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 5, 15, 28, 38, 51, and 61 are cancelled by Applicant.
Claims 1-4, 6-14, 16-27, 29-37, 39-50, 52-60 and 62-70 are currently pending and are herein under examination.
Claims 1-4, 6-14, 16-27, 29-37, 39-50, 52-60 and 62-70 are rejected.
Priority
The instant application claims domestic benefit as a divisional application of U.S. Patent Application No. 18/115,924, filed on March 1, 2023, which is a divisional application of U.S. Patent Application No. 17/838,129, filed on June 10, 2022, which claims the benefit of U.S. Provisional Application 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 1-4, 6-14, 16-27, 29-37, 39-50, 52-60 and 62-70 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 the IDS is included with this Office Action.
Drawings
The objection to the drawings is withdrawn in view of amendments filed 02/18/2026. The drawings filed 08/30/2023 are accepted.
Specification
The objection to the specification is withdrawn in view of amendments filed 02/18/2026.
Claim Objections
The objection to claims 12, 35, 48 and 58 are withdrawn in view of claim amendments.
Withdrawn Rejections
35 USC 112(b)
The rejection of claims 3, 5-6, 8, 14, 22, 26, 28-29, 31-32, 37 and 48-70 is withdrawn in view of claim amendments. The rejection of claim 8, 31 and 54 for reciting “intensive lipid-lowering agent” is withdrawn because Applicant’s argument was found persuasive (pg. 25, last para. of remarks filed 02/18/2026).
35 USC 103
The rejection of claims 5, 15, 28, 38, 51 and 61 under 35 USC 103 is withdrawn because Applicant canceled these claims.
35 USC 101
The rejection of claims 5, 15, 28, 38, 51 and 61 under 35 USC 101 is withdrawn because Applicant canceled these claims.
Double Patenting
The provisional rejection of claim 1 on the ground of nonstatutory double patenting as being unpatentable over claims 18 and 19 of copending Application No. 17/838,135 in view of Pichardo-Almarza et al. is withdrawn in view of claim amendment.
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 48-50, 52-60 and 62-70 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 maintained from the previous Office action.
Claims dependent from a rejected claim are also rejected unless otherwise noted.
Claim 48, line 7, recites “the patient” which renders the claim indefinite. It is unclear if it refers to the phrase “a patient” recited in claim 48 line 3 or 6. Clarify what the phrase refers to.
Response to Arguments under 35 USC 112(b)
Applicant's arguments filed 02/18/2026 have been fully considered but they are not persuasive because the rejection of claim 48 was not addressed properly by correcting for “the patient”.
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 1-4, 6-14, 16-27, 29-37, 39-50, 52-60 and 62-70 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 1-4, 6-14, 16-27, 29-37, 39-50, 52-60 and 62-70 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 disease-associated pathway activation data or disease-associated molecule levels, or both, derived from the non-invasively obtained data from each actual patient in the cohort of actual patients to generate a patient-specific systems biology model for each of the patients in the cohort of patients; training each of the patient-specific systems biology models with 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 to the candidate dyslipidemia management agent in each of the patient-specific systems biology models for the cohort of patients to obtain a patient-specific simulated therapeutic effect in each of the patient-specific systems biology models; comparing a therapeutic effect in each of the patient-specific systems biology models for each patient in the cohort of patients before and after simulating the therapeutic response by the candidate dyslipidemia management agent; and quantifying a simulated therapeutic response by the agent at a cohort level based on the comparing step.”
Claims 3, 26 and 49 recite “wherein the disease-associated pathway activation data or disease-associated molecule levels, or both, in the systems biology model comprise an alteration in a level of a gene, a protein, or a metabolite.”
Claim 4, 27 and 50 recite “wherein the candidate dyslipidemia management agent is a statin.”
Claim 6, 29 and 52 recite “wherein the statin is atorvastatin.”
Claim 7, 30 and 53 recite “wherein the candidate dyslipidemia management agent is a hypertriglyceridemia lowering agent, a hypercholesterolemia lowering agent, or an agent that increases an atheroprotective effect.”
Claim 8, 31 and 54 recite “wherein the candidate dyslipidemia management agent is an intensive lipid-lowering agent.”
Claim 9, 32 and 55 recite “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 10, 33 and 56 recite “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 11, 34 and 57 recite “wherein the systems biology model includes one or more pathways representing: …”
Claim 12, 35 and 58 recite “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 patient-specific systems biology models 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 the defined therapeutic effect pathway level, or both, on one or more of the other molecules or pathways represented in the patient-specific systems biology models.”
Claim 13, 36 and 59 recite “wherein simulating the therapeutic response comprises setting an increased level of plaque stability in the patient-specific systems biology models.”
Claim 14, 37 and 60 recites “wherein the step of modifying the systems biology model using disease-associated molecules levels further comprises using disease gene transcript levels derived from the non-invasively obtained data.”
Claim 17, 40 and 63 recites “processing the non-invasively obtained imaging data to obtain quantitative plaque morphology data including structural anatomy data, tissue composition data, or both.”
Claim 18, 41 and 64 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 19, 42 and 65 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 20, 43 and 66 recite “wherein the pathways in the patient-specific systems biology models are compartmentalized into cell-specific networks.”
Claim 21, 44 and 67 recite “wherein the cell-specific networks include at least an endothelial cell network, a macrophage network, and a vascular smooth muscle cell network.”
Claim 22, 45 and 68 recites “further comprising if the report indicates that the candidate dyslipidemia management agent is a potential therapeutic agent, conducting further testing of the potential therapeutic agent.”
Claim 23, 46 and 69 recite “wherein the plaque is an atherosclerotic plaque.”
Claim 24, 47 and 70 recite “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).”
Claim 25 recites “further comprising selecting a patient from the cohort of patients for inclusion in a clinical trial when the quantifying indicates an improvement in disease status for the patient at a level above an inclusion criteria threshold.”
Claim 48 recites “further comprising determining any adverse side effects from the quantifying at the cohort level, and selecting a patient from the cohort of patients for exclusion from a clinical trial when the quantifying indicates an adverse side effect for the patient at a level above an exclusion criteria threshold. further comprising selecting a patient from the cohort of patients for inclusion in a clinical trial when the quantifying indicates an improvement in disease status for the patient at a level above an inclusion criteria threshold.”
Limitations reciting a mental process.
Above cited claims 1, 12-13, 17-19, 22, 25, 35-36, 40-42, 45, 48, 58-59, 63-65 and 68 are 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 that includes pathway activation data includes acquiring a data in the form of math 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 using pathway data or molecule levels to create patient specific systems biology models includes inputting into the model patient-specific measurements such as molecule levels. 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 simulating a therapeutic effect in each of the patient-specific systems biology models includes performing the differential equations related to the pathways or equations described on specification pg. 31. The BRI of comparing a therapeutic effect for each patient before and after simulating the therapeutic response includes evaluating data. The BRI of quantifying a simulated therapeutic response of the agent at a cohort level includes performing the computations of each patient-specific systems biology model to determine how the cohort as a whole respond to the simulated therapy effect.
The BRI of claims 12, 35 and 58 include performing the same operations described above regarding claim 1 on a specific set of molecules/pathways in the systems biology model.
The BRI of claims 13, 36 and 59 includes modifying the parameters of the differential equations in the systems biology model.
The BRI of claims 17-19, 40-42 and 63-65 includes a physician examining x-rays images or CT scans to calculate wall thickness and calcification.
The BRI of claims 22, 45 and 68 includes performing further calculations using the systems biology model with altered parameters.
The BRI of claims 25 and 48 includes making a determination/selection based upon analyzing data.
Limitations reciting a mathematical concept.
Above cited claims 1, 12-13, 35-36 and 58-59 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 paragraphs below discuss 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 it “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 and patient-specific systems biology models include calibrating, manipulating, and performing the mathematical operations of the equations. The same BRI of claim 1 applies to claims 12-13, 35-36 and 58-59.
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 data related to a plaque of the patient.
Limitations included in the recited judicial exception.
Claims 3, 11, 20-21, 24, 26, 34, 43-44, 47, 49, 57, 66-67 and 70 further limit the judicial exception in claim 1 of the systems biology model because the pathways, molecule levels, and lipoproteins are all numerical parameters within the mathematical equations of the model.
The BRI of claims 4-10, 26-33 and 49-56 includes them being part of the judicial exception in claim 1 of the systems biology model because the candidate dyslipidemia management agent is a numerical parameter within the mathematical equations of the model.
The BRI of claims 14, 37 and 60 includes them being part of the judicial exception in claim 1 of the systems biological model because the molecule levels are numerical parameters within the mathematical equations of the model.
The BRI of claims 23, 46 and 69 includes them being part of the data that is collected, which is part of the abstract idea.
As such, claims 1-4, 6-14, 16-27, 29-37, 39-50, 52-60 and 62-70 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 comprising: receiving non-invasively obtained data related to a plaque from each patient in a cohort of patients with known or suspected atherosclerotic cardiovascular disease;”
Claim 2 recites “providing a report indicating the candidate dyslipidemia management agent is a potential therapeutic agent when the quantifying indicates the candidate dyslipidemia management agent provides an improvement in disease status at the cohort level.”
Claim 16, 39 and 62 recite “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 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.”
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)), insignificant extra-solution activity (MPEP § 2106.05(g)), and field of use limitations (MPEP § 2106.05(h)). The paragraphs below discuss the additional elements recited above in the instant claims.
The limitation in claim 1 of a computer-implemented method 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.
Claim 2 equates to insignificant, extra-solution activity of necessary data outputting because it outputs the result of the judicial exception.
Claims 1, 16, 39 and 62 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 the model to generate patient-specific models.
As such, claims 1-4, 6-14, 16-27, 29-37, 39-50, 52-60 and 62-70 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.
The limitation in claim 1 of a computer-implemented method 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 1-2, 16, 39 and 62 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 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 1-4, 6-14, 16-27, 29-37, 39-50, 52-60 and 62-70 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 that Examiner did not explain how mathematical equations can performed in the human mind and that the specification makes clear that a human cannot perform them (pg. 27, last para. – pg. 28, para. 1) (pg. 29, para. 2, last sentence). Applicant’s argument is not persuasive because:
As shown in the Office action passage cited by Applicant (pg. 28 of remarks), Examiner explained that the systems biology model can comprise differential equations. A human can perform differential equations on pen and paper. MPEP 2106.04(a)(2).III recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.” See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674.
Applicant argues that the claims do not recite an equation or algorithm (pg. 28, last para.). Applicant’s argument is not persuasive because:
A claim does not have to 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 textual replacement 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 SME Example 39 (pg. 29, para. 2). 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. Also the BRI of the systems biology model in claim 1 includes differential equations, which is not AI.
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. 29, last para.).
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 (pg. 30, para. 1-2). For example, comparing models is a mental process as well as modifying a model because a human can alter parameters of a differential equation. The claims recite mathematical concepts as discussed above.
Applicant argues that claim 1 does not recite a natural phenomenon because it performs computer simulations and does not recite a diagnosis (pg. 30, para. 3-4). 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 argues that claims 3, 11, 20-21, 24, 26, 34, 43-44, 47, 49, 57, 66-67 and 70 do not recite a mathematical concept (pg. 31, para. 2). Applicant’s argument is not persuasive because:
The systems biology model can be a differential equation. When claim 3 is viewed in combination with claim 1 step of “modifying”, it includes inserting numerical values representing alterations of gene levels into the differential equation. Even if this did not recite a mathematical concept, it recites a mental process because a human can modify numerical values in an equation.
Applicant argues that claim 1 limitation of receiving non-invasively obtained imaging data is not mere data gathering activity because it is used to train/modify the model (pg. 31, para. 3-4). Applicant’s argument is not persuasive because:
MPEP 2106.05(g)(3) recites the following when discussing criteria for determining whether an additional element is insignificant, extra-solution activity: “Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output.” The receiving step is a necessary data gathering step used to perform the judicial exception. As such, it equates to data gathering, which does not integrate into a practical application.
Applicant argues that the claims as a whole must be analyzed and not viewed in isolation when evaluating an inventive concept (pg. 31, last para. – pg. 32, 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. 32, 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 and the specification examples demonstrate how the invention improves upon existing technology (pg. 32, last para. – pg. 33, para. 2). 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 and because Examiner has examined all additional elements under both Step 2A, Prong 2 and Step 2B (pg. 33, para. 3).
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.
Claim 1-4, 6-14, 16-27, 29-37, 39-50, 52-60 and 62-70 are rejected under 35 U.S.C. 103 as being unpatentable over Pichardo-Almarza et al. (“Almarza”; NPL ref. 173 on IDS filed 10/17/2023; 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 candidate dyslipidemia management agent for treating 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 (pg. 6908, col. 2, para. 2).
receiving non-invasively obtained imaging data related to a plaque from each actual patient in a cohort of actual patients with known or suspected atherosclerotic cardiovascular disease;
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 imaging data of actual patients.
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 patients may be part of a cohort [155]. 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] (receiving non-invasively obtained data).
It would have been prima facie obvious to have parameterized the QSP model of Almarza with the predicted biological properties/analytes acquired from non-invasive imaging data of real patients in a cohort as taught by Buckler. Buckler states that biological properties/analytes are obtained advantageously through non-invasive methods [136], which advantageously utilizes 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;
The instant specification defines “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 1-3). Biological pathways are defined in the specification as “a series of actions among molecules that leads to a certain product or a change” (pg. 12, lines 4-5).
Almarza discloses a quantitative systems pharmacology (QSP) model of cholesterol-lowering drugs and their effect on atherosclerosis (pg. 6907-6908 § 3). The QSP model is a multiscale approach that describes important biological/physiological mechanisms related to atherosclerotic plaque progression combined with the effect of blood flow conditions and how this impacts LDL and monocyte penetration in arterial walls. The models include biological pathways related to atherosclerosis (pg. 6908 §3.2) such as the biochemical pathway related to cholesterol, including LDL and oxLDL, and the effect of the biochemical pathway of Simvastatin on the biochemical pathways of cholesterol (pg. 6909; Figure 8), wherein concentrations of the molecules are present within the model (molecule levels) (pg. 6905, sec. 1.3.3) (pg. 6904 § 1.3.1). Therefore, in accordance with the definitions of the specification, the QSP model of Almarza equates to a systems biology model that represents interconnected biological pathways that contain molecules leading to products or change (e.g., cholesterol and Simvastatin pathways as well as the interplay in the arterial wall).
modifying the systems biology model using disease-associated pathway activation data or disease-associated molecule levels, or both, derived from the non-invasively obtained imaging data from each actual patient in the cohort of actual patients to generate a separate patient-specific systems biology model for each of the actual patients in the cohort of actual patients;
Almarza teaches performing 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 disease-associated pathway activation data or disease-associated molecule levels, or both, derived from the non-invasively obtained data from each patient in the cohort of patients to generate a … systems biology model for each of the patients in the cohort of patients) (pg. 6908, col. 2, para. 2).
However, Almarza does not teach that the pathways or molecule levels were derived from the non-invasively obtained data from real patients in a cohort or creating separate patient-specific models for each real patient.
Buckler teaches quantifying biological properties/analytes such as gene expression directly from non-invasively acquired radiological imagining data [136]. Buckler teaches that each real patient may be in a cohort [155].
However, neither Almarza nor Buckler teach generating patient-specific models for each real patient.
Schobel discloses using machine learning models to predict if a patient has an increased risk of developing a clinical outcome (abstract). In one embodiment, 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]. Each real patient in a cohort has a separate model created [174].
When Almarza, Buckler, and Schobel are taken together, the QSP model is simulated with patient-specific data derived from non-invasive imagining to generate patient-specific QSP models for each patient.
It would have been prima facie obvious to have updated the QSP model of Almarza with patient-specific data to obtain a patient-specific QSP model for each real patient in a cohort 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 is provided by Schobel who states that updating a model with patient-specific data is advantageous for predicting a patient-specific clinical outcome [79]. Thus it would be beneficial to update the QSP model of Almarza with patient specific data.
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 QSP model updated with patient-specific data 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]. There also would have been a reasonable expectation of success because Almarza teaches that the QSP model opens the door for predictive modelling in individual patients (pg. 6908, col. 2, para. 2).
training each of the patient-specific systems biology models with 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 with the 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).
However, Almarza does not teach the that the QSP model is patient-specific.
Almarza in combination with Buckler and Schobel discloses the patient-specific QSP model, as discussed above.
simulating a therapeutic response to the candidate dyslipidemia management agent in each of the patient-specific systems biology models for the cohort of actual patients to obtain a patient-specific simulated therapeutic effect in each of the patient-specific systems biology models; comparing a therapeutic effect in each of the patient-specific systems biology models for each actual patient in the cohort of patients before and after simulating the therapeutic response by the candidate dyslipidemia management agent; 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. The comparison between the model with and without the simulated therapeutic effect is inherent in performing the simulation because the simulated effect only has meaning if the values are compared to baseline levels of molecules (i.e., the model updated with patient-specific data before the simulation is performed).
However, Almarza does not teach that the QSP model is patient-specific.
Almarza in combination with Buckler and Schobel disclose the patient-specific QSP model.
quantifying a simulated therapeutic response by the agent at a cohort level based on the comparing step.
Almarza performs simulations using the QSP model on a virtual patient population and was able to simulate each patient’s trajectory defined by quantifying the effect of a statin on plaque volume (pg. 6908, col. 2, para. 2). Almarza also teaches that QSP models can be used to define inter-subject variability response to drugs, wherein a QSP model using a virtual population predicted a better reduction in LDL-C when using a combined therapy of a PCSK9 inhibitor and a statin (pg. 6908, col. 1, para. 1). As discussed in the limitations regarding “comparing a therapeutic effect”, the simulation of the cohort necessitates comparison between baseline (pre-simulation) and modeled drug effect in the QSP model (post-simulation).
However, Almarza does not teach a real cohort of patients. As discussed above, Buckler and Schobel disclose the real patient cohort and each having a patient-specific model.
Claims 2, 4, 7, 27, 30, 50 and 53:
Almarza uses the simulation results for personalized decision-making (p. 6903, col. 2), which necessitates reporting the result of the simulation. Simvastatin was used in the modeling (statin) (hypercholesterolemia lowering agent).
Claims 3, 26 and 49:
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).
Claims 6, 29 and 52:
Almarza teaches modeling with atorvastatin (pg. 6905, col. 2, para. 2).
Claims 8-9, 31-32 and 54-55:
Almarza teaches a QSP model that models a combination therapy of a statin and a PCSK9 inhibitor (pg. 6908; Fig. 7).
Claims 10, 33 and 56:
Almarza references a PKPD model that modeled a combined therapy of ezetimibe and atorvastatin for the treatment of dyslipidemia (pg. 6906 § 2.3).
Claims 11, 34 and 57:
Almarza’s QSP model shows cholesterol metabolism in Figure 8 (fluid shear stress and atherosclerosis) (abstract).
Claims 12, 35 and 58:
Almarza teaches that the QSP model includes terms representing the concentrations of individual molecules that are affected (directly or indirectly) by the drug (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 (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).
Claims 13, 36 and 59:
Almarza teaches that the QSP model simulates the effect of drugs on atherosclerotic plaque progression (abstract) (Figure 8).
Claims 14, 37 and 60:
Almarza teaches performing simulations in the QSP model using physiological characteristics of a virtual population 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). However, Almarza does not teach modifying the QSP model with disease gene transcript levels derived from non-invasively obtained data.
Buckler teaches that the radiological imagining data may be of arteries that have atherosclerotic plaque [69] [91] [93] (Figure 22), and 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 QSP model of Almarza to include gene expression levels related to atherosclerotic plaque derived from non-invasively obtained imaging data as taught by Buckler. The motivation for doing so is taught by Buckler who states that gene expression profiling is done advantageously through non-invasive means [50] [68] and because the gene expression profiling can be used to determine/tune algorithms that correlate biological properties/analytes to pathologies with a known relationship to clinical outcome [136]. One of ordinary skill in the art would have had a reasonable expectation of success by using the predicted gene expression levels related to atherosclerotic plaque of Buckler in the QSP model of Almarza because Almarza states that the QSP model can use levels of different biomarkers associated with atherosclerotic plaque (pg. 6905 § 1.3.3).
Claims 16-19, 39-42 and 62-65:
Almarza teaches that the QSP model contains concentrations of molecules (pg. 6905 § 1.3.3), wherein the virtual population’s physiological parameters are simulated and inputted into the QSP model (pg. 6908, col. 1, para. 1) (pg. 6908, col. 2, para. 2). However, Almarza does not teach receiving non-invasively obtained imaging data related to plaque or processing the imaging data to acquire quantitative plaque morphology data including structural anatomy data or tissue composition data that is then used to modify the QSP model.
Buckler shows in Figure 1 acquiring non-invasively obtained radiological images 121A from actual patients 50 [136] [150], wherein the images can be from cardiac computed tomography angiography [136] (claim 37). The patients are part of a cohort [155]. The radiological imagining data may be of arteries that have atherosclerotic plaque [69] [91] [93] (Figure 22). The imagining data can be used to quantify characteristic of plaque structure and plaque composition (quantitative plaque morphology data including structural anatomy data, tissue composition, or both) [168]. The plaque structure includes remodeling, stenosis, dilation, and wall thickness (Table 3) [362]. The plaque composition includes lipid-rich necrotic core, calcification, and intraplaque hemorrhage [165] (Table 4).
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 patient-specific predicted biological properties/analytes derived from non-invasive imaging data 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]. Buckler also states that when modeling a vascular setting it is advantageous to evaluate plaque structure and plaque composition [168], and that it is advantageous to use predicted biological properties/analytes derived from non-invasively obtained imaging data to provide surrogate measures for predicting clinical outcome or guiding treatment [136].
One of ordinary skill in the art would have had a reasonable expectation of success because Almarza models atherosclerotic plaque progression which already uses biomarkers associated with plaque. The combination would provide further parameters to model atherosclerotic plaque progression. There also would have been a reasonable expectation of success because Buckler states that CCTA has been established for evaluation of coronary atherosclerotic plaques [50] and the properties/analytes of Buckler can be used to create more deterministic and patient-specific prognostics as well as measurements of response to drugs [17].
Claims 20-21, 43-44 and 66-67:
Almarza teaches a QSP model that includes cell-specific compartments, including macrophage cells (pg. 6909; Fig. 8).
Claims 22, 45 and 68:
Almarza teaches that the quantitative models can determine if a drug has no disease reducing effect, which demonstrates how quantitative decision-making “facilitated a quick decision to stop development of the drug, saving millions of US dollars to the Company in R&D and expensive clinical trials” (pg. 6906 § 2.3). Although Almarza does not teach conducting further tests when the drug indicates improvement in a cohort, it would have been prima facie obvious over the teachings of Almarza to further test a drug if it predicted to improve patient disease status because quantitative decision-making helps decide which drugs should be developed.
Claims 23-24, 46-47 and 69-70:
Almarza shows in Figure 8 that the QSP models simulates the effect of statins in LDL and atherosclerotic plaque progression.
Claim 25:
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 patient is not selected for inclusion in a clinical trial when the quantifying does not indicate an improvement. As such, claim 25 is not required to be performed.
Claim 48:
Almarza teaches that quantitative models can estimate adverse side effects and uses the models to simulate a clinical trial with more than 1,000 patients (determining any adverse side effects from the quantifying at the cohort level) (pg. 6906 § 2.3) (pg. 6907 § 2.5.2). Regarding the remaining limitations in claim 48 of “selecting a patient from the cohort of patients for exclusion/inclusion”, the BRI of these limitations includes them being contingent limitations, for the same reasons discussed above regarding claim 25. As such, these limitations are not required to be performed by the claim.
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 disclose or suggest the method of claim 1 because it has nothing to do with screening candidate dyslipidemia agents (pg. 34, para. 1-2). Applicant’s argument is not persuasive because claim 1 does not require an active step of “screening a candidate dyslipidemia management agent” because this is an intended use recited in the preamble.
Applicant argues that Almarza does not use non-invasively obtained imaging data related to plaque from real patients because the QSP model is based on virtual patients (pg. 34, para. 4). 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. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The combination of Almarza, Buckler, and Schobel teach a patient-specific QSP models derived using non-invasively obtained imaging data of atherosclerotic plaques from patients.
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. 34, last para. – pg. 35, para. 2). Applicant’s argument is not persuasive because:
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]. This rational is reiterated from the above 103 rejection.
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. 35, para. 2). Applicant’s argument is not persuasive because Almarza teaches the QSP model and screening candidate drug agents is an intended use recited in the preamble. 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 models, not Almarza alone in combination with Buckler (pg. 35, 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. 35, last para. – pg. 36, para. 2). Applicant’s argument is not persuasive because:
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 1-3). The specification defines biological pathway as “a series of actions among molecules that leads to a certain product or a change” (pg. 12, lines 4-5).
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 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 a model is not generated for a cohort (pg. 36, para. 3 – pg. 37, para. 2). Applicant’s argument is not persuasive because:
One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. The combination of Almarza, Buckler, and Schobel a patient-specific systems biology model for each real patient in a cohort. Almarza’s 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). This indicates a cohort of patients with each having their own model. The patient-specific QSP models are achieved by Schobel and Buckler who update a diagnostic prediction model with patient-specific data to derive patient-specific clinical outcome predictions.
Applicant appears to argue 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. 36, last para. – pg. 37, para. 2). Applicant’s arguments are not persuasive because:
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 uses 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 Buckler and Schobel to arrive at the patient-specific QSP model.
MPEP 2141.03.I recites "A person of ordinary skill in the art is also a person of ordinary creativity, not an automaton." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 421, 82 USPQ2d 1385, 1397 (2007). "[I]n many cases a person of ordinary skill will be able to fit the teachings of multiple patents together like pieces of a puzzle." Id. at 420, 82 USPQ2d 1397. Office personnel may also take into account "the inferences and creative steps that a person of ordinary skill in the art would employ." Id. at 418, 82 USPQ2d at 1396.
Applicant states that none of the alleged combinations disclose claim 1 and that Almarza does not teach using data from real-patients (pg. 37, para. 3-4). Applicant’s argument is not persuasive for the same reasons stated above and because Applicant attacks the references individually.
Applicant’s argument regarding inherency in Almarza is not persuasive because it relates to Examiner’s explanation regarding updating a model with patient-specific data to perform a simulation. In the context of Almarza, simulating with patient specific data in a QSP model inherently requires a comparison of the model before and after simulation in order to discern how the patient-specific data affected the model simulation (pg. 37, last para.).
Applicant’s remark regarding screening potential subjects is not persuasive because claim 1 does not require such a limitation (pg. 37, last para.).
Applicant argues that one of skill would not have combined the references to arrive at instant claim 1 (pg. 38, para. 1-2). Applicant’s argument is not persuasive for the same reasons described in the rejection and responses above.
Applicant’s remark regarding neural networks is not persuasive because the instant claims do not recite neural networks (pg. 38, para. 2).
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.
Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 18 and 19 of copending Application No. 17/838,135 (“Application ‘135”) in view of Pichardo-Almarza et al. (“Almarza”; NPL ref. 173 on IDS filed 10/17/2023; Current pharmaceutical design 22, no. 46 (2016): 6903-6910; previously cited) and Buckler et al. (“Buckler”; US 2019/0180153 A1; previously cited on PTO892 mailed 11/18/2025).
This rejection is newly recited in view of 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 Application ‘135.
Application ‘135 does not teach imaging data or “comparing a therapeutic effect in each of the patient-specific systems biology models for each patient in the cohort of patients before and after simulating the therapeutic response by the candidate dyslipidemia management agent”.
Almarza teaches a QSP model that 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. The comparison between the model with and without the simulated therapeutic effect is inherent in performing the simulation because the simulated effect only has meaning if the values are compared to baseline levels of molecules (i.e., the model updated with patient-specific data before the simulation is performed). This is performed for each patient in a cohort or 1,000 patients (patient-specific systems biology models) (pg. 6909, col. 2, para. 2).
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 atherosclerotic plaque drug simulation of Application ‘135 by comparing the data in the model before and after simulation in order to determine how a drug affects the disease. One of ordinary skill in the art would have had a reasonable expectation of success for the combination because comparing baseline data with results after a simulation provides a basis to understand how the simulation affects the model.
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 patients may be part of a cohort [155]. These images may be from non-invasively acquired radiological imagining data [136]. The radiological imagining data may be of arteries that have atherosclerotic plaque (receiving non-invasively obtained data) [69] [91] [93] (Figure 22).
It would have been prima facie obvious have modified Application ‘135 to use imaging data 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 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].
This is a provisional nonstatutory double patenting rejection.
Claims 1, 3, 11, 26, 34, 49 and 57 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 23-24 and 28-29 of U.S. Patent No. US 11,887,701 B2 (“Patent ‘701”) in view of Pichardo-Almarza et al. (“Almarza 2015”; CPT: Pharmacometrics & Systems Pharmacology 4, no. 1 (2015): 41-50; previously cited on PTO892 mailed 11/18/2025).
This rejection is maintained from the previous Office action.
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 ‘701. The following table shows the claims of Patent ‘701 that read on the limitations of the instant claims.
Instant Application
Patent ‘701
1
23 and 29
3, 26, 49
24
11, 34, 57
28
Regarding instant claim 1, Patent ‘701 does not teach “quantifying a simulated therapeutic response by the agent at a cohort level.”
Almarza 2015 teaches a systems pharmacology approach to study the effect of statins on early-stage atherosclerosis in humans (title). Figure 2D a cohort response to simvastatin.
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 Patent ‘701 for screening a drug for atherosclerotic disease by comparing before and after data of the simulation to quantify a cohort level of response in order to determine if the drug had an overall effect on the cohort. The motivation for doing so is taught by Almarza 2015 who recites “systems pharmacology is expected to have an impact across all stages of drug research and development, ranging from very early discovery programs to large-scale Phase 3/4 patient studies” (pg. 41, col. 1, para. 1). One of ordinary skill in the art would have had a reasonable expectation of success for the combination because Patent ‘701 already teaches performing drug simulations on subject-specific systems biology models, wherein the combination would result in further data analysis of the simulations.
Claims 1, 3, 11, 26, 34, 49 and 57 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 19-20 and 22 of U.S. Patent No. US 11,887,713 B2 (“Patent ‘713”) in view of Pichardo-Almarza et al. (“Almarza 2015”; CPT: Pharmacometrics & Systems Pharmacology 4, no. 1 (2015): 41-50; previously cited on PTO892 mailed 11/18/2025).).
This rejection is maintained from the previous Office action.
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 the claims of Patent ‘713 that read on the limitations of the instant claims.
Instant Application
Patent ‘713
1
19
3, 26, 49
20
11, 34, 57
22
Regarding instant claim 1, Patent ‘713 does not teach “quantifying a simulated therapeutic response by the agent at a cohort level.”
Almarza 2015 teaches a systems pharmacology approach to study the effect of statins on early-stage atherosclerosis in humans (title). Figure 2D a cohort response to simvastatin.
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 Patent ‘713 for screening a drug for atherosclerotic disease by comparing before and after data of the simulation quantify a cohort level of response in order to determine if the drug had an overall effect on the cohort. The motivation for doing so is taught by Almarza 2015 who recites “systems pharmacology is expected to have an impact across all stages of drug research and development, ranging from very early discovery programs to large-scale Phase 3/4 patient studies” (pg. 41, col. 1, para. 1). One of ordinary skill in the art would have had a reasonable expectation of success for the combination because Patent ‘713 already teaches performing drug simulations on subject-specific systems biology models, wherein the combination would result in further data analysis of the simulations.
Response to arguments under Double Patenting
Applicant requests to hold in abeyance the Double Patenting rejections (pg. 39, para. 1). The provisional rejection of claim 1 over copending Application No. 17/838,135 is withdrawn due to claim amendment, but a new ground of rejection is applied with a new reference. The other two 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.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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