DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
CLAIM STATUS
Claims 1-20 are rejected. Claims 9 and 19 are objected to.
Priority
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 (1-20) because the provisional applications do not recite limitations in independent claims 1 and 11 (lines 6-26 and 5-26, respectively). Domestic benefit of claims 2-10 and 12-20 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 all claims will be considered to be the filing date of the instant application, 06/10/2022.
Information Disclosure Statement
The references in the information disclosure statements (IDSs) filed 09/23/2022, 01/17/2023, 08/02/2024, 03/17/25, 05/30/2025, 07/01/2025, and 08/15/2025 have been considered.
Drawings
Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Figures 1, 2(A-J), 3A, 3E, and 4A submitted on 08/19/2022 are in color.
The drawings are further objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters "310" and “516" have both been used to designate biofluid health inferences; reference characters “230” and “420” have both been used to designate communications module; and reference characters “793” and “383” have both been used to designate medical history data. In addition, reference character “300” has been used to designate both AI-enabled Health Ecosystem and intelligence-enabled health ecosystem; reference character “310” has been used to designate both biofluid health inferences and biofluid threshold; reference character “350” has been used to designate both physiological model and physiological threshold; reference character “383” has been used to designate both medical history data and medical test data; reference character “420” has been used to designate both communications module and configurator module; reference character “460” has been used to designate serializer, serializer module and subscribers; and part “480” has been used to designate local storage and subscribers.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 9 and 19 are objected to because of the following informalities: They contain grammatical errors in the limitations as they refer to “an unannotated genes”. Appropriate correction is required.
Claim Interpretation
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 par. [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
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 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. Claims 1 and 11 are rejected for the reasons listed below and claims 2-10 and 12-20 are included by virtue of dependence on claims 1 and 11.
Claims 1 and 11 recite the limitation "ranking the potential nodal points" in line 19. There is insufficient antecedent basis for this limitation in the claims because “the potential nodal points” was not introduced previously.
Claims 1 and 11 recite the limitation “identifying the disease driver” in line 23. There is insufficient antecedent basis for this limitation in the claims because “the disease driver” was not introduced previously.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and natural phenomenon without significantly more.
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:
The limitations for identifying individuals having a disease (claims 1 and 11, line 5), physiological functions (claims 1 and 11, line 15), biological function of a gene (claims 1 and 11, line 17, claims 10 and 20), a disease driver (claims 1 and 11, line 23), and a drug to treat the disease (claims 1 and 11, line 25, claims 5 and 15) and changes to a cell or protein caused by a disease (claims 6-7 and 16-17); and the limitations for partitioning samples into subgroups (claims 1 and 11, line 6 and claims 3 and 13), analyzing public data with natural language processing (claims 8 and 18) and ranking genes (Claims 1 and 11 line 19) 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 observation, evaluation, judgment, and opinion (MPEP 2106.04(a)(2), subsection III.
The limitations for performing case versus control differential enrichment analysis (claims 1 and 11 (lines 9 and 12), and claims 9 and 19), measuring 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 11 (line 4), 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.
Additionally, claims 1-20 recite limitations that are directed to identifying correlations between genetic states and a disease or a treatment for a disease. The courts have identified a correlation between genotype and patient risk or diagnosis as a natural phenomenon in Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, 887 F.3d 1117, 1135-36, 126 USPQ2d 1266, 1281 (Fed. Cir. 2018) and Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017). Since the claims recite a similar concept, the instant claims are also directed to natural phenomena, and thus are also subject to the “natural phenomenon” judicial exception (see MPEP 2106.04(b)). As such, claims 1-20 recite an abstract idea and a natural phenomenon (Step 2A, Prong 1: YES).
14. 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 or insignificant extra-solution activity.
Specifically, the claims recite the following additional elements:
Claims 1 (line 3), 11 (line 2), 6, 7, 9, 16, 17, 19 recite storing medical data.
Claims 11 (line 2) recites computer readable storage media to store medical data.
Claims 11 (line 4), 12-17, 19 and 20 recite using a hardware computer processor to analyze medical data.
Claims 5-7 and 15-17 recite modeling of protein-drug interactions using computational fluid dynamics.
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 to apply computational fluid dynamics and 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. Therefore, these additional elements do not integrate the judicial exception into a practical application. As such, claims 1-20 are directed to an abstract idea and a natural phenomenon (Step 2A, Prong 2: NO).
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.
The instant claims recite the following additional elements:
Claims 1 (line 3), 11 (line 2), 6, 7, 9, 16, 17, 19 recite storing medical data.
Claims 11 (line 2) recites computer readable storage media to store medical data.
Claims 11 (line 4), 12-17, 19 and 20 recite using a hardware computer processor to analyze medical data.
Claims 5-7 and 15-17 recite modeling of protein-drug interactions using computational fluid dynamics.
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 computational fluid dynamics was well-understood, routine and conventional before the effective filing date of the instant application as indicated in review articles (Yoo et al., 2020; Ayyaswamy et al., 2013;).
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, par. 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 a mesoscale model of noncarriers functionalized with antibodies which bind to antigens on the extracellular surface amid fluid flow and glycocalyx interactions has been developed, validated, and the absolute binding free energy has been computed (Ayyaswyamy, p. 010101-2, para.5).
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 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
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 are quotations of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 9, 11, 12, 14 and 19 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Narain et al. (US 2020/0185063 A1; published June 11, 2020; effectively filed on December 5, 2018), hereafter referred to as Narain, as evidenced by Narain et al. (US 2012/0258874 A1; published on October 11, 2012; effectively filed on March 02, 2012), hereafter referred to as Narain 2012.
Claims 1 and 11 are directed to 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. This limitation is anticipated by Narain.
Narain discloses a method and system for identifying biomarkers for a clinical outcome related to administration of an agent (Narain abstract). The method comprises storing patient data including clinical records data and molecular profile data (Narain, par. [ 0172]). Example data types disclosed include data from wearable mobile devices (physiological data), medical history data, and patient demographic data (contextual information) (Narain, par. [ 0169]); as well as molecular profile data including genomics and transcriptomics data (genetics data) (Narain, par. [0024]).
Narain further discloses a system comprising a database; a memory; and a processor in communication with the memory (Narain, par. [ 0033]). The database comprises storage devices for storing data (Narain, par. [ 0194]) including molecular profile data and clinical records data for a plurality of subjects (Narain, par. [0015]).
Claims 1 and 11 are further directed to a method and system for 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. These claim elements are anticipated by Narain.
Narain discloses that the method includes stored clinical outcome data from patients containing the state or status of a disease (Narain, par. [ 0025]). By the method, the disease (for example prostate cancer) can also be identified from molecular biomarker data (Narain, par. [0424]). Narain 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, par. [0033]). Attributes can include clinical benefit of a drug (Narain, par. [ 0128 0174 and 0478]), or clinical outcome to prior treatment (Narain, par. [0010]).
Narain 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, par. [ 0033]).
Claims 1 and 11 are further directed to a method and system for detecting and storing physiological or medical test anomalies that are enriched in a patient subgroup versus a control group. It also directed to genetics differential analysis to identify genetic anomalies affecting a patient subgroup versus a control group. These claim elements are anticipated by Narain.
Narain 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, par. [0034]). Narain 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, par. [0492]). Narain also discloses that differentially expressed genes are measured between patients that have a particular clinical outcome versus those that do not (Narain, par. [0013]).
Narain 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, 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, par. [0033]). Narain 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, claim 67 and par. [0033]). The system includes a database comprises storage devices for storing the data (Narain, par. [ 0194]).
Claims 1 and 11 are further directed to a method and system for analyzing patient subgroups to identify physiological functions effected by physiological or medical test anomalies and identify biological functions effected by the genes having the genetic anomalies. These claim elements are anticipated by Narain.
Narain discloses that the method includes identifying physiological and biological functions from the data. 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, par. [ 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 (par. [ 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, par. [0335]).
Narain 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, par. [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, par. [0035]).
Claims 1 and 11 are further directed to a method and system for ranking the potential nodal points from the genes that are most likely to have caused the largest number of genetic anomalies enriched in a selected subgroup of patients, and identifying the disease driver based on the effected functions. These claim elements are anticipated by Narain.
Narain discloses the method includes generation of causal relationship networks comprised of genomic or transcriptomic 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, par. [0009 and 0024]). This identification of drivers based on degree of connectivity to the outcome is a method of ranking the genes (nodal points). In other embodiments, nodes are ranked by statistical significance of differential expression (Narain, par. [0338]).
Narain further discloses the system comprises analysis module configured to analyze the causal relationship networks, which can include networks comprised only of genes (discussed in the above paragraph). The module can be configured to identify nodes corresponding to the one or more outcome drivers including identifying as outcome drivers, variables corresponding to nodes connected to the clinical outcome in one or more of the generated causal relationship networks by relationships having a degree of connection equal to or less than n (Narain, par. [ 0036]).
Claims 1 and 11 are further directed to a method and system for identifying a drug to treat the disease in a disease subgroup by identifying a drug that binds to a protein made by the disease driver. These claim elements are anticipated by Narain.
Narain discloses that the entire contents of Narain 2012 is incorporated by reference into the disclosure (par. [0267]). . Through that incorporation, Narain 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 2012, par. [0062]). Narain 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, par. [0455-0456]). Narain further teaches producing therapeutically useful humanized IgG, IgA and IgE antibodies (Narain 2012, par. [0628]), and that these antibodies may be used as therapeutic agents in treating cancers (Narain 2012, par. [0633]).
Narain discloses a system comprising 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, par. [ 0044]) including identifying a drug to treat disease.
Accordingly, claims 1 and 11 lack novelty and are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Narain because Narain disclosed every limitation of claims 1 and 11 in a US patent application and the application was publicly available and effectively filed before the effective filing date of the instant application.
Claims 2 and 12 are directed to a method and system of claims 1 and 11 and further determining whether the selected subgroup has a statistically significant disease signature compared to the control group. These claims are anticipated by Narain.
Narain discloses the method of Claim 1 (see section 21 above). Narain further 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 further teaches that significance of differential expression of variables between patient subsets can be measured using t-test or limma or regression analysis (Narain, par. [0013]).
Narain discloses the system of Claim 11 (see section 21 above). Narain 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, par. [ 0203]) and that statistical significance can be calculated in various ways including calculating p-values or false discovery rates (for differential expression analysis) (Narain, par. [ 0338]).
Accordingly, claims 2 and 12 lack novelty and are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Narain because Narain disclosed the limitations of these claims in a US patent application and the application was publicly available and effectively filed before the effective filing date of the instant application.
Claims 9 and 19 are directed to the method and system of claims 1 and 11 and further 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. These claims are anticipated by Narain.
Narain discloses the method of Claim 1 (see section 21). Narain further discloses the method includes performing differential analysis to identify genes with novel roles in drug induced cardiotoxicity (Narain 2012, par. [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 2012, par. [0484]).
Narain discloses the system of claim 11 (see section 21). 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, par. [0044]) and a database comprising storage devices for storing data (Narain, par. [ 0194]).
Accordingly, claims 9 and 19 lack novelty and are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Narain because Narain disclosed every limitation of these claims in a US patent application and the application was publicly available and effectively filed before the effective filing date of the instant application.
Claim 14 is directed to the system of claim 11, further including 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. These claims are anticipated by Narain.
Narain teaches the system of claim 11 (see section 21). Narain further 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 (Narain, par. [0522]). Narain further teaches predicting severe adverse effects prior to treatment by determining statistically significant differentially expressed variables (Narain, FIG. 35). Narain 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 (Narain, par. [ 0044]), including identifying a drug to treat disease based on identification of a statistically significant disease signature.
Accordingly, claim 14 lacks novelty and is rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Narain because Narain disclosed every limitation of this claim in a US patent application and the application was publicly available and effectively filed before the effective filing date of the instant application.
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.
Claims 3, 4, 10, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Narain evidenced by Narain 2012 as applied to claims 1, 9, 11, and 19 above.
Claims 3 and 13 are directed to the method and system of claims 1 and 11, 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.
Narain teaches the method of Claim 1 (see section 21). Narain does not explicitly teach selecting a subgroup of patients based on absence of a statistically significant disease signature. Narain 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 2012, par. [0023]). In addition, Narain teaches selecting and analyzing subgroups of patients that do not have clinical outcomes (Narain, claims 13, 14, and 18). Narain further teaches the method comprises selecting multiple patient subgroups based on combinations of attributes including responsiveness to treatment (Narain, par. [0022, 0220]) and serious adverse events to treatment (Narain, par. [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 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 (par. [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, par. [0182])).
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 2012, par. [0010]), including deciding to not treat a patient (Narain par. [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 par. [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.
Claim 4 is directed to the method of claim 1 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.
Narain teaches the method of Claim 1 (see section 21). Narain 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 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 par. [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 also teaches a specific example of using levels of a nodal point, PDIA3, to select a cancer treatment plan with Coenzyme Q10. Narain 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 par. [ 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 par. [0182])).
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 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 par. [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.
Claims 10 and 20 are directed to the method and system of claims 9 and 19 and further identifying a biological function of a gene in another animal that is correlated with the unannotated gene.
Narain teaches the method and system of claims 9 and 19 (as discussed above in the 102-rejection Section 19), but does not explicitly teach characterizing a gene in another animal that is correlated with an unannotated gene.
Narain teaches that the disclosed method and system can be applied to humans or animals. Narain 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, par. [0480-0481]). Narain further teaches that the method and system can be applied to virtually any organism, including humans or animals (Narain, par. [0382]) and that disease modeling steps and mechanistic characterization of the identified drugs can be performed in either animal or human cells (Narain 2012, par. [0311], [0716]). Narain 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 2012, par. [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, par. [0212]. Narain further teaches correlations between parent and child nodes in the networks can be measured as Pearson correlation coefficients. (Narain, par. [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, par. [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, par. [0335]) (for example using gene set enrichment analysis).
Based on these teachings by Narain, 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 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, par. [0480]). There is reasonable expectation of success as the method for doing so is implicit in the Narain disclosure and predicting biological functions of unannotated genes based on molecular network structure was routine in the art before the effective filing date.
In conclusion, claims 3, 4, 10, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Narain evidenced by Narain 2012.
Claims 5-8 and 15-18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Narain evidenced by Narain 2012 (as applied to claims 1 and 11 in section 21), and further in view of Lexma (US 2019/0340318 A1; effectively filed on May 03, 2019; disclosed in the IDS), herein referred to as Lexma.
Claims 5 and 15 are directed to the method and system of claims 1 and 11 wherein the drug that binds to the protein made by the disease driver is identified using computational fluid dynamics (CFD) to model cellular conditions, protein shape and a plurality of drugs.
Narain teaches the method and system of claims 1 and 11 (see section 21) and identifying the disease driver by computational modelling of cellular environments (using for example Bayesian causal relationship networks (Narain, par. [0189]), but Narain does not specifically teach modeling protein shape with CFD. Lexma teaches a method and system for drug discovery using 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, par. [0008]). Lexma further teaches the processors and computer readable storage medium configured to execute the method (Lexma, par. [0007, 0012, 0099]).
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 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, par. [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, par. [0008]). This application of CFD to the method of selecting drugs based on drug-target interaction has a reasonable expectation of success because CFD modeling improves our understanding of the molecular-scale factors affecting these interactions.
Claims 6 and 16 are directed to the method and system of claims 5 and 15, wherein shapes of multiple proteins in multiple disease states are stored and the shape of a protein in a disease state is identified and modelled by CFD. Narain teaches the method and system of independent claims 1 and 11 (see section 21), but does not teach storage or modeling of protein shapes with CFD (limitations of claims 5, 6, 15 and 16). Lexma teaches modeling with CFD in claims 5 and 15 and further teaches storing multiple proteins in multiple states and modelling the disease state of a protein. Specifically, Lexma teaches modeling proteins (Lexma, par. [0042]) and storing data from multiple scientific data sources and storing a plurality of scientifically accurate poses for each protein (Lexma, par. [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, par. [0079-0081]). Lexma further teaches the processors and computer readable storage medium configured to execute the method (Lexma, par. [0007, 0012, 0099]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to apply the teaching of Lexma (using CFD to model a protein in a disease state) with the method and system of Narain to improve drug discovery by simulating protein transitions activated by surrounding flows (including unfolding, refolding, allostery, cleavage, and substrate binding), which can allow for the virtual assessment of drug performance before engaging experimental studies, which can be costly and time-consuming (Lexma, par. [0008]). This application of CFD to the method of selecting drugs based on drug-target interaction has a reasonable expectation of success because CFD modeling improves our understanding of the molecular-scale factors affecting these interactions.
Claims 7 and 17 are directed to the methods and systems of claims 6 and 16, wherein cellular conditions of multiple diseases are stored and one cellular condition caused by the disease is selected and modelled using CFD. Narain teaches the method and system of independent claims 1 and 11 (see section 21), but does not teach modeling with CFD disclosed in claims 5-7 and 15-17. Lexma teaches modeling with CFD as described in the above sections 32 and 33. Lexma further 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, par. [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, par. [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, par. [0079-0081]. Molding the cellular environment (solution/fluid modeling) with molecular dynamics is necessary to accurately model the protein aggregation (fibril formation) (Lexma, par. [0082]). Lexma further teaches the processors and computer readable storage medium configured to execute the method (Lexma, par. [0007, 0012, 0099]).
It would be obvious for a person having ordinary skill in the art before the effective filing date to combine the teaching of Narain with the teaching of Lexma to apply CFD to accurately depict the interplay of cause-and-effect of a protein molecule within a surrounding solvent in a cell-like environment (Lexma, par. [0051]) to improve drug discovery and development by allowing for the virtual assessment of drug performance before performing costly and time consuming experimental studies (Lexma, par. [0008]). This application of CFD to the method of selecting drugs based on drug-target interaction has a reasonable expectation of success because CFD modeling improves our understanding of the molecular-scale factors affecting these interactions.
Claims 8 and 18 are directed to 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).
Narain teaches the method and system of claims 1 and 11 (see section 21) and further teaches analyzing medical research data using natural language processing. Narain 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, par. [0305]).
Narain 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, par. [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, par. [0044]). Lexma further teaches the processors and computer readable storage medium configured to execute the method (Lexma, par. [0007, 0012, 0099]).
It would be obvious for a person having ordinary skill in the art before the effective filing date to combine the teaching of Narain with the teaching of Lexma to apply CFD to accurately depict the interplay of cause-and-effect of a protein molecule within a surrounding solvent in a cell-like environment (Lexma, par. [0051]) to improve drug discovery and development by allowing for the virtual assessment of drug performance before performing time-consuming and costly experimental studies (Lexma, par. [0008]). This application of CFD to the method of selecting drugs based on drug-target interaction has a reasonable expectation of success because CFD modeling improves our understanding of the molecular-scale factors affecting these interactions.
In conclusion, claims 5-8 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Narain in view of Lexma.
Conclusion
In conclusion, no claims are allowed. Claims 1-20 are rejected. Claims 9 and 19 are objected to.
References not cited
Prior art disclosing methods and systems for personalized discovery with CFD for liver disease (Cirit et al., US 2021/0156846 A1) and for neurodegenerative disease (Cirit et al., US 2021/0101146 A1) were considered but not cited.
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/J.J.S./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685