DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are currently pending and under exam herein.
Claims 1-20 are rejected.
Priority
The instant application claims the benefit to U.S. Provisional Application No. 63/195,818, filed June 2, 2021. The claim to the benefit of priority is acknowledged.
Information Disclosure Statement
The Information Disclosure Statements filed 19 December 2022 is in compliance with the provisions of 37 CFR 1.97 and have therefore been considered.
Drawings
The drawings filed on 08/31/2022 are accepted.
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.
Claim 1 recites the limitation "the vascular calcification analysis process" in lines 4-5. There is insufficient antecedent basis for this limitation in the claim, making it unclear. The rejection might be overcome by for example, by replacing “the vascular calcification analysis process” with “a vascular calcification analysis process” upon first recitation.
Claims 17-20 recite the limitation "The method of claim 1", however, claim 1 is an apparatus claim. There is insufficient antecedent basis for those limitation in the claims, resulting in lack of clarity. The rejection might be overcome by for example, by amending claims 17-20 to depend from a method claim (e.g. claim 16), or by amending the claims to recite apparatus limitations consistent with claim 1.
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.
Claim 20 is rejected under 35 U.S.C. 112(a) 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, at the time the application was filed, had possession of the claimed invention.
Claim 20 recites “The method of claim 1, further comprising administering a treatment regimen based on the causal relationship structure”. Claim 1 is an apparatus and claim 20 seems to be adding a step of administering a treatment to the apparatus. However, the components of the apparatus of claim 1 (a processor and memory) are not able to administer a treatment to a patient. The specification does not provide a sufficient disclosure of the limitation of “administering a treatment regimen” to demonstrate to one of ordinary skill in the art that the inventor possessed the invention at the time the application was filed. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
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 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims depend from apparatus claim, yet recite steps of performing a method, rendering the statutory category of the claims unclear.
In the interest of compact prosecution, if claims 17-20 were amended to depend from claim 16 for example, examination would proceed as follows:
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgment, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-9 are directed to an apparatus (device) to perform a chronic kidney and/or end-stage renal diseases (CKD/ESRD) condition analysis process. Claims 10-15 are directed to a computer-implemented method (process) to perform a chronic kidney and/or end-stage renal diseases (CKD/ESRD) condition analysis process. Claim 16-20 are directed to a computer-implemented method (process) of vascular calcification analysis.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claims 1 and 10 recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
An apparatus, comprising: at least one processor; a memory coupled to the at least one processor, the memory comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform a chronic kidney and/or end-stage renal diseases (CKD/ESRD) condition analysis process to determine a CKD/ESRD condition model configured to model a CKD/ESRD condition, the vascular calcification analysis process to: receive input data associated with at least one patient, perform a dynamical system learner process to build a collection of dynamical system models, determine a model rank for at least a portion of the collection of dynamical system models, and determine an optimal dynamical system model for modeling the CKD/ESRD condition for the at least one patient (i.e., mental processes and mathematical concepts)
Dependent claim 2 and claim 11 further recites:
the instructions, when executed by the at least one processor, to cause the at least one processor to pre-process the input data to impute missing values (i.e., mathematical concepts)
Dependent claim 3 and claim 12 further recites:
the instructions, when executed by the at least one processor, to cause the at least one processor to perform a causal analysis of the input data to generate causal information (i.e., mental processes and mathematical concepts)
Dependent claim 4 and claim 13 further recites:
the causal information comprising a causal diagram (i.e., mental processes and mathematical concepts)
Dependent claim 5 and claim 14 further recites:
the model rank configured to indicate model performance for dynamical relationships between variables in the input data (i.e., mathematical concepts)
Dependent claim 6 and claim 15 further recites:
the collection of dynamical system models to model one or more of the following variables: pre-treatment pulse pressure (P), Neutrophils-Lymphocytes ratio (pNL), Serum calcium concentration (Cca), Intact Parathyroid Hormone (CPTH), Serum albumin concentration (g/dL) (CAb), Serum phosphorus concentration (Cp), or Alkaline Phosphatase (CAP). (i.e., mathematical concepts)
Dependent claim 7 further recites:
the instructions, when executed by the at least one processor, to cause the at least one processor to: receive patient information for a patient; analyze the patient information using one of the collections of dynamical models to predict a CKD/ESRD condition process for the patient based on modeled variables (i.e., mental processes and mathematical concepts)
Dependent claim 8 further recites:
the input data comprising a time series of system observables and a library of functions configured as an operator on the input data (i.e., mathematical concepts)
Dependent claim 9 further recites:
the dynamical system models comprising differential equations that describe a time evolution of at least one variable of the input data (i.e., mathematical concepts)
Independent claims 16 recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
A computer-implemented method of vascular calcification analysis, the method comprising, via a processor of a computing device: determining a vascular calcification model configured to model vascular calcification of a virtual patient to determine a causal relationship between at least one patient characteristic and a vascular calcification indicator; and generate a causal relationship structure configured to visualize a causal relationship between the at least one patient characteristic and the vascular calcification indicator (i.e., mental processes and mathematical concepts)
Dependent claim 17 further recites:
the vascular calcification indicator comprising one of pulse pressure (PP) or pulse wave velocity (i.e., mental processes and mathematical concepts)
Dependent claim 18 further recites:
the at least one patient characteristic comprising at least one of parathyroid hormones (PTH), calcium (Ca), phosphate (P04), calcium-phosphate product (CaPO4), neutrophil-lymphocyte ratio (NLR), and albumin (Alb) (i.e., mathematical concepts)
Dependent claim 19 further recites:
the causal relationship structure comprising at least one of a causality fingerprint or a causality pathway map (i.e., mental processes and mathematical concepts)
Dependent claim 20 further recites:
administering a treatment regimen based on the causal relationship structure (i.e., mental processes)
Therefore claims 1-20 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
Claims 4-6, 8-9, 13-15, 17-19 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements recited in claims 1 and 10 include at least one processor and a memory coupled to the at least one processor, as well as receiving input data. Claims 2-3, 11-12 and 16 recite the at least one processor. Claim 7 recites the at least one processor and receiving patient information. The additional element of least one processor and a memory coupled to the at least one processor, and receiving input data/receiving patient data are insignificant extra-solution activity that are part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g)).
Claim 20 recites the additional element of “administering a treatment regimen based on the causal relationship structure”. Although this limitation indicates that a treatment is to be administered, it does not provide any information as to how the patient is to be treated or what the treatment is, but instead covers any possible treatment that a medical professional decides to administer to the patient. As such, there are no meaningful constraints on the administering step such that the particular treatment or prophylaxis consideration would apply because it is not limited to any particular manner or type of treatment. See MPEP 2106.04(d)(2).
When all limitations in claims 1-20 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because the reasons noted below.
Claims 4-6, 8-9, 13-15, 17-19 do not recite any elements in addition to the judicial exception,
and thus, are part of the judicial exception.
The additional elements recited in claims 1-3, 7, 10-12, 16 and 20 are identified above, and carried over from Step 2A: Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at step Step 2A: Prong Two was re-evaluated at step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional element of claims 1-3, 7, 10-12 and 16 include:
at least one processor
memory coupled to the at least one processor
One or more processors and memory are conventional computer components. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
The additional elements of receiving input data (claim 1 and claim 10) and the processor receiving patient information (claim 7) are considered data gathering. The additional element of data gathering does not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field, they do not require or set forth a particular machine, they do not effect a transformation of matter, nor do they provide a non-conventional or unconventional step. Data gathering steps constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claim 20: the additional element of “administering a treatment regimen based on the causal relationship structure” does not rise to the level of significantly more than the judicial exception. The treatment limitation is not particular, as it does not specify a particular dosage, timing, or manner of administration so is therefore generic (see MPEP 2106.05(a)). It instead merely instructions to “apply” the exception in a generic way. Thus, the administration step does not integrate the mental analysis step into a practical application (see MPEP 2106.04(a)). Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, claims 1-20 as a whole do not amount to significantly more than the exception itself.
[Step 2B: NO]
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5-10, 14-15, and 17-18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Moss et al. “A Computational Model for Emergent Dynamics in the Kidney.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 367, no. 1896, 13 June 2009, pp. 2125–2140), in view of Huybrechts et al. “Modeling the Implications of Changes in Vascular Calcification in Patients on Hemodialysis.” Kidney International, vol. 67, no. 4, 1 Apr. 2005, pp. 1532–1538).
Claim 1 and Claim 10 are drawn to a device and computer-implemented method containing one or more processors and memory, the memory stores instructions that when executed cause the processors to perform an analysis related to chronic kidney disease and/or end-stage renal diseases (CKD/ESRD). The analysis is used to create or select a model that represents or predicts the kidney disease condition. The device performs a vascular calcification analysis by taking data from one or more patients, using a learning process to create a multiple dynamic model that describe how variables change over time, ranking the model to see which ones perform better and choosing the best model for representing the patient’s CKD/ESRD condition.
With respect to the limitation of an apparatus, comprising: at least one processor, Moss et al. teach “A computational model for emergent dynamics in the kidney” (title, page 2125) and “They are also well suited to simulation, since network automata are well-known models of computation” (para. 4, lines 9-10, page 2126), the computational model necessarily requires a processor to execute it.
With respect to the limitation of a memory coupled to the at least one processor, the memory comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform a chronic kidney and/or end-stage renal diseases (CKD/ESRD) condition analysis process, to determine a CKD/ESRD condition model configured to model a CKD/ESRD condition, the vascular calcification analysis process to: receive input data associated with at least one patient, perform a dynamical system learner process to build a collection of dynamical system models, determine a model rank for at least a portion of the collection of dynamical system models, and determine an optimal dynamical system model for modeling the CKD/ESRD condition for the at least one patient.
Moss et al. teach a computational kidney model that changes over time by applying update rules. To do this, the model must store its current state (memory) and parameters in memory and use a processor to run the rules and calculate the next state (one or more processors), “The dynamics of the system is governed by the update rules. The dynamics of the system is governed by the update rules. The update rules for the tubules are equations” (b-The single nephron model, para.3, lines 1-2, page 2130).
Moss et al. further teaches performing computational analysis of kidney disease behavior by investigating the effects of two diseased nephrons within a computational kidney model (para 3., lines 3-4, page 2137) and “An example is to study the effects of kidney disease on system dynamics, and to investigate the stability under perturbations due to disease” (para.5, lines 4-6, page 2126).
Moss et al. further teaches receiving physiological input data, “we have used the model to explore the effects of changes in input parameters including hydrostatic and osmotic pressures and concentrations of ions, such as sodium and chloride” (abstract, lines 11-13, page 2125).
Moss et al. further teaches performing a dynamical system learner process to build a collection of dynamical system models, “A network model is used to investigate the stability of systems of nephrons and interactions between nephrons” and different nephron groups such as “two-, eight- and 72-nephron models” are explored (abstract, lines 7-15, page 2125). By building and running these different kidney models, Moss et al. create many system models and compare how they behave, which corresponds to building a collection of dynamical system models.
With respect to claim 8, Moss et al. teach using a time-based measurement data and applying a set of mathematical functions to that data, “the dynamics of such systems are explored” and that “Nephron behaviour can fluctuate widely” (abstract, lines 3-4, page 2125), meaning that the model follows kidney system values as they change over time. Moss et al. further teach “a network model is used to investigate the stability of systems of nephrons” (abstract, line 7, page 2125) and that “the effects of coupling between nephrons are explored” (abstract, line 14, page 2125), meaning the model applies defined rules that describe how nephrons interact t update those changing values.
With respect to claim 9, Moss et al. teach the dynamical system models including equations to show how at least one variable from the input data changes over time, “The dynamics of the system is governed by the update rules. The update rules for the tubules are equations” (b-The single nephron model, para.3, lines 1-2, page 2130). Moss et al. further teach system variables as functions of time, “H2O(t)r” and “Na(t)r” (equation 3.8, para.2, page 2132), meaning that these variables change over time according to the governing equations.
Moss et al. does not teach the limitations of the computational model to perform an analysis related to chronic kidney disease and/or end-stage renal diseases (CKD/ESRD) specifically; a vascular calcification analysis process to receive the input data from at least one patient; determine a model rank for at least a portion of the collection of dynamical system models, and determine an optimal dynamical system model for modeling the CKD/ESRD condition for the at least one patient.
With respect to the limitation of a model to perform an analysis related to chronic kidney disease and/or end-stage renal diseases (CKD/ESRD), Huybrechts et al. teach “A breakdown of the various etiologies of ESRD in the study population is provided in Table 3” (Table 3, col. 2, para.2, lines 8-9, page 1534).
With respect to the limitation of a vascular calcification analysis process to receive the input data from at least one patient, Huybrechts et al. teach “Modeling the implications of changes in vascular calcification in patients on hemodialysis” (title, page 1532) and “Data on 179 patients on hemodialysis treated at one center in France included biochemical values during the year prior to study entry, patient characteristics, and cardiovascular events over an average of 4 years” (abstract, para.2, lines 1-4).
With respect to the limitation of determine a model rank for at least a portion of the collection of dynamical system models, and determine an optimal dynamical system model for modeling the CKD/ESRD condition for the at least one patient, Huybrechts et al. teach that different regression models were evaluated and that one was chosen as the best performing model, “The final regression model for the calcification score is presented in Table 1” (Table 1, col.2, para.1, lines 1-2, page 1534). Choosing a final regression model necessarily involves comparing models and determining which one performs better, which corresponds to determining a model rank and selecting and optimal model. Huybrechts et al. further teach applying the selected model to individual patients, “The appropriate retransformed estimates are obtained by multiplying the exponential of the individual patients’ predicted log EBT score with a smearing estimator (Table 1, col.2, para.1, lines 4-7, page 1534), meaning that the chosen model is used to generate patient specific predictions.
With respect to claims 5 and 14, Huybrechts et al. teach assigning a rank to models that indicate how well each model captures time-based relationship between variables in the input data, “The final regression model for the calcification score is presented in Table 1” (Table 1, col.2, para.1, lines 1-2, page 1534), meaning that choosing a final regression model necessarily involves comparing models (ranking them). Huybrechts et al. further teach that the regression analysis includes time-to-event modeling “Cox proportional hazards analysis” (col.1, para.3, line 7, page 1534), meaning it incorporates time and models time-dependent relationships between variables and outcomes, thereby indicating model performance for dynamical relationships in the input data.
With respect to claims 6 and 15, Huybrechts et al. teach having multiple time-based mathematical models used to represent or predict one or more specific measured variables: pre-treatment pulse pressure (P), Neutrophils-Lymphocytes ratio (pNL), Serum calcium concentration (Cca), Intact Parathyroid Hormone (CPTH), Serum albumin concentration (g/dL) (CAb), Serum phosphorus concentration (Cp), or Alkaline Phosphatase (CAP), “Multivariate regression analyses were carried out”(col1., para.5, lines 2-3, pages 1533) and “The final regression model for the calcification score is presented in Table 1” (Table 1, col.2, para.1, lines 1-2, page 1534), showing that multiple regression models were evaluated and a final model was selected based on performance. Huybrechts et al. further teach that variables such as “Mean blood pressure, Pulse pressure, Calcium, Parathyroid hormone, Albumin” (Table 2, page 1535) were obtained and that “predialysis serum calcium and phosphate were determined twice monthly” (col.2, para.5, lines 1-2, page 1533).
With respect to claim 7, Huybrechts et al. teach receiving patient information and analyzing the patient information using one of a collection of dynamical models to predict a ESRD condition process based on modeled variables, “A breakdown of the various etiologies of ESRD in the study population is provided in Table 3” (Table 3, col. 2, para.2, lines 8-9, page 1534) and “Data on 179 patients on hemodialysis treated at one center in France included biochemical values during the year prior to study entry, patient characteristics, and cardiovascular events over an average of 4 years” (abstract, para.2, lines 1-4). Huybrechts et al. further teach analyzing this patient information using regression-based models “Multivariate regression analyses were carried out” (col1., para.5, lines 2-3, pages 1533) and that “Cox proportional hazards analysis” (col.1, para.3, line 7, page 1534) to model outcomes overtime.
With respect to claim 17, Huybrechts et al. teach the vascular calcification indicator comprising one of pulse pressure (PP) or pulse wave velocity, “systolic blood pressure, pulse pressure, heart rate, and aortic pulse wave velocity (PWV), determined with transcutaneous Doppler flow recordings” (col.1, para 1., lines 1-3, page 1534).
With respect to claim 18, Huybrechts et al. teach the at least one patient characteristic comprising at least one of parathyroid hormones (PTH), calcium (Ca), phosphate (P04), calcium-phosphate product (CaPO4), neutrophil-lymphocyte ratio (NLR), and albumin (Alb), “Mean blood pressure, Pulse pressure, Calcium, Parathyroid hormone, Albumin” (Table 2, page 1535) were obtained and that “predialysis serum calcium and phosphate were determined twice monthly” (col.2, para.5, lines 1-2, page 1533).
It would have been obvious to one of ordinary skill in the art at the time of the invention was made to modify the computational kidney model of Moss et al. by using the vascular calcification modeling techniques of Huybrechts et al. because Moss et al. specifically mentions that their computer model can mimic how a real kidney behaves and can be used to study how kidney disease affects kidney function and stability (para.5, lines 1-6, page 2126), while Huybrechts et al. shows that its modeling equations are “key inputs for formal cost-effectiveness analyses” abstract, para.4, lines 4-6). A person of ordinary skill in the art would therefore have been motivated to combine these teachings because and Huybrechts et al. shows a cost effective, patient focused ESRD vascular calcification techniques, which complements and supports the computational kidney model of Moss et al. that can be used to study kidney disease behavior and stability. One would have had a reasonable expectation of success for making the combination because all references are not only related to the same field, but provide compatible approaches for modeling kidney disease and vascular calcification using patient data in a cost-effective way.
Claims 2 and 11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Moss et al. “A Computational Model for Emergent Dynamics in the Kidney.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 367, no. 1896, 13 June 2009, pp. 2125–2140), in view of Huybrechts et al. “Modeling the Implications of Changes in Vascular Calcification in Patients on Hemodialysis.” Kidney International, vol. 67, no. 4, 1 Apr. 2005, pp. 1532–1538), as applied to claims 1, 5-10, 14-15, and 17-18 above, and in further view of Montez-Rath et al. (“Addressing Missing Data in Clinical Studies of Kidney Diseases.” Clinical Journal of the American Society of Nephrology, vol. 9, no. 7, 7 July 2014, pp. 1328–1335).
Claims 2 and 11 are drawn to the instructions stored in the memory that when executed cause the processors to pre-process patient data by filling missing values.
Moss et al. and Huybrechts et al. teach a computational kidney model and vascular calcification modeling techniques as detailed in claims 1, 5-10, 14-15, and 17-18 above.
Moss et al. and Huybrechts et al. do not teach pre-processing patient data by filling missing values.
Montez-Rath et al. teach various approaches for handling missing data in clinical studies of kidney diseases, including multiple imputation methods (abstract, page 1).
It would have been obvious to one of ordinary skill in the art at the time of the invention was made to modify a computational kidney model and vascular calcification modeling techniques of Moss et al. and Huybrechts et al. to include the preprocessing techniques of Montez-Rath et al., because the study provides data imputation examples in kidney disease field and “concrete guidance on their use” (abstract, line 10, page 1). A person of ordinary skill in the art would therefore have been motivated to combine these teachings because Montez-Rath et al. shows “multiple imputation methods that rely on assumptions about missingness that are more flexible” (abstract, line 10, page 1). One would have had a reasonable expectation of success for making the combination because all references are not only related to the same field, but Montez-Rath et al. shows that its preprocessing techniques, such as multiple imputation offers “accessibility and reasonable flexibility of assumptions” (para.4, lines 13-14, page 6) and help overcome the issue of missing data, which is common problem.
Claims 3-4, 12-13, 16 and 19 are rejected under 35 U.S.C. 103(a) as being unpatentable over Moss et al. “A Computational Model for Emergent Dynamics in the Kidney.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 367, no. 1896, 13 June 2009, pp. 2125–2140), in view of Huybrechts et al. “Modeling the Implications of Changes in Vascular Calcification in Patients on Hemodialysis.” Kidney International, vol. 67, no. 4, 1 Apr. 2005, pp. 1532–1538), as applied to claims 1, 5-10, 14-15, and 17-18 above, and in further view of Staplin et al. “Use of Causal Diagrams to Inform the Design and Interpretation of Observational Studies: An Example from the Study of Heart and Renal Protection (SHARP).” Clinical Journal of the American Society of Nephrology: CJASN, vol. 12, no. 3, 2017, pp. 546–552).
Claims 3 and 12 are drawn to the instructions stored in the memory that when executed cause the processors to analyze patient data to determine cause and effect relationships, and generate information describing those relationships. Claims 4 and 13 are drawn to the causal information from the analysis is presented as a causal diagram and Claim 19 is drawn to the causal relationship structure comprising at least one of a causality fingerprint or a causality pathway map. Claim 16 is drawn to a computer-based method for analyzing blood vessel calcification, via a processor of a computing device by determining a mathematical model that represents blood vessel calcification in a simulated patient, use the model to figure out how a patient factor affects a calcification measurement and create a causal relationship structure which are displayed and show the relationship between the patient characteristic and calcification indicator.
Moss et al. and Huybrechts et al. teach a computer-based method for analyzing blood vessel calcification, via a processor of a computing device by determining a mathematical model that represents blood vessel calcification, use the model to figure out how a patient factor affects a calcification measurement, as detailed in claims 1, 5-10, 14-15, and 17-18 above. Moss et al. further teaches a “virtual patient”, as modeling of a kidney is entirely done in silico, therefore being a virtual organism, part of the “One contribution of 15 to a Theme Issue ‘The virtual physiological human: tools and applications II’”.
Moss et al. and Huybrechts et al. do not teach determining cause and effect relationships (use the model to figure out how a patient factor affects a calcification measurement), and generating information describing those relationships, the causal information from the analysis presented as a causal diagram (and create a causal relationship structure which are displayed and show the relationship between the patient characteristic and calcification indicator) and the causal relationship structure comprising at least one of a causality fingerprint or a causality pathway map.
Staplin et al. teach causal analysis using causal diagrams and causal pathways, “A causal diagram is a graphical tool that enables the visualization of the relationships between the exposure of interest, the outcome being studied, and all other characteristics (variables)” (col.2, para.2, lines 1-4, page 547) and that causal diagrams “comprise a set of variables (nodes: often represented by letters) with arrows drawn between them to show the directions of the assumed causal relationships” (col.2, para.2, lines 8-11, page 547). Staplin et al. further teach causal pathways, “The DAG in Figure 5B illustrates what has happened. Many of the causal pathways (shown in green in Figure 5A) are now blocked, because all of these additional factors were assumed to be effect mediators” (Figure 5A, col.2, para. 2, lines 16- 19, page 550).
It would have been obvious to one of ordinary skill in the art at the time of the invention was made to modify a computational kidney model and vascular calcification modeling techniques of Moss et al. and Huybrechts et al. to include the causal analysis of Staplin et al. because they explain how to determine which variables truly affect an outcome (abstract, lines 8-10, page 546). A person of ordinary skill in the art would therefore have been motivated to combine these teachings because Staplin et al. shows that the “the use of causal diagram is still appropriate” and that “Causal diagrams should, therefore, be considered at all stages when embarking on such analyses and made available alongside the results to make it clear which assumptions have been made” (col.1, para.1, lines 1-13, page 552). One would have had a reasonable expectation of success for making the combination because Staplin et al. makes the model more reliable by showing which factors actually cause an outcome, instead of just being correlated with it.
Claims 20 is rejected under 35 U.S.C. 103(a) as being unpatentable over Moss et al. “A Computational Model for Emergent Dynamics in the Kidney.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 367, no. 1896, 13 June 2009, pp. 2125–2140), in view of Huybrechts et al. “Modeling the Implications of Changes in Vascular Calcification in Patients on Hemodialysis.” Kidney International, vol. 67, no. 4, 1 Apr. 2005, pp. 1532–1538), as applied to claims 1, 5-10, 14-15, and 17-18 above, and in further view of Meid et al. (“Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data.” Clinical Epidemiology, vol. 12, 2 Nov. 2020, pp. 1223–1234)
Claim 20 is drawn to administering a treatment that is selected based on the identified cause and effect relationships.
Moss et al. and Huybrechts et al. teach a computational kidney model and vascular calcification modeling techniques as detailed in claims 1, 5-10, 14-15, and 17-18 above.
Moss et al. and Huybrechts et al. do not teach administering a treatment that is selected based on the identified cause and effect relationships.
Meid et al. teach administering a treatment regiment based on causal relationship structure, “using a causal inference framework” (title, page 1223) to decide which treatment a patient should get, “Based on these data, we suggest to clearly communicate the estimated likelihood of different outcomes, to take into account the patient’s opinion and preferences, and, based on this, to provide treatment advice to support patient and physician in choosing an appropriate (drug) treatment option” (discussion, lines 14-19, pages 1230-2131).
It would have been obvious to one of ordinary skill in the art at the time of the invention was made to modify a computational kidney model and vascular calcification modeling techniques of Moss et al. and Huybrechts et al. to include the treatment administration based on causal relationship of Meid et al. because they provide “a straightforward technique to predict the effects of such decision rules when applied in routine care” (abstract, lines 20-21, page 1223). A person of ordinary skill in the art would therefore have been motivated to combine these teachings because Meid et al. suggests that its model has “the potential to efficiently supplement clinical guidelines and support healthcare professionals in creating personalized treatment plans using decision support tools” (abstract, lines 21-23, page 1223). One would have had a reasonable expectation of success for making the combination because Meid et al. shows that causal models can be reliably applied in routine care to support patient treatment decisions.
Conclusion
No claims are allowed.
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/A.S.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687