DETAILED ACTION
Notices to Applicant
This communication is a non-final rejection. Claims 1-20, as filed 11/16/2024, are currently pending and have been considered below.
Priority is generally acknowledged as shown on the filing receipt with the earliest priority date being 05/24/2022.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter 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
The claim(s) recite(s) subject matter within a statutory category such as a process, machine, and/or article of manufacture.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mathematical concepts and mental processes. A core concept of the claimed invention is creating a flow model describing one or more dynamic activities between the plurality of compartments, the one or more dynamic activities including transition rates between compartments, influx into compartments, and efflux out of compartments over time, wherein the flow model includes a plurality of model parameters each related to the one or more dynamic activities. This limitation can be represented by a mathematical equation and defines how population values in each compartment change over time as a function of rate parameters, and thus amounts to a mathematical concept. See MPEP 2106.04(a)(2)(I).
The claimed invention further recites mental processes because creating a plurality of compartments of dialysis patients, formatting the historical data, identifying a set of second model parameters, and analyzing the flow model to determine a predictive impact of the treatment. Each of these steps could be performed by a clinician or economist mentally thinking about patient data, grouping the patients into cohorts, performing analysis, and formatting the data for other types of analysis.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims. For example, claims 2 and 3 recite additional mental processes like determinations and estimations. Claims 4 and 9-10 provide labels for the data that is part of the abstract ideas. Claim 5-8 perform computations that are mathematical concepts or mental processes. Similar logic applies to the other dependent claims.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. The additional elements include the “computer program product comprising computer-executable code embodied in a non-transitory computer-readable medium” and “one or more computing devices.” The additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements:
amount to mere instructions to apply an exception. For example, the computer implementation includes generic computing devices like laptop computers and mobile devices in the specification, as published, in paragraph [0077] and amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
generally link the abstract idea to a particular technological environment or field of use such as creating the compartments “from a census of dialysis patients” which simply labels the data source without providing any technical details indicating that gathering data from a census of dialysis patients requires a technological solution or meaningfully limits the claim, see MPEP 2106.05(h))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields. For example, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i), performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii), electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii), and/or storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over McEwan (McEwan P, Morgan AR, Boyce R, Green N, Song B, Huang J, Bergenheim K. Cardiorenal disease in the United States: Future health care burden and potential impact of novel therapies. J Manag Care Spec Pharm. 2022 Apr;28(4):415-424. doi: 10.18553/jmcp.2022.21385. Epub 2022 Jan 12.) in view of Wong (Wong, L. Y., Liew, A. S. T., Weng, W. T., Lim, C. K., Vathsala, A., Toh, M. P. H. S., Projecting the Burden of Chronic Kidney Disease in a Developed Country and Its Implications on Public Health, International Journal of Nephrology, 2018, 5196285, 9 pages, 2018.) and Official Notice.
Regarding claim 1, the prior art discloses: A computer program product comprising computer-executable code embodied in a non-transitory computer-readable medium that, when executed on one or more computing devices, performs the steps of:
--creating a plurality of compartments of dialysis patients from a census of dialysis patients based on one or more shared attributes from a plurality of attributes related to the dialysis patients;
McEwan teaches creating compartments from patients in a patient population on page 416: We developed a compartmental Markov model with an annual time cycle to model an evolving prevalent US patient population with T2DM, HF, or CKD over the period 2021-2030 (either in isolation or combined). Specifically, the model was designed to estimate the expected future prevalence of the following health states, taking into consideration disease incidence, an aging population, and increases in life expectancy: T2DM0 (no CKD or HF), CKD0 (no T2DM or HF), HF0 (no T2DM or CKD), T2DM + HF (no CKD), T2DM + CKD (no HF), HF + CKD (no T2DM), and T2DM + HF + CKD.
McEwan does not expressly disclose a census dialysis patients, but Wong teaches this: “A Markov model with nine mutually exclusive health states was developed according to the clinical course of CKD,” Abstract, “The yearly national incidence of CKD stage 5 was available from the National Registry Disease Office (NRDO)” (page 3), and “The CDMS links administrative and clinical information of patients who seek care at NHG, which includes three acute care public hospitals and nine primary care clinics. The integrated data enables information access and longitudinal tracking of patient outcomes across different care settings from inpatient, emergency department and specialist outpatient clinics to primary care clinics,” (page 2).
--receiving historical data related to the census of dialysis patients and the one or more shared attributes;
McEwan discloses “Targeted peer-reviewed data characterizing disease-specific resource utilization relating to hospital bed days, emergency department, intensive care, outpatient, nurse visits, physician office visits, home visits, dialysis sessions, and transplantations were applied to the disease prevalence projections,” (page 416).
--creating a flow model describing one or more dynamic activities between the plurality of compartments, the one or more dynamic activities including transition rates between compartments, influx into compartments, and efflux out of compartments over time, wherein the flow model includes a plurality of model parameters each related to the one or more dynamic activities;
McEwan discloses this.
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--formatting the historical data as a set of first model parameters;
McEwan discloses gathering historical published patient data that is formatted into model parameters: “Population level T2DM incidence and prevalence are modeled for 3 specific age groups (18-44, 45-64, and 65+ years) with HF stratified by age (< 65 vs ≥ 65 years), CKD, and diabetes status. CKD incidence, prevalence, and mortality were stratified by stage, diabetes, and HF status,” (page 417)
--inputting the set of first model parameters into the flow model;
McEwan discloses inputting formatted historical data into the compartmental flow model “to first estimate disease prevalence and incidence using published evidence and then apply therapeutic benefits using trial reported hazard ratios (HRs),” (page 418).
--identifying a set of second model parameters, each of the second model parameters dependent on a treatment for the dialysis patients;
McEwan identifies a set of treatment-dependent variables (i.e., “trial reported hazard ratios”) that is used to estimate disease prevalence and incidence using published evidence and then apply therapeutic benefits using trial reported hazard ratios. The model then “considers the impact of treatment uptake for the following SGLT2 inhibitors: dapagliflozin (in all disease states), empagliflozin (in T2DM, HF + T2DM and CKD + T2DM) and canagliflozin (in T2DM, HF + T2DM, CKD + T2DM),” page 418. The treatment-dependent second model variable relates to CKD/ESRD (i.e., dialysis patient) outcomes.
--analyzing the flow model to determine a predictive impact of the treatment on the set of second model parameters;
McEwan discloses that “the model was used to explore the potential impact of selected therapies and their associated therapeutic profile on future disease burden, by extrapolating the results of relevant clinical trials to representative patient populations,” (page 417). McEwan’s model generates quantitative predictions like “treatment with dapagliflozin has the potential to reduce disease prevalence by 8.0% and estimated cumulative service delivery costs by 3.6% by 2030” (Abstract). “Expected Percentage Reductions Compared With Standard of Care in Direct Health Care Costs” in FIGURE 4.
--outputting, from the flow model, population data for the plurality of compartments wherein dialysis patients within one or more of the plurality of compartments are given the treatment.
McEwan discloses outputting population data by health state (i.e., compartment) under a treatment scenario in Supplementary Table 2. “Expected Percentage Reductions Compared With Standard of Care in Direct Health Care Costs” in FIGURE 4.
One of ordinary skill in the art before the effective filing date would have been motivated to expand the kidney disease modeling of McEwan to include the patient registry of Wong because this would allow for quantification of the disease burden (Wong page 2).
Additionally, it can be seen that each element is taught by either McEwan or Wong. The patient registry of Wong does not affect the normal functioning of the elements of the claim which are taught by McEwan. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of McEwan with the teachings of Wong since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
McEwan and Wong do not expressly disclose that the models are implemented as program code embodied on a non-transitory computer-readable medium executed by computing devices. The Examiner takes official notice that implementing statistical models such as compartmental Markov models with computers executing program code was well-known in the art at the time of invention. See MPEP 2144.03. It would have been obvious to one of ordinary skill in the art before the effective filing date to implement the models and calculations of McEwan and Wong in this manner, as doing so would have provided the well-recognized benefits of computational speed and accuracy.
Regarding claim 2, McEwan further discloses: wherein analyzing the flow model includes determining differential predictive impacts of the second model parameters (“Treatment effects were considered in terms of a reduction of hospitalization for heart failure (HHF) events, a reduced incidence of CKD and ESRD and a slower rate of estimated glomerular filtration rate decline in patients with prevalent CKD. Where treatment prevented an initial HHF event in patients with CKD or T2DM, this was assumed to prevent the incidence of HF in these patients. Cost benefits of treatment are captured in terms of cost savings from a reduced utilization of resource use due to events avoided,” page 418; Supplementary Figure 2).
Regarding claim 3, McEwan further discloses: providing an initial estimation of one or more of the second model parameters before analyzing the flow model (treatment hazard ratios, i.e., “second model parameters” are derived from clinical trial data as initial estimates, Table 1; “The analytical approach undertaken was to first estimate disease prevalence and incidence using published evidence and then apply therapeutic benefits using trial reported hazard ratios (HRs) as presented in Table 1,” page 418).
Regarding claim 4, McEwan further discloses: wherein the one or more shared attributes include at least one of a medical diagnosis, a treatment indication, and a current treatment (“Specifically, the model was designed to estimate the expected future prevalence of the following health states, taking into consideration disease incidence, an aging population, and increases in life expectancy: T2DM0 (no CKD or HF), CKD0 (no T2DM or HF), HF0 (no T2DM or CKD), T2DM + HF (no CKD), T2DM + CKD (no HF), HF + CKD (no T2DM), and T2DM + HF + CKD,” page 416).
Regarding claim 5, McEwan further discloses: outputting, from the flow model, population data including a time dependency (“Disease prevalence predictions for each forecast year, for the period 2021-2030 and for each of the 7 health states above were calculated,” page 417).
Regarding claim 6, McEwan does not expressly disclose, but Wong further teaches: receiving updated historical data and changing one of the plurality of compartments, the plurality of attributes, and the set of first model parameters based in the updated historical data (“The CDMS links administrative and clinical information of patients who seek care at NHG, which includes three acute care public hospitals and nine primary care clinics. The integrated data enables information access and longitudinal tracking of patient outcomes across different care settings from inpatient, emergency department and specialist outpatient clinics to primary care clinics”).
One of ordinary skill in the art before the effective filing date would have been motivated to expand the kidney disease modeling of McEwan, Wong, and the Examiner’s official notice to further include the updated data of Wong because this would allow the disease burden quantification to become more accurate as new data is reported (Wong page 2).
Regarding claim 7, McEwan further discloses: wherein analyzing the flow model includes varying a treatment inclusion rate (“Table 1 also provides estimates of the generalizability of each respective trial indicating the proportion of the relevant diagnosed population to which a treatment effect is applied,” page 418).
Regarding claim 8, McEwan further discloses: wherein analyzing the flow model includes performing a sensitivity analysis of the plurality of model parameters (Sensitivity Analysis on page 420).
Regarding claim 9, McEwan further discloses: wherein the sensitivity analysis identifies one or more model parameters impacted by sodium-glucose co-transporter 2 inhibitors (“The model considers the impact of treatment uptake for the following SGLT2 inhibitors: dapagliflozin (in all disease states), empagliflozin (in T2DM, HF + T2DM and CKD + T2DM) and canagliflozin (in T2DM, HF + T2DM, CKD + T2DM). Furthermore, the potential impact of treatment uptake with sacubitril and valsartan in patients with HF was also evaluated,” page 418).
Regarding claim 10, McEwan further discloses: wherein the treatment includes at least one of a sodium-glucose co-transporter 2 inhibitor medication (“The model considers the impact of treatment uptake for the following SGLT2 inhibitors: dapagliflozin (in all disease states), empagliflozin (in T2DM, HF + T2DM and CKD + T2DM) and canagliflozin (in T2DM, HF + T2DM, CKD + T2DM). Furthermore, the potential impact of treatment uptake with sacubitril and valsartan in patients with HF was also evaluated,” page 418) and dialysis (“Targeted peer-reviewed data characterizing disease-specific resource utilization relating to hospital bed days, emergency department, intensive care, outpatient, nurse visits, physician office visits, home visits, dialysis sessions, and transplantations were applied to the disease prevalence projections,” page 416).
Claims 11-18, 19, and 20 are substantially similar to claims 1-8, 10, and 1 (respectively) are rejected with the same reasoning.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Cha (US20210183471A1) discloses modeling techniques for predicant risk of renal function decline.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA BLANCHETTE whose telephone number is (571)272-2299. The examiner can normally be reached on Monday - Thursday 7:30AM - 6:00PM, EST.
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/JOSHUA B BLANCHETTE/Primary Examiner, Art Unit 3624