 24-peak IgG-glycan modeling to address correlation, overfitting, and batch effects; Liu teaches stepwise dimensionality reduction of a 24-peak IgG-glycan dataset to address multicollinearity; and Haakensen teaches logit transformation because relative glycan peak areas are compositional data unsuitable in raw form for most statistical analysis. One of ordinary skill therefore would have had a reason to combine these teachings when developing a quantitative model of the estrogen-responsive IgG-glycan data identified by Ercan. A reasonable expectation of success existed because Krištić had already demonstrated validated numerical prediction using quantitative IgG glycans, Uh had successfully implemented LASSO/cross-validation and ComBat with the same general 24-peak IgG platform, and Liu had successfully reduced correlated IgG-glycan predictor sets.
Applicant emphasizes that claim 1 narrows 24 candidates to exactly 10 peaks. However, Liu establishes the known use of stepwise feature selection to reduce a 24-peak correlated IgG-glycan predictor set. The number ultimately retained is an empirical consequence of applying the selection criterion to the particular response variable and dataset being modeled. The claim does not identify the ten peaks in claim 1 itself; it recites the result of the feature-selection process. Applicant’s argument concerning the particular R²=0.551 likewise does not establish nonobviousness. The rejection does not contend that Krištić expressly predicts E2 or expressly discloses the identical 0.551 value. Rather, Krištić demonstrates that an IgG-glycan predictive model can attain substantial validated predictive performance, reporting approximately 58% explained variation and R² values in the approximately 54%–61% region. This evidence supports the reasonable expectation that quantitative IgG glycans contain sufficient predictive information to support a model exhibiting performance in the general region recited by Applicant. The particular R² obtained after application of the known model-development and validation procedures to E2-responsive glycan data does not negate the articulated reason for making the combination or the demonstrated expectation that a predictive IgG-glycan model would successfully result. Accordingly, Applicant’s argument that no individual reference teaches the complete amended method does not overcome the rejection of claim 1.
Applicant states that claims 2–12 depend directly or indirectly from claim 1 and argues that they are patentable at least by virtue of their dependency. This argument is not persuasive as to claims 2–11. Dependency from claim 1 does not independently establish patentability where the rejection accounts for the limitations incorporated from claim 1 and additionally accounts for the further limitations of each dependent claim. As set forth in detail in the rejections above, Ercan and Krištić teaches the age-related female IgG-glycan context and PNGase F release pertinent to claims 2 and 3, respectively; Ercan teaches UPLC analysis pertinent to claim 4; Pučić and Krištić supply the fluorescent-labeling and standardized IgG glycan-peak teachings pertinent to claim 5; Morris supplies the particular RF labeling reagent pertinent to claim 6; Krištić, Pučić, and Vanhooren supply the additional quantitative/logarithmic glycan-model teachings pertinent to claim 7; Coburn supplies the E2/menopausal-status teachings pertinent to claims 8, 10, and 11; and Melo supplies the three-month longitudinal E2 context pertinent to claim 9. The respective motivations and reasonable expectations of success are set forth in each rejection above. Accordingly, the mere dependency of claims 2–11 from claim 1 does not overcome the respective §103 rejections.
Applicant argues that claim 12 provides an additional therapeutic application because accurate determination of multiday-average E2 would permit timely initiation of HRT, selection of an appropriate therapy and dose, and treatment-response monitoring. This argument does not overcome the §103 rejection of claim 12. As explained above, the combination applied to claim 1 provides the determined multiday-average E2 concentration. Sriprasert then supplies the clinical treatment teaching absent from that combination. Sriprasert evaluates hormone therapy in postmenopausal women receiving different E2/P4 doses and repeatedly measures attained E2 levels during therapy. Sriprasert teaches that blood E2 levels are associated with treatment efficacy, that administered E2 dose and attained serum E2 concentration are not equivalent, and that different E2 doses produce different attained E2 concentrations. Most importantly, Sriprasert expressly explains that attained E2 levels relate to possible HT treatment efficacy and teaches that healthcare providers may adjust the treatment dose for each individual woman to maximize the treatment efficacy and minimize side effects. Applicant’s own argument therefore identifies essentially the same clinical utility motivating the combination: using physiologically meaningful E2 information to guide HRT management. Once the claim 1 combination supplies an E2 concentration representative of the female subject, Sriprasert provides an express reason for using E2 information in individualized HT management because attained E2 relates to treatment efficacy and administered dose affects attained E2 concentration.
A skilled artisan would reasonably have expected success because Sriprasert demonstrates repeated measurement of serum E2 during hormone therapy, a dose-dependent relationship between administered E2 and attained E2, and clinical relevance of attained E2 to treatment efficacy. The rejection does not require Sriprasert alone to disclose Applicant’s IgG-glycan method; that method is supplied by the claim 1 combination, while Sriprasert supplies the additional reason for applying the resulting E2 information to HRT adjustment. Accordingly, Applicant’s arguments do not overcome the rejection of claim 12 under 35 U.S.C. 103.
Double Patenting
Applicant states that a Terminal Disclaimer has been filed against U.S. Patent No. 11,454,636 and therefore contends that the double-patenting rejection is moot. The Terminal Disclaimer has been reviewed and determined to be proper and effective, the nonstatutory double-patenting rejection based on U.S. Patent No. 11,454,636 is withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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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/E.O./Examiner, Art Unit 1677
/BAO-THUY L NGUYEN/Supervisory Patent Examiner, Art Unit 1677 September 28, 2026
