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 .
Formal Matters
Applicant's response, filed 22 October 2025, 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.
Status of Claims
Claims 1-18 are currently pending and have been examined.
Claims 1 and 10-18 have been amended.
Claims 1-18 have been rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed, 06/22/2023, in parent Application No. KR10-2021-0114310, filed on 08/28/2021.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 28 Aug. 2021 claiming benefit to KR10-2021-0114310.
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.
Claims 10-18 rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims 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, at the time the application was filed, had possession of the claimed invention. Claim 10 recites a processor and a memory storing an algorithm which were not in the originally filed disclosure. Examiner notes Applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitations in the application as filed (see 2163.04(I) regarding the burden on Examiner with regard to the written description requirement). Claims 11-18 depend on claim 10 and do not remedy the written description requirement issues of claim 10. As dependent claims inherit the deficiencies of the claims they depend on, they are also rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-9 and 10-18 are drawn to a method and system, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method for generating a personalized biological age prediction model, the method being performed in a system for generating the personalized biological age prediction model to generate the biological age prediction model from medical checkup data collected from a medical checkup system in part performing the steps of an age range setting process of setting, by a user, an age range (x to y) to be used as training data in order to generate binary logistic regression models; a binary logistic regression model generation process of setting each age unit as one unit in the age range set in the age range setting process, dividing the training data into two groups of an underage group (UAGm) and an overage group (OAGm) for each age unit, and generating the binary logistic regression models (Mx to My) for respective age units; an age prediction probability calculation process of calculating a probability (Pm) to be predicted as the overage group (OAGm) for each individual, who is a sample target, according to the binary logistic regression models; a cutoff extraction process of setting the underage group (UAGm) and the overage group (OAGm) as two-part response variables, setting the probability (Pm) to be predicted as the overage group (OAGm) as a predictor variable, and extracting a cutoff (Cm) through Receiver Operating Characteristic (ROC) curve analysis; an age prediction probability correction process of calculating an excess probability (Dm) to be predicted as the overage group (OAGm) by applying (Pm−Cm) calculation to subtract the cutoff (Cm) from the probability (Pm) to be predicted as the overage group (OAGm); an excess age calculation process of obtaining an individual's excess age by obtaining a weighted mean (Δi) for every excess probability (Dm) to be predicted as the overage group (OAGm) obtained through the age prediction probability correction process; and a biological age calculation process of obtaining a biological age by adding the individual's excess age obtained through the excess age calculation process to a chronological age; and providing the biological age to the individual.
Independent claim 10 recites a system for generating a personalized biological age prediction model in part performing the steps of collect medical checkup data provided, and store and manage the medical checkup data; determine valid training data from the checkup data according to a set training data reference age range (x to y) and checkup item information; generate binary logistic regression models (Mx to My) for respective age units within the age range (x to y) set for the training data; calculate a probability (Pm) to be predicted as an overage group (OAGm) for each individual in the training data according to the binary logistic regression models; set an underage group (UAGm) and the overage group (OAGm) as two-part response variables, set the probability (Pm) to be predicted as the over-age group (OAGm) as a predictor variable, and extract a cutoff (Cm) through ROC curve analysis; apply (Pm−Cm) calculation to subtract the cutoff (Cm) from the probability (Pm), which is to be predicted as the overage group (OAGm), calculate an excess probability (Dm) to be predicted as the individual overage group (OAGm), and correct the probability (Pm), which is to be predicted as the overage group (OAGm); obtain an individual's excess age by obtaining a weighted mean (Δi) for every excess probability (Dm) to be predicted as the overage group (OAGm); calculate a biological age from a chronological age by using the individual's excess age; output the calculated biological date; and store and manage the medical checkup data and the training data set.
These steps for calculating a biological age for an individual amount to a mathematical concept which includes mathematical relationships, mathematical formulas or equations, and mathematical calculations. The mathematical concept need not be expressed in mathematical symbols but not merely limitations that are based on or involve a mathematical concept (MPEP § 2106.04(a)(2)(I)(A) citing the abstract idea grouping for mathematical concepts for mathematical relationships). The steps of obtaining data and outputting the results amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982)).
Dependent claim 2 recites, in part, wherein the training data in the binary logistic regression model generation process is organized according to checkup item information, and the checkup item information is composed of health insurance checkup item data comprising: physical examination indices such as body mass index, waist circumference, systolic blood pressure, and diastolic blood pressure; and blood test indices such as three types of liver levels (i.e., AST, ALT, and γ-GTP), creatinine, three types of cholesterol (i.e., HDL, LDL, and TG), fasting blood glucose, and hemoglobin.
Dependent claim 3 recites, in part, a checkup item information setting process of retrieving and setting to add or delete the checkup item information used as the training data, wherein the training data in the binary logistic regression model generation process is organized according to the checkup item information.
Dependent claim 4 recites, in part, a condition information setting process of setting condition information for the training data in the binary logistic regression model generation process.
Dependent claim 5 recites, in part, wherein the condition information in the condition information setting process is male and female gender information.
Dependent claim 6 recites, in part, wherein, in the binary logistic regression model generation process, the binary logistic regression models (Mx to My) are generated for the respective age units by setting each age unit as one unit in the set age range, dividing the training data for each age unit into the two groups of the underage group (UAGm) and the overage group (OAGm), setting the two groups of the underage group (UAGm) and the overage group (OAGm) as the response variables, and setting the training data as the predictor variable.
Dependent claim 7 recites, in part, wherein, in the age prediction probability calculation process, calculating of the probability (Pm) to be predicted as the overage group (OAGm) for each individual, who is the sample target, according to the binary logistic regression model is calculated by Equation below:
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,
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where, Y: individual’s aging status, p(Y = OAGm): probability to be predicted as overage group OAGm, Yi: i-th individual’s aging status, i = 1, 2, …,: sample number, m = 26 (as x), 27,..., 75 (as y): chronological age observed in training data, CA: chronological age, Xk: k-th independent variable, βk: regression coefficient of k-th independent variable, and p: number of independent variables
Dependent claim 8 recites, in part, wherein, in the excess age calculation process, the individual’s excess age is calculated by Equation below, expressing a mean of a sum of each value obtained by multiplying the excess probability (Dm) (where, m = 26,..., 75) calculated for each individual by corresponding age (= m):
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where, N: sample number i = 1, 2, …, N, Δi : weighted mean of (Pim - Cm), and Cm: cutoff value Cm obtained through age prediction probability calculation process (cutoff of Pm to predict individual′s aging status from ROC curve analysis).
Dependent claim 9 recites, in part, wherein, in the excess age calculation process, the individual’s excess age is obtained by the weighted mean of every excess probability (Dm) to be predicted as the overage group (OAGm), and by applying an additional weight (Wm) to be applied, and the weighted mean is calculated by Equation below:
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where, N: sample number i = 1, 2, …, N, Δi: weighted mean of (Pim - Cm), Cm: cutoff value Cm obtained through age prediction probability calculation process (cutoff of Pm to predict individual′s aging status from ROC curve analysis), and Wm: weight applied for model to predict CA ≥ m.
Dependent claim 11 recites, in part, provide a process enabling a user to retrieve and set the age range and the checkup item information.
Dependent claim 12 recites, in part, provide a process enabling the user to set condition information for determining the training data.
Dependent claim 13 recites, in part, where the condition information is male and female gender information.
Dependent claim 14 recites, in part, wherein the binary logistic regression models (Mx to My) are generated for the respective age units by setting each age unit as one unit in the set age range, dividing the training data for each age unit into two groups of the underage group (UAGm) and the overage group (OAGm), setting the two groups of the underage group (UAGm) and the overage group (OAGm) as the response variables, and setting the training data as the predictor variable.
Dependent claim 15 recites, in part, wherein the checkup item information is composed of health insurance checkup item data comprising: physical examination indices such as body mass index, waist circumference, systolic blood pressure, and diastolic blood pressure; and blood test indices such as three types of liver levels (i.e., AST, ALT, and γ-GTP), creatinine, three types of cholesterol (i.e., HDL, LDL, and TG), fasting blood glucose, and hemoglobin.
Dependent claim 16 recites, in part, wherein calculating the probability (Pm) to be predicted as the overage group (OAGm) for each individual, who is a sample target, is performed according to the binary logistic regression models by using Equation below:
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,
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where, Y: individual’s aging status, p(Y = OAGm): probability to be predicted as overage group OAGm, Yi: i-th individual’s aging status, i = 1, 2, …,: sample number, m = 26(x), 27,..., 75(y): (chronological age observed in training data), CA: chronological age, Xk: k-th independent variable, βk: regression coefficient of k-th independent variable, and p: number of independent variables.
Dependent claim 17 recites, in part, obtain the individual’s excess age by obtaining the weighted mean (Δi) for every probability (Dm) to be predicted as the overage group (OAGm) through Equation below:
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where, N: sample number i = 1, 2, …, N, Δi: weighted mean of (Pim - Cm), and Cm: cutoff value Cm (cutoff of Pm to predict individual′s aging status from ROC curve analysis).
Dependent claim 18 recites, in part, obtain the individual’s excess age by obtaining the weighted mean (Δi) for every probability (Dm) to be predicted as the overage group (OAGm) through Equation below:
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where, N: sample number i = 1, 2, …, N, Δi: weighted mean of (Pim-Cm), Cm: cutoff value Cm (cutoff of Pm to predict individual′s aging status from ROC curve analysis), and Wm: weight applied for model to predict chronological age CA ≥ m.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 10 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1 and 10 recite a medical checkup system. Claim 10 recites a processor and a memory storing an algorithm. The specification only specifies in Fig. 13 that the medical checkup system includes generic computer hardware via the desktop illustration. The use of a medical checkup system, in this case to send checkup data over a network, only recites the medical checkup system as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claims 1 and 10 recite a medical checkup system. Claim 10 recites a processor and a memory storing an algorithm. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional element does not amount to significantly more than the above-identified judicial exception. The additional element merely provides a conventional computer implementation.
Claims 1-18 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 22 October 2025 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that a user interaction is not a mathematical concept. Examiner has classified the majority of the claim under the mathematical concept judicial exception. However, Examiner has provided that receiving and communicating data is considered method of organizing human activity in the rejection above under Step 2A Prong 1.
Next, Applicant asserts that the calculation of a biological age amounts to an improvement to technology. an improvement to the abstract idea of calculating a biological age does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to abstract idea of predicting an individual’s biological age. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN JACKSON whose telephone number is (571) 272-5389 and fax number is (571) 273-1626. The examiner can normally be reached on Monday – Thursday, 6:30 AM - 4:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long, can be reached on (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 3682