DETAILED ACTION
Applicant's response, filed 23 March 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 5 and 10-11 are cancelled.
Claims 1-4, 6-9, and 12-13 are pending.
Claims 1-4, 6-9, and 12-13 are rejected.
Priority
The present application, filed on May 24, 2022 claims priority to application KR10-2022-0056033 (06 May 2022), KR10-2021-001771964 (03 December 2021), and all the benefits accruing therefrom under 35 U.S.C 119. The application currently has the effective filing date of 03 December 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted 27 March 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings submitted 24 May 2022 have been accepted by the examiner.
Claim Objections
The objections to claims 3, 8, and 12-13 are withdrawn, in view of the claim amendments.
The objection to claim 11 is withdrawn, in view of cancellation of the claim.
Claim Interpretation
Claims 1 and 7 no longer invoke interpretation under 35 U.S.C 112(f) in view of the amendments and are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art.
Claim Rejections - 35 USC § 112
The previous rejections to claims 2-3 and 6-8 under 35 U.S.C 112(b) are withdrawn in view of the claim amendments.
Claim Rejections - 35 USC § 101
Applicant’s arguments that the amendments to the independent claims recite a concrete, ordered, workflow which yield a specific, actionable output instead of merely calculating a number (page 2, para. 2) are persuasive.
As such, the previous rejections to claims 1-4, 6-9, and 12-13 are withdrawn, in view of claim amendments.
The previous rejections to claims 5 and 10-11 are withdrawn in view of claim cancellations.
Claim Rejections - 35 USC § 102
Applicant’s arguments, that Dugué and Holmes fail to teach or suggest the amended limitations (page 3, para. 1-4), have been fully considered and are persuasive.
As such, the previous rejections to claims 1, 6, and 12-13 are withdrawn, in view of claim amendments.
The previous rejection to claim 11 is withdrawn in view of the claim cancellation.
Claim Rejections - 35 USC § 103
Applicants’ arguments, that Dugué nor Holmes individually and combined fail to teach or suggest the amended limitations (page 3, para 6-7), have been fully considered and are persuasive.
As such, the previous rejections to claims 1-4, 6-9, and 12-13 are withdrawn, in view of claim amendments; and the rejections to claims 5 and 10-11 are withdrawn in view of claim cancellations.
The following rejections are newly recited and necessitated by claim amendments.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-9, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Han et al. (Int. J. Environ. Res. Public Health, Vol. 15: 11; 2018, previously cited) in view of Dugué et al (2020; JNCI Cancer Spectrum; Vol. 5: 1, previously cited), Kresovich et al., (JNCI; Vol. 111; no. 10; 2019, newly cited), Barlow et al. (J Clinical Epidemiology; Vol. 52, no. 12, newly cited), and Holmes (2016/0180050, previously cited).
Han et al. describes research on how a subject’s relationship with nutrition can affect their biological age (BA) and risk of chronic disease.
Claims 1 and 6 are drawn to systems and methods of calculating a subject’s risk of cancer. The system obtains and uses age, gender, clinical biomarker information, and a lifestyle questionnaire data with information about family history of cancer, smoking, drinking, and exercise; identifying whether the subject is male or female; calculates biological age based on the biomarker information; and calculates a corrected biological age with questionnaire information.
Han et al. teaches a system that obtains and uses data including the participants’ gender, age, lifestyle questionnaire (page 3, table 1), and biomarker test results (page 2, column 1). Han et al. teaches evaluating males and females separately (page 3, column 1); and using the data to calculate biological age (page 2, column 1).
Han et al. further teaches exercise habits, alcohol intake, smoking status, family history of chronic disease, and subjective health status were the variables that most significantly influenced the adjusted biological age (page 5, column 1); defining family history of chronic disease to include at least one of diabetes mellitus, dyslipidemia, or hypertension (page 3, column 1); and using SAS software to execute the calculations within the system (page 3, column 1).
Claims 1 and 6 are further drawn to calculating the risk of cancer incidence, as the corrected biological age multiplied by the value of relative risk, in which the value of relative risk is an exponential value of a regression coefficient for biological age; and the regression coefficient is statistically derived from each individual cancer per one year increase of biological age calculated using a cox proportional hazard model.
Han et al. does not teach calculating the risk of cancer incidence.
Dugué et al. describes a prospective study on Biological Aging Measures Based on Blood DNA Methylation and Risk of Cancer.
Dugué et al. shows use of PhenoAge, a biological age (BA) calculation algorithm which utilizes a selection of clinical biomarkers (page 2, column 1), as one of the many methods to calculate BA (page 2, column 2). Dugué et al. shows collecting gender data; and calculating the risk of colorectal, gastric, kidney, lung, mature B-cell, prostate, and urothelial cancer incidence (page 2, column 1).
Dugué et al. shows the application of conditional logistic regression to calculate the risk of cancer incidence, per 5-year increase of biological age (page 3, column 1); and using multiplication, within Pearson correlations, to determine the association of cancer incidence with biological age (page 2, column 2).
Though Dugué et al. measures the statistical analysis of cancer incidence per 5 BA years instead of 1, this merely represents an obvious alteration of scale/proportion, in accordance with MPEP § 2144.04 IV(A).
Dugué et al. further shows the biological age of this cancer risk prediction system can remain unadjusted, in Model 1; or adjusted for lifestyle factors including smoking, alcohol intake, and exercise, in Model 2 (page 2, column 2).
Dugué et al. further teaches a Sister Study revealed a reasonably strong association with PhenoAge [citation 30] using a hazard ratio per 5-year increase: 1.13, which is of similar magnitude to our findings for colorectal, kidney, lung, mature B-cell, and urothelial cancers (page 7, column 1).
Dugué et al. does not explicitly teach the hazard ratio to calculate the cancer incidence is Cox proportional.
Kresovich et al. describes Methylation-Based Biological Age and Breast Cancer Risk calculations, the cited Sister Study article.
Kresovich et al. teaches to examine associations with breast cancer risk, we used a case-cohort Cox proportional hazards model to calculate hazard ratios, 95% confidence intervals, and two-sided P values [citation 36]; and used chronological age as the primary time-scale (page 2, column 2).
Kresovich et al. does not explicitly teach the value of relative risk is an exponential value of a regression coefficient for biological age.
Barlow et al. describes best practices of case-cohort designs; and is citation 36 of Kresovich et al.
Barlow et al. teaches assuming a standard exponential form for the relative risk (page 2, column 1); and using a standard Cox regression model, if covariates are evaluated on everyone (page 2, column 1).
Claims 1 and 6 are further drawn to generating comprehensive result information regarding: the subject’s nominal age, corrected biological age, a result of the risk of each individual cancer incidence, and statistical information about cancer incidence for each individual cancer in a certain sex and age.
Han et al. in view of Dugué et al., Kresovich et al., and Barlow et al. do not teach generating comprehensive result information.
Holmes et al. describes methods for determining health risks.
Holmes et al. teaches applying the subject's values to the disease-specific logistic regression risk model of the system to generate and output the subject's risk of developing a health condition [0151], such as cancer [0042]; the arithmetic difference between the chronological age of the subject and the calculated age of the subject [0170]; and illustrating the logistical approximation of age and gender-specific incidence data for individual cancer types [0006].
Claims 1 and 6 are further drawn to classifying the risk of cancer incidence into five grades: good, caution, warning, risk, and high risk.
Holmes et al. teaches using the risk model, personalized risk scores can be calculated for each subject based on the predictors [0037], which can be reported as high, average, low; a percentile of a population; or a bell curve [0040].
Holmes et al. further teaches in some embodiments, a subject with a calculated disease risk and age equivalent to a 100-year-old subject can be considered to be at a very high risk of developing a health condition; and in some embodiments, a subject with a calculated disease risk and age equivalent to a 0-year-old subject can be considered to be at a very low risk of developing a health condition [0059].
Claims 1 and 6 are further drawn to selecting a predetermined prevention practice guide corresponding to the classified grade.
Holmes et al. teaches features of the invention include: a final deliverable of a relative risk rating that shows potential risks for developing the condition assessed; and additional links to further information on the particular health condition, including original content created for the online portal… which can give members a way to identify risks before issues arise and provide guidance on prevention and management related to specific risks [0127].
Holmes et al. further teaches providing health recommendations to a subject, such as changes in lifestyle, physical activity, diet, medication, supplements, environmental factors, sun exposure, genetic testing, therapeutic intervention, and screening for health conditions, based on the health data [0079], calculated age, and risks [0104].
Claims 1 and 6 are further drawn to transmitting the comprehensive analysis result information including the grade and predetermined prevention practice guide to a terminal device possessed by the subject in an API or printable file format through a communication network that can provide a customized management service for the subject.
Holmes et al. further teaches a global network that can transmit a product of the invention [0015]; any tool, interface, engine, application, program, service, command, or other executable item can be provided as a module encoded on a computer-readable medium in computer executable code, in which each module can perform any function described herein to provide a result, such as an output, to a user [0097]; a system of the invention configured for use on any suitable device, for example, personal computer, tablet, or smartphone [0077]; and the subject using an application of the system on her smartphone [0102].
Holmes et al. further teaches the final deliverable allows users to take control of their own health and understand risk factors in advance of illness [0127].
Claims 2 and 7 are directed to two formulas that calculate BA, dependent on gender, and a selection of biomarkers to include within the calculations. The formulas are defined by their method of adding the results of multiple regression analysis calculations to quantify a relationship between biomarker results and chronological age of a subject.
Han et al. shows using waist circumference (WC), fasting blood glucose level (FBS), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) biomarker data to calculate biological age (page 2, column 1). Han et al. further shows distinct BA calculation formulas, dependent on the gender of the subject (page 2, column 1). Han et al. shows adding (page 2, column 1) the results of analysis of variance (ANOVA) calculations that quantify relationships between the biomarkers, nutrition data, and chronological age (page 3, column 1).
Claims 3 and 8 are drawn to a formula that determines an alternative BA calculation using information about a subject’s lifestyle information, such as: family history, smoking, drinking, and exercise. The formula is defined by its use of regression analysis to quantify the difference between the original biological age calculation, chronological age, and lifestyle factors.
Han et al. shows the calculation of an adjusted biological age with the consideration of additional independent variables (page 5, table 2). Han et al. shows exercise habits, alcohol intake, smoking status, family history of chronic disease, and subjective health status were the variables that most significantly influenced this adjusted biological age (page 5, column 1). Han et al. further shows performing multiple linear regression analysis to explore and determine these associations between the variables, biological age, and chronological age (page 6, figure 2).
Han et al. does not show smoking, drinking, exercise, or family history information defined according to claims 3 and 8. However, Han shows that exercise is a binary status, with a weekly threshold of 150 minutes; drinking is defined as more than seven (males) or five (females) drinks on a single occasion at least twice a week (page 3, column 1); and smoking status is defined as current smoker, ex-smoker, or non-smoker (page 4, table 1).
These definitions obtain the data required to measure and define smoking, drinking, and exercise in the same manner as the claimed invention. Furthermore, the disclosure recites that the amount of smoking, drinking, and exercise information need not be limited to a specific unit amount (specification [0055]). Therefore, information about these lifestyle factors collected by Han is within the metes and bounds of the current disclosure.
Holmes et al. further teaches calculating ages for subjects with a family history of bladder cancer, colon cancer, breast cancer, kidney cancer, lung cancer, ovarian cancer, pancreatic cancer, and skin cancer [0098].
Claims 4 and 9 are drawn to when the subject is a male, selecting at least one individual cancer from: oral cancer, pharyngeal cancer, esophageal cancer, stomach cancer, small intestine cancer, colorectal cancer, liver cancer, gallbladder cancer, pancreatic cancer, laryngeal cancer, lung cancer, skin cancer, prostate cancer, kidney cancer, bladder cancer, brain cancer, thyroid cancer, lymphoma, myeloma, and leukemia;
and when the subject is female, selecting at least one individual cancer from: oral cancer, pharyngeal cancer, esophageal cancer, stomach cancer, small intestine cancer, colorectal cancer, liver cancer, gallbladder cancer, pancreatic cancer, laryngeal cancer, lung cancer, skin cancer, breast cancer, uterine cancer, ovarian cancer, kidney cancer, bladder cancer, brain cancer, thyroid cancer, lymphoma, myeloma, or leukemia.
Dugué et al. teaches collecting gender data for each subject; and calculating the risk of colorectal, gastric, kidney, lung, mature B-cell, prostate, and urothelial cancer incidence (page 2, column 1).
Claim 12 is directed to a computer server with a processor and memory with instruction that when executed perform the method of claim 6.
Claim 13 is directed to a service server with a processor and memory with instructions that when executed perform the method of claim 6; and transmit the comprehensive analysis result information to a terminal device possessed by the subject through a communication network.
Regarding claims 12-13, Holmes et al. teaches a global network that can transmit a product of the invention [0015]; and any tool, interface, engine, application, program, service, command, or other executable item can be provided as a module encoded on a computer-readable medium in computer executable code, in which each module can perform any function described herein to provide a result, such as an output, to a user [0097].
Holmes et al. further teaches processors with multiple cores can be used, whether in a single computer system, in a cluster, or distributed across systems over a network comprising a plurality of computers, cell phones, and/or personal data assistant devices [0114]; a system of the invention configured for use on any suitable device, for example, personal computer, tablet, or smartphone [0077]; and the subject uses an application of the system on her smartphone to input her health data [0102].
Therefore Han et al. teaches a method of calculating biological age (BA) and adjusting it for lifestyle factors. Though Han et al. does not teach incorporating family history of cancer into the adjusted/corrected biological age calculation, nor calculating the risk of cancer incidence, it teaches biological age is significantly associated with higher risks of chronic diseases (page 1, column 1), including cancer and is needed to predict mortality (page 2, column 1). Therefore, Han et al. provides sufficient motivation for one of ordinary skill in the art to use the result of its BA calculation in a method of predicting the incidence risk of chronic diseases, such as cancer.
Dugué et al. teaches applying several unique methods of calculating BA to computer-implemented cancer risk prediction algorithms. Therefore, it would be obvious that the known BA calculation method in Han et al. can similarly be applied to the known cancer risk prediction algorithm in Dugué et al. with a reasonable expectation of success.
Dugué et al. further teaches a comparable method of calculating cancer incidence that uses a hazard ratio. Kresovich et al. teaches the comparable method and further clarifies that the hazard ratio is a Cox proportional hazard ratio. As such, Dugué et al. teaches that calculating cancer incidence with a cox proportional hazard ratio is one of a finite number of techniques that can be performed on applicable data in order to yield predictable results.
Barlow et al. is further cited within Kresovich et al. and teaches calculating the Cox proportional hazard ratio involves taking the exponential value of a regression coefficient. As such, applying this known technique to the base method of the cancer incidence calculation would be obvious to one of ordinary skill in the art with the expectation of predictable results based on the teaching of Dugué et al. and Kresovich et al.
Holmes et al. teaches methods of summarizing and displaying the biological age, cancer incidence, risk grade, and prevention plan information; and provides motivation for one of ordinary skill in the art to provide this information to the user in order to improve control over health outcomes.
Holmes et al. further teaches performing age calculations with a family history of cancer variable. Therefore, applying the identical variable within analogous biological age calculations used to quantify cancer risk would be obvious to one of ordinary skill in the art in order to yield predictable results and an improved system that accounts for the prediction of a mortality factor, as disclosed by Dugué et al.
Though Holmes et al. does not explicitly teach classifying cancer risk into good, caution, warning, risk, and high risk, it does teach classifying health risk into five analogous categories: very low, low, average, high, and very high risk; and teaches that the health risk can further be reported in any suitable format [0040]. As such, applying a five-level cancer incidence risk scale, as generically recited, would be obvious to one of ordinary skill in the art via using a known technique to improve similar methods in the same way, in which the results would be predictable.
Furthermore, Holmes et al. provides sufficient motivation for one of ordinary skill in the art to gather, display and transmit the comprehensive analysis information the subject through a network and/or device with a reasonable expectation of success and improved system based on the benefit to the user in understanding and having autonomy over their health.
Response to Arguments
Applicants’ arguments, that Dugué and Han individually and combined fail to teach or suggest the amended limitations (page 3, para 6-7), have been fully considered and are persuasive.
Examiner responds that upon consideration of the claim amendments, the amended limitations are obvious in view of the newly recited references, as detailed above.
Applicant argues that Han utilizes only 5 metabolic syndrome markers, but fails to teach or suggest the extensive, whole body clinical biomarker combinations required by dependent claims 2 and 7 (page 4, para 3).
Examiner responds dependent claims 2 and 7 explicitly recite “biomarker information to include at least one or more of” the biomarker combinations. As such, Han teaches this limitation by including 5 of the biomarkers recited in claims 2 and 7, as indicated by applicant.
Applicant argues that Dugué nor Han teaches or suggests one of ordinary skill in the art to arrive at the highly specific, multi-layered ordered combination of 28/32 biomarkers + lifestyle correction formulas + individualized cancer targeting roadmap architecture created by the chain dependency format without impermissible hindsight.
Examiner responds the reference combinations, which arrive at the architecture created by the recited claims, have appropriate rationale support via KSR International Co. v. Teleflex Inc. (KSR), 550 U.S. 398, 82 USPQ2d 1385 (2007); and do not explicitly cite/rely upon the applicant’s disclosure.
Furthermore, the reference combinations comply with MPEP 2145 (X)(A), which states: any judgment on obviousness is in a sense necessarily a reconstruction based on hindsight reasoning, but so long as it takes into account only knowledge which was within the level of ordinary skill in the art at the time the claimed invention was made and does not include knowledge gleaned only from applicant’s disclosure, such a reconstruction is proper.
Conclusion
No claims are currently allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Milana Thompson whose telephone number is (571)272-8740. The examiner can normally be reached Monday - Friday, 9:00-6:00 ET.
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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