DETAILED ACTION
Applicant’s response filed 02/18/2026 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
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 4 and 14 are cancelled by Applicant.
Claims 1-3, 5-13 and 15-23 are currently pending and are herein under examination.
Claims 1-3, 5-13 and 15-23 are rejected.
Priority
The instant application claims domestic benefit as a divisional application of U.S. Patent Application Serial No. 18/115,924, filed on March 1, 2023, which is a divisional application of U.S. Patent Application Serial No. 17/838,129, filed on June 10, 2022, which claims the benefit of U.S. Provisional Application Serial No. 63/209,164, filed on June 10, 2021 and U.S. Patent Application Serial No. 17/693,229, filed on March 11, 2022. The claims to domestic benefit are acknowledged. As such, the effective filing date for claims 1-23 is 06/10/2021.
Information Disclosure Statement
The IDS filed 02/18/2026 follows the provisions of 37 CFR 1.97 and has been considered in full. A signed copy of the list of references cited from this IDS is included with this Office Action.
Drawings
The objection to the drawings is withdrawn in view of amendments. The drawings filed 08/30/2023 are accepted.
Specification
The objection to the specification is withdrawn in view of amendments.
Withdrawn Rejections
35 USC 112(b)
The rejections of claims 2, 4-5 and 11 under 35 USC 112(b) is withdrawn in view of claim amendment and arguments. The rejection of claim 7 under 35 USC 112(b) is withdrawn in view of Applicant’s persuasive argument regarding “intensive lipid-lowering agent” being a term of the art that would be understood by one or ordinary skill in the art in the context of the claims.
Claim Objections
The objection to claims 11 and 13 are withdrawn in view of claim amendments.
Claim Rejections - 35 USC § 112
35 USC 112(b)
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 1-3, 5-13 and 15-23 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
This rejection is newly recited as necessitated by claim amendment.
Claim 1, line 19, recites “the updated subject-specific systems biology model” which lacks antecedent basis. Provide antecedent basis. Furthermore, claims 2-3, 5-13 and 15-23 are also rejected because they depend on rejected claim 1 and do not resolve the issue of indefiniteness.
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 1-3, 5-13 and 15-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more.
Any newly recited portions herein are necessitated by claim amendment.
Step 1:
Step 1 asks whether the claims recite statutory subject matter. In the instant application, claims 1-3, 5-13 and 15-23 recite a method. As such, these claims recite statutory subject matter (Step 1: YES).
Step 2A, Prong 1:
Claims that recite statutory subject matter are analyzed under Step 2A, Prong 1 to determine if they recite any concepts that equate to an abstract idea, law of nature or natural phenomena. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claim 1 recites “accessing a systems biology model of atherosclerotic cardiovascular disease, wherein the systems biology model has been trained with disease-associated pathway activation data or disease-associated molecule levels, or both, for each pathway or molecule in the systems biology model, respectively; modifying the systems biology model using personalized pathway activation data or personalized molecule levels, or both, derived from the non-invasively obtained data from the potential subject to generate a subject-specific systems biology model; training the subject-specific systems biology model with predicted pathway activation data or predicted molecule levels, or both, derived from information relating to an effect on one or more lipoproteins by a candidate dyslipidemia management agent based on a known mechanism of action of the candidate dyslipidemia management agent; simulating a therapeutic response by the potential subject to the candidate dyslipidemia management agent in the updated subject-specific systems biology model to obtain a simulated therapeutic effect; comparing the updated subject-specific systems biology model with and without the simulated therapeutic response;”
Claim 2 recites “wherein the estimated pathway activation data or molecule levels, or both, comprise an alteration in a level of a gene, a protein, or a metabolite.”
Claim 3 recites “wherein the candidate dyslipidemia management agent is a statin.”
Claim 5 recites “wherein the statin is atorvastatin.”
Claim 6 recites “wherein the candidate dyslipidemia management agent is a hypertriglyceridemia lowering agent, a hypercholesterolemia lowering agent, or an agent that increases an atheroprotective effect.”
Claim 7 recites “wherein the candidate dyslipidemia management agent is an intensive lipid-lowering agent.”
Claim 8 recites “wherein the intensive lipid-lowering agent is a proprotein convertase subtilisin kexin type 9 (PCSK9) inhibitor or a cholesteryl ester transfer protein (CETP) inhibitor.”
Claim 9 recites “wherein the candidate dyslipidemia management agent comprises one or more of niacin, fish oil, ezetimibe, a bile acid sequestrant, an adenosine triphosphate-citrate lyase (ACL) inhibitor, an omega-3 fatty acid ethyl ester, or a marine-derived omega-3 polyunsaturated fatty acid (PUFA).”
Claim 10 recites “wherein the systems biology model includes one or more pathways representing: …”
Claim 11 recites “wherein simulating the therapeutic response by the potential subject to the candidate dyslipidemia management agent in the updated subject- specific systems biology model comprises: determining a first set of molecules or pathways, or both, known to be affected by the candidate dyslipidemia management agent; defining a therapeutic effect molecule level for each molecule in the first set of molecules or a therapeutic effect pathway level, or both, based on one or more known mechanisms of action of the candidate dyslipidemia management agent on the set of molecules or pathways, or both; and estimating a therapeutic effect molecule level for each molecule or a therapeutic effect pathway level, or both, in a second set of molecules or pathways represented in the trained systems biology model other than in the first set of molecules or pathways, or both, based on a simulated effect of the defined therapeutic effect molecule levels of the first set of molecules or a therapeutic effect pathway level, or both, on one or more of the other molecules or pathways represented in the updated systems biology model.”
Claim 12 recites “wherein simulating the therapeutic response comprises setting an increased level of plaque stability in the updated subject-specific systems biology model.”
Claim 13 recites “wherein updating the systems biology model using subject personalized molecule levels further comprises using disease gene transcript levels derived from the non-invasively obtained data.”
Claim 16 recites “processing the non-invasively obtained imaging data to obtain quantitative plaque morphology data including structural anatomy data, tissue composition data, or both.”
Claim 17 recites “wherein the structural anatomy data comprises data relating to a level of any one or more of remodeling, wall thickening, ulceration, stenosis, dilation, or plaque burden.”
Claim 18 recites “wherein the tissue composition data comprises data relating to a level of any one or more of calcification, lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), matrix, fibrous cap, or perivascular adipose tissue (PVAT).”
Claim 19 recites “wherein pathways in the updated subject-specific biological systems model are compartmentalized into cell-specific networks.”
Claim 20 recites “wherein the cell-specific networks include at least an endothelial cell network, a macrophage network, and a vascular smooth muscle cell network.”
Claim 21 recites “if the report indicates that the potential subject's atherosclerotic cardiovascular disease would likely be improved by the candidate dyslipidemia management agent at a level above an inclusion criteria threshold, providing a recommendation that the potential subject should be included in the clinical trial.”
Claim 23 recites “wherein the one or more lipoproteins comprise one or more of a low-density lipoprotein (LDL), a glycosylated LDL (glyLDL), an oxidized LDL (oxLDL), a minimally-modified LDL (mmLDL), a very-low-density lipoprotein (VLDL), or a high-density lipoprotein (HDL).”
Limitations reciting a mental process.
Claims 1, 11-12 and 16-18 contain limitations recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind. The paragraphs below discuss the limitations in these claims that recite a mental process under their broadest reasonable interpretation (BRI).
Regarding claim 1, the BRI of accessing a systems biology model includes obtaining a mathematical formula because the specification recites “pathways or cell signaling networks are described with mathematical formalisms using differential equations or other mathematical formalism that capture behavior such as … approximations or biochemical reactions/relations” (pg. 33, lines 15-21). The BRI of modifying the systems biology model includes inputting measurements such as molecule levels into the model patient-specific. Specification pg. 31 and Figure 4 describe generating the model using equations. The BRI also includes calibrating the model as described in specification pg. 35. The BRI of training a systems biology model includes least-square regression. The BRI of simulating a therapeutic effect in the patient-specific model includes performing the differential equations related to the pathways or equations described on specification pg. 31. The BRI of comparing includes comparing the outputs/parameters of the model before and after simulating.
The BRI of claim 11 includes performing the same operations described above regarding claim 1 on a specific set of molecules/pathways in the model.
The BRI of claim 12 includes modifying the parameters of the differential equations in the systems biology model.
The BRI of claims 16-18 includes a physician examining x-rays images or CT scans to calculate wall thickness and calcification.
Limitations reciting a mathematical concept.
Above cited claims 1 and 11 recite a mathematical concept because they are similar to the concepts of organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts. The paragraph below discusses the limitations in these claims that recite a mathematical concept under their broadest reasonable interpretation (BRI).
As discussed above in the mental process section, the BRI of the systems biology model includes “mathematical formalisms using differential equations or other mathematical formalisms that capture behavior such as mass transfer, reaction dynamics that stem from enzymes, various inhibitory processes, and other approximations to biochemical reactions/relations” (specification pg. 33). The BRI of modifying, training, and simulating using the systems biology model includes calibrating, parameterizing and performing the calculations of the model to simulate a therapeutic effect.
Limitations reciting a natural phenomenon.
Claim 1 recites a natural phenomenon because it is similar to the natural relationship between a patient’s CYP2D6 metabolizer genotype and the risk that the patient will suffer QTc prolongation after administration of a medication called iloperidone, Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, 887 F.3d 1117, 1135-36, 126 USPQ2d 1266, 1281 (Fed. Cir. 2018), which the courts have established as a natural phenomenon. Claim 1 predicts how a patient will react to a dyslipidemia management agent based upon pathways and molecules related to a plaque of the subject.
Limitations reciting organizing human activity.
Above cited claim 21 equates to organizing human activity because it is similar to managing personal behavior or relationships or interactions between people (See MPEP 210604(a)(2).II.C). The BRI of claim 21 includes a healthcare provider communicating to the subject whether they should enroll in a clinical trial.
Limitations included in the recited judicial exception.
Claims 2-10, 13, 19-20 and 23 are included in the judicial exception in claim 1 of the systems biology model because they further limit modifying the model and further limit the dyslipidemia agent, pathways, molecule levels, and lipoproteins which are all numerical parameters within the mathematical equations of the model.
As such, claims 1-3, 5-13 and 15-23 recite an abstract idea and a natural phenomenon (Step 2A, Prong 1: YES).
Additional Elements:
Once limitations have been identified that recite a judicial exception, the claims are evaluated for additional elements. The additional elements are then analyzed under Step 2A, Prong 2 then Step 2B. The instant claims recite the following additional elements:
Claim 1 recites “a computer-implemented method … the method comprising: receiving non-invasively obtained imaging data related to a plaque from the potential subject; providing a report indicating whether the potential subject's atherosclerotic cardiovascular disease would likely be improved or unaffected by the candidate dyslipidemia management agent, or whether the potential subject would suffer an adverse effect from the candidate dyslipidemia management agent, or both.”
Claim 15 recites “wherein the imaging data is radiological imaging data obtained by computed tomography (CT), dual energy computed tomography (DECT), spectral computed tomography (spectral CT), computed tomography 108 angiography (CTA), cardiac computed tomography angiography (CCTA), magnetic resonance imaging (MRI), multi-contrast magnetic resonance imaging (multi-contrast MRI), ultrasound (US), positron emission tomography (PET), intra-vascular ultrasound (IVUS), optical coherence tomography (OCT), near-infrared radiation spectroscopy (NIRS), or single-photon emission tomography (SPECT) diagnostic images, or any combination thereof.”
Claim 22 recites “wherein the plaque is an atherosclerotic plaque.”
These above recited additional elements are analyzed below under both Step 2A, Prong 2 and Step 2B:
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)) and to insignificant extra-solution activity (MPEP § 2106.05(g)). The paragraphs below discuss the additional elements recited above in the instant claims.
Claim 1 recites a computer-implemented method. There are no limitations that these components require anything other than a generic computer and/or generic computing system. Therefore, this limitation equates to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
The above cited limitation in claim 1 of providing a report equates to insignificant, extra-solution activity of necessary data outputting because it outputs the result of the judicial exception (MPEP 2106.05(g)(3)).
Claims 15 and 22 and the limitation in claim 1 of receiving non-invasively obtained data equate to insignificant, extra-solution activity of necessary data gathering because they gather data necessary to perform the judicial exception in claim 1 of modifying and updating the model to generate a subject-specific model (MPEP 2106.05(g)(3)).
As such, claims 1-3, 5-13 and 15-23 are directed to an abstract idea and a natural phenomenon (Step 2A, Prong 2: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The paragraphs below discuss the additional elements recited above in the instant claims.
Claim 1 recites a computer-implemented method. There are no limitations requiring anything other than a generic computer and/or generic computing system. Therefore, these limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claims 1, 15 and 22 equate to insignificant, extra-solution activity of necessary data gathering/outputting as discussed above in section Step 2A, Prong 2. Under Step 2B, limitations that equate to insignificant, extra-solution activity are evaluated for whether or not they are WURC (MPEP 2106.05(II)). The BRI of these claims include that they are computer-implemented, especially because the specification on pg. 37-38 and in Figure 7A show that the non-invasively obtained data is received at a computer system. The BRI of providing a report includes transmitting a report over a computer. Therefore, these limitations equate to transmitting/receiving data over a network which the courts have established as a WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they equate to WURC functions of a generic computer (i.e. receiving/transmitting data over a network). Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-3, 5-13 and 15-23 are not patent eligible.
Response to Arguments under 35 USC 101
Applicant's arguments filed 02/18/2026 have been fully considered but they are not persuasive.
Applicant argues claim 1 cannot be performed by a human (pg. 18, para. 1). Applicant’s argument is not persuasive because:
Examiner explained how a human, using pen and paper, could perform the method of claim 1 in the previous Office action mailed 11/18/2025 (pg. 7-8). The systems biology model includes differential equations (pg. 33, lines 15-21 of specification). A human can perform differential equations.
Applicant argues that the claims do not recite an equation or algorithm (pg. 18, para. 2). Applicant’s argument is not persuasive because:
Applicant appears to believe that a claim must recite a literal formula or equation in order for a claim to recite a mathematical equation/formula. A claim can recite a mathematical formula or equation using a textural replacement (MPEP 2106.04.A.2.I.B). As such, “a systems biology model” is a textural replace for a differential equation as described in specification pg. 33, lines 15-21.
Applicant argues that claim 1 does not recite any mathematical relationships, formulas, or equations and is similar to Subject Matter Eligibility Example 39 (pg. 18, last para. – pg. 19, para. 1). Applicant’s argument is not persuasive because:
The fact pattern of instant claim 1 is distinct from Example 39. Instant claim 1 does not require a neural network nor training a neural network on image data. Instant claim 1 does recite mathematical concepts as discussed in the response above.
Applicant’s remarks regarding the AI-SME Update and Memo are noted but are not persuasive because the instant claims do not recite an AI model (pg. 19, para. 2).
Applicant’s remarks regarding a claim that recites a judicial exception and a claim that merely involves a judicial exception is noted but are not persuasive because claim 1 recites a judicial exception, as discussed in the rejection above (pg. 19, last para. – pg. 20, para. 2). For example, comparing models is a mental process.
Applicant argues that claim 1 does not recite a natural phenomenon because it performs computer simulations and does not recite a diagnosis (pg. 20, last para.). Applicant’s argument is not persuasive because even though simulations are performed, they use a natural correlation between dyslipidemia agents and atherosclerotic cardiovascular disease. MPEP 2106.04(b)(I) lists laws of nature and natural phenomena that do not recite a diagnosis.
Applicant’s remarks regarding claims 2-10, 13 19-20 and 23 are noted but are not persuasive because, as discussed above, claim 1 recites mathematical concepts (pg. 21, para. 2).
Applicant argues that the data outputting of “providing a report” is clearly the result of the trained systems biology model. Then argues that the data gathering steps are integrated by the final report (pg. 21, para. 4). Applicant’s argument is not persuasive because:
Examiner stated on pg. 11 of the previous Office action that “providing a report” outputs the result of the judicial exception. Outputting the result of a judicial exception does not integrate into a practical application (MPEP2106.05(g)(3)).
Applicant argues that the claims as a whole must be analyzed and not viewed in isolation when evaluating an inventive concept (pg. 22, para. 1). Applicant’s argument is not persuasive because only additional elements are viewed under Step 2B to determine an inventive concept. Examiner has evaluated the additional elements individually and in combination. No inventive concept was found.
Applicant argues that none of the limitations equate to mere instructions to apply a judicial exception in a generic way or in a generic computing environment (pg. 22, para. 1). Applicant’s argument is not persuasive because the limitation in claim 1 of “a computer-implemented method” equates to mere instructions to implement an abstract idea in a computer.
Applicant argues that claim 1 contains a practical application in the field of systems biology models (pg. 22, last para. – pg. 23, para. 3). Applicant’s argument is not persuasive because the improvement is a result of the systems biology model, which recites a judicial exception. MPEP 2106.05(a) recites “the judicial exception alone cannot provide the improvement.” MPEP 2106.05(A)(II) recites “an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
Applicant’s remarks regarding Step 2B are noted but are not persuasive because claim 1 recites judicial exceptions, as discussed above (pg. 23, para. 4-5).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claim 1-3, 5-13 and 15-23 are rejected under 35 U.S.C. 103 as being unpatentable over Pichardo-Almarza et al. (“Almarza”; NPL ref. 173 on IDS filed 11/15/2023; Current pharmaceutical design 22, no. 46 (2016): 6903-6910) in view of Buckler et al. (“Buckler”; US 2019/0180153 A1; previously cited on PTO892 mailed 11/18/2025) and Schobel et al. (“Schobel”; WO 2020/037244 A1; previously cited on PTO892 mailed 11/18/2025).
Any newly recited portions herein are necessitated by claim amendment.
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claim 1:
A computer-implemented method of screening a potential subject for enrollment in a clinical trial testing safety or efficacy, or both, of a candidate dyslipidemia management agent for atherosclerotic cardiovascular disease, the method comprising:
Almarza discloses a quantitative systems biology (QSP) model to determine the effect of a cholesterol-lowering drugs on the biological and physiological mechanisms related to atherosclerotic plaque progression (abstract) (Figure 8). The QSP model can be used for stratified medicine and reducing a number of clinical efficacy trials (pg, 6906, col. 1, para. 3) (pg. 6908, col. 2, para. 2).
receiving non-invasively obtained imaging data related to a plaque from the potential subject;
Almarza teaches using physiological characteristics from 1,000 virtual patients related to atherosclerotic plaque to simulate the sensitivity of plaque growth and statin response to physiological conditions such as characteristics of blood flow and geometry of the artery (pg. 6908, col. 2, para. 2).
However, Almarza does not teach that the physiological parameters are received from non-invasively obtained data.
Buckler discloses a hierarchical analytics framework that quantifies biological properties/analytes from radiological imaging data and characterizes one or more pathologies based on the quantified biological properties/analytes (abstract). Figure 1 shows acquiring images 121A from actual patients 50 [150]. These images may be from non-invasively acquired radiological imagining data [136]. The radiological imagining data may be of arteries that have atherosclerotic plaque [69] [91] [93] (Figure 22). Data acquired from non-invasive images includes blood vessel geometry [164], gene transcripts [136], and hemodynamics such as blood flow velocity [167] [362].
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have parameterized the QSP model of Almarza with the predicted biological properties/analytes of Buckler because Buckler states that biological properties/analytes are obtained advantageously through non-invasive methods [136]. Buckler also states that their methods advantageously utilize radiological imagining to produce surrogate measures for predicting clinical outcome or guiding treatment [136].
There would have been a reasonable expectation of success because Buckler states that CCTA has been established for evaluation of coronary atherosclerotic plaques [50] and because imaging phenotypes can be correlated with large-scale genomic and proteomic analyses which has potential to impact therapy strategies by creating more deterministic and patient-specific prognostics as well as measurements of response to drugs [17]. Therefore, the combination of Buckler and Almarza would provide non-invasive clinical parameter measurements that could be used to create patient-specific prognostic model in relation to predicting treatment response to drugs.
accessing a systems biology model of atherosclerotic cardiovascular disease, wherein the systems biology model has been trained with disease-associated pathway activation data or disease-associated molecule levels, or both, for each pathway or molecule in the systems biology model, respectively;
Almarza discloses a QSP model of cholesterol-lowering drugs and their effect on atherosclerosis (pp. 6907–6908 § 3) (pg. 6909; Figure 8), wherein concentrations of the molecules are present within the model (pg. 6905, sec. 1.3.3). However, Almarza does not previously train the QSP model.
Schobel uses machine learning models to predict if a patient has an increased risk of developing a clinical outcome (abstract). A model is trained on clinical parameters of first subjects, wherein a subset of model parameters is selected from the clinical parameters, then each model is used to predict clinical outcomes of the first subjects [9]. The model with the best performance metric is outputted as the prediction model which is used to predict clinical outcomes of a second patient [9]. The best model, which is trained on various subjects, can take in new data to make predictions of clinical outcomes such as by using clinical data specific to a particular subject in order to predict outcomes for the specific subject, resulting in an iterative, self-improvement process [79].
It would have been prima facie obvious to have trained the QSP model of Almarza with disease associated molecule levels shown in Figure 8 of Almarza, as taught by Schobel. Motivation for doing so is taught by Schobel who recites that training models enables selection of a best performing model [9]. There would have been a reasonable expectation of success because the combination requires training a model.
modifying the systems biology model using personalized pathway activation data or personalized molecule levels, or both, derived from the non-invasively obtained imaging data from the potential subject to generate a subject-specific systems biology model; training the subject-specific systems biology model with predicted pathway activation data or predicted molecule levels, or both, derived from information relating to an effect on one or more lipoproteins by a candidate dyslipidemia management agent based on a known mechanism of action of the candidate dyslipidemia management agent;
Almarza performs simulations in the QSP model using a virtual population of 1,000, wherein each virtual patient had corresponding parameters of LDL levels in blood, blood viscosity, and lumen radius (pg. 6908, col. 2, para. 2). The simulation “was able to simulate each patient’s trajectory defined by quantifying the effect of the drug and adherence to regime on plaque volume” (modifying the systems biology model using personalized pathway activation data or personalize molecule levels) (pg. 6908, col. 2, para. 2). The result of these simulations generated predicted molecule levels based on effect of mechanism of action of a dyslipidemia agent (Figure 8).
However, Almarza does not teach that the personalized pathways or molecule levels were derived from the non-invasively obtained data.
Buckler quantifies biological properties from non-invasively acquired radiological imagining data (personalized activation data or personalized molecule levels derived from the non-invasively obtained data) [136]. The properties/analytes may be anatomic structure, tissue composition, biological function, gene expression correlates, and the like [164-168] [305].
However, neither Almarza nor Buckler generate a subject-specific model or train the subject-specific model.
Schobel uses machine learning models to predict if a patient has an increased risk of developing a clinical outcome (abstract). Patient-specific clinical data is inputted into a predictive model that predicts an expected outcome for a particular subject [79]. The clinical data can include gene and serum protein expression [83-84] as well as imaging data [89]. Machine learning models predict if a patient has an increased risk of developing a clinical outcome (abstract). Machine learning models are trained on clinical parameters of first subjects, wherein a subset of model parameters are selected from the clinical parameters, and then the each model is used to predict clinical outcomes of the first subjects [9]. The model with the best performance metric is outputted as the prediction model which is then used to predict a clinical outcome of a second patient [9]. The best prediction model, which is trained on various subjects, can take in new data to make predictions of clinical outcomes such as by using clinical data specific to a particular subject in order to predict outcomes for the specific subject, resulting in an iterative, self-improvement process [79].
When Almarza, Buckler, and Schobel are taken together, the QSP model is fitted with patient-specific data derived from non-invasive imagining to generate a subject-specific QSP model, wherein the subject-specific QSP model is trained through an iterative, self-improvement process.
It would have been prima facie obvious to have modified and trained the QSP model of Almarza with patient-specific data to obtain a trained patient-specific QSP model as taught by Schobel and Buckler. The motivation for doing so is provided by Almarza who states that the QSP model could be used for predictive modeling in individual patients (pg. 6908, col. 2, para. 2). Further motivation for modifying/training Almarza’s QSP model to be patient-specific lies in Schobel who states that inputting new data specific to a particular patient into a best model, trained on various subjects, is beneficial for predicting patient-specific clinical outcomes [79].
One of ordinary skill in the art would have had a reasonable expectation of success by combining Buckler and Schobel to Almarza to obtain a patient-specific QSP model parameterized and trained with patient-specific data because inputting patient-specific data into a QSP model requires updating the previously established parameters, thereby fine-tuning them to be specific patient, which is taught by Schobel.
simulating a therapeutic response by the potential subject to the candidate dyslipidemia management agent in the updated subject-specific systems biology model to obtain a simulated therapeutic effect; comparing the updated subject-specific systems biology model with and without the simulated therapeutic response; and
Almarza teaches that the QSP model simulates the effect of cholesterol-lowering drugs on atherosclerosis (pp. 6907–6908 § 3), which includes effects on biological pathways related to atherosclerosis (p. 6908 § 3.2), biochemical pathways related to cholesterol, including oxLDL (p. 6909, Fig. 8), and the QSP models include concentrations of the molecules within the model (p. 6905 § 1.3.3). These teachings indicate simulating the therapeutic effect based on a change in the parameters in the model.
However, Almarza does not teach the patient-specific systems biology model.
Almarza in combination with Buckler and Schobel disclose the patient-specific QSP model, as discussed above.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the QSP model of Almarza to be updated with patient-specific data as taught by Schobel and Buckler because Schobel states that updating a model with patient-specific data is advantageous for predicting a patient-specific clinical outcome [79]. One of ordinary skill in the art would have had a reasonable expectation of success because Almarza states that the QSP model can be used for individual patient predictive modeling (pg. 6908, col. 2, para. 2) and because Schobel states that their methods are designed for improving performance of diagnostic prediction technology [57] such as by optimizing clinical parameters of a diagnostics model (i.e., the QSP model of Almarza) [65] [69]. The combination would have reasonably resulted in a patient-specific outcome prediction model for a lipid-lowering agent using parameters derived from non-invasive imaging data.
providing a report indicating whether the potential subject's atherosclerotic cardiovascular disease would likely be improved or unaffected by the candidate dyslipidemia management agent, or whether the potential subject would suffer an adverse effect from the candidate dyslipidemia management agent, or both.
Almarza uses the simulation results for personalized decision-making (p. 6903, col. 2), which necessitates reporting the result of the simulation. Almarza also discloses simulations that predict better reduction in LDL-C levels when a combined therapy of PCSk9 inhibitor and statins are administered compared to monotherapy (pg. 6908, col. 1, para. 1). This is an example of reporting an improvement.
Regarding claim 2, Almarza teaches that the molecules used in the QSP simulations can be metabolites and proteins (pg. 6907-6909, sec. 3). Almarza also teaches that molecules affected by the drug are associated by the known mechanism of action of the drug and the biochemical/cellular processes that it affects (pg. 6905, col. 1, para. 6-7).
Regarding claims 3 and 6, Almarza teaches modeling with a 4 mg daily dose of simvastatin (statin) (high dose statin) (hypertriglyceridemia lowering agent) (pg. 6904, col. 2, para. 3).
Regarding claim 5, Almarza teaches modeling with atorvastatin (pg. 6905, col. 2, para. 2).
Regarding claims 7-8, Almarza teaches a QSP model that models a combination therapy of a statin and a PCSK9 inhibitor (pg. 6908; Fig. 7).
Regarding claim 9, Almarza references a PKPD model that modeled a combined therapy of ezetimibe and atorvastatin for the treatment of dyslipidemia (pg. 6906 § 2.3).
Regarding claim 10, Almarza’s QSP model shows cholesterol metabolism in Figure 8 (abstract).
Regarding claim 11, Almarza teaches that the QSP model includes terms representing the concentrations of individual molecules that are affected (directly or indirectly) by the drug (determining a first set of molecules) (pg. 6905, col. 1, para. 6-7). The molecules affected by the drug are associated by the known mechanism of action of the drug and the biochemical/cellular processes that it affects (defining a therapeutic effect … based on one or more known mechanisms of action of the agent) (pg. 6905, col. 1, para. 6-7). Almarza also teaches using QSP models for simulations with molecules (estimating a therapeutic effect molecule level for each molecule) (pg. 6907-6909, sec. 3).
Regarding claim 12, Almarza teaches that the QSP model simulates the effect of drugs on atherosclerotic plaque progression (abstract) (Figure 8).
Regarding claim 13, Almarza teaches a QSP model (Figure 8). However, Almarza does not teach using disease gene transcript levels derived from non-invasively obtained data. Buckler states that gene expression profiles can be quantified from the non-invasively acquired radiological imaging data [136].
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the clinical parameters of Almarza’s QSP model to include disease gene expression levels derived from non-invasive imaging data as taught by Buckler because Buckler states that their method is advantageously non-invasive [50] [68] and because Buckler states that imaging phenotypes can be correlated with large-scale genomic and proteomic analyses which has potential to impact therapy strategies by creating more deterministic and patient-specific prognostics as well as measurements of response to drugs [17]. One of ordinary skill in the art would have had a reasonable expectation of success by using the predicted gene expression levels of disease of Buckler in the QSP model of Almarza because it would have provided patient-specific expression parameters for the QSP model of Almarza.
Regarding claims 15-18, Almarza does not teach the limitations in claims 14-18. Buckler shows in Figure 1 acquiring images from patients 121A. These images may be from non-invasively acquired radiological imagining data which includes computer tomography [136]. The radiological imagining data may be of arteries that have plaque [69]. The imagining data can be paired with an additional dataset with sub-regions labeled by objectively verifiable tissue composition [70]. Example labels for vascular tissue can be lumen, calcification, LRNC, and IPH [70]. Buckler also discloses color coding wall thickness in radiological imagining data (structural anatomy data) [77].
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have parameterized the clinical parameters of the QSP model of Almarza using the predicted biological properties/analytes derived from non-invasive imaging as taught by Buckler. Buckler teaches motivation for doing so by reciting “For patients where the necessity of invasive procedures is uncertain, predicting MACCE non-invasively would be beneficial and feasible with CCTA which gives an overall estimate of disease burden and risk of future events” [50].
One of ordinary skill in the art would have had a reasonable expectation of success to parameterize the clinical parameters of Almarza’s QSP model with the predicted biological properties/analytes of Buckler because Buckler states that CCTA has been established for evaluation of coronary atherosclerotic plaques [50] and because Buckler states that imaging phenotypes can be correlated with large-scale genomic and proteomic analyses which has potential to impact therapy strategies by creating more deterministic and patient-specific prognostics as well as measurements of response to drugs [17]. Buckler also states that their methods advantageously utilize radiological imagining to produce surrogate measures for predicting clinical outcome or guiding treatment [136].
Regarding claims 19-20, Almarza teaches a QSP model that includes cell-specific compartments, including macrophage cells (pg. 6909; Fig. 8).
Regarding claim 21, this claim is being interpreted as a contingent limitation. MPEP 2111.04(II) recites “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” Thus, a recommendation is not provided when the report indicates that the disease does not improve. As such, claim 21 is not required to be performed.
Regarding claims 22-23, Almarza shows in Figure 8 that the QSP models simulates the effect of statins in LDL and atherosclerotic plaque progression.
Response to Arguments under 35 USC 103
Applicant's arguments filed 02/18/2026 have been fully considered but they are not persuasive.
Applicant argues that Almarza does not relate to instant claim 1 because it does not screen subjects for enrollment in clinical trials testing safety or efficacy (pg. 24, para. 1-2). Applicant’s argument is not persuasive for the following reasons:
The language of “screening a potential subject for enrollment in a clinical trial testing safety or efficacy” is an intended used recited in the preamble. The steps of the method do not recite an active step of screening. As such, this limitation is not required by claim 1.
Applicant argues that Almarza does not use imaging data from real patients (pg. 24, para. 3). Applicant’s argument is not persuasive because:
Applicant argues against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. Buckler acquires imagining data from real patients in order to create patient-specific prognostic models [17] [136] [150] (Figure 22).
Applicant argues that Almarza teaches away from using imaging of plaque because Almarza recites “it is very difficult to assess via imaging alone which plaques are unstable; atherosclerotic plaques are a challenging imaging target” (pg. 24, last para. – pg. 25, para. 2 of Applicant’s remarks). Applicant’s argument is not persuasive for the following reasons:
Almarza teaches that imaging plaque is difficult and invasive (pg. 6903, col. 2, para. 2), but does not teach that plaque imaging data cannot or should not be used. MPEP 2145(X)(D)(1) recites “A known or obvious composition does not become patentable simply because it has been described as somewhat inferior to some other product for the same use." In re Gurley, 27 F.3d 551, 553, 31 USPQ2d 1130, 1132 (Fed. Cir. 1994). Almarza teaches imaging tools are invasive whereas Buckler teaches and is relied upon for non-invasive imaging tools.
Buckler teaches that imaging phenotypes create more deterministic and patient-specific prognostics as well as measurements of response to drug or other therapy [17]. Buckler discloses improved spatial, temporal, and contrast resolution [6] [61-62] and advantageous non-invasive imaging and differentiation of stable from unstable plaques [68] [79]. One of ordinary skill would have recognized that the plaque imaging phenotypes could have been used as input data into Almarza given the improvements and advantages of the methods of Buckler. Additionally, Buckler teaches that the imaging phenotypes can be used as surrogate measures for predicting clinical outcome or guiding treatment [136].
It is also noted that Applicant’s argument hinges on imaging of unstable plaques. However, instant claim 1 does not recite “unstable” plaques.
Applicant argues that because Buckler does not teach systems biology models or screening candidate drug agents that there is no motivation to combine Almarza and Buckler (pg. 25, para. 2). Applicant’s argument is not persuasive because Almarza teaches these limitations. Buckler is used to acquire non-invasive quantitative biological properties used in patient-specific prognostic models [136], such as the model in Almarza.
Applicant’s argument regarding “shear speculation” is noted but is not persuasive because it is Schobel in combination with Almarza and Buckler that teach a patient-specific model, not Almarza alone in combination with Buckler (pg. 25, para. 2).
Applicant argues that the QSP model of Almarza is not a systems biology model because Almarza states that the QSP model is “in-between two different worlds (PK-PD and Systems Biology)” (pg. 25, last para. – pg. 26, para. 2). Applicant’s argument is not persuasive for the following reasons:
The QSP model of Almarza equates to a systems biology model as defined by the instant specification. The specification defines a systems biology model as “a model that is used to represent a set of interconnected biological pathways potentially used to simulate changes across those pathways under defined conditions” (pg. 11, lines 3-5). The specification defines biological pathway as “a series of actions among molecules that leads to a certain product or a change” (pg. 12, lines 6-7).
Almarza states that the QSP model is used to “understand the PK of Simvastatin and its effect on LDL and atherosclerotic plaque evolution and vascular remodeling, which is the clinical endpoint. The multiscale approach adopted describes the most important biological/physiological mechanisms related to atherosclerotic plaque progression combined with the effect of blood flow conditions and how this has an impact on the LDL and Monocytes penetration in the arterial wall. Simulations of a virtual population composed of 1000 patients with different physiological characteristics and simulated during 20 years showed the sensitivity of the plaque growth and statin response to different physiological conditions (e.g. characteristics of the blood flow and the geometry of the artery) and was able to simulate each patient’s trajectory defined by quantifying the effect of the drug and adherence to regime on plaque volume” (pg. 6908, col. 2, para. 2). Almarza also shows in Figure 8 the QSP model which contains biological pathways such as the mechanism of action for Simvastatin and its effect on LDL as well as the biological pathways that lead to foam cells (i.e., oxidized LDL and macrophages). Therefore, the QSP model of Almarza is a systems biology model that represents interconnected biological pathways, i.e., the PK of Simvastatin and its effect on LDL, atherosclerotic plaque evolution, and vascular remodeling as seen in Figure 8.
Applicant argues that the QSP model of Almarza is not a patient-specific systems biology model because Almarza uses a virtual population rather than real, actual patients and that the model is not designed to be patient-specific for screening enrollment in clinical trials. Applicant argues that Almarza does not suggest a patient-specific model using real patients (pg. 26, para. 3 – pg. 27, para. 4). Applicant’s argument is not persuasive for the following reasons:
One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. As discussed in the rejection above, the combination of Almarza, Buckler, and Schobel disclose a patient-specific systems biology model. Almarza states that their QSP model can be used for predictive modeling in individual patients (pg. 6908, col. 2, para. 2). Schobel predicts clinical outcomes in a subject having a risk of a specific outcome by using clinical parameters [3], wherein the clinical parameters are specific to a subject and are used to predict an expected outcome for the subject [79]. Schobel states that patient-specific predictions are made by a diagnostic prediction model [56], wherein the prediction model was trained on data from various subjects then updated with data from a particular patient to derive the patient specific predictions [79].
When the teachings of Almarza and Schobel are taken together, they suggest that the predictive QSP model of Almarza, which is trained on virtual populations, can be made patient-specific by adding patient specific clinical data as taught by Schobel. The motivation for producing a patient-specific model is stated by Almarza who recites “This novel approach opens up the way to use QSP for predictive modelling in individual patients” (pg. 6908, col. 2, para. 2). The patient-specific QSP model is then achieved by the teachings of Schobel and Buckler who update a diagnostic prediction model with patient-specific data to derive patient-specific clinical outcome predictions.
It is also noted that instant claim 1 does not require an active step of screening subjects for clinical trials because this limitation is embedded in the preamble and is an intended use.
Applicant argues that data from actual subjects is not used to indicate an improvement (pg. 28, para. 2). Applicant’s argument is not persuasive because the combination of Almarza, Buckler, and Schobel disclose a patient-specific model that outputs predictions to drug therapy.
Applicant argues that the following phrase from Almarza does not support a reasonable expectation of success to create a patient-specific model: “This novel approach opens up the way to use QSP for predictive modelling in individual patients, although there is obviously much work to be done” (pg. 27, last para. – pg. 28, para. 2). Applicant’s arguments are not persuasive for the following reasons:
The quote from Almarza suggests that the QSP model can be used with data from an actual patient to derive a patient-specific model. Examiner then uses the teachings from Buckler and Schobel to demonstrate how the predictive QSP model can be updated with patient-specific data to derive a patient-specific QSP model.
Regarding “although there is still much work to be done”, this quotation does not preclude the possibility that the QSP model could be made patient-specific using actual patient data. Rather, it suggests that modifications to the model need to be made in order to make it patient-specific, which is why Examiner relies upon the teachings of Buckler and Schobel to arrive at the patient-specific QSP model.
Applicant’s concluding remarks are noted but are not persuasive for the same reasons discussed above (pg. 28, para. 3 – pg. 29, para. 1). Again, claim 1 does not require screening subjects for clinical trials as this is an intended used recited in the preamble, and the method steps do not recite an active step of screening.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 3 and 11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 17 and 19-20 of U.S. Patent No. 11,869,186 B2 (“Patent ‘186).
Any newly recited portions herein are necessitated by claim amendment.
Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are anticipated by the claims in Patent ‘186. The following table shows claims in Patent ‘186 that anticipate the claims of the instant application:
Instant Application
Patent ‘186
Claim
Limitations
Claim
Limitations
1
receiving non-invasively obtained data related to a plaque from the potential subject;
17
receiving non-invasively obtained data related to a plaque from the patient;
accessing a systems biology model of atherosclerotic cardiovascular disease, wherein the systems biology model has been trained with disease-associated pathway activation data or disease-associated molecule levels, or both, for each pathway or molecule in the systems biology model, respectively;;
accessing a systems biology model of atherosclerotic cardiovascular disease,
modifying the systems biology model using personalized pathway activation data or personalized molecule levels, or both, derived from the non-invasively obtained data from the potential subject to generate a subject-specific systems biology model;
updating the systems biology model using personalized levels of molecules derived from the non-invasively obtained data from the patient to generate a patient-specific systems biology model;
training the subject-specific systems biology model with predicted pathway activation data or predicted molecule levels, or both, derived from information relating to an effect on one or more lipoproteins by a candidate dyslipidemia management agent based on a known mechanism of action of the candidate dyslipidemia management agent;
updating the patient-specific systems biology model with information relating to an effect on low-density lipoprotein (LDL) levels by a lipid lowering agent, inflammation levels by an anti-inflammatory agent, and glucose levels by an anti-diabetic agent based on known mechanisms of action of each of the agents;
simulating a therapeutic response by the potential subject to the candidate dyslipidemia management agent in the updated subject-specific systems biology model to obtain a simulated therapeutic effect;
simulating a therapeutic response by the patient to a combination of any two or more of the lipid lowering agent, the anti-inflammatory agent, and the anti-diabetic agent in the updated patient-specific systems biology model to obtain simulated therapeutic effects for two or more combinations;
comparing the updated subject-specific systems biology model with and without the simulated therapeutic response; and
comparing the updated patient-specific systems biology model with and without the simulated therapeutic effects for each of the two or more combinations;
providing a report indicating whether the potential subject's atherosclerotic cardiovascular disease would likely be improved or unaffected by the candidate dyslipidemia management agent, or whether the potential subject would suffer an adverse effect from the candidate dyslipidemia management agent, or both.
17
identifying one or more contraindications associated with the combination of any two or more of the lipid lowering agent, the anti-inflammatory agent, and the anti-diabetic agent based on the comparison; and providing a report indicating one or more contraindications associated with the combination of any two or more of the lipid-lowering agent, the anti-inflammatory agent, and the anti-diabetic agent for the patient.
Patent ‘186 claims 20 and 19 reads on instant claims 3 and 11, respectively.
Claims 1, 3 and 11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-10, 12-14 and 24 of U.S. Patent No. 11,887,713 B2 (“Patent ‘713) in view of Pichardo-Almarza et al. (“Almarza”; NPL ref. 173 on IDS filed 11/15/2023; Current pharmaceutical design 22, no. 46 (2016): 6903-6910).
Any newly recited portions herein are necessitated by claim amendment.
Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are an obvious variation of the claims in Patent ‘713. The following table shows claims in Patent ‘713 that read on the claims of the instant application:
Instant Application
Patent ‘713
Claim
Limitations
Claim
Limitations
1
receiving non-invasively obtained data related to a plaque from the potential subject;
1
receiving non-invasively obtained imaging data related to a plaque from the patient;
accessing a systems biology model of atherosclerotic cardiovascular disease, wherein the systems biology model has been trained with disease-associated pathway activation data or disease-associated molecule levels, or both, for each pathway or molecule in the systems biology model, respectively; modifying the systems biology model using personalized pathway activation data or personalized molecule levels, or both, derived from the non-invasively obtained data from the potential subject to generate a subject-specific systems biology model;
generating virtual 'omics data that include predicted molecule levels of the patient, by applying the neural network to the non-invasively imaging obtained imaging data from the patient; providing the virtual 'omics data to a systems biology model of atherosclerotic cardiovascular disease to generate a patient-specific systems biology model, wherein (i) the systems biology model represents a plurality of pathways associated with atherosclerotic cardiovascular disease,
training the subject-specific systems biology model with predicted pathway activation data or predicted molecule levels, or both, derived from information relating to an effect on one or more lipoproteins by a candidate dyslipidemia management agent based on a known mechanism of action of the candidate dyslipidemia management agent;
updating the patient-specific systems biology model with information relating to an effect on glucose levels by an anti-diabetic agent based on a known mechanism of action of the anti-diabetic agent;
simulating a therapeutic response by the potential subject to the candidate dyslipidemia management agent in the updated subject-specific systems biology model to obtain a simulated therapeutic effect;
simulating a therapeutic response by the patient to the anti-diabetic agent in the updated patient-specific systems biology model to obtain a simulated therapeutic effect,
comparing the updated subject-specific systems biology model with and without the simulated therapeutic response; and
wherein the simulated therapeutic effect is based on change in the predicted molecule levels in the updated patient-specific systems biology model with and without the anti-diabetic agent.
providing a report indicating whether the potential subject's atherosclerotic cardiovascular disease would likely be improved or unaffected by the candidate dyslipidemia management agent, or whether the potential subject would suffer an adverse effect from the candidate dyslipidemia management agent, or both.
24
providing a report, in response to determining that the simulated therapeutic effect indicates an improvement for the patient, recommending the anti-diabetic agent for the patient.
Claim 1 of Patent ‘713 differs from instant claim 1 in that the former relates to an anti-diabetic agent and does not simulate an effect on one or more lipoproteins. Almarza performs simulations with a QSP model and shows the sensitivity of plaque growth and statin response to different physiological conditions (e.g. characteristics of the blood flow and the geometry of the artery) and was able to simulate each virtual patient’s trajectory defined by quantifying the effect of the drug and adherence to regime on plaque volume (pg. 6908, col. 2, para. 2; Figure 8). The drug is Simvastatin which is a lipid-lowering drug on LDL (pg. 6908, col. 2, para. 2; Figure 8). Figure 8 shows the effect of the modeling with LDL.
It would have been prima facie obvious to one of ordinary skill in the art to have modified Patent ‘713 to include simulating an effect of a dyslipidemia management agent on lipoproteins as taught by Almarza because Patent ‘713 teaches in claim 12 recommending a combined therapy of the anti-diabetic agent with a lipid-lowering drug. One of ordinary skill in the art would have had a reasonable expectation of success for simulating the effect of two drugs and on LDL in the systems biology model of Patent ‘713 because Almarza shows that a QSP model was capable of simulating an effect of a PCSK9 inhibitor and a statin on LDL-C (pg. 6908, col. 1, para. 1).
Patent ‘713 claims 3-10 and 12-14 read on instant claims 10-20 and 22.
Claims 1, 10-20 and 22 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-10, 14-16 and 30 of U.S. Patent No. 11,887,701 B2 (“Patent ‘701) in view of Pichardo-Almarza et al. (“Almarza”; NPL ref. 173 on IDS filed 11/15/2023; Current pharmaceutical design 22, no. 46 (2016): 6903-6910).
Any newly recited portions herein are necessitated by claim amendment.
Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are an obvious variation of claims in Patent ‘701. The following table shows claims in Patent ‘701 that read the claims of the instant application:
Instant Application
Patent ‘701
Claim
Limitations
Claim
Limitations
1
receiving non-invasively obtained data related to a plaque from the potential subject;
1
receiving non-invasively obtained imaging data related to a plaque from the patient;
accessing a systems biology model of atherosclerotic cardiovascular disease, wherein the systems biology model has been trained with disease-associated pathway activation data or disease-associated molecule levels, or both, for each pathway or molecule in the systems biology model, respectively; modifying the systems biology model using personalized pathway activation data or personalized molecule levels, or both, derived from the non-invasively obtained data from the potential subject to generate a subject-specific systems biology model;
generating virtual 'omics data that include predicted molecule levels of the patient, by applying the neural network to the non-invasively obtained imaging data from the patient; providing the virtual 'omics data to a systems biology model of atherosclerotic cardiovascular disease to generate a patient-specific systems biology model, wherein (i) the systems biology model represents a plurality of pathways associated with atherosclerotic cardiovascular disease,(ii) each pathway in the plurality of pathways corresponds to one or more of an IL-1, IL1p, TNF, IL12/23, IL17, or other cytokine molecule, (iii) the systems biology model includes a disease-associated molecule level for each molecule in the systems biology model, and (iv) the patient-specific systems biology model includes predicted molecule levels that are updated from the disease-associated molecule level;
training the subject-specific systems biology model with predicted pathway activation data or predicted molecule levels, or both, derived from information relating to an effect on one or more lipoproteins by a candidate dyslipidemia management agent based on a known mechanism of action of the candidate dyslipidemia management agent;
updating the patient-specific systems biology model with information relating to an effect on inflammation by an anti-inflammatory agent based on a known mechanism of action of the anti-inflammatory agent;
simulating a therapeutic response by the potential subject to the candidate dyslipidemia management agent in the updated subject-specific systems biology model to obtain a simulated therapeutic effect;
simulating a therapeutic response by the patient to the anti-inflammatory agent in the updated patient-specific systems biology model to obtain a simulated therapeutic effect,
comparing the updated subject-specific systems biology model with and without the simulated therapeutic response; and
wherein the simulated therapeutic effect is based on change in the predicted molecule levels in the updated patient-specific systems biology model with and without the anti-inflammatory agent.
1
providing a report indicating whether the potential subject's atherosclerotic cardiovascular disease would likely be improved or unaffected by the candidate dyslipidemia management agent, or whether the potential subject would suffer an adverse effect from the candidate dyslipidemia management agent, or both.
30
providing a report, in response to determining that the simulated therapeutic effect indicates an improvement for the patient, recommending the anti-inflammatory agent for the patient.
Claim 1 of Patent ‘701 differs from instant claim 1 in that the former relates to an anti-inflammatory agent and does not simulate an effect on one or more lipoproteins. Almarza performs simulations with a QSP model and shows the sensitivity of plaque growth and statin response to different physiological conditions (e.g. characteristics of the blood flow and the geometry of the artery) and was able to simulate each virtual patient’s trajectory defined by quantifying the effect of the drug and adherence to regime on plaque volume (pg. 6908, col. 2, para. 2; Figure 8). The drug is Simvastatin which is a lipid-lowering drug on LDL (pg. 6908, col. 2, para. 2; Figure 8). Figure 8 shows the effect of the modeling with LDL.
It would have been prima facie obvious to one of ordinary skill in the art to have modified Patent ‘701 to include simulating an effect of a dyslipidemia management agent on lipoproteins as taught by Almarza because Patent ‘701 teaches in claim 14 recommending a combined therapy of the anti-inflammatory agent with a lipid-lowering drug. One of ordinary skill in the art would have had a reasonable expectation of success for simulating an effect of two drugs on LDL in the systems biology model of Patent ‘701 because Almarza shows that a QSP model was capable of simulating an effect of both a PCSK9 inhibitor and a statin on LDL-C (pg. 6908, col. 1, para. 1).
Patent ‘701 claims 3-10 and 14-16 read on instant claims 10-20 and 22.
Response to arguments under Double Patenting
Applicant requests to hold in abeyance the Double Patenting rejections (pg. 29 of Applicant’s remarks). The Double Patenting rejections are maintained.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Inquiries
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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