DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed July 4, 2025.
Claims 1 and 8 are pending.
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 .
Response to Amendments
Claims 1 and 8 remain pending.
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.
Claim 1 and 8 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1 and 8:
Eligibility Step 1 (does the subject matter fall within a statutory category?): Independent Claim 1 falls within the statutory category of machine. Independent Claim 8 falls within the statutory category of method.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1 and 8 claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in the independent claims are (Claim 1 being representative):
assessing risks of T2DM complications
obtain current disease progressions and learn assessment parameters of a plurality of patients with T2DM, wherein the current disease progressions refer to whether the plurality of patients currently has end-stage renal disease, arteriosclerotic heart disease, chronic heart failure, ischemic stroke, retinopathy or amputation;
the assessment parameters at least comprise an age of the plurality of patients at the current disease progressions and a plurality of risk factors;
and the plurality of risk factors comprise glycated hemoglobin, systolic blood pressure, body mass index, low-density lipoprotein, high-density lipoprotein, total cholesterol, triglyceride, creatinine, and urine protein and creatinine ratio of the plurality of patients;
and use a nonhomogeneous Markov chain depending on the age of the plurality of patients to describe status changes of the plurality of patients when inputting the current disease progressions and the age into the nonhomogeneous Markov chain, and use the assessment parameters as a baseline value to expresses influence on status transitions with a Cox proportion hazards ratio model,
and construct a risk equation based on the impact of transfer, wherein the risk equation can calculate a plurality of risk values for one patient to develop a plurality of complications to be assessed from a current disease progression after a predetermined time, based on hazard of the plurality of complications developing at an age of the one patient and hazard of the plurality of complications
developing after the predetermined time in a Weibull distribution, wherein magnitudes of the plurality of risk values represent occurrence probability and occurrence sequence of the plurality of complications, wherein the plurality of complications comprise end-stage renal disease, arteriosclerotic heart disease, chronic heart failure, ischemic stroke, retinopathy, and amputation, wherein the plurality of risk values of the plurality of complications are sorted to obtain a sorted result, and health care workers and patients make care decisions and disease management to reduce occurrence of one of the complications and improve quality of life of the patients according to the sorted result, and a risk value of the one of the complications is greater than risk values of the others of the complications.
This abstract idea is “managing personal behavior or relationships or interactions between people” within certain methods of organizing human activity along with Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I) as the independent claims are directed to mathematical equations calculating risk of T2DM complications and by following these rules and instructions for health care workers to make care decisions for a patient.
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claims 1 and 8 the judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional claim elements below:
a computer
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
Within the noted above claim the additional element, a computer, is performing the abstract idea and recited as merely “apply-it”
Independent claim 8 recites no additional elements therefore purely treated as the abstract idea:
Accordingly, independent claims 1 and 8 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as analyzed above in step 2A prong 2, are merely applying the abstract idea with implementation by a computer and therefore, do not amount to significantly more. The claims are patent ineligible.
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.
Claim 1 and 8 are rejected to under 35 U.S.C. 103 as being unpatentable over HONGPENG et. al (CN111297329A) in view of Rosenthal et. al (US2021/0000792A1), in further view of Cottin et. al (hereinafter Cottin) (US20220293270A1) and in even further view of Morris et. al (hereinafter Morris) (US20110105852A1)
As per claim 1, HONGPENG teaches:
A system for assessing risks of T2DM complications, comprising: a computer, configured to: obtain current disease progressions and learn assessment parameters of a plurality of patients with T2DM,…[…]…; (page 2, para. 7 discloses, “a data collection module” and page 2 para. 3 discloses, “the public health data includes longitudinal indicators such as blood sugar, blood pressure, blood lipids, uric acid, etc. repeatedly measured at different times, and whether cardiovascular diseases such as coronary heart disease and stroke occur and when they occur” and page 3 para. 5 discloses, “Extract blood glucose, blood pressure, blood lipids, uric acid and other indicators repeatedly measured at different times from the physical examination database; extract blood glucose and medication data from the diabetes health management file. The data that needs to be collected also include the onset of coronary heart disease and stroke in the chronic disease database and the onset time, and the cause of death, onset time, and death time in the death report database.”)
…[…]…the assessment parameters at least comprise an age of the plurality of patients at the current disease progressions and a plurality of risk factors; (see page 2 para. 5 “which uses Cox A proportional hazard model was established to predict the risk of coronary heart disease in patients with type 2 diabetes through the gender, age at diagnosis of diabetes” and see page 2 para 1 and 3 discloses, “the survival sub-model adopts a COX proportional hazard regression model…[…]… It is assumed that the survival outcome is related to the expected value of the longitudinal variable at the time point, the change trend at the time point, and the cumulative effect at all times.” and see page 3 para. 5 discloses, “The data collected above is divided into longitudinal variables and outcome variables for analysis. Longitudinal variables include dynamic variables (such as blood sugar, systolic blood pressure, uric acid, etc.) and static variables (such as gender, smoking, drinking). The outcome variables are stroke, coronary heart disease, etc. Whether and when cardiovascular disease occurs.”)
and the plurality of risk factors comprise glycated hemoglobin, systolic blood pressure, body mass index, low-density lipoprotein, high-density lipoprotein, total cholesterol, triglyceride,…[…]… (“through the gender, age at diagnosis of diabetes, smoking status, and the values of systolic blood pressure, cholesterol, low-density lipoprotein, and glycosylated hemoglobin.” And “BMI represents body mass index, sex represents gender, γ1, γ2, and γ3 are the corresponding coefficients, y blood glucose represents fasting blood glucose value, y systolic blood pressure represents systolic blood pressure The value of y uric acid represents the value of uric acid, and α1, α2, and α3 are the corresponding related parameters.” And “First, fit a univariate joint model to fasting blood glucose, systolic blood pressure, diastolic blood pressure, cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, uric acid, heart rate and other variables, and consider the time-point expected value and time-point change trend of the variables”)
and use the assessment parameters as a baseline value to expresses influence on status transitions with a Cox proportion hazards ratio model, and construct a risk equation based on the impact of transfer, wherein the risk equation can calculate a plurality of risk values for one patient to develop a plurality of complications to be assessed from a current disease progression after a predetermined time, based on hazard of the plurality of complications developing at an age of the one patient and hazard of the plurality of complications developing after the predetermined time …[…]…(page 1-2 paras 12 and 1-3 discloses, “the joint model includes a longitudinal sub-model and a survival sub-model, wherein the longitudinal sub-model adopts a linear mixed effects model, and the survival sub-model adopts a COX proportional hazard regression model. The longitudinal sub-model formula: yki(t)=xki(t)βk+zki(t)bki, where yki(t) represents the variable value of the k-th variable of the i-th diabetic patient at time t, xki(t) ) And zki(t) are the coefficients of fixed effect βk and random effect bki, respectively. The survival sub-model formula: hi(t)=h0(t)exp[γ·wi(t)+α·fi*(Ti)], where hi(t) indicates that the i-th diabetic patient has a heart attack at time t The risk function of vascular disease, h0(t) represents the baseline risk function, wi(t) is the covariate, the corresponding coefficient is γ, fi*(Ti) is the function related to the longitudinal variable, and the correlation parameter α connects the two sub-models Together, suppose that the survival outcome is related to the expected value of the longitudinal variable at a point in time, the trend of change at the point in time, and the cumulative effect at all times.” And “The correlation parameter α connects the two sub-models. It is assumed that the survival outcome is related to the expected value of the longitudinal variable at the time point, the change trend at the time point, and the cumulative effect at all times.” And see page 2 para. 5 “which uses Cox A proportional hazard model was established to predict the risk of coronary heart disease in patients with type 2 diabetes through the gender, age at diagnosis of diabetes” and see page 4 para. 1 discloses, “the formula is: h=h0exp(γ1·qzage+γ2·BMI+γ3·sex+α1·y
blood sugar +α2·y systolic blood pressure +α3·y uric acid),”)
HONGPENG does not teach the underlined portions:
wherein the current disease progressions refer to whether the plurality of patients currently has end-stage renal disease, arteriosclerotic heart disease, chronic heart failure, ischemic stroke, retinopathy or amputation
and the plurality of risk factors comprise glycated hemoglobin, systolic blood pressure, body mass index, low-density lipoprotein, high-density lipoprotein, total cholesterol, triglyceride, creatinine, and urine protein and creatinine ratio.
and use a nonhomogeneous Markov chain depending on the age of the plurality of patients to describe status changes of the plurality of patients when inputting the current disease progressions and the age into the nonhomogeneous Markov chain,
in a Weibull distribution, wherein magnitudes of the plurality of risk values represent occurrence probability and occurrence sequence of the plurality of complications, wherein the plurality of complications comprise end-stage renal disease, arteriosclerotic heart disease, chronic heart failure, ischemic stroke, retinopathy, and amputation, wherein the plurality of risk values of the plurality of complications are sorted to obtain a sorted result, and health care workers and patients make care decisions and disease management to reduce occurrence of complications and improve quality of life of the patients according to the sorted result.
However, Rosenthal does teach the underlined portions:
wherein the current disease progressions refer to whether the plurality of patients currently has end-stage renal disease (see table 1 “renal disease”), arteriosclerotic heart disease (“atherosclerotic cardiovascular disease” / a type of arteriosclerotic heart disease), chronic heart failure (“heart failure”), ischemic stroke (‘non - fatal ischemic events or strokes”), retinopathy (see table 3 “retinopathy”), or amputation (see table 1 “or amputation secondary to vascular disease”). ([0193] discloses, “Participants were men and women with type 2 diabetes ( glycated hemoglobin27.0 % and s10.5 % ) either 30 years or older with a history of symptomatic atherosclerotic cardiovascular disease , or 50 years or older with two or more of the following risk factors for cardiovascular disease : duration of diabetes 10 years , systolic blood pressure > 140 mmHg while on one or more antihypertensive agents , current smoker , microalbuminuria or macroalbuminuria , or high - density lipoprotein ( HDL ) cholesterol < 1 mmol / L . Participants were required to have an estimated glomerular filtration rate at entry of > 30 ml / min / 1.73 m² and to the criteria listed in Table 1 below.” And [0037] discloses, “In certain embodiments , the present invention is directed to methods for reducing or preventing a cardiovascular event , cardiovascular hospitalization, non-fatal myocardial infarction , non - fatal ischemic events or strokes , or cardiovascular mortality.” And [0045] discloses, “In certain embodiments , the present invention is directed to methods for preventing or reducing cardiovascular events in a patient with heart failure ( including Class I through Class IV , preferably Class II through Class IV , more preferably Class III or Class IV )”)
and the plurality of risk factors comprise glycated hemoglobin, systolic blood pressure, body mass index, low-density lipoprotein, high-density lipoprotein, total cholesterol, triglyceride, creatinine, and urine protein and creatinine ratio.([0077] discloses, “In certain embodiment of the present invention , the patient is over 50 years of age and exhibits or presents with two or more risk factors of vascular disease ( including but not limited to elevated urinary albumin : creatinine ratio.”)
in a Weibull distribution, wherein magnitudes of the plurality of risk values represent occurrence probability and occurrence sequence of the plurality of complications, wherein the plurality of complications comprise end-stage renal disease, arteriosclerotic heart disease, chronic heart failure, ischemic stroke, retinopathy, and amputation, wherein the plurality of risk values of the plurality of complications are sorted to obtain a sorted result, and health care workers and patients make care decisions and disease management to reduce occurrence of complications and improve quality of life of the patients according to the sorted result. (see table 1 “renal disease”), (“atherosclerotic cardiovascular disease” / a type of arteriosclerotic heart disease), (“heart failure”), (‘non - fatal ischemic events or strokes”), (see table 3 “retinopathy”), (see table 1 “or amputation secondary to vascular disease”) / see ([0193] discloses, “Participants were men and women with type 2 diabetes ( glycated hemoglobin27.0 % and s10.5 % ) either 30 years or older with a history of symptomatic atherosclerotic cardiovascular disease , or 50 years or older with two or more of the following risk factors for cardiovascular disease : duration of diabetes 10 years , systolic blood pressure > 140 mmHg while on one or more antihypertensive agents , current smoker , microalbuminuria or macroalbuminuria , or high - density lipoprotein ( HDL ) cholesterol < 1 mmol / L . Participants were required to have an estimated glomerular filtration rate at entry of > 30 ml / min / 1.73 m² and to the criteria listed in Table 1 below.” And [0037] discloses, “In certain embodiments , the present invention is directed to methods for reducing or preventing a cardiovascular event , cardiovascular hospitalization, non-fatal myocardial infarction , non - fatal ischemic events or strokes , or cardiovascular mortality.” And [0045] discloses, “In certain embodiments , the present invention is directed to methods for preventing or reducing cardiovascular events in a patient with heart failure ( including Class I through Class IV , preferably Class II through Class IV , more preferably Class III or Class IV )”)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Rosenthal with teaching of HONGPENG since the combination of the two references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A). It can be seen that each element claimed is present in either HONGPENG or Rosenthal . Various additional complications as cited (as taught by Rosenthal) does not change or affect the complications of HONGPENG. Complications related to type 2 diabetes mellitus would be considered no matter the type as it is part of the risk equations. The functionality of the claimed invention appears to operate independently of specific complication being used therefore the elements in HONGPENG and Rosenthal do not interfere with each other, the results of the combination would be predictable.
However, HONGPENG and Rosenthal do not teach the underlined portions:
and use a nonhomogeneous Markov chain depending on the age of the plurality of patients to describe status changes of the plurality of patients when inputting the current disease progressions and the age into the nonhomogeneous Markov chain,
in a Weibull distribution, wherein magnitudes of the plurality of risk values represent occurrence probability and occurrence sequence of the plurality of complications, wherein the plurality of complications comprise end-stage renal disease, arteriosclerotic heart disease, chronic heart failure, ischemic stroke, retinopathy, and amputation, wherein the plurality of risk values of the plurality of complications are sorted to obtain a sorted result, and health care workers and patients make care decisions and disease management to reduce occurrence of complications and improve quality of life of the patients according to the sorted result.
However, Cottin does teach the underlined portions:
and use a nonhomogeneous Markov chain depending on the age of the plurality of patients to describe status changes of the plurality of patients when inputting the current disease progressions and the age into the nonhomogeneous Markov chain, ([0152] discloses, “Here , the modeling of the illness - death process may comprise defining the three processes by making specific assumptions on cancer evolution over time ( see Reference 24 ) . For the transitions 0- > 1 and 0-2 , the specific implementation may consider time non - homogeneous markovian processes . For transition 1- > 2 , the modeling may comprise performing a time transformation , and considering a time homogeneous semi - markovian process ( the probability of transiting from state 1 to state 2 at time t depends only on the duration t - T , already spent in 1 ) . Wherever convenient , the modeling may comprise using the duration variable d = t - T , instead of the time variable t for the transition 1 + 2 . Under these assumptions , the modeling may aim at modeling the transition probabilities fkl . ) over time . See an illustration of the illness - death process in FIG . 1.” And see [0102] discloses, “The illness may be any disease which can be characterized through an intermediate state and an absorbing state . In examples where the multi - state model is an illness death model , the illness may be any deadly / lethal disease , i.e. , a disease known for potentially incurring death at a statistical rate which is non - negligible over the human population ( for example higher than 1 % or 5 % ) . The illness may for example be a cancer disease , for example a breast cancer . In other examples , the illness may be the Alzheim er's disease or a cardiovascular disease . Alternatively , the absorbing state may be non - lethal . For instance , age - related macular degeneration disease may evolve towards blindness which is the final absorbing state.” And see [0106] discloses, “The core characteristics of a patient for example comprise their age , body mass index ( BMI ) , comorbidities , sex , and / or any other general characteristic medically relevant.” And see [0087] discloses, “A computer - implemented method is provided for machine - learning a function ( i.e. , determining via a machine learning process a neural network function , that is , a function comprising or consisting of one or more neural networks ) . The function is configured to be fed with an input and to provide a certain output , in accordance with the following . The input is a plurality of covariates ( i.e. , pieces of data ) that represent medical characteristics of a patient . The output is defined with respect to ( i.e. , in reference to ) a multi - state model of an illness ( e.g. , an illness - death model ) . In other words , the multi - state model is predetermined . The ( e.g. , illness - death ) multi - state model has ( i.e. , is characterized by ) ( e.g. , three ) states and ( e.g. , irreversible ) transitions between the states . Given this , the output is a distribution of transition - specific ( i.e. , transition - specific ) probabilities for each interval of a set of intervals . The set of intervals is such that it forms a subdivision of a follow - up period . In other words , the output is a distribution of probabilities constant values , with exactly one probability constant value per each respective time interval of a follow - up period and per each respective transition of the multi - state model . Said probability constant value ( e.g. , a single number , e.g. , between 0 and 1 ) represents probability that the patient experiences the respective transition ( and thus corresponding changes states ) during said respective time interval . The machine learning method comprises providing an input dataset of covariates and ( e.g. , illness - death ) time - to - event data of a set of patients . The machine - learning method further comprises training the function based on the input dataset” and see [0119] discloses, “In the case of an illness - death model , the log likelihood may comprise a first sub - term representing a log contribution from the initial state , and a second sub - term representing a log contribution from the intermediate state . The log - likelihood may be equal to the sum of the two terms . The first sub - term may optionally be derived under a time non - homogenous markovian assumption for the transitions from the initial state , and / or the second sub - term may optionally be derived under a time homogenous semi markovian assumption for the transition ( s ) from the inter mediate state.” / examiner notes that the illness death model analyzes age and cancer evolution (limited type of disease progression) as a medical characteristic of a patient therefore necessarily input and is based in homogenous semi markovian assumptions which is simply the name of the assumption made in context of a nonhomogeneous Markov chain)
in a Weibull distribution, wherein magnitudes of the plurality of risk values represent occurrence probability and occurrence sequence of the plurality of complications[0217] discloses, “The simulations aim to generate the processes To ( together with D. ) and T2 , such that the illness - death times Tki , for ( k , 1 ) e { ( 01 ) , ( 02 ) , ( 12 ) } , are simulated through Cox transition - specific hazard functions : Tki - k \ t \ X ; ) = axlº ( t ) exp ( 8k ( X ; Bxl ) ) where gxl . ) is a transition - specific risk function , Bx = Bx / " ) , Bki ? ) , Bk13 ) , Bkl { 4 } ) ? with Bulpe R2 for 15p54 are fixed effect coefficients , and axiº ( . ) is the baseline hazard function . The three baseline hazard functions are generated as follows : Ox1 ° ( . ) - Weibull ( scale = 0.01 , shape = 1.2 ).”)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Cottin with teaching of HONGPENG and Rosenthal since the combination of the references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A). It can be seen that each element claimed is present in either HONGPENG, Rosenthal, or Cottin. Various additional computer simulation techniques include statistical methods as cited (as taught by Cottin) does not render the teachings of HONGPENG inoperative. The functionality of the claimed invention appears to operate by use of multiple statistical techniques being used therefore the elements in HONGPENG, Rosenthal, and Cottin do not interfere with each other, the results of the combination would be predictable.
However, HONGPENG, Rosenthal, and Cottin do not teach the underlined portions:
in a Weibull distribution, wherein magnitudes of the plurality of risk values represent occurrence probability and occurrence sequence of the plurality of complications, wherein the plurality of complications comprise end-stage renal disease, arteriosclerotic heart disease, chronic heart failure, ischemic stroke, retinopathy, and amputation, wherein the plurality of risk values of the plurality of complications are sorted to obtain a sorted result, and health care workers and patients make care decisions and disease management to reduce occurrence of one of the complications and improve quality of life of the patients according to the sorted result, and a risk value of the one of the complications is greater than risk values of the others of the complications.
However, Morris teaches the underlined portion:
in a Weibull distribution, wherein magnitudes of the plurality of risk values represent occurrence probability and occurrence sequence of the plurality of complications, wherein the plurality of complications comprise end-stage renal disease, arteriosclerotic heart disease, chronic heart failure, ischemic stroke, retinopathy, and amputation, wherein the plurality of risk values of the plurality of complications are sorted to obtain a sorted result, and health care workers and patients make care decisions and disease management to reduce occurrence of one of the complications and improve quality of life of the patients according to the sorted result, and a risk value of the one of the complications is greater than risk values of the others of the complications.([0085] discloses, “FIG. 6 illustrates a process of using data imputation to determine risk scores and rank risks of health outcomes. In general, the process is configured to receive patient-level data as input, and to generate an indication of (a) risks of health care outcomes and (b) benefits of treatments as outputs.” And see [0086] discloses, “ The outcomes for which risks are determined may include, in various embodiments, cardiovascular disease (CVD) including MI and strokes whether fatal or non-fatal; actual onset of diabetes mellitus (DM); and complications of DM Such as foot ulcers, blindness, and end-stage renal dis ease (ESRD).” And see [0102] discloses, “At step 618, the process generates output to a display device, storage or printer comprising a list of interventions for which a patient is eligible, risk values arising if the person stops all medications, and risks by disease category. In an embodiment, step 618 may involve providing a ranked list of different interventions or recommendations and their associated benefits, when the different interventions produce different benefits for the patient.” And see [0044]-[0050] / examiner notes that prior art of record teaches the diseases are sorted based on low and high risk in conjunction with implementation of interventions thus necessarily trying to give the best intervention based on risk reduction of disease categories where one risk may be higher or lower at baseline based on patient information that another)
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Morris with teaching of HONGPENG, Rosenthal, and Cottin as the functionality of the claimed invention would have improved accuracy and increased efficiency of resources to treat the sorted risks that may have the highest impact on the patient in a positive manner and HONGPENG, Rosenthal, and Cottin already teach the need to treat disease progression effectively (see HONGPENG page 2 para. 9 and Rosenthal ([0071] and Cottin ([0088])), therefore the elements of risk being ranked do not interfere with the overall functionality of HONGPENG and are rather just another manipulation of data thus the results of combining would be predictable.
As per claim 8, it is a method claim which repeats limitations of claim 1, the corresponding system claim, as a series of process steps as opposed to a collection of elements. Since the collective teaching as well as motivations to combine HONGPENG, Rosenthal, Cottin, and Morris disclose the structural elements that constitute the system of claim 1, it is respectfully submitted that they perform the underlying process steps, as well. As such, the limitations of claim 8 are rejected for the same reasons given above for claim 1.
Response to Arguments Regarding 35 U.S.C § 101 Rejection
Applicant argues on pages 1-3 that claim 1 is rejected under 35 U.S.C. § 101 as directed to non-statutory subject matter should be withdrawn in view of the following amendments to claim 1. Applicant point out that according to technical feature of "health care workers and patients make care decisions and disease management to reduce occurrence of one of the complications and improve quality of life of the patients according to the sorted result" as recited in the amended claim 1, a person having ordinary skill in the art would easily assume that the health care workers would certainly give corresponding medical treatment to patients to reduce occurrence probability of complications having higher rankings in the sorted result. Therefore, Applicant submits that the amended claim 1 has indicated that "the claim must show some tangible outcome such as administering medical treatment based on the calculated result" as suggested by the Examiner in the interview conducted on February 11, 2025. For example, when the sorted result (as shown in FIG. 5 of the as-filed application) shows that the risk value of arteriosclerotic heart disease is significantly higher than the risk values of other complications, doctor would certainly give appropriate drugs to patients to reduce occurrence probability of the arteriosclerotic heart disease. Therefore, the amended has integrated into a practical application, reconsideration and withdrawal of the 101 rejection are kindly requested.
Applicant's arguments have been fully considered but they are not persuasive. The MPEP § 2106 states the following enumerated groupings of abstract idea:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
The MPEP § 2106 states, “It is essential that the broadest reasonable interpretation (BRI) of the claim be established prior to examining a claim for eligibility. The BRI sets the boundaries of the coverage sought by the claim and will influence whether the claim seeks to cover subject matter that is beyond the four statutory categories or encompasses subject matter that falls within the exceptions. See MyMail, Ltd. v. ooVoo, LLC, 934 F.3d 1373, 1379, 2019 USPQ2d 305789 (Fed. Cir. 2019) ("Determining patent eligibility requires a full understanding of the basic character of the claimed subject matter"), citing Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1273-74, 103 USPQ2d 1425, 1430 (Fed. Cir. 2012); In re Bilski, 545 F.3d 943, 951, 88 USPQ2d 1385, 1388 (Fed. Cir. 2008) (en banc ), aff'd by Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) ("claim construction … is an important first step in a § 101 analysis"). Evaluating eligibility based on the BRI also ensures that patent eligibility under 35 U.S.C. 101 does not depend simply on the draftsman’s art. Alice, 573 U.S. 208, 224, 110 USPQ2d at 1984, 1985 (citing Parker v. Flook, 437 U.S. 584, 593, 198 USPQ 193, 198 (1978) and Mayo, 566 U.S. at 72, 101 USPQ2d at 1966). See MPEP § 2111 for more information about determining the BRI.”
Furthermore, the MPEP § 2106.05(a) states, “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691…..It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016) (a method of translating a logic circuit into a hardware component description of a logic circuit was found to be ineligible because the method did not employ a computer and a skilled artisan could perform all the steps mentally). Similarly, a claimed process covering embodiments that can be performed on a computer, as well as embodiments that can be practiced verbally or with a telephone, cannot improve computer technology. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1328, 122 USPQ2d 1377, 1381 (Fed. Cir. 2017) (process for encoding/decoding facial data using image codes assigned to particular facial features held ineligible because the process did not require a computer)….. However, 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.”
No additional elements are amended into recitation of the claim and as stated in the MPEP § 2106 the abstract idea cannot provide an improvement but rather it must come from the additional elements. Furthermore, MPEP § 2106.04(d)(2) Examples of "treatment" and prophylaxis " limitations encompass limitations that treat or prevent a disease or medical condition, including, e.g., acupuncture, administration of medication, dialysis, organ transplants, phototherapy, physiotherapy, radiation therapy, surgery, and the like. For example, an immunization step that integrates an abstract idea into a specific process of immunizing that lowers the risk that immunized patients will later develop chronic immune-mediated diseases is considered to be a particular prophylaxis limitation that practically applies the abstract idea. See, e.g., Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1066–68, 100 USPQ2d 1492, 1500-01 (Fed. Cir. 2011). Examiners should keep in mind that in order to qualify as a "treatment" or " prophylaxis " limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis " consideration. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used.
Therefore, while a it may be true that a doctor would certainly give appropriate drugs to patients to reduce occurrence probability of the arteriosclerotic heart disease the affirmative recitation of treatment or prophylaxis is not recited in the claim. Examiner maintains the 35 USC § 101 rejection.
Response to Arguments Regarding 35 U.S.C § 103 Rejection
As stated on remarks on pages 3-15 amended claim 1 and 8 are rejected under 35 U.S.C. § 103.
Applicant’s argues The amended claim 1 recites "use a nonhomogeneous Markov chain depending on the age of the plurality of patients to describe status changes of the plurality of patients when inputting the current disease progressions and the age into the nonhomogeneous Markov chain" and "health care workers and patients make care decisions and disease management to reduce occurrence of one of the complications and improve quality of life of the patients according to the sorted result, and a risk value of the one of the complications is greater than risk values of the others of the complications". In view of the aforementioned amendment, Applicant respectfully traverses the said rejections based on at least the grounds set forth in detail below.
Specifically, paragraphs [0106] and [0152] of Cottin only revealed time non-homogeneous markovian processes and core characteristics of a patient comprising their age, body mass index (BMI), comorbidities, sex, and/or any other general characteristic medically relevant. However, Cottin did not teach to input current disease progressions of patients with T2DM into nonhomogeneous Markov chain.
Therefore, Applicant submits that Cottin fails to disclose the technical feature of "use a nonhomogeneous Markov chain depending on the age of the plurality of patients to describe status changes of the plurality of patients when inputting the current 7 disease progressions and the age into the nonhomogeneous Markov chain" as recited in the amended claim 1.
Examiner does not find applicant’s argument persuasive. Cottin teaches in [0152] “Here , the modeling of the illness - death process may comprise defining the three processes by making specific assumptions on cancer evolution over time ( see Reference 24 ) . For the transitions 0- > 1 and 0-2 , the specific implementation may consider time non - homogeneous markovian processes . For transition 1- > 2 , the modeling may comprise performing a time transformation , and considering a time homogeneous semi - markovian process ( the probability of transiting from state 1 to state 2 at time t depends only on the duration t - T , already spent in 1 ) . Wherever convenient , the modeling may comprise using the duration variable d = t - T , instead of the time variable t for the transition 1 + 2 . Under these assumptions , the modeling may aim at modeling the transition probabilities fkl . ) over time . See an illustration of the illness - death process in FIG . 1.” And see [0102] discloses, “The illness may be any disease which can be characterized through an intermediate state and an absorbing state . In examples where the multi - state model is an illness death model , the illness may be any deadly / lethal disease , i.e. , a disease known for potentially incurring death at a statistical rate which is non - negligible over the human population ( for example higher than 1 % or 5 % ) . The illness may for example be a cancer disease , for example a breast cancer . In other examples , the illness may be the Alzheim er's disease or a cardiovascular disease . Alternatively , the absorbing state may be non - lethal . For instance , age - related macular degeneration disease may evolve towards blindness which is the final absorbing state.” And see [0106] discloses, “The core characteristics of a patient for example comprise their age , body mass index ( BMI ) , comorbidities , sex , and / or any other general characteristic medically relevant.” And see [0087] discloses, “A computer - implemented method is provided for machine - learning a function ( i.e. , determining via a machine learning process a neural network function , that is , a function comprising or consisting of one or more neural networks ) . The function is configured to be fed with an input and to provide a certain output , in accordance with the following . The input is a plurality of covariates ( i.e. , pieces of data ) that represent medical characteristics of a patient . The output is defined with respect to ( i.e. , in reference to ) a multi - state model of an illness ( e.g. , an illness - death model ) . In other words , the multi - state model is predetermined . The ( e.g. , illness - death ) multi - state model has ( i.e. , is characterized by ) ( e.g. , three ) states and ( e.g. , irreversible ) transitions between the states . Given this , the output is a distribution of transition - specific ( i.e. , transition - specific ) probabilities for each interval of a set of intervals . The set of intervals is such that it forms a subdivision of a follow - up period . In other words , the output is a distribution of probabilities constant values , with exactly one probability constant value per each respective time interval of a follow - up period and per each respective transition of the multi - state model . Said probability constant value ( e.g. , a single number , e.g. , between 0 and 1 ) represents probability that the patient experiences the respective transition ( and thus corresponding changes states ) during said respective time interval . The machine learning method comprises providing an input dataset of covariates and ( e.g. , illness - death ) time - to - event data of a set of patients . The machine - learning method further comprises training the function based on the input dataset” and see [0119] discloses, “In the case of an illness - death model , the log likelihood may comprise a first sub - term representing a log contribution from the initial state , and a second sub - term representing a log contribution from the intermediate state . The log - likelihood may be equal to the sum of the two terms . The first sub - term may optionally be derived under a time non - homogenous markovian assumption for the transitions from the initial state , and / or the second sub - term may optionally be derived under a time homogenous semi markovian assumption for the transition ( s ) from the inter mediate state.”
Examiner notes therefore under BRI that Cottin does teach that the illness death model analyzes age and cancer evolution (limited type of disease progression) as a medical characteristic of a patient therefore necessarily input and is based in homogenous semi markovian assumptions which is simply the name of the assumption made in context of a nonhomogeneous Markov chain model such as the illness death model as disclosed in the art.
Applicant also argues: In addition, as described in paragraphs [0018]-[0023], [0072] and FIG. 1 of the as-filed application, the current disease progression (complication status) of patient with T2DM has a great influence on the subsequent disease development, and the risk equation using the current disease progression (complication status) of patient with T2DM to assess risks of T2DM complications provides highly accurate risk assessment results. FIG. 6 and paragraph [0102] of Morris only revealed to rank risks of health outcomes and generate an indication of treatments. However, Morris did not explicitly teach that the care decisions and disease management should first reduce occurrence probability of complications having higher rankings in the sorted result. Therefore, Applicant submits that Morris fails to disclose the technical feature of "health care workers and patients make care decisions and disease management to reduce occurrence of one of the complications and improve quality of life of the patients according to the sorted result, and a risk value of the one of the complications is greater than risk values of the others of the complications" as recited in the amended claim 1. In addition, Applicant submits that according to the highly accurate risk assessment results, the health care workers would give appropriate drugs to patients to reduce occurrence probability of complications that rank higher in the sorted result first, but the health care workers would not give a lot of drugs to the patients to reduce occurrence probability of all complications. Accordingly, the health care workers may prescribe the right cure for an illness, and the patients can avoid affecting their quality of life due to taking too many drugs.
Examiner appreciates applicant’s further argument but does not find it persuasive. Morris in combination with the prior cited references teaches “health care workers and patients make care decisions and disease management to reduce occurrence of one of the complications and improve quality of life of the patients according to the sorted result, and a risk value of the one of the complications is greater than risk values of the others of the complications" in paragraph [0085] discloses, “FIG. 6 illustrates a process of using data imputation to determine risk scores and rank risks of health outcomes. In general, the process is configured to receive patient-level data as input, and to generate an indication of (a) risks of health care outcomes and (b) benefits of treatments as outputs.” And see [0086] discloses, “ The outcomes for which risks are determined may include, in various embodiments, cardiovascular disease (CVD) including MI and strokes whether fatal or non-fatal; actual onset of diabetes mellitus (DM); and complications of DM Such as foot ulcers, bl