Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Summary
This is a Non-Final Office action based on the 17/079110 application RCE filed on 05/22/2025.
Claims 1, 3-8, 11-12, & 21-33 are pending and have been fully considered.
Claims 30-33 are newly added.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/22/2025 has been entered.
Claim Rejections - 35 USC § 112
Claims 1, 3-8, 11-12, & 21-33 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 & 21 recite the limitation "the determined testosterone level" in step d). There is insufficient antecedent basis for this limitation in the claim. It is assumed that applicant intends to refer to the predicted testosterone level which is predicted in Claims 1 & 21, step c), but applicant should stick with the same terminology throughout to prevent confusion.
Further with respect to Claims 1 & 21, they all contain mention of “the identified set of variants,” “the identified variants,” and “the genetic variants.” It seems these are all possibly the same thing in the claims, but it is unclear due to the different terminology if this is actually the case or not. If it instead is the case that “the identified set of variants,” contain more than “the genetic variants,” “with male-specific effect on testosterone,” for Claim 1 and with “female-specific effect on testosterone,” for Claim 21, then this should be made clear in the claims to prevent confusion. Claims 30-33 are also unclear due to this since they also use “the genetic variants.”
With respect to Claims 31 & 33 they read confusingly since simplified read…”wherein the genetic variants,” “comprise genetic variants.” It is noted that the genetic variation should be in the claimed genes, but it is unclear if genetic variants comprising genetic variants mean that more variation is present.
With respect to Claims 30 & 32, it is claimed that “identifying the genetic variants,” is performed. However, in base Claims 1 & 21 a set of variants is already identified and it is already states that both the “identified variants,” and the genetic sequencing data, “comprise the genetic variants with the male specific effect on testosterone.” Therefore--- aren’t “the genetic variants,” already identified in Claims 1 & 21, even though the term “identifying,” isn’t uses specifically for “the genetic variants.”
Claims 3-8, 11-12, & 22-33 are rejected by virtue of being dependent on Claims 1 & 21.
Claim Rejections - 35 USC §103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
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.
1.Claims 1 & 3-6, 11-12 & 30-31 are rejected under 35 U.S.C. 103 as being obvious over OHLSSON in Genetic Determinants of Serum Testosterone Concentrations in Men, in view of RIVAS in Diagnosing and managing low serum testosterone and in further view of BEIM in US 20170351806.
With respect to Claim 1, OHLSSON teach of methods of investigating genetic determinants of serum testosterone concentrations in men(abstract). This includes obtaining genetic sequencing data from male individuals, identifying a set of variants and determining using a model the testosterone level based on the variants from the genetic sequencing data(“meta-analysis of genome-wide association data in 8,938 men from seven cohorts and followed up the genome-wide significant findings in one in silico (n = 871) and two de novo replication cohorts (n = 4,620) to identify genetic loci significantly associated with serum testosterone concentration in men”, abstract).
It was found that two single-nucleotide polymorphisms at the sex hormone-binding globulin (SHBG) locus (17p13-p12) were identified as independently associated with serum testosterone concentration (rs12150660, p = 1.2610241 and rs6258, p = 2.3610222). Subjects with $3 risk alleles of these variants had 6.5-fold higher risk of having low serum testosterone than subjects with no risk allele. The rs5934505 polymorphism near FAM9B on the X chromosome was also associated with testosterone concentrations (p = 5.6610216). The rs6258 polymorphism in exon 4 of SHBG affected SHBG’s affinity for binding testosterone and the measured free testosterone fraction (p,0.01) (abstract).
GWAS (genetic wide association studies) were performed to identify variants in different individuals to see if any variant is associated with the high or low testosterone trait, and additive linear regression model was developed to determine this (Page 3, column 2, paragraph 2, 6 lines from bottom).
OHLSSON teach of using only male participants and forming of the model with only male participants and data, therefore making this model male specific and only male trained and further teach that the model is performed on a computer/through computation (abstract, Page 5, column 1, line 5 & Table 1, Page 9, first column, paragraph 4, line 9). Though--- it is noted that there is no positive training step in Claim 1 and training is only mentioned in the predicting step (c ).
OHLSSON does not teach of treating the patient who has high or low testosterone as claimed.
RIVAS is used to remedy this and teaches of a method of diagnosing and treating patients with low testosterone and the study is specific to men(abstract). RIVAS teaches of using testosterone replacement therapy as the treatment, when low testosterone level is detected/measured (Page 323, column 1, paragraph 3). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have reasonable expectation of success of/to use testosterone replacement therapy as is done in RIVAS if low testosterone level is found, in the method of OHLSSON due to the advantages it shows for improving secondary sexual characteristics, sexual function, sense of well-being, and bone mineral density (RIVAS, Page 323, column 1, paragraph 3).
OHLSSON and RIVAS teach of the claimed invention as shown above. They do not teach of the model being a predictive machine learning computational model.
BEIM is used to remedy this and teaches of a method and system to assess female and male infertility(abstract), utilizing genetic sequencing data and detecting genetic variants including polymorphisms (abstract, paragraph 0006), and through detecting testosterone levels (paragraph 0295, Table 6). BEIM further teaches of using a linear predictive model (linear predictive models like linear regression are a fundamental type of supervised machine learning algorithm/s) (paragraph 0210, 0209), and further of training with female and male genetic data (paragraph 0014). Thought BEIM teaches of using both female and male data- this also make using either separately obvious as well—specifically on Table 6 in BEIM, they teach of marking out if the gene or biomarker is male or female specific (Table 6). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have had reasonable expectation of success of/to use the predictive model of BEIM to determine testosterone level as is done in OHLSSON And RIVAS due to the need in the art for better methods to assess genetic variations and predict ahead of time problems associated with infertility (BEIM, paragraph 0006, 0005).
With respect to Claim 3, OHLSSON teach of using genome-wide data (whole genome) (abstract).
With respect to Claim 4, OHLSSON teach of the data being at a loci by capture (“identify genetic loci significantly associated with serum testosterone,” GWAS captures sequence variants common in the population (abstract, Page 4, column 2, 4 lines from bottom, Page 6, column 1, first line).
With respect to Claim 5, OHLSSON and RIVAS teach of the claimed invention as shown above. They do not teach of the sample being a biopsy.
BEIM teach of a method a system to assess female and male infertility(abstract), utilizing genetic sequencing data and detecting genetic variants including polymorphisms (abstract, paragraph 0006), and through detecting testosterone levels (paragraph 0295, Table 6). BEIM further teach of using biopsies as the sample (paragraph 0087). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have had reasonable expectation of success of/to use biopsies as the sample as is done in BEIM in the method of OHLSSON and RIVAS since this is a clinical acceptable manner for tissue collection (BEIM, paragraph 0087).
With respect to Claim 6, OHLSSON teach of determining a risk of have low serum testosterone and of using a model and scoring to do this (abstract, Page 3, column 2, last paragraph, Page 9, column 1, paragraph 4, line 6). OHLSSON et al. do not call out “polygenic risk score,” specifically. BEIM teach of calculating genetic regions/variants with risk score (paragraph 0025). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have had reasonable expectation of success of/to calculate risk score as is done in BEIM in the method of OHLSSON and RIVAS to make use and learn from the data obtained.
With respect to Claim 11, BEIM teach of performing a blood test (paragraph 0097).
With respect to Claim 12, BEIM teach of determining infertility(abstract).
With respect to Claim 30, OHLSSON teaches of using only male participants and forming of the model with only male participants and data, therefore making this model male specific and only male trained and further teach that the model is performed on a computer/through computation (abstract, Page 5, column 1, line 5 & Table 1, Page 9, first column, paragraph 4, line 9). RIVAS further teaches of the claims as shown above.
OHLSSON and RIVAS teach of the claimed invention as shown above. They do not teach of the model being a predictive machine learning computational model.
BEIM is used to remedy this and teaches of a method and system to assess female and male infertility(abstract), utilizing genetic sequencing data and detecting genetic variants including polymorphisms (abstract, paragraph 0006), and through detecting testosterone levels (paragraph 0295, Table 6). BEIM further teaches of using a linear predictive model (linear predictive models like linear regression are a fundamental type of supervised machine learning algorithm/s) (paragraph 0210, 0209), and further of training with female and male genetic data (paragraph 0014). Thought
BEIM teaches of using both female and male data- but also this also makes using either separately obvious as well—specifically on Table 6 in BEIM, they teach of marking out if the gene or biomarker is male or female specific for conditions (Table 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have had reasonable expectation of success of/to use the predictive model of BEIM to determine testosterone level as is done in OHLSSON And RIVAS due to the need in the art for better methods to assess genetic variations and predict ahead of time problems associated with infertility (BEIM, paragraph 0006, 0005).
With respect to Claim 31, BEIM teaches of detection of jmjd1c, prmt6, and msh5 (Table 1 & Table 6). More specifically, BEIM teaches of jmjd1c being specifically associated with male phenotypes wherein there is a decrease male germ cell count and abnormal spermatogonia (Page 54, Table 6, last row).
2. Claims 7-8 are rejected under 35 U.S.C. 103 as being obvious over OHLSSON in Genetic Determinants of Serum Testosterone Concentrations in Men, in view of RIVAS in Diagnosing and managing low serum testosterone and in further view of BEIM in US 20170351806 and in further view of ZAMPIERI in Machine and deep learning meet genome-scale metabolic modeling.
With respect to Claims 7-8, OHLSSON and RIVAS and BEIM teach of the claimed invention as shown above. They do not teach of using penalized multivariate regression of LASSO (batch screened).
ZAMPIERI is used to remedy this. ZAMPIERI teach of omic data analysis procedures and specifically of combining machine learning and constraint-based modeling(abstract). ZAMPIERE further teach of using LASSO procedures (Page 8, supervised fluxomic analysis, paragraph 2 & table 1), investigating batch effects (Page 16, paragraph 3, line 7-9), of penalizing deviations(Page 12, first paragraph, second line), and of using multivariate regression(Page 14, generation of constraint based models and fluxomic data, paragraph 2, lines 3-5, Page 3, types of machine learning approaches, paragraph 1, last line, page 8, supervised fluoxomic analysis, table 1- including linear regression). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have had reasonable expectation of success of/to use these procedures as is done in ZAMPIERI in the methods of OHLSSON and RIVAS and BEIM due to the advantages they have in identifying phenotypic extremes (Page 13, paragraph 3) and also due to the need in the art for better integration of constraint based and machine learning modeling (Page 2, paragraph 3).
3.Claims 21-25, 28-29 & 32-33 are rejected under 35 U.S.C. 103 as being obvious over SMEETH in Polygenic risk for circulating reproductive hormone levels and their influence on hippocampal volume and depression susceptibility in view of ROHR in The impact of testosterone imbalance on depression and women’s health and in further view of BEIM in US 20170351806.
With respect to Claim 21, SMEETH teach of methods of determining polygenic risk for circulating reproductive hormone levels(abstract). This includes obtaining genetic sequencing data from female individuals, identifying a set of variants and determining using a model the testosterone and other hormones levels based on the variants from the genetic sequencing data. Specifically, SMEETH teach of performing a genome wide association study (GWAS) on genomic data and using this data to generate a polygenic risk score for testosterone and other hormones. SMEETH further teach of using 100% female participants in some cases (abstract). SMEETH teaches of from the participants genetic data, identifying the best combination of SNP (single nucleotide polymorphisms, variants from the genetic data) for each reproductive hormone including testosterone, and from that determining a best-fit polygenic risk score (Page 4/24, second paragraph). SMEETH further teach of performing linear regression (Page 23, first paragraph).
Further, SMEETH teaches of using only female participants and forming of the model with only female participants and data, therefore making this model female specific and only female trained and further teach that the model is performed on a computer/through computation utilizing software to calculate the score & model (Page 6/23, last paragraph). Though--- it is noted that there is no positive training step in Claim 21 and training is only mentioned in the predicting step (c ).
SMEETH does not teach of treating the women found to have high or low testosterone levels. Further- if it’s still unclear that one would be interested in studying (and modeling) for a female only group, ROHR is used to remedy both of these things.
ROHR teach of a method of determining the affect testosterone imbalance has on women’s health(abstract). ROHR further teach of analyzing and modeling specifically for only women participants (Page S33, column 2, 12.), and of treating the women with testosterone reducing drugs and of drugs which will treat testosterone deficiencies (Page S36, 16.- S38, column 1, paragraph 2). ROHR teach that these treatments can include oral contraceptives (Page S37, column 1, paragraph 2). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have had reasonable expectation of success of/to model for a treatment of testosterone imbalances in women in the method of SMEETH due to the need in the art to better determine levels of testosterone in women and treat them as an avenue to treat depression (ROHR, abstract).
SMEETH and ROHR teach of the claimed invention as shown above. They do not teach of the model being predictive.
BEIM teach of a method and system to assess female and male infertility(abstract), utilizing genetic sequencing data and detecting genetic variants including polymorphisms (abstract, paragraph 0006), and through detecting testosterone levels (paragraph 0295, Table 6). BEIM further teach of using a linear predictive model (paragraph 0210, 0209) (linear predictive models like linear regression are a fundamental type of supervised machine learning algorithm/s), and further of training with female and male genetic data (paragraph 0014). Thought BEIM teaches of using both female and male data- this also make using either separately obvious as well—specifically on Table 6 in BEIM they teach of marking out if the gene or biomarker is male or female specific (Table 6). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have had reasonable expectation of success of/to use the predictive model of BEIM to determine testosterone level as is done in SMEETH and ROHR due to the need in the art for better methods to better methods to assess ahead of time, genetic variations associated with infertility (BEIM, paragraph 0006, 0005).
With respect to Claim 22, SMEETH teach of using genome-wide data (GWAS) (whole genome) (abstract).
With respect to Claim 23, SMEETH teach of using GWAS analysis (GWAS captures sequence variants common in the population the data being at a loci by capture) (abstract).
With respect to Claim 24, SMEETH and ROHR teach of the claimed invention as shown in the above rejection. They do not teach of the sample being a biopsy.
BEIM teach of a method a system to assess female and male infertility(abstract), utilizing genetic sequencing data and detecting genetic variants including polymorphisms (abstract, paragraph 0006), and through detecting testosterone levels (paragraph 0295, Table 6). BEIM further teach of using biopsies as the sample (paragraph 0087). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have had reasonable expectation of success of/to use biopsies as the sample as is done in BEIM in the method of SMEETH and ROHR since this is a clinical acceptable manner for tissue collection (BEIM, paragraph 0087).
With respect to Claim 25, SMEETH teach of determining polygenic risk score(abstract).
With respect to Claim 28, SMEETH teach of performing a clinical assessment(abstract). BEIM also teaches of performing a blood test (paragraph 0097).
With respect to Claim 29, BEIM teach of determining infertility(abstract).
With respect to Claim 32, SMEETH teaches of using only female participants and forming of the model with only female participants and data, therefore making this model female specific and only female trained and further teach that the model is performed on a computer/through computation utilizing software to calculate the score & model (Page 6/23, last paragraph). SMEETH does not teach of treating the women found to have high or low testosterone levels. Further- if it’s still unclear that one would be interested in studying (and modeling) for a female only group, ROHR is used to remedy both of these things.
ROHR teach of a method of determining the affect testosterone imbalance has on women’s health(abstract). ROHR further teach of analyzing and modeling specifically for only women participants (Page S33, column 2, 12.), and of treating the women with testosterone reducing drugs and of drugs which will treat testosterone deficiencies (Page S36, 16.- S38, column 1, paragraph 2). ROHR teach that these treatments can include oral contraceptives (Page S37, column 1, paragraph 2). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have had reasonable expectation of success of/to model for a treatment of testosterone imbalances in women in the method of SMEETH due to the need in the art to better determine levels of testosterone in women and treat them as an avenue to treat depression (ROHR, abstract).
SMEETH and ROHR teach of the claimed invention as shown above. They do not teach of the model being predictive.
BEIM teach of a method and system to assess female and male infertility(abstract), utilizing genetic sequencing data and detecting genetic variants including polymorphisms (abstract, paragraph 0006), and through detecting testosterone levels (paragraph 0295, Table 6). BEIM further teach of using a linear predictive model (paragraph 0210, 0209) (linear predictive models like linear regression are a fundamental type of supervised machine learning algorithm/s), and further of training with female and male genetic data (paragraph 0014). Thought BEIM teaches of using both female and male data- this also make using either separately obvious as well—specifically on Table 6 in BEIM they teach of marking out if the gene or biomarker is male or female specific (Table 6). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have had reasonable expectation of success of/to use the predictive model of BEIM to determine testosterone level as is done in SMEETH and ROHR due to the need in the art for better methods to better methods to assess ahead of time, genetic variations associated with infertility (BEIM, paragraph 0006, 0005).
With respect to Claim 33, BEIM teaches of detection of MCM9 and LIPE (Table 6) and also of detection of stag3 (table1). BEIM also teaches of MSM9 being associated with female issues (Page 53, second from last full column group).
4. Claims 26-27 are rejected under 35 U.S.C. 103 as being obvious over SMEETH in Polygenic risk for circulating reproductive hormone levels and their influence on hippocampal volume and depression susceptibility in view of ROHR in The impact of testosterone imbalance on depression and women’s health and in further view of BEIM in US 20170351806 and in further view of ZAMPIERI in Machine and deep learning meet genome-scale metabolic modeling.
With respect to Claims 26-27, SMEETH and ROHR and BEIM teach of the claimed invention as shown above. They do not teach of using penalized multivariate regression of LASSO (batch screened).
ZAMPIERI is used to remedy this. ZAMPIERI teach of omic data analysis procedures and specifically of combining machine learning and constraint-based modeling(abstract). ZAMPIERE further teach of using LASSO procedures (Page 8, supervised fluxomic analysis, paragraph 2 & table 1), investigating batch effects (Page 16, paragraph 3, line 7-9), of penalizing deviations(Page 12, first paragraph, second line), and of using multivariate regression(Page 14, generation of constraint based models and fluxomic data, paragraph 2, lines 3-5, Page 3, types of machine learning approaches, paragraph 1, last line, page 8, supervised fluoxomic analysis, table 1- including linear regression). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention and one would have had reasonable expectation of success of/to use these procedures as is done in ZAMPIERI in the methods of SMEETH and ROHR and BEIM due to the advantages they have in identifying phenotypic extremes (Page 13, paragraph 3) and also due to the need in the art for better integration of constraint based and machine learning modeling (Page 2, paragraph 3).
Response to Arguments
Applicant's arguments filed 05/22/2025 have been fully considered but they are not persuasive.
The examiner notes that a 112 rejection was added due to the amendments made 05/22/2025. Though is seems applicant intends that the “identified variants,” “set of identified variants,” and the “genetic variants,” are all slightly different things--- especially the line--- “the identified set of variants comprises genetic variants,” makes this especially confusing.
With respect to the prior art, applicant argues that none of the prior art teach of training the models with only male (in the case of claim 1) or only female (in the case of Claim 21) specific genetic variants. With respect to this--- the examiner points out that no specific “training,” step is claimed in the independent claims.
This is only found in the newly added dependent claims. The prior art rejections above has shown how they make these limitations obvious.
Further, as shown in the rejections above—even if initially using both female and male data, the prior art that teaches of training the models with both female and model genetic data together—also makes it obvious to one of ordinary skill in the art to separate out and use only male or female data. The pieces of prior art that use both, merely are using additional data which can equate to an additional step in the claimed method, in comparison to what is claimed.
For claim 1, applicant argues that OHLSSON does not teach of a trainable predictive linear model, but instead teaches of an “additive genetic linear regression model.” Applicant argues the same thing for Claim 21 with respect to the SMEETH reference. With respect to this, due to the instant amendments, an additional piece of prior art was used, BEIM. BEIM was used in the last office action, but not for the teaching of the predictive models, and hadn’t been used to teach of independent claim 1 before. It would have been obvious to one of ordinary skill in the art to use the predictive model of BEIM instead of the model as is done in OHLSSON or SMEETH due to the need in the art for better methods to assess genetic variations and predict ahead of time problems associated with infertility (BEIM, paragraph 0006, 0005). Further--- as shown above OHLSSON teaches of modeling being male specific, while SMEETH teaches of modeling being female specific, albeit without the machine-learning part. However, the combination of the instant references still make the claims obvious.
It is noted that the prior art only teach of small subsets of the genetic variants claimed in newly added Claims 31 & 33. The claims recite only needing the genetic variants to comprise, “one or more,” of these variants, so the instant prior art reads on the claims. However--- if the training of the algorithms requires training (again, which isn’t currently positively claimed as a step in independent Claims 1 & 21) to include ALL of the variants claimed in Claims 31 & 33, and if applicant has support for this in the instant disclosure, this could be a possible avenue for amendment.
All claims remain rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA M FRITCHMAN whose telephone number is (303)297-4344. The examiner can normally be reached 9:30-4:30 MT Monday-Friday.
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/REBECCA M FRITCHMAN/Primary Examiner, Art Unit 1758