DETAILED ACTION
Notices to Applicant
This communication is a final rejection. Claims 1-8 and 10-21, as filed 07/13/2026, 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8, 10-10, and 21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Specifically, claims 1 and 11 recite “the plurality of dialysis patients,” but this claim lacks antecedent basis so the scope of the claim is not determinable.
Claim Rejections - 35 USC § 112(a)
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claim 21 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) 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, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The claim includes “at least one of: a number of dialysis sessions, a quantity of dialysis equipment, a number of clinical personnel, or a level of facility capacity to be allocated to one or more of the plurality of compartments.” Applicant points to [0031] and [0065] of the Specification for support. The closest support appears to be in [0065]: “This step may also include acting upon the provided recommendation-in the above example, acting upon the recommendation to increase resources may include providing additional equipment, facilities, funding, etc.” That passage describes equipment and facility capacity, not a number of dialysis sessions. The term “session” is not found in the Specification at all. Similarly, no support is found for “a number of clinical personnel.”
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 1-8 and 10-21 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. Claim 1 recites:
1. 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: (additional element – generally applying the ab)
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; (abstract idea – mental process because a person can sort a roster of patients into groups sharing a diagnosis or treatment)
receiving historical data related to the census of dialysis patients and the one or more shared attributes; (insignificant extra-solution activity – mere data-gathering)
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; (abstract idea – mathematical concept and mental process; how a population of a compartment changes over time as a function of rate parameters)
formatting the historical data as a set of first model parameters; (abstract idea – mental process)
inputting the set of first model parameters into the flow model; (insignificant extra-solution activity – mere data-gathering)
identifying a set of second model parameters, each of the second model parameters dependent on a treatment for the dialysis patients;
analyzing the flow model to determine a predictive impact of the treatment on the set of second model parameters; and (abstract idea – mental processes and mathematical concepts)
outputting a recommendation for resource allocation to one or more of the plurality of dialysis patients based on the predictive impact of the treatment (abstract idea – certain methods of organizing human activity, namely, managing the resources of an enterprise; to the extent that this is non-abstract, it is insignificant extra-solution activity that amounts to outputting a result)
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; (additional element - insignificant extra-solution activity that amounts to outputting a result)
causing allocation of one or more healthcare resources to the one or more of the plurality of dialysis patients based on the recommendation, wherein the one or more healthcare resources include at least one of equipment, facilities, or funding. (additional element – applying the abstract idea with a computer and generally linking the abstract idea to a particular field of use).
Claim 1 is exemplary, but the same logic applies to claims 11 and 20.
Step 2A Prong One
The broadest reasonable interpretation of these steps includes mathematical concepts, mental processes, and certain methods of organizing human activity as described above because the italicized portions are analogous to work of a planner in a dialysis clinic. But for generic computer language like “one or more computing devices”, sorting a census of dialysis patients into groups, tabulating past rates for those groups, selecting which of those rates a therapy is expected to change, and judging how the therapy moves the group counts is analogous to steps a human planner would perform when planning patient rosters on paper, reading historical rates on printed tables, marking the rates a new treatment is expected to affect, and working out how many patients the program will have to treat and what to buy for them.
Dependent claims further narrow or define the abstract idea embodied in the claims. For example, claims 2, 3, 12, and 13 recite additional mental processes like determinations and estimations. Claims 4 10, 14, and 19 apply labels to the data which is part of the abstract ideas. Claim 5-8 and 15-18 perform computations that are mathematical concepts or mental processes. Claim 21 further specifies the content of the output.
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, mobile devices, and non-transitory computer-readable media 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))
add insignificant extra-solution activity such as receiving historical data which amounts to mere data gathering and outputting the recommendation and the population data amounts to data output see MPEP 2106.05(g))
generally link the abstract idea to a particular technological environment or field of use such as delivery of dialysis care to a patient population, MPEP 2106.05(h).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. For example, claims 5, 15, and 21 recite limitations amounting to invoking computers as tools to perform the abstract idea. 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 the historical data and the census amounts to receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i). Running the flow model forward year by year amounts to performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii). Maintaining the census and the compartment counts amounts to electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii), and 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 1-8, 10-19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Gilbertson (Gilbertson et al., "Projecting the number of patients with end-stage renal disease in the United States to the year 2015" J Am Soc Nephrol. 2005 Dec;16(12):3736-41 – cited in PTO-892 dated 03/16/2026) in view of 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.) and Bozkir (Bozkir et al., "Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study" European Journal of Operational Research 304 (2023) 276–291).
Regarding claim 1, Gilbertson discloses:
--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 (“A recent USRDS projection of the growth in the ESRD population through 2010 showed a near doubling of this population to 650,000 individuals, with 520,000 prevalent on dialysis…The former represents individuals with diabetes as the cause of renal failure; the latter comprises those with renal failure for any other cause… created by the cross-classification of seven age groups” p. 3736);
--receiving historical data related to the census of dialysis patients and the one or more shared attributes (“ESRD incident counts by age, race, and cause of renal failure for 1978 to 2000… Actual ESRD prevalence in 1978 was used in the model” p. 3737);
--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 (“discrete-time, annually incremented, nonstationary Markov model was developed to model the ESRD population from 1978 to 2000 and project to the year 2015… Patients who develop ESRD are assigned to the incident block; if they remain alive to the end of the calendar year, then they are moved to the prevalent block for the beginning of the next year… transition probabilities (probability of moving from one state in the model to another state)” p. 3736; “In each cell, the number of prevalent patients and number of deaths were recorded; the remaining patients were “aged” 1 yr, and new incident patients were added,” p. 3737);
--formatting the historical data as a set of first model parameters (“ESRD incident counts by age, race, and cause of renal failure for 1978 to 2000 were divided by diabetic and nondiabetic population counts during that same period to obtain ESRD incidence rates by age, race, and cause of renal failure… the 1-yr probability of death was calculated for each year for each of the 42 groups within it” p. 3737);
--inputting the set of first model parameters into the flow model (“Each “year” of the model was implemented by multiplying the number of individuals in each cell by the corresponding transition probability for each possible model transition. In each cell, the number of prevalent patients and number of deaths were recorded; the remaining patients were “aged” 1 yr, and new incident patients were added. This process was repeated for each year through 2015, using actual incident counts for each year through 2000 and extrapolated incident counts through 2015,” p. 3737).
Gilbertson does not expressly disclose but McEwan teaches:
--identifying a set of second model parameters, each of the second model parameters dependent on a treatment for the dialysis patients (“then apply therapeutic benefits using trial reported hazard ratios (HRs) as presented in Table 1,” p. 418; “Treatment effects were considered in terms of a reduction of hospitalization for heart failure (HHF) events, a reduced incidence of CKD,” p. 418);
--analyzing the flow model to determine a predictive impact of the treatment on the set of second model parameters (“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,” p. 417; “However, treatment with dapagliflozin has the potential to reduce disease prevalence by 8.0% and estimated cumulative service delivery costs by 3.6% by 2030,” p. 415);
--outputting a recommendation for resource allocation to one or more of the plurality of dialysis patients based on the predictive impact of the treatment (“Cost benefits of treatment are captured in terms of cost savings from a reduced utilization of resource use due to events avoided,” p. 418; “Expected Percentage Reductions Compared With Standard of Care in Direct Health Care Costs, Number of Hospitalizations, and Disease Prevalence,” p. 421; 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 (“Disease prevalence predictions for each forecast year, for the period 2021-2030 and for each of the 7 health states above were calculated,” p. 417; “Disease prevalence was estimated to be reduced by 208,500 by 2025 and 321,000 by 2030 with dapagliflozin,” p. 420);
A POSITA before the effective filing data would have been motivated to expand Gilbertson’s projection of the dialysis population to include the treatment-dependent parameters and treatment-impact analysis of McEwan because Gilbertson identifies treatment as the factor its own model leaves out and states that treatment changes the projected population, “New treatments for diabetes also may slow the increase” and should the death rate fall, “the dialysis and transplant populations will grow even faster than predicted with our model,” p. 3741, and because McEwan’s model exists to supply what a burden projection otherwise “misses the opportunity to communicate the implications upon health care system capacity associated with disease management within a dynamic prevalent population,” p. 416.
Gilbertson and McEwan do not expressly disclose, but Bozkir teaches:
--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 (“The proposed models are solved on a computer with an Intel(R) Core (TM) i7-9750H CPU @ 2.60 GHz processor and a 16 GB RAM. Gurobi 9.0.3 is used to solve the instances,” p. 20);
--causing allocation of one or more healthcare resources to the one or more of the plurality of dialysis patients based on the recommendation, wherein the one or more healthcare resources include at least one of equipment, facilities, or funding (“the clinic needs to determine the number of available dialysis machines to allocate to each unit that serves different patient cohorts,” p. 1; “the hospital assigns dialysis machines to the units in the dialysis clinic thereby setting their capacity to serve different patient cohorts during the week,” p. 3; “As shown in the figure, seven, five and two machines are currently allocated to standard, isolated and quarantine units, respectively,” p. 8).
A POSITA before the effective filing data would have been motivated to expand the projection of Gilbertson and McEwan to include the machine allocation of Bozkir because this “can support decisions for determining the size of units by considering daily treatment schedules,” p. 9, and “can support the hospital to cohort patients during a pandemic effectively and manage scarce health resources efficiently,” p. 25. Gilbertson already directs its projection to that end, stating that the projection matters “so that policy makers and health care providers can plan appropriately,” p. 3736, and that otherwise “the level of health care resources that currently are allocated to the care of these patients will eventually be unable to meet the demand,” p. 3736.
Gilbertson’s projection of the dialysis population to include the treatment-dependent parameters and treatment-impact analysis of McEwan because Gilbertson identifies treatment as the factor its own model leaves out and states that treatment changes the projected population, “New treatments for diabetes also may slow the increase” and should the death rate fall, “the dialysis and transplant populations will grow even faster than predicted with our model,” p. 3741, and because McEwan’s model exists to supply what a burden projection otherwise “misses the opportunity to communicate the implications upon health care system capacity associated with disease management within a dynamic prevalent population,” p. 416.
Additionally, each element is taught by either Gilbertson, McEwan, or Bozkir. McEwan’s treatment-dependent hazard ratios and Bozkir’s machine allocation do not affect the normal functioning of the elements taught by Gilbertson such as the incident and prevalent blocks and the annual transition probabilities that patient move between then. The combination is the assembly of old elements, each performing as it did separately with predictable results. Therefor the combination of McEwan, Bozkir, and Gilbertson would have been obvious.
Regarding claim 2, Gilbertson does not expressly disclose, but McEwan teaches: 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).
The motivation to combine is the same as in claim 1.
Regarding claim 3, Gilbertson does not expressly disclose, but McEwan teaches: 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).
The motivation to combine is the same as in claim 1.
Regarding claim 4, Gilbertson further discloses: wherein the one or more shared attributes include at least one of a medical diagnosis, a treatment indication, and a current treatment (“The former represents individuals with diabetes as the cause of renal failure; the latter comprises those with renal failure for any other cause,” p. 3736).
Regarding claim 5, Gilbertson further discloses: outputting, from the flow model, population data including a time dependency (“Figures 4 and 5 show ESRD incidence and prevalence estimates to the year 2015,” p. 3739).
Regarding claim 6, Gilbertson further discloses: 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 second implementation used counts and probabilities based on 1978 through 2000 to project 2001 through 2015. Because projected numbers did not agree exactly with actual numbers for 2000, incident counts, prevalent counts, and numbers of deaths were recalibrated to actual counts in the year 2000 to project to 2015,” p. 3737).
Regarding claim 7, Gilbertson does not expressly disclose, but McEwan teaches: 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).
The motivation to combine is the same as in claim 1.
Regarding claim 8, Gilbertson further discloses: wherein analyzing the flow model includes performing a sensitivity analysis of the plurality of model parameters (“putting sampling distributions around model parameters and running the model 1000 times, each time sampling each model parameter from the appropriate distribution,” p. 3737; “Upper limits were obtained by increasing ESRD incidence and diabetes prevalence rates beyond the year 2000, decreasing the probability of death among patients with ESRD,” p. 3737).
Gilbertson does not expressly disclose but McEwan teaches:
the sensitivity analysis identifying 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),” page 418; “hazard ratios from the clinical trials were modeled using upper and lower confidence intervals,” p. 420).
The motivation to combine is the same as in claim 1.
Regarding claim 10, Gilbertson does not expressly disclose but McEwan further teaches: 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). The motivation to combine is the same as in claim 1.
Claims 11-17 and 19 are substantially similar to claims 1-7, and 10 (respectively) are rejected with the same reasoning.
Regarding claim 18, Gilbertson further discloses: wherein analyzing the flow model includes performing a sensitivity analysis of the plurality of model parameters (“putting sampling distributions around model parameters and running the model 1000 times, each time sampling each model parameter from the appropriate distribution,” p. 3737).
Regarding claim 21, Gilbertson and McEwan do not expressly disclose, but Bozkir teaches: wherein the recommendation for resource allocation specifies at least one of: a number of dialysis sessions, a quantity of dialysis equipment, a number of clinical personnel, or a level of facility capacity to be allocated to one or more of the plurality of compartments (“number of dialysis machines that must be allocated to each unit,” p. 4; “number of dialysis machines allocated to dialysis unit j,” p. 13; “Depending on the estimated demands for the upcoming week, the hospital assigns dialysis machines to the units in the dialysis clinic thereby setting their capacity to serve different patient cohorts during the week,” p. 3). The motivation to combine is the same as in claim 1.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Gilbertson, in view of McEwan, Bozkir, and 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).
Regarding claim 20, the claim is substantially similar to claim 1 and is rejected with the same reasoning. The Examiner further notes: that Gilbertson, McEwan, and Bozkir do not expressly disclose but Wong teaches: a database connected to a network, the database including data related to a plurality of dialysis patients, the data including a plurality of attributes of the plurality of dialysis patients (“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” p. 2).
One of ordinary skill in the art before the effective filing date would have been motivated to expand the projection of Gilbertson, McEwan, and Bozkir to include the linked patient database of Wong because forecasting on such data “can aid in the planning of future healthcare resources and manpower, which in turn translates into improvement in the healthcare system, better preventive care, and favourable patient outcomes,” (Wong p. 7).
Additionally, each element is taught by Gilbertson, McEwan, Bozkir, or Wong. Wong’s patient database does not affect the normal functioning of the elements taught by Gilbertson, Bozkir, and Wong, so the combination is no more than an assembly of old elements performing as it does separately with predictable results.
Response to arguments
Applicant's arguments filed 07/13/2026 have been fully considered and are discussed below.
Regarding the subject matter ineligibility rejections, Applicant argues that the claimed invention is not directed to a judicial exception (Step 2A Prong One) because the characterization “abstracts away the specific ordered combination recited in the claims.” Remarks pages 12. This is not persuasive because this prong asks what the claim recites, and Applicant points to limitations that are analyzed as additional elements. The ordered combination of the abstract ideas and additional elements are considered at Step 2A Prong Two and Step 2B.
Applicant argues that the claimed invention integrates any abstract idea into a practical application (Step 2A Prong Two) because the amended claim 1 recites “causing allocation of one or more healthcare resources,” which “is the disclosed operational use of the modeled treatment impact.” Remarks pages 13-14. This is not persuasive because the instant claims recite the allocation of the resources at a level that is equivalent to outputting the result of the analysis. For example, claim 1 does not require that equipment be moved between two facilities; the claim can be satisfied by sending an alert to a practice manager describing the desired equipment move. The claims recite no transformation and no technique for producing anything beyond this alert.
Applicant argues that the census of dialysis patients is a specific technological constraint, but claim 1 only recites “a census of dialysis patients.” The claim recites no particular data structure, no acquisition or interface technology, and no constraints on how the census is obtained. Naming the population to which an analysis is applied is a field of use limitation. See MPEP 2106.05(h).
Applicant argues that the claimed invention improves a technical process, namely, that the “two-parameter-set architecture…is a specific modeling architecture that structures the flow model in a way that improves upon generic Markov models.” Remarks page 15. This is not persuasive because, unlike in Desjardins where the claim recited the mechanism by which the model’s own operation was improved, claim 1 here recites a result computed on a generic processor. Benefits like separating a treatment signal from baseline population dynamics flow from automating the planner’s logical processes and are not technical improvements.
Applicant argues that the claimed invention amounts to an unconventional arrangement as in BASCOM because the Examiner has not identified evidence that the entire claimed invention is well-understood, routine, and conventional. Remarks page 16. Applicant’s arguments are not persuasive because they conflate eligibility and obviousness. As the Supreme Court emphasizes: “[t]he ‘novelty' of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter.” Diehr, 450 U.S. at 188-89 (emphasis added). The Federal Circuit further guides that “[eligibility and novelty are separate inquiries.” Two-Way Media Ltd. v. Comcast Cable Commc' ns, LLC, 874 F.3d 1329, 1340 (Fed. Cir. 2017). The Examiner analyzed the combined result of the additional elements above consistent with the 101 analysis. For example, the Examiner noted that receiving the historical data and the census amounts to receiving or transmitting data over a network which is well-understood, routine, and conventional according to MPEP 2106.05(d)(II)(i).
The 101 rejections are thus maintained.
Regarding the prior art rejections, Applicants arguments are moot in view of the new grounds of rejection applied above.
Conclusion
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office Action (See MPEP 706.07(a)). Accordingly, 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.
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/JOSHUA B BLANCHETTE/ Primary Examiner, Art Unit 3624
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