DETAILED ACTION
1. Applicant’s response, filed 23 April 2026, has been fully considered. The following rejections and /or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
2. 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
3. Claims 5-6 and 15-16 are cancelled.
Claims 1-4, 7-14 and 17-20 are currently pending and under examination herein.
Claims 1-4, 7-14 and 17-20 are rejected.
Priority
4. The instant application claims domestic benefit to provisional applications 63/209,307, 63/209,291 and 63,209,298 filed on 06/10/2021. Domestic benefit is not acknowledged for all claims (1-4, 7-14 and 17-20) because the provisional applications do not recite limitations in independent claims 1 and 11 (lines 6-39 and 5-39, respectively). Domestic benefit of claims 2-4, 7-10, 12-14 and 17-20 is not acknowledged by virtue of dependence on claims 1 and 11. Identifying subgroups of patients based on common attributes is not described in the provisional applications, nor is the proposed analysis of subgroups of patients. Therefore, the effective filing date of claims 1-4, 7-14 and 17-20 will be considered to be the filing date of the instant application, 10 June 2022.
Drawings
5. The objection to the drawings is withdrawn in view of the amendment to the drawing filed 23 April 2026.
Claim Objections
6. The objection to claims 9 and 19 are withdrawn in view of the claim amendments filed 23 April 2026.
Claim Interpretation
7. Claims 1 and 11 use the term “potential nodal points”. The term “nodal points” has a generally accepted meaning in the art of points of intersection or hubs in a graph. The term “potential nodal points” was introduced in para. [0007] of the specification. Based on this paragraph and the generally accepted meaning in the art, for the purpose of review, “potential nodal points” will be considered to be genes having genetic anomalies that have potentially causative relationships with other data points in the patient data (including genetic anomalies, physiological functions and biological functions) and may be ranked based on the number of associations with other genetic anomalies or selected based on other associations in the dataset.
Claim Rejections - 35 USC § 112
8. The rejection of claims 1-20 under 35 U.S.C 112(b) is withdrawn in view of the claim amendments filed 23 April 2026.
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.
9. Claims 1-4, 7-14 and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and natural phenomenon without significantly more. Any newly recited portions herein are necessitated by claim amendment.
Step 2A Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claims 1 and 11 recite: identifying, from the stored medical data, a group of individuals having a disease
Claims 1 and 11 recite: repeatedly partitioning the group of individuals having the disease to select a subgroup of the individuals having a common attribute
Claims 1 and 11 recite: for each selected subgroup: detecting physiological anomalies or medical test anomalies that are more prevalent in the physiological data or the medical history data of the selected subgroup than in the physiological data or the medical history data of a control group
Claims 1 and 11 recite: performing genetics differential analysis to identify genetic anomalies affecting one or more genes that are more prevalent in the genetics data of the selected subgroup than in the genetics data of the control group
Claims 1 and 11 recite: identifying physiological functions affected by the physiological anomalies or medical test anomalies
Claims 1 and 11 recite identifying biological functions affected by the genes having the genetic anomalies
Claims 1 and 11 recite identifying potential nodal points by using a genetic pathway database to identify genes along a genetic pathway that includes the genes having the genetic anomalies
Claims 1 and 11 recite: identifying a disease driver from among the earliest genes along the identified genetic pathway by using at least one gene-phenotype catalogue to identify a gene that is commonly associated with both the affected physiological functions and the affected biological functions
Claims 1 and 11 recites: identifying at least one change to the shape of the protein caused by the disease
Claims 1 and 11 recites: selecting a drug from a drug shape database having a shape that binds to the modified shape of the protein made by the disease driver
Claims 2 and 12 recite: the method of claim 1, further comprising: determining whether the selected subgroup has a statistically significant disease signature compared to the control group
Claims 3 and 13 recite: the method of claim 1, wherein the group of individuals is partitioned to select a different subgroup having a different attribute in response to a determination that the selected subgroup does not have a statistically significant disease signature compared to the control group.
Claims 4 and 14 recite: the method of claim 1, wherein the potential nodal points, the disease driver, or the drug to treat the disease in individuals having the attribute is identified in response to a determination that the selected subgroup does not have a statistically significant disease signature compared to the control group
Claims 7 and 17 recite: selecting at least one cellular condition change caused by the disease
Claims 8 and 18 recite: the method of claim 7, wherein the cellular conditions changes caused by the plurality of diseases and the changes to shapes of a plurality of proteins caused by the plurality of diseases are identified by analyzing published medical research using natural language processing
Claims 9 and 19 recite: the method of claim 1, further comprising: performing genetics differential analysis to identify a genetic anomaly affecting an unannotated gene
Claims 10 and 20 recite: the method of claim 9, further comprising: identifying a biological function affected by a gene in another animal that is correlated with the unannotated gene
The limitations for identifying individuals having a disease (claims 1 and 11), identifying physiological functions affected by the anomalies (claims 1 and 11), identifying biological function affected by a gene (claims 1 and 11, claims 10 and 20), identifying a disease driver (claims 1 and 11) and identifying genes along a genetic pathway (claims 1 and 11), detecting physiological anomalies (claims 1 and 11), selecting changes to a cellular condition caused by a disease (claims 7 and 17), partitioning samples into subgroups (claims 1 and 11), selecting a subgroup (claims 3 and 13), selecting a drug (claims 1 and 11), identifying at least one change to the structure (claims 1 and 11) and analyzing public data with natural language processing (claims 8 and 18) equate to evaluating data and making a decision based on that evaluation which, under the broadest reasonable interpretation, can be practically performed in the human mind. Therefore, these limitations fall within the “mental process” grouping of abstract ideas because they cover concepts performed in the human mind including reviewing information, extracting relationships, categorizing concepts, drawing conclusions from text (MPEP 2106.04(a)(2)(III)).
The limitations for performing case versus control differential enrichment analysis (claims 1 and 11, and claims 9 and 19), identifying correlations of gene expression values (claims 10 and 20) and calculating statistical significance of a differential signature (claims 2, 3, 4, 12, 13, and 14), by broadest reasonable interpretation, require mathematical calculations. These include applying arithmetic calculations and functions (division, multiplication, addition, subtraction and square root calculations) to calculate mean, sample size, standard deviations, variance and covariance. Therefore, these limitations recite “mathematical calculations” and so fall into the “mathematical concepts” grouping of abstract ideas. These limitations also fall into “mental process” grouping because the mathematical calculations are simple enough to be practically performed in the human mind. Even if most humans would use a physical aid, like a pen and paper or a calculator, to make such calculations, the use of a physical aid would not negate the mental nature of this limitation See MPEP 2106.04(a)(2), subsection III.B.
While claims 1 and 11 recite looking up data in a database or catalogue, this does not preclude the steps being mental processes as the computer merely performs the abstract analysis faster. While claims 11, 12-17, 19 and 20 recite performing some aspects of the analysis with a “hardware computer processor”, there are no additional limitations that indicate that this processor requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-4, 7-14 and 17-20 recite an abstract idea (Step 2A, Prong 1: YES).
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). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment, insignificant extra-solution activity, and instructions to apply known computational fluid dynamics (CFD) in a generic way.
Specifically, the claims recite the following additional elements:
Claim 11 recites: a non-transitory computer readable storage media that stores medical data that includes physiological data, medical history data, contextual information, and genetics data
Claim 11 recites: a hardware computer processor
Claim 1 recites: storing medical data that includes physiological data, medical history data, contextual information, and genetics data
Claims 1 and 11 recite: for each selected subgroup: storing physiological anomalies or medical test anomalies that are more prevalent in the physiological data or the medical history data of the selected subgroup than in the physiological data or the medical history data of a control group
Claims 1 and 11 recite: identifying a drug to treat the disease in individuals having the attribute by using computational fluid dynamics to model a shape of a protein made by the disease driver
Claims 1 and 11 recite: storing changes to shapes of a plurality of proteins caused by a plurality of diseases
Claims 1 and 11 recites: using the computation fluid dynamics to model a modified shape of the protein caused by the disease
Claims 7 and 17 recite: using computation fluid dynamics to model the cellular conditions as modified by the at least one cellular condition change caused by the disease
Claims 7 and 17 recite: the method of claim 1, further comprising: storing cellular conditions changes caused by a plurality of diseases
Claims 9 and 19 recite: storing an annotation that the unannotated gene may be related to an affected physiological function of a physiological anomaly or a medical test anomaly
There are no limitations that indicate that the claimed hardware computer processor or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The limitations directed to storing medical data are insignificant extra-solution activities, which are incidental to the method and system and are merely nominal or tangential additions to the claim.
The additional elements that apply known computational fluid dynamics (CFD) techniques to identify the shape of a protein caused by a disease and selecting a drug based on a protein shape merely invoke generic computational tools to perform the abstract analytical process and do not reflect an improvement to CFD itself. The claims merely invoke only the idea of a solution or outcome (predicting drug-target interactions by modeling protein shape and/or cellular conditions) and a generic technological tool at a high level of abstraction, without reciting a specific manner of achieving the results that meaningfully limits the judicial exception. Therefore, the recitation is equivalent to the words “apply it” and there is no concrete technological improvement (see MPEP 2106.05(f)).
Therefore, these additional elements do not integrate the judicial exception into a practical application. As such, claims 1-4, 7-14 and 17-20 are directed to an abstract idea (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). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment or well-understood, routine and conventional activities.
As discussed above, there are no additional limitations to indicate that the claimed hardware computer processor requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Limitations that merely add an insignificant extra-solution activity, do not amount to an inventive concept, particularly when the activities are well-understood and conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). Storing medical data and retrieve information in memory are well-understood, routine, conventional computer functions as recognized by Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
In addition, modeling with CFD and adapting CFD modeling approaches in biomolecular contexts was well-understood, routine and conventional before the effective filing date of the instant application as indicated in review articles (Yoo et al., 2020, Current Opinion in Structural biology, Vol. 64, p. 88-96; Ayyaswamy et al., 2013, Journal of Nanotechnology in Engineering and Medicine, Vol. 4 p. 010101-1-010101-15).
Yoo et al. reviews the application of molecular dynamics simulations in protein–nucleic acid interactions and indicates they have been studied extensively using the all-atom molecular dynamics approach (Yoo, p. 88, para. 4). For example, they review several recent all-atom MD studies that have investigated the source of CRISPR-Cas9’s unintended interaction with off-target DNA sequences (Yoo, p. 92, para. 5).
Ayyaswyamy et al. reviews several multiscale modeling approaches rooted in computational fluid dynamics and nonequilibrium statistical mechanics to accurately resolve fluid, thermal, as well as adhesive interactions governing nanocarrier motion and their binding to endothelial cells lining the vasculature (Ayyashyamy, abstract). For example, they describe development and validation of a mesoscale model of carriers functionalized with antibodies, which bind to antigens on an extracellular surface amid fluid flow and glycocalyx interactions to calculate the absolute binding free energy (Ayyaswyamy, p. 010101-2, para.5). Many protein drug interactions are antibody-antigen interactions.
The additional elements generically invoke known CFD technique as an analytical tool without reciting a specific technological improvement or unconventional implementation. The claims merely apply existing, conventional, computational modeling techniques to a new domain (protein-drug interactions), which does not by itself supply an inventive concept. There are no meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-4, 7-14, and 17-20 are not patent eligible.
Response to Arguments
Applicant’s arguments filed 23 April 2026 have been fully considered but they are not persuasive.
10. Regarding arguments A1 and C1 – assertion that there are two improvements to technology (Step 2A prong 2):
Argument A1 and C1: The applicant asserts that dual pathway convergence analysis (combining genetic and physiological data) is an improvement to technology (drug discovery) (p. 24, para. 3, p. 29, para. 2).
Response to Argument A1 and C1: This argument is not persuasive because the identification steps (i.e. identifying physiological functions, biological functions, genes along a pathway and a disease driver), which are part of the judicial exception, are providing the improvement to technology. MPEP 2106.05(a) sets forth that the judicial exception alone cannot provide the improvement. The applicant did not provide an argument or explanation as to how the improvement is provided by the additional elements, either alone or in combination with the judicial exception.
Argument A2 and C1: The applicant asserts that computational fluid dynamics (CFD)-based protein shape modeling in a disease modifier cellular environment is an improvement to technology (drug discovery) (p. 25 and p. 29, para. 2).
Response to Argument A2 and C1: The argument not persuasive because the claimed limitations are not commensurate in scope with the asserted improvement to technology and thus do not reflect the disclosed improvement in technology. The asserted improvement to technology is “CFD-based protein shape modeling in a disease-modifier cellular environment” but the claimed invention does not perform protein shape modeling in the disease-modifier cellular environment, rather CFD modeling of protein shape modified by a disease (claimed in claim 1) is performed independent of CFD modeling the cellular conditions modified by the disease (claimed in claim 7). There is no indication in the claimed invention of a disease model comprising of both changes to protein shape and changes to cellular conditions.
Additionally, The argument is not persuasive because the asserted improvement to technology is not in the CFD modeling itself, but in using the disease modifier cellular environment, which is provided by the ‘selecting of cellular condition changes caused by the disease’ and identifying by analyzing published medical research the cellular conditions changes caused by the diseases and changes to shapes of a plurality of proteins caused by the diseases (recited in claims 7 and 17 and 8 and 18). Therefore the judicial exception is providing the improvement to technology rather than the additional elements. MPEP 2106.05(a) sets forth that the judicial exception alone cannot provide the improvement. The applicant did not provide an argument or explanation as to how the improvement is provided by the additional elements, either alone or in combination with the judicial exception.
Regarding arguments B – assertion that there is not a judicial exception (step 2A prong 1):
Argument B1: The applicant asserts that the claimed process cannot practically be performed in the human mind.
Response to argument B1: This argument is not persuasive. The word practically implies feasibility. If there is no real-time constraint in the claimed method, then a step of evaluating, judgment or a calculation that a person can do can be considered a mental process. The limitations directed to analyzing published medical research using natural language processing (in claims 8 and 18), and limitations directed to searching a ‘genetic pathway database’ and a ‘genotype-phenotype catalogue’ (that have been added to claims 1 and 11 in amendment after initial examination) still fall into the mental process grouping of abstract ideas. The amendments do not change the designation as mental processes as the human mind is capable of searching publications for existing data and capable of searching lists of molecular interactions and annotations to find those involving the protein of interest. Humans are also capable of using these data to draw conclusions for example by drawing protein interaction maps by hand, which could be made up of a series of nodes and edges to represent the proteins and their interactions and coloring of the nodes based on known biological or physiological processes. Although a person would take longer than a computer to search the literature, a database or a catalogue and it would be less convenient, they can still be practically performed by a human and thus are still mental processes.
Regarding limitations directed to modeling with computational fluid dynamics (CFD) (which were claims 5-7 and 15-17 in the original claims, but are now in claims 1, 7, 11 and 17 in the amended claims), the examiner agrees that modeling with CFD is not a mental process; however, these limitations were categorized as additional elements rather than mental processes in the rejection.
Argument B2: The applicant asserts that the claims do not “set forth or describe” any mathematical calculation.
Response to argument B2: This argument is not persuasive. Limitations in claims 1-4 and 9, 11 and 19 describe mathematical calculations. A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A claim does not have to recite the word "calculating" or recite an equation in order to be considered a mathematical calculation; textual replacement for equations such as determining a ratio is still considered math. See MPEP 2106.04(a)(2)(B-C). Determining statistical significance (in claims 2-4) involves basic arithmetic and performing differential analysis (in claims 1, 9, 11 and 19) involves subtraction or division. The example 47 in the 2025 memo cited by the applicant is different from the instant claims because it is directed to a neural network claim that does not recite or imply any mathematical calculations in the claim, textual or otherwise.
Argument B3: The applicant asserts that the claims are not directed to a natural phenomenon
Response to argument B3: The applicants arguments are persuasive and the rejection has been dropped.
Regarding argument C2 – assertion that recitation of CFD is not generic (step 2A prong 2):
Argument C2: The applicant asserts that CFD is not an insignificant extra solution activity and that CFD was not recited in a generic or high-level manner.
Response to argument C2: The applicants argument that CFD is not an insignificant extra solution activity are persuasive; however, the applicants assertion that CFD is not recited at a generic or high-level manner is not persuasive. As indicated in the response to arguments A2 and C1, while the idea of a solution to a technical problem is recited (that modeling the correct cellular environment parameters specific to a disease state are needed to accurately predict protein shape and protein binding predictions using CFD), there is no claim of precisely what the solution is. An improvement to technology is not described. It is simply stated that parameter values would be obtained from the literature. Example improvements include: reciting a value of a parameter that would be used in CFD to improve predictions, a recited improved experimental method to identify disease-specific environmental conditions, a recited improved method for data mining to find the disease conditions, or recited methods to improve the identification of which parameters are important to modify in the disease state. There are no technical details of CFD described in the specification (i.e. there are no equations described, no specific CFD model indicated, no simulation parameters, boundary conditions, computational architecture or technological implication) and no improvements to CFD stated in the claims or described in the specification that would integrate the abstract idea into a practical application.
Regarding arguments D – assertion that there is an inventive concept (Step 2B)
Argument D: Step 2B: The applicant asserts that as an ordered combination, the claims recite “significantly more” than mere instructions to apply the judicial exception because neither Yoo et al nor Ayyaswamy et al., (prior art references cited) provide evidence of the ordered combination of specific claimed physiological and genetic pathway analysis AND CFD-process is conventional.
Response to Argument D: The argument is not persuasive. The inventive concept is furnished by the combination of additional elements beyond the judicial exception, thus the inventive concept comes from the combination of additional elements, not the combination of additional elements and judicial exceptions (see MPEP 2106.05(I)). The additional elements are directed to using CFD to model molecular interactions (including protein-antibody interactions (which are a type of protein-drug interactions) and the cited art (Yoo et al. and Ayyaswamy et al.) evidence that CFD in this context was well-understood routine and conventional at the time of the effective filing date of the invention.
The second argument, that the supreme court has recognized that claims that ‘improve a technological process’ transform the process into an ‘inventive application’ is a moot point. While this assertion is true, in this application, as described in the responses to arguments A-C, there is no clear improvement to technology.
Claim Rejections - 35 USC § 102
11. The rejection of claims 1-2, 9, 11, 12, 14, and 19 under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Narain et al. (US 2020/0185063 A1; previously cited), as evidenced by Narain et al. (US 2012/0258874 A1; previously cited) is withdrawn in view of the claim amendments filed 23 April 2026.
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.
12. The rejection of claims 5-6 and 15-16 under 35 U.S.C. 103 as being unpatentable over Narain et al. (US 2020/0185063 A1; previously cited), as evidenced by Narain et al. (US 2012/0258874 A1; previously cited), in view of Lexma (Melchionna et al. US 2019/0340318 A1; previously cited) is withdrawn in view of the cancellation of these claims in the claim amendments filed 23 April 2026.
13. Claims 1-4, 7-14 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Narain et al. (US 2020/0185063 A1; previously cited), as evidenced by Narain et al. (US 2012/0258874 A1; previously cited), in view of Jagannathan et al. (Hindawi Sleep Disorders, 2017, Vol. 2017, p. 1-8; newly cited) and further in view of Lexma (Melchionna et al. US 2019/0340318 A1; previously cited) and Omar et al. (PLOS one, 2018, Vol. 13, p. 1-12; newly cited). The italicized text corresponds to the instant claim limitations. This rejection is newly recited and necessitated by claim amendment.
Regarding claim 11 (lines 3-5), Narain et al. discloses a system comprising a processor and a non-transitory computer-readable medium storing instructions that when executed causes a processing device to implement any of the methods in the disclosure (Narain et al., para. [0033, 0044]; a non-transitory computer readable storage media that stores medical data that includes physiological data, medical history data, contextual information, and genetics data; a hardware computer processor).
Regarding Claims 1 and 11 (lines 3-4), Narain et al. discloses a method and system for identifying biomarkers for a clinical outcome related to administration of an agent (Narain et al. abstract). The method comprises storing patient data including clinical records data and molecular profile data (Narain et al., para. [ 0172]). Example data types disclosed include data from wearable mobile devices (physiological data), medical history data, and patient demographic data (contextual information) (Narain et al., para. [ 0169]); as well as molecular profile data including genomics and transcriptomics data (genetics data) (Narain et al., para. [0024]). Narain et al. further discloses a system comprising a database; a memory; and a processor in communication with the memory (Narain et al., para. [ 0033]). The database comprises storage devices for storing data (Narain et al., para. [ 0194]) including molecular profile data and clinical records data for a plurality of subjects (a method and system for personalized drug discovery comprising a non-transitory computer readable storage media that stores medical data including physiological data, medical history data, contextual information and genetics data).
Regarding claims 1 and 11 (lines 5-7), Narain et al. discloses that the method includes stored clinical outcome data from patients containing the state or status of a disease (Narain et al., para. [0025]). By the method, the disease (for example prostate cancer) can also be identified from molecular biomarker data (Narain et al., para. [0424]). Narain et al. further discloses that the method includes “slicing” (partitioning) patient data sets into two or more subgroups using one or more attributes based on the clinical record data to generate two or more selected data sets (Narain et al., para. [0033]). Attributes can include clinical benefit of a drug (Narain et al., para. [0128 0174 and 0478]), or clinical outcome to prior treatment (Narain et al., para. [0010]).
Narain et al. further discloses that the system includes a processor with a slicing (partitioning) module configured to select two or more subgroups of patient data using one or more criteria based on the clinical records data to generate two or more selected data sets (Narain et al., para. [0033]) (identifying from the stored medical data a group of patients having a disease, and repeatedly partitioning the group to select a subgroup of patients having a common attribute).
With respect to claims 1 and 11 (lines 8-14), Narain et al. discloses that the method includes using clinical records data to subgroup patients including one or more of pharmacokinetics data, medical history data, laboratory test data, and data from a mobile wearable device (Narain et al., para. [0034]). Narain et al. further discloses that patient medical test data measured by proteomic, metabolic and lipidomic analysis of blood or urine samples are compared between patient groups with clinical benefit versus no clinical benefit to a particular drug to identify differences between the groups (e.g. levels of Coenzyme Q10) (Narain et al., para. [0492]). Narain et al. also discloses that differentially expressed genes are measured between patients that have a particular clinical outcome versus those that do not (Narain et al., para. [0013]).
Narain et al. further discloses the system includes a processor with a clinical records module configured to process clinical records for each subject and merge them with other patient data, and a slicing (partitioning) module configured to select two or more subgroups of the merged data based on the clinical records data to generate two or more selected data sets (Narain et al., claim 67). The analysis module is configured to analyze one or more data sets (each selected from patient subsets) to identify one or more potential biomarkers for a clinical outcome related to administration of the agent (Narain et al., para. [0033]). Narain et al. discloses a processor that comprises and omics module configured to process molecular profile data for each subject in a plurality of subjects, the molecular profile data for each subject comprising genomics and transcriptomic data and a slicing module (Narain et al., claim 67 and para. [0033]). The system includes a database and storage devices for storing the data (Narain, para. [ 0194]) (for each selected subgroup: detecting and storing physiological anomalies or medical test anomalies that are more prevalent in the physiological data or the medical history data of the selected subgroup than in the physiological data or the medical history data of a control group; performing genetics differential analysis to identify genetic anomalies affecting one or more genes that are more prevalent in the genetics tata of the selected subgroup than in the genetics data of the control group).
Concerning Claims 1 and 11 (lines 15-18), Narain et al. discloses that the method includes identifying physiological and biological functions from the data. Narain et al. teaches that functional insights can be gained by integrating the physiological, medical test data and/or molecular profile data for each subgroup of patients to make causal relationship networks for determining differential causal relationships (Narain et al., para. [ 0011]; illustrated in Figs. 41 and 43). Narain et al. further discloses using Bayesian causal relationship networks and the Narain et al. Interrogative Biology Informatics Suite for understanding a wide variety of biological processes including disease pathophysiology (para. [ 0189]). Narain et al. further discloses that biological functions effected by significantly differentially expressed genes between patient subgroups can be identified using pathway membership information form KEGG, BioCarta, Reactome, and NCI (Narain et al., para. [0335]). Narain et al. further discloses the system includes a processor containing an omics module configured to process molecular profile data of patients (e.g. genomics and transcriptomic data), an integration module configured to integrate the molecular profile data with clinical records data (Narain et al., para. [0033]), and an analysis module configured to generate one or more causal relationship networks based on one or more of the selected data sets (Narain et al., para. [0035]) (identify physiological functions affected by physiological anomalies or medical test anomalies; identify biological functions affected by the genes having the genetic anomalies).
Regarding Claims 1 and 11 amended limitations (lines 19-28), Narain et al. discloses their method includes generation of causal relationship networks comprised of collected patient data (genomic, metabolomic, transcriptomic and/or proteomics data and clinical records data) from the patient subgroups and analysis of the networks to identify outcome drivers (disease drivers) corresponding to nodes (nodal points) connected to the clinical outcome with a degree of connection equal to or less than n where n is 6, 5, 4, 3, 2 or 1 (Narain et al., para. [0009, 0024, 0036 0172]). Narain et al. further discloses using regression analysis to find differentially expressed features and using pathway analysis of the features using information from KEGG, BioCarta, REactome and NCI)(para. [0331-0335]). Narian et al. further discloses that physiological functions (such as tumor size) can be used as selection criteria and that the outcome variable (physiological functions) are nodes in the network connected to molecular variables (genes or proteins) by edges (para. 0130, 0216). (Identifying potential nodal points by using a genetic pathway database to identify genes along a genetic pathway that includes the genes having the genetic anomalies; Identifying a disease driver from among the earliest genes along the identified genetic pathway by using at least one gene-phenotype catalog to identify a gene that is commonly associated with both the affected physiological functions and the affected biological functions).
Regarding claims 1 and 11 amended limitations (lines 19-28), Narin et al. is silent to specifically using a genetic pathway database to identify the potential nodal points and specifically using at least one gene-phenotype catalog to identify a gene that is commonly associated with both the affected physiological functions and the affected biological functions). However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Jagannathan et al.
Regarding claims 1 and 11 amended limitations (lines 19-28), Jagannathan et al. teaches a method of identifying possible drug targets associated with obstructive sleep apnea based on phenotype data and biological function data. Jagannathan et al. discloses first selecting candidate genes based on association of genes/biomarkers with the physiological function ‘sleep apnea’ by selecting genes from the literature (based on key terms including “apnea, obstructive sleep” and “apneas, obstructive sleep”) and based on gene annotations in DisGeNET (a gene-phenotype catalog that integrates information form OMIM and 3 other repositories. (p. 2, col. 1, para. 4 – col. 2, para. 1). Jagannathan et al. further discloses prioritizing gene candidates based on gene set enrichment analysis (using ToppGene suite) to enrich for genes associated with one of 10 biological functions related to obstructive sleep apnea including peptide hormone binding, G-protein coupled receptor, cytokine receptor binding, cytokine activity and others (p. 2, col. 2, para. 2-3; Fig. 1). Jagannathan et al. further disclose performing genetic network analysis using Ingenuity Pathway Analysis to prioritize the candidates. Four selected candidates were serotonin receptors, HTR2A, HTR1B, HTR2C and SLC6A4, found upstream of other genes in the network (i.e. earlier along the pathway)(p. 2, col. 2, para. 2-3; Fig. 3). Jagannathan et al. further shows that the candidate drug targets were further significantly associated with other comorbidities (physiological functions) associated with obstructive sleep apnea including eating disorders, major depression, blood pressure, dyslipidemia, and glucose metabolism disorder (Fig. 2; p. 6, col. 1, para. 2) (Identifying potential nodal points by using a genetic pathway database to identify genes along a genetic pathway that includes the genes having the genetic anomalies; Identifying a disease driver from among the earliest genes along the identified genetic pathway by using at least one gene-phenotype catalog to identify a gene that is commonly associated with both the affected physiological functions and the affected biological functions).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Jagannathan et al. taught that our current knowledge of biomarkers for obstructive sleep apnea is limited by data collection techniques, disease complexity, and potential confounding factors and that their systems biology approach of combining analysis of multidimensional data with phenotypic data can identify novel biochemical markers and pathways of obstructive sleep apnea (p. 2, col. 1, para. 2-3). Therefore, one of ordinary skill in the art would have been motivated to utilize the methods of utilizing gene-phenotypic catalog and genetic pathway databases taught by Jagannathan et al. in the method of disease biomarker selection taught by Narian et al., in order to identify possible targets for obstructive sleep apnea. Furthermore, one of ordinary skill in the art would predict that the methods taught by Jagannathan et al. could be readily added to the system of Narian et al. with a reasonable expectation of success because they both pertain to systems-level analysis of multiple large-scale datasets for disease biomarker/target discovery. The invention is therefore prima facie obvious.
Regarding Claims 1 and 11 limitations on lines 29-37, Narain et al. discloses that the entire contents of Narain et al. 2012 is incorporated by reference into the disclosure (para. [0267]). Through that incorporation, Narain et al. teaches that in analysis of causal relationship networks comprising patient data, some of the causal relationships (and the genes associated therewith) may be responsible for causing a disease condition and thus are a potential target for therapeutic intervention (Narain et al. 2012, para. [0062]). Narain et al. further teaches that drug targets for various disease states are developed based on marker nucleic acids identified in the system as well as proteins that are encoded by the markers (“marker proteins,”) (Narain 2012, para. [0455-0456]). Narain further teaches producing therapeutically useful humanized IgG, IgA and IgE antibodies (Narain 2012, para. [0628]), and that these antibodies may be used as therapeutic agents in treating cancers (Narain 2012, para. [0633]). Narian et al. and Jagannathan et al. are silent to identifying a drug to treat the disease in individuals having the attribute by; using computational fluid dynamics to model a shape of a protein made by the disease driver; storing changes to shapes of a plurality of proteins caused by a plurality of diseases; identifying at least one change to the shape of the protein caused by the disease; using the computation fluid dynamics to model a modified shape of the protein caused by the disease. However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Lexma.
Regarding claims 1 and 11 limitations on lines 29-37, Lexma teaches a method and system for drug discovery using computational fluid dynamics (CFD) for modeling biochemical transitions activated by cellular conditions (surrounding flows), protein shape (including unfolding, refolding, allostery, cleavage, and substrate binding), for the virtual assessment of drug performance (Lexma, para. [0008]). Lexma further teaches storing multiple proteins in multiple states and modelling the disease state of a protein. Specifically, Lexma teaches modeling proteins (Lexma, para. [0042]) and storing data from multiple scientific data sources and storing a plurality of scientifically accurate poses for each protein (Lexma, para. [0011]). Lexma further teaches modeling a protein in a disease state using CFD. For example, Lexma teaches using CFD to model protein aggregation (amyloid fibril formation), which underlies neurodegeneration in Alzheimer’s disease to enable drug design to prevent this aggregation (Lexma, para. [0079-0081]). Lexma further teaches the processors and computer readable storage medium configured to execute the method (Lexma, para. [0007, 0012, 0099]) (identifying a drug to treat the disease in individuals having the attribute by; using computational fluid dynamics to model a shape of a protein made by the disease driver; storing changes to shapes of a plurality of proteins caused by a plurality of diseases; identifying at least one change to the shape of the protein caused by the disease; using the computation fluid dynamics to model a modified shape of the protein caused by the disease).
It would have been obvious to a person of ordinary skill in the art before the effective filing date modify the method and system of Narain et al. and Jagannathan et al. with CFD modeling taught by Lexma because using CFD modeling would allow for simulation of biochemical transitions activated by surrounding flows, including unfolding, refolding, allostery, cleavage, and substrate binding, which are multiscale and multiphysics problems that other modeling approaches fail to address (Lexma, para. [0035]). Application of CFD would improve drug discovery by enabling virtual assessment of drug performance before engaging experimental studies, which can be costly and time consuming (Lexma, para. [0008]). This application of CFD to the method of patient stratification and identification of biomarkers and drug targets taught by Narin et al. and Jagannathan et al. has a reasonable expectation of success because both methods include identifying drug target candidates from patient data in disease states. The invention is therefore prima facie obvious.
Regarding claims 1 and 11 amended limitations on lines 38 and 39, Narain et al. Jagannathan et al. and Lexma are silent to selecting a drug from a drug shape database in the limitation: selecting a drug from a drug shape database having a shape that binds to the modified shape of the protein made by the disease driver. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Omar et al.
Omar et al. teaches a method of virtual screening to find drug activators for G245S mutant of p53 as a potential therapeutic for cancer. Omar et al. discloses that by selecting compounds from the ZINC15 database using DOCKTITE protocol in the Molecular Operating Environment software to only include drug-like molecules with moderate to standard reactivity, followed by performing covalent docking at C124 of G245S-mp53 to identify potential activators of the mutant protein. Their method identified thiosemicarbazones and halo-carbonyls as the best potential G245S-mp53 activators (p. 3, para. 4 – p. 5, para. 3; p. 7, para. 3 - p. 10, para. 2; selecting a drug from a drug shape database having a shape that binds to the modified shape of the protein made by the disease driver).
It would have been obvious to a person of ordinary skill in the art before the effective filing date modify the method and system of drug target identification taught by Narain et al. Jagannathan et al. and Lexma with the method of screening the drug shape database taught by Omar et al. because restoring wild type activity to mp53 is a promising strategy to treat cancer and this method would enable finding potential G245S-mp53 activators (p. 7, para. 2). This application of screening the drug shape database to the method of patient stratification and identification of biomarkers and drug targets taught by Narin et al. Jagannathan et al. and Lexma has a reasonable expectation of success because both methods include identifying drug target candidates from patient data in disease states.
Regarding claims 2 and 12, Narain et al. discloses the method includes identifying one or more variables with significant differential expression between a dataset from patients that exhibit a clinical outcome versus patients that did not. Narain et al. further teaches that significance of differential expression of variables between patient subsets can be measured using t-test or limma or regression analysis (Narain et al., para. [0013]). Narain et al. further discloses the system contains an analysis module that is a hardware-implemented module configured to conduct statistical analysis for identification of differentially expressed variables (Narain et al., para. [0203]) and that statistical significance can be calculated in various ways including calculating p-values or false discovery rates (for differential expression analysis) (Narain et al., para. [0338]) (determining whether the selected subgroup has a statistically significant disease signature compared to the control group).
Regarding claims 3 and 13, Narain et al. Jagannathan et al., Lexma and Omar et al. do not explicitly teach selecting a subgroup of patients based on absence of a statistically significant disease signature. Narain et al. teaches that in certain embodiments, the AI-based informatics platform receives all data input from the patient data and molecular data without applying statistical cut-off points (Narain et al. 2012, para. [0023]). In addition, Narain et al. teaches selecting and analyzing subgroups of patients that do not have clinical outcomes (Narain et al., claims 13, 14, and 18). Narain et al. further teaches the method comprises selecting multiple patient subgroups based on combinations of attributes including responsiveness to treatment (Narain et al., para. [0022, 0220]) and serious adverse events to treatment (Narain et al., para. [0021, 0221]). It is implicit that in some embodiments this would include selecting from responsive patients those with no significant differential expression for adverse event markers. Narain et al. also teaches examples of using biomarkers to select patients for clinical trials who were refractory to treatment (e.g. that exhibited no clinical benefit) and those that did not experience the adverse event (para. [0174]). For both examples, it is implicit that in some embodiments, patient subgroups would be selected based on absence of significant case versus control differential expression for the marker(s) using the statistical methods disclosed (e.g. a two-sample t-test or limma methodology, or regression analysis (Narain et al., para. [0182])) (wherein a group of individuals is partitioned to select a different subgroup having a different attribute in response to determination that the subgroup does not have a statistically significant disease signature compared to the control group).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to select a subgroup of patients after determination that the subgroup does not have a statistically significant disease signature compared to the control group to improve disease diagnosis and intervention and obtain insight into causality of drug toxicity (Narain et al. 2012, para. [0010]), including deciding to not treat a patient (Narain et al. para. [0018]). This would also have been done to apply to clinical trials for cancer drugs to create partitioned data sets of patients responsive to an agent (e.g., that exhibited an overall clinical benefit) and patients who were refractory (e.g., that exhibited no clinical benefit) (Narain et al. para. [0174]). There is reasonable expectation of success in selecting subgroups based on absence of statistically significant differential expression because establishing a threshold of statistical significance is a routine optimization step in analysis of multivariate disease signatures, and various different significance thresholds are routinely used in the art. The invention is therefore prima facie obvious.
Regarding claim 4, Narain et al. does not explicitly teach selecting the disease driver or the treatment based on lack of a statistically significant disease signature in case versus control groups. However, Narain et al. discloses the method includes developing companion diagnostic plasma biomarkers to select subgroups that are predicted to not to experience severe adverse events to a treatment or drug (Narain et al. para. [0221]); it is implicit that in some embodiments, this would involve selecting a subgroup that does not have statistically significant differential expression of biomarkers of adverse events in case versus control comparisons. Narain et al. also teaches a specific example of using levels of a nodal point, PDIA3, to select a cancer treatment plan with Coenzyme Q10. Narain et al. teaches that high levels of serum PDIA3 expression correlates with clinical responsiveness to treatment of cancer with Coenzyme Q10 and that this biomarker has utility in customizing treatment plans to minimize the exposure of the patients to unnecessary treatments which may not provide any benefits and could carry serious risks due to toxic side effects (Narain et al. para. [ 0357]). It is implicit that selecting this unresponsive population would involve selecting a subgroup with lack of a significant disease signature using one of the statistical analysis methods disclosed (e.g. a two-sample t-test or limma methodology, or regression analysis (Narain et al. para. [0182])). (wherein the potential nodal points, the disease driver, or the drug to treat the disease is identified in response to a determination that the selected subgroup does not have a statistically significant disease signature compared to the control group).
Based on these examples, selecting a disease driver or treatment based on absence of statistically significant disease signature is an inherent result that is implicit in the Narain et al. disclosure. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to select a subgroup of patients after determining that the subgroup does not have a statistically significant disease signature compared to the control group to customize treatment plans and specifically to minimize the exposure of the patients to unnecessary treatments which may not provide any benefits and could carry serious risks due to toxic side effects (Narain et al. para. [0357]). There is reasonable expectation of success in selecting subgroups based on absence of statistically significant differential expression because establishing a threshold of statistical significance is a routine optimization step in analysis of multivariate disease signatures, and various different significance thresholds are routinely used in the art. The invention is therefore prima facie obvious.
With respect to claim 14, Narain et al. discloses using regression analysis to identify statistically significant differentially expressed variables between a disease and control group for prediction of responsivity and efficacy of a drug to treat the disease (para. [0522]). Narain et al. further teaches predicting severe adverse effects prior to treatment by determining statistically significant differentially expressed variables (Fig. 35). Narain et al. further discloses a non-transitory computer-readable medium storing instructions that when executed causes a processing device to implement any of the methods disclosed in the patent application (para. [0044]), including identifying a drug to treat disease based on identification of a statistically significant disease signature. (a processor configured to identify the potential nodal points, the disease drive, or the drug to treat the disease in individuals having the attribute in response to a determination that the selected subgroup has a statistically significant disease signature compared to the control group).
Regarding claims 7 and 17, Lexma teaches storing multiple cellular conditions and modeling a cellular condition of a disease. Specifically, the event/parameter database of Lexma includes storing plurality of environments (Lexma, para. [0040]). This database allows the users to simulate and test the interactions of the structures with certain environments, including certain surrounding fluids (e.g. temperature, salinity, pH, osmolality, and/or viscosity) (Lexma, para. [0040]). Lexma further teaches modeling the cellular condition of a disease with CFD. The CFD modeling applied to Alzheimer’s disease described in the above section includes modeling parameters of cellular environments affecting diffusion of proteins and trafficking in cellular crowding (Lexma, para. [0079-0081]). Molding the cellular environment (solution/fluid modeling) with molecular dynamics is necessary to accurately model the protein aggregation (fibril formation) (Lexma, para. [0082]). Lexma further teaches the processors and computer readable storage medium configured to execute the method (Lexma, para. [0007, 0012, 0099]). (methods and systems of claims 1 and 11, further comprising storing cellular conditions changes caused by a plurality of diseases; selecting at last one cellular condition change caused by the disease; and using computational fluid dynamics to model the cellular conditions as modified by the at least one cellular condition change caused by the disease.
Regarding claims 8 and 18, Narain et al. teaches analyzing medical research data using natural language processing. Narain et al. teaches an AI-based data mining platform (Narain incorporated patent [0781]), which includes using pathway data from medical research in publicly available databases and using a type of NLP called “word clouds” to visualize the frequency of pathway members identified by proteomics regression analysis. (Narain et al., para. [0305]).
Narain et al. does not teach modeling with CFD described in dependent claims 5-8 and 15-18. Lexma teaches modeling with CFD as described in the above sections 32-34. Lexma further teaches a method and system of CFD modeling where conditions and protein states are acquired from medical research data (Lexma, para. [0044]). Lexma teaches that the digital models are built based on scientific information, such as publicly-available data repositories including experimentally-determined data, including structure, dynamics for a particle or fluid. For example, a digital model may include raw structural data, such as a set of coordinates from a protein databank (PDB) file, wherein a simulation engine is configured to utilize such raw structural data in a modeling, animation or simulation environment. The Protein Data Bank archives close to 100,000 PDB files of molecular structures (Lexma, para. [0044]). Lexma further teaches the processors and computer readable storage medium configured to execute the method (Lexma, para. [0007, 0012, 0099]) (the method and system of claims 7 and 17, wherein the cellular conditions and protein shapes of the disease states are identified by analyzing medical research data using natural language processing (NLP)).
Pertaining to claims 9 and 19, Narain et al. discloses the method includes performing differential analysis to identify genes with novel roles in drug induced cardiotoxicity (Narain et al. 2012, para. [0469-0490]). For example, TIMP1 (TIMP metalloprotease inhibitor 1), is involved with remodeling of the extra cellular matrix in association with MMPs (matrix metalloproteinases); in the disclosure, the method identifies that TIMP1 expression is correlated with fibrosis of the heart, and that TIMP1 expression is indued by hypoxia of vascular endothelial cells. Using these data, TIMP1 was identified was determined to be a novel predictor of drug induced cardiac toxicity (a medical test anomaly) (Narain et al. 2012, para. [0484]). Narain et al. discloses that the system includes a non-transitory computer-readable medium storing instructions that when executed causes a processing device to implement any of the method in the disclosure (Narain et al., para. [0044]) and a database comprising storage devices for storing data (Narain et al., para. [ 0194]) (performing genetics differential analysis to identify a genetic anomaly affecting and unannotated gene and storage of an annotation that the gene may be related to an effected physiological function or a medical test anomaly).
Pertaining to claims 10 and 20, Narain et al. does not explicitly teach identifying a biological function affected by a gene in another animal that is correlated with the unannotated gene. Narain et al. teaches that the disclosed method and system can be applied to humans or animals. Narain et al. teaches identifying genes and mechanisms of drug-cardiotoxicity and selecting drugs to prevent cardiotoxicity in a subgroup of subjects, which can be human or non-human animals (Narin 2012, para. [0480-0481]). Narain et al. further teaches that the method and system can be applied to virtually any organism, including humans or animals (Narain et al., para. [0382]) and that disease modeling steps and mechanistic characterization of the identified drugs can be performed in either animal or human cells (Narain et al. 2012, para. [0311], [0716]). Narain et al. further teaches for disease drivers, identifying amino acid residues that are conserved among the homologs of various species (e.g., murine and human) in the analysis process (Narain et al. 2012, para. [0588]).
Narain further teaches that the method and system is based on interpreting correlations in gene expression and other data from the subjects and that the correlations are used to annotate gene functions: Narain teaches that Bayesian networks are cause-and-effect graphs that best describe the underlying correlation structure in the input data (Narain, para. [0212]. Narain further teaches correlations between parent and child nodes in the networks can be measured as Pearson correlation coefficients. (Narain, para. [0215]). Narain further teaches that the structures of networks (based these on correlations) are used to annotate functions for genes. For example, Narain teaches that “hubs” of molecular activity are potential intervention targets for external control of the hub’s biological process (e.g. using the molecules in the hub as a potential therapeutic target) (Narain, para. [0133]). Narain further teaches that biological functions effected by significantly differentially expressed genes between subject subgroups can be identified using pathway membership information from KEGG, BioCarta, Reactome, and NCI gene sets (Narain, para. [0335]) (for example using gene set enrichment analysis) (identifying a biological function affected by a gene in another animal that is correlated with the unannotated gene).
Based on these teachings by Narain et al., the identification of a biological function of a gene in another animal that is correlated with an unannotated gene is obvious because it is implicit in the method. Doing so would involve applying the method to an animal as disclosed including analysis of correlation between genes based on expression values or other attributes, and functional annotation of genes based on these correlations as disclosed. Therefore, it would have been obvious to a person with ordinary skill in the art before the effective filing to use the method and system of Narain et al. to identify a function effected by a gene in another animal that is correlated with an unannotated gene. The motivation for doing this is to develop personalized treatments for animals and humans; for example, to alleviate, reduce or prevent drug-induced cardiotoxicity in a subject in need thereof, comprising administering to a subject (e.g., a mammal, a human, or a non-human animal) an agent identified by the method, thereby reducing or preventing drug-induced cardiotoxicity in the subject (Narain et al., para. [0480]). There is reasonable expectation of success as the method for doing so is implicit in the Narain et al. disclosure and predicting biological functions of unannotated genes based on molecular network structure was routine in the art before the effective filing date. The invention is therefore prima facie obvious.
Response to Arguments
Applicant’s arguments filed 23 April 2026 have been fully considered but they are not persuasive.
14. Argument VII-A and VII-C: The applicant asserts that Narain (including Narain 2012) does not teach or suggest the purposeful search of the physiological and genetic pathways of the selected subset of patients (guided by the affected physiological and biological functions of that subset of patients) recited in the claims.
Response to argument VII-A and VII-C:
Regarding the argument that the claims of the Narian patent application do not teach the invention, this argument is not convincing. It is the disclosure of Narian as a whole, not only the claims that were cited as prior art.
Regarding the argument that Narain (prior art) does not purposely search the existing physiological and genetic pathways (dual pathways) of the affected patient, this argument is not persuasive for two reasons:
1) the instant application does not claim a search of existing physiological and genetic pathways; rather it only claims ‘identifying physiological functions’ and identifying ‘biological functions’ (without mention a ‘search’ or ‘pathways’) (claims 1 and 11 lines 15-18)
2) As indicated in the initial office action, Narian discloses identifying physiological functions and biological functions caused by the anomalies. Narain teaches that functional insights can be gained by integrating the physiological, medical test data and/or molecular profile data for each subgroup of patients to make causal relationship networks for determining differential causal relationships (Narain, para. [ 0011]; illustrated in Figs. 41 and 43). Narain further discloses using Bayesian causal relationship networks and the Narain Interrogative Biology Informatics Suite for understanding a wide variety of biological processes including disease pathophysiology (para. [ 0189]). Narain further discloses that biological functions effected by significantly differentially expressed genes between patient subgroups can be identified using pathway membership information form KEGG, BioCarta, Reactome, and NCI (Narain, para. [0335]).
Regarding the assertion that the process is fundamentally different from the claimed processing steps in a number of respects (raised in arguments VII-A and VII-C, the four points are addressed below:
The applicant asserts that Narain’s ordered steps are different than the instant claims because ‘they start with a known drug that has already been administered and works backward to identify biomarkers that explain disparate patient response to the drug that has already been selected and administered’. This argument is not persuasive because using broadest reasonable interpretation, the claims of the instant application include such analyses that begin with patients treated with a drug. There is no limitation in the instant application that indicates that the ‘individuals’ cannot be patients that have previously been administered a drug. Furthermore, there is also no limitation that indicates that the ‘common attribute’ used to subgroup the group of individuals having a disease (claims 1 and 11 lines 6-7 of the instant application) cannot be the previous administration of a drug. Also, Narain et al. teaches that attributes can include clinical benefit of a drug (Narain et al., para. [0128 0174 and 0478]), or clinical outcome to prior treatment (Narain et al., para. [0010]). Finally, as discussed in the 35 U.S.C. 103 rejection above in the section starting with ‘Claims 1 and 11 limitations on lines 29-37’, Narain et al. discloses that the entire contents of Narain et al. 2012 is incorporated by reference into the disclosure, which includes using the methods to identify drug targets.
The applicant asserts that Narain’s system does not separately identify "physiological functions affected by the physiological anomalies" (presumably without including patient genetic data). This argument is not persuasive because there is no indication in the claim of the instant application that the identification of physiological and biological functions need to be separately identified in a separate analysis. Secondly, the disclosure of Narain et al. includes scenarios where the physiological functions affected by the physiological anomalies are identified separately (i.e. without using genetic data). As indicated in the previous office action, Narain teaches that functional insights can be gained by integrating the physiological, medical test data and/or molecular profile data for each subgroup of patients to make causal relationship networks for determining differential causal relationships (Narain, para. [ 0011]; illustrated in Figs. 41 and 43). Narain further discloses using Bayesian causal relationship networks and the Narain Interrogative Biology Informatics Suite for understanding a wide variety of biological processes including disease pathophysiology (para. [0189]). This disclosure is inclusive of leaving out the molecular profile data (i.e. patient genetic data) by use of the term “and/or” and it is inclusive of identifying pathophysiology as an output. Thirdly, as evidenced by (Su et al., 2013, BioData Mining Vol. 6, p. 1-12), Bayesian causal relationship networks are directed acyclic graphs, wherein nodes are represented by random variables and directed edges represent stochastic dependencies between variables. The graphs include sets of conditional probability distributions between different features characterizing the stochastic dependencies represented by edges (p. 2, para. 3-5). Therefore, they can identify for example, a conditional dependence of tumor size with a patient’s cancer stage independently of a genetic feature (which may or may not have a separate relationship with a patient’s cancer stage), and thus they can identify dependent relationships of physiological functions with physiological anomalies or medical test anomalies (that are perhaps not correlated with the genetic data) even if genetic data are included in the analysis.
Regarding a newly added limitation to the amended claims (in claims 1 and 11, lines 19-28), the applicant asserts that Narain et al. does not identify potential nodal points by using a ‘genetic pathway database’ to trace a genetic pathway of the affected patients. The examiner agrees with this assertion, but as the limitations were added in the amendment made after the first office action, a new prior art reference has been added that teaches this limitation. Please see the teachings of Jagannathan et al. in 35 U.S.C 103 section directed to ‘claims 1 and 11 limitations on lines 19-28’.
Regarding the newly added limitation to the amended claims (in claims 1 and 11, lines 19-28), the applicant asserts that Narain et al. does not use a gene-phenotype catalogue (such as OMIM) to perform a purposeful search for a gene commonly associated with both physiological and biological dysfunctions. The examiner agrees with this assertion, but as limitation was added in the amendment made after the first office action, a new prior art reference has been added that discloses this limitation. Please see the teachings of Jagannathan et al. in 35 U.S.C 103 section directed to ‘claims 1 and 11 limitations on lines 19-28’.
15. Argument VII-B and VII-D: The applicant asserts that Narain (including Narain 2012) does not teach or suggest the computational drug selection based on protein binding recited in the claims.
Response to argument VII-B and VII-D:
The examiner agrees with the assertion that Narain et al. does not teach limitations directed to computational fluid dynamics that have been moved from claims 5-6 to claims 1-11 lines 29-37, but as indicated in the first office action, these limitations were taught by lexma (please see 35 U.S.C 103 section directed to claims 1 and 11 lines 29-37). The examiner also agrees with the assertion that Narain et al. does not teach limitations directed to “selecting a drug from a drug shape database having a shape that binds to the modified shape of the protein made by the disease driver” (claims 1 and 11, lines 38-39). However, as this limitation was added in the amendment made after the first office action, a new prior art reference (Omar et al.) has been added that teaches this limitation. Please see the 35 U.S.C 103 section directed to Claims 1 and 11 lines 38-39. Regarding the assertion that Narian’s “markers of a disease state” are not equivalent to “genes responsible for causing a disease condition”, the applicants argument is not persuasive. The paragraph cited by the applicant (Narian et al. 2012 para. [0062]) is evidence for the markers of a disease state being potential targets for therapeutics. The paragraph states: “With respect to any given biological system for which the subject interrogative biological assessment is applied, some (or maybe all) of the causal relationships (and the genes associated therewith) may be “determinative” with respect to the specific biological problem at issue, e.g., either responsible for causing a disease condition (a potential target for therapeutic intervention) or is a biomarker for the disease condition (a potential diagnostic or prognostic factor).”
Conclusion
16. 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.
E-mail Communications Authorization
17. Per updated USPTO Internet usage policies, Applicant and/or applicant's representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300): "Recognizing that Internet communications are not secure, / hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. / understand that a copy of these communications will be made of record in the application file."
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273- 8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Inquiries
18. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNIFER SMITH whose telephone number is (571)272-7801. The examiner can normally be reached Monday-Friday 7:00 AM - 3:00 PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise, can be reached (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.J.S./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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