DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions>
Claim Status
Claims 1-3, 5-6, 9-10, 12-14, 16-18, and 45-58 are currently pending and under exam herein.
Claims 4, 7-8, 11, 15, and 19-44 have been cancelled by preliminary amendment.
Priority
The instant Application is a Continuation of US 17/881,253, filed 4 August 2022, now abandoned, which is a Continuation of US 15/861,293, filed 3 January 2018, now US Patent 11,456,054, which is a Continuation of US 13/411,460, filed 2 March 2012, now US Patent 9,886,545, and which further claims the benefit of priority to US Provisional Applications 61/448,587, filed 2 March 2011 and 61/593,848, filed 1 February 2012. The instant claims, however, include limitations not found in priority document 61/448,587. As such, the earliest priority date accorded the instant application is 1 February 2012.
Drawings
The Replacements Drawings filed on 13 November 2023 are accepted.
Information Disclosure Statement
No Information Disclosure Statement has been filed herein.
Specification
Note: All reference to the “Specification” in this Office Action refers to PG Publication US2024/0062844.
Abstract
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. In certain patents, particularly those for compounds and compositions, wherein the process for making and/or the use thereof are not obvious, the abstract should set forth a process for making and/or use thereof. If the new technical disclosure involves modifications or alternatives, the abstract should mention by way of example the preferred modification or alternative.
The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
Where applicable, the abstract should include the following:
(1) if a machine or apparatus, its organization and operation;
(2) if an article, its method of making;
(3) if a chemical compound, its identity and use;
(4) if a mixture, its ingredients;
(5) if a process, the steps.
Extensive mechanical and design details of apparatus should not be given.
The abstract of the disclosure is objected to because the abstract is not descriptive of the invention claimed. Correction is required. See MPEP § 608.01(b).
Disclosure
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01. Please see[0615] and [0711] of the Specification.
The above are examples of corrections that need to be addressed. The list may not be exhaustive and it is kindly suggested that Applicant review the entire Specification for errors and make corrections appropriately.
The use of the term REFs™, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks. See as example, [0022]; [0041]; [0099]. This list is not exhaustive and there may be other instance in the Specification of trademarks and/or names.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-6, 9-10, 12, 14, 16-18, 45-46, and 51-58 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The instant rejection reflects the framework as outlined in the MPEP at 2106.04:
Framework with which to Evaluate Subject Matter Eligibility:
(1) Are the claims directed to a process, machine, manufacture or composition of matter;
(2A) Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
(2B) If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1 Analysis: Are claims directed to process, machine, manufacture/composition of matter
With respect to step (1): yes, the claims are directed to a method of generating a causal relationship network model for a biological system and a method of generating a causal relationship network model of a disease process.
Step 2A, Prong 1 Analysis: Do claims recite abstract idea
With respect to step (2A)(1), the claims recite abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the (2A)(1) evaluation, the claims are found herein to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and in conjunction with mathematical concepts (in particular mathematical relationships and formulas).
Note: The claims elements are italicized herein to highlight the judicial exceptions in the claim steps and underlined to represent the additional claim elements.
Claim 1:
A method of generating a causal relationship network model of a biological system for identification of a modulator of the biological system, the method comprising:
(1) obtaining a first data set from cells associated with the biological system, the first data set representing measured expression levels of one or more genes in the cells associated with the biological system, measured lipidomics data for the cells associated with biological system, measured metabolomics data for the cells associated with the biological system, or a combination of the aforementioned;
(2) obtaining a second data set from the cells associated with the biological system, the second data set representing a measured functional activity or a measured cellular response of the cells associated with the biological system;
(3) generating a computer-implemented causal relationship network model relating the expression levels of the one or more genes in the cells associated with the biological system, the lipidomics data for the cells associated with the biological system, the metabolomics data for the cells associated with the biological system, or the combination of the aforementioned and the functional activity or cellular response of the cells associated with the biological system based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code, wherein said operation is directed to making associations between gene expression and data, wherein doing so is directed to a mathematical process by which comparative analysis is performed (with the aid of a computer as a tool). The Specification discloses such at least at [0063];
(4) identifying a causal relationship unique in the biological system based on the computer-implemented causal relationship network model, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of the biological system, wherein the steps directed to “identifying” are mental operations whereby one could simply assess the data from a relationship to identify a modulator based on given data. Further the Specification includes operation using the mathematic operations of Bayesian networks [0158], for example There are no steps beyond this operation that would suggest otherwise, under the Broadest Reasonable Interpretation (BRI) of the claim herein.
Claim 2:
wherein the modulator stimulates or promotes the biological system
Claim 3:
wherein the modulator inhibits the biological system
Claim 5:
wherein the cells associated with the biological system were subject to an environmental perturbation prior to or during measurements for the first data set, and the comparison cells were not subject to the environmental perturbation prior to or during measurements for the first comparison data set.
Claim 6:
wherein the environmental perturbation comprises one or more of a contact with an agent, a change in culture condition, an introduced genetic modification or mutation, and a vehicle that causes a genetic modification or mutation.
Claim 9:
wherein the second data set is obtained through one or more of bioenergetics profiling, a cell proliferation assay, an apoptosis assay, an organellar function assay, and a genotype-phenotype association actualized by functional models selected from ATP, ROS, OXPHOS, and Seahorse assays.
Claim 10:
wherein step (3) is carried out by an artificial intelligence AI-based informatics platform
Claim 12:
wherein the AI-based informatics platform receives all data input from the first data set and the second data set without applying a statistical cut-off point
Claim 14:
wherein the unique causal relationship is identified as part of a differential causal relationship network that is uniquely present in the cells associated with the biological network, and absent in the comparison cells, wherein said operations are further directed to limiting the abstract steps in above claim 1 and include mental operations of in a computer environment or using a computer as a tool for establishing a relationship amongst data.
Claim 16:
A method of generating a causal relationship network model of a disease process for identification of a modulator of the disease process, the method comprising:
(1) obtaining a first data set from disease-related cells, the first data set representing measured expression levels of one or more genes in the disease-related cells, measured lipidomics data for the disease-related cells, measured metabolomics data for the disease-related cells, or a combination of the aforementioned;
(2) obtaining a second data set from the disease-related cells, the second data set representing a measured functional activity or a measured cellular response of the disease related-cells;
(3) generating a computer-implemented causal relationship network model relating the expression levels of the one or more genes in the disease-related cells, the lipidomics data for the disease-related cells, the metabolomics data for the disease-related cells, or the combination of the aforementioned and the functional activity or cellular response of the disease-related cells based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code, wherein said operation is directed to making associations between gene expression and data, wherein doing so is directed to a mathematical process by which comparative analysis is performed (with the aid of a computer as a tool). The Specification discloses such at least at [0063];
(4) identifying a causal relationship unique in the disease process based on the computer-implemented causal relationship network model, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of the disease process, wherein the steps directed to “identifying” are mental operations whereby one could simply assess the data from a relationship to identify a modulator based on given data. Further the Specification includes operation using the mathematics of Bayesian networks [0158], for example There are no steps beyond this operation that would suggest otherwise, under the Broadest Reasonable Interpretation (BRI) of the claim herein.
Claim 17:
wherein the disease process is cancer, diabetes, obesity or cardiovascular disease.
Claim 18:
wherein the cancer is lung cancer, breast cancer, prostate cancer, melanoma, squamous cell carcinoma, colorectal cancer, pancreatic cancer, thyroid cancer, endometrial cancer, bladder cancer, kidney cancer, a solid tumor, leukemia, non- Hodgkin lymphoma, or a drug-resistant cancer
Claim 45:
wherein an environment of the cells associated with the biological system represents a characteristic aspect of the biological system
Claim 46:
wherein the environment comprises a hypoxia condition, a hyperglycemic condition, a lactic acid rich culture condition, or combinations thereof
Claim 51:
further comprising validating the identified unique causal relationship in a biological system, wherein steps of validating are directed to mentally making an association between data and verifying said data and are therefore under the BRI of the claim said claim is mental in nature, as there are no further steps to define said operation.
Claim 52:
further comprising identifying the unique causal relationship based on a structure of the computer-implemented causal relationship network model, wherein the operation of “identifying” is directed to a mental operation that may be performed by merely looking at the data and making an informed assessment. As such, under the BRI of the claim said step is abstract.
Claim 53:
wherein the computer-implemented causal relationship network model based on measurements from cells associated with the biological system is a first computer-implemented causal relationship network model; and
wherein the method further comprises:
obtaining a first comparison data set from comparison cells, the first comparison data set representing measured expression levels of one or more genes in the comparison cells, measured lipidomics data for the comparison cells, measured metabolomics data for the comparison cells, or a combination of the aforementioned;
obtaining a second comparison data set for the comparison cells, the second comparison data set representing a measured functional activity or a measured cellular response of the comparison cells; and
generating a computer-implemented second causal relationship network model relating the expression levels of the one or more genes in the comparison cells, the lipidomics data for the comparison cells, the metabolomics data for the comparison cells, or the combination of the aforementioned, and the functional activity or cellular response of the comparison cells based on the first comparison data set and the second comparison data set using the programmed computing system, wherein said operation is directed to the abstract category of mathematical operation and mental processes wherein making associations between gene expression and data is directed to a comparative analysis performed (with the aid of a computer as a tool). The Specification discloses such at least at [0063];
wherein identifying the causal relationship unique in the biological system based on the computer-implemented first causal relationship network model comprises:
generating a computer-implemented differential causal relationship network from the first causal relationship network model and the second causal relationship network model using a computing device
identifying the causal relationship unique in the biological system from the generated differential causal relationship network, wherein the steps directed to generating are addressed above and further wherein “identifying” is a mental operation whereby one could simply assess the data from a relationship to identify a relationship based on given data. Further the Specification includes operation using Bayesian networks [0158], for example. There are no steps beyond this operation that would suggest otherwise, under the Broadest Reasonable Interpretation (BRI) of the claim herein.
Claim 54:
wherein generating the computer-implemented differential causal relationship network from the first causal relationship network model and the second causal relationship network model comprises:
i) for each relationship between two nodes in a selected one of the first causal relationship network model and the second causal relationship network model, determining if the other causal relationship network model includes a relationship between the same two nodes, and, where the other causal relationship network model includes a relationship between the same two nodes, determining if the relationship between the same two nodes in the other causal relationship network model has at least one significantly different parameter than that of the relationship in the selected causal relationship network model; and
ii) forming the differential causal relationship network by including the relationships in the selected causal relationship network model that are absent from the other causal relationship network model and including the relationships in the selected causal relationship network model that have at least one significantly different parameter in the other causal relationship network model, wherein the above recitations are directed further to the abstract ideas that is a mental operation of making relationship associations between data using mathematical processes of graph theory and network analysis representing data as a vertex and representing points where lines, edges or pathways connect. Further the Specification includes operation using Bayesian networks [0158], for example. There are no steps beyond said operations under the BRI of the claim and thus said operations are abstract.
Claim 55:
wherein the at least one significantly different parameter is a directionality of the relationship or a quantitative magnitude of the strength of the relationship, wherein said operation further limits the abstract idea as above.
Claim 56:
generating a graphical representation of the generated differential causal relationship network; and storing the graphical representation of the generated differential causal relationship network; or displaying the graphical representation of the generated differential causal relationship network, wherein said operation are directed to mental operations using mathematics directed to graph representations and are therefore abstract.
Claim 57:
further comprising generating a delta-delta causal relationship network based on the first differential causal relationship network and a second differential causal relationship network generated solely based on data obtained from control cells, wherein establishing delta-delta relationships is directed to a mental operation via mathematical processes of change calculations using a Δ function. As such said operation further limits the abstract idea.
Claim 58:
wherein the control cells are normal cells
Hence, the claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined herein to each cover performance either in the mind (calculations by hand or pen and paper or computer as a tool) and performance by mathematical operation. There are no specifics as to the methodology involved in, for example, “generating” or in “identifying” as explained above. Thus, under the BRI, one could simply, for example, perform said operation with pen and paper, or, alternatively with the aid of a generic computer as a tool to perform said operations. These recitations are similar to the concepts of collecting information, analyzing it and providing certain results from the collection and analysis (Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations (Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in (Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind with pen and paper, and can include mathematical concepts.
Further, see MPEP § 2106.04(a)(2), subsection III. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation (see, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674: noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016): holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind" (see Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016): holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Step 2A, Prong 2 Analysis: Integration to a Practical Application
Because the claims do recite judicial exceptions, direction under (2A)(2) provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application (MPEP 2106.04(d). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim is said to fail to integrate the abstract idea into a practical application (MPEP 2106.04(d).III).
With respect to the instant recitations, the claims recite the additional elements as underlined above. Specifically said steps are those that include ones that require a computer-implementation and storage and processor with instructions executable by a processor.
Further steps directed to additional elements in the claim are those that further limit the data as in claims 1 (claims 2-3, 5-6, 9-10, 12, 45-46, 53, and 58) and 16 (claims 17-18).
As the computer implementation includes stored data, said stored data serves as the vehicle for data gathering in the claim. Additional elements in the instant claims directed to data gathering perform functions of collecting the data needed to carry out the abstract idea. Data gathering does not impose any meaningful limitation on the abstract idea, or on how the abstract idea is performed. Data gathering steps are not sufficient to integrate an abstract idea into a practical application. (MPEP 2106.05(g).
Further, the computer, processor, storage and instructions are part of a general purpose computer system and there are no details herein wherein of how the specific computer structures are used to implement the judicial exceptions beyond generic computing operations, i.e., the computer elements of the claims do not provide improvements to the functioning of the computer itself (see: DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (see: Diamond v. Diehr); nor do they utilize a particular machine (see: Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the judicial exception using a computer, and therefore the claim does not provide integration into a practical application of any judicial exception.
Step 2B Analysis: Do Claims Provide an Inventive Concept
The claims are lastly evaluated using the (2B) analysis, wherein it is determined that because the claims recite abstract ideas, and do not integrate that abstract ideas into a practical application, the claims also lack a specific inventive concept. Applicant is reminded that the judicial exception alone cannot provide the inventive concept or the practical application and that the identification of whether the additional elements amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to the instant claims, the additional elements of data gathering described above do not rise to the level of significantly more than the judicial exception. As directed in the Berkheimer memorandum of 19 April 2018 and set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the claim elements are directed data gathering elements as in 2A, prong 2 and that under the assessment herein under 2B encompass steps that are routine, well-understood and conventional in the art. For example, the prior art to Nikolskaya et al. (US 8,000,949) to discloses data obtaining operations to provide data for network generation of biological data, for example [Figure 1; Figure 3]. It is noted that actual “measurement” steps via specific assays are not performed by the instant claims. However even if assays for gene expression, lipid data etc. were to be performed, said operations are also routine and conventional practice in the art an would further not provide for an inventive concept under 2B, as the courts have recognized the following laboratory techniques as well-understood, routine, conventional activity in the life science arts when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)II.): determining the level of a biomarker in blood by any means (Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1362, 123 USPQ2d 1081, 1088 (Fed. Cir. 2017)); detecting DNA or enzymes in a sample (Sequenom, 788 F.3d at 1377-78, 115 USPQ2d at 1157); Cleveland Clinic Foundation 859 F.3d at 1362, 123 USPQ2d at 1088 (Fed. Cir. 2017)).
With respect to the claims to the computer and processor, storage and instruction, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The instant Specification discloses that computer processors and systems, as example, are generic computing systems [e.g., 0302]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than an abstract idea (see MPEP 2106.05(b)I-III).
The dependent claims have been analyzed with respect to step 2B and none of these claims provide a specific inventive concept, as they all fail to rise to the level of significantly more than the identified judicial exception. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
1. Claims 1-3, 5-6, 9, 14, 16-18, 45-46, and 51-58 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over US 8,000,949 (Nikolskaya et al.).
With respect to Independent claim 1, Nikolskaya teaches a method for
generating a causal relationship network model of a biological system for identification of a modulator of the biological system (drug discovery in terms of target prioritization and identification of multi-gene/multi-proteins biomarkers; col 18, ln 34-36), the method comprising:
obtaining a first data set from cells associated with the biological system, the first data set representing measured expression levels of one or more genes in the cells associated with the biological system, measured lipidomics data for the cells associated with the biological system, measured metabolomics data for the cells association with the biological system, or a combination of the aforementioned (establishing a model for the biological system (System Model; col 4, ln 25), using cells associated with the biological system (cellular response; col 18, ln 55-58, fine mapping can be performed in order to compare the tissue and call type specific response; col 19, ln 38- 40; global gene expression profiles, proteomics or metabolomics profiles; col 18, In 34-37, fine mapping can be performed in order to compare the tissue and cell type specific response, different time points, drug dosage...etc.; col 19, ln 38-41 );
obtaining a second data set from the cells associated with the biological system, the second data set representing a measured functional activity or measured cellular responses of the cells associated with the biological system, (active proteins perform certain cellular functions {such as a metabolic transformation of malonyl into acetyl-CoA in this example}, which can be presented as one-step interactions in the space of thousands of metabolic transformations regulated at multiple levels from the cell membrane receptors to transcription factors. The intersection of the experimental data with the interactions content on the networks {derived from experimental literature} provides the closest possible view of the activated cellular machinery in a cell-either signaling or metabolism; col 18, ln 63-col 19, ln 6);
generating a causal relationship network model relating the expression levels of one or more genes in the cells associated with the biological system, the lipidomics data for the cells associated with the biological system, the metabolomics data associated with the biological system , or the combination of the aforementioned and the functional activity or cellular response of the cells associated with the biological system based on the first data set and the second data set (pathways are connected to each other and linked to relevant information to form a functional model; col 8, ln 17-18) using a programmed computing system (computational reconstruction of relevant metabolic networks; col 4, ln 32-33); and
identifying a causal relationship unique in the biological system based on the computer-implemented causal relationship network model, wherein a gene, lipid, or metabolite associated with the unique causal relationship, is identified as a modulator of the biological system (a collection of human tissue-specific and condition-specific biochemical pathways are linked by common intermediates into maps or models. These models serve as a framework to integrate complementary types of high-throughput data and to establish mechanisms underlying clinical manifestations of diseases"; col 8 ln 5-11; "comparing System Reconstructions made for normal and diseased organs or tissues, thus providing important information about possible regulatory mechanisms and potential drug targets"; col 5, ln 6-9). Although Nikolskaya does not explicitly recite wherein the model is based solely on the first and second data sets, Nikolskaya teaches wherein "mapping can be performed in order to compare the tissue and call type specific response, different time points, drug dosage; different patients from the same cohort, etc. For instance, we have compared gene expression patterns from mammary gland duct epithelium of two breast cancer patients, one from pre-invasive DSIC stage, another with invasive cancer. Both data sets were used for building the initial networks, and then visualized separately" (col 19, ln 34-46). It would have been prima facie obvious to a person of ordinary skill in the art at the time of the invention to limit the network-building data to that acquired from samples processed using a single system in a single experiment or set of experiments in order to enable comparisons of the data to each other, and to avoid potential errors in target identification by using data from outside the experimental scope, based on the teaching of Nikolskaya.
Regarding claims 2 and 3, Nikolskaya discloses modulators as activators or inhibitors (Example 7, col. 29, ln 31-35).
With respect to claim 9, Nikolskaya discloses data sets with respect to cellular functions, such as apoptosis, DNA repair, cell cycle checkpoints or fatty acid metabolism (col. 18, ln. 37 to col. 19, ln. 25).
With respect to claims 45 and 46, Nikolskaya teaches wherein the first data set further represents one or two or more of transcriptomic (expression; col 19, ln 34-46), and SNP data (col 5, ln 16-20) thus teaching characterizing the cells associated with the biological system. Further, Nikolskaya discloses that experimental adjustments may be made by choosing experiment specific interactions and removing or adding specific interaction mechanisms, for example (col. 19, ln. 16-25). In addition, Nikolskaya teaches wherein the pathways examined may include those affected by insulin (Fig. 38, 39), as well as glucose metabolism (Fig. 35b), and as such, it would have been obvious to a person of ordinary skill in the art to include wherein a characteristic aspect of the disease process comprised hyperglycemia, based on the inclusion of glucose metabolism and insulin in the pathways assessed, since both were involved in control of the levels of blood sugar.
With respect to claims 51 and 52, Nikolskaya discloses cross-validation of network models (col. 19, ln. 26-35; col. 20, ln. 6-9) and identification of a concise network. Under the BRI of the instant claim said identification may include a structure of a final network (col. 34, ln. 51-65).
With respect to claim 53, Nikolskaya discloses wherein the computer-implemented causal relationship network model based on measurements from cells associated with the biological system is a first computer-implemented causal relationship network model; and wherein the method further comprises: obtaining a first comparison data set from comparison cells, the first comparison data set representing measured expression levels of one or more genes in the comparison cells, measured lipidomics data for the comparison cells, measured metabolomics data for the comparison cells, or a combination of the aforementioned; obtaining a second comparison data set for the comparison cells, the second comparison data set representing a measured functional activity or a measured cellular response of the comparison cells (determination of a network of relevant biochemical pathways; col 4, In 29-30; comparing System Reconstructions made for normal and diseased organs or tissues, thus providing important information about possible regulatory mechanisms and potential drug targets; col 4, In 53-56), the method comprising: establishing a model for the disease process (models serve as a framework to integrate complementary types of high-throughput data and to establish mechanisms underlying clinical manifestations of diseases; col 8, In 8-11), using disease related cells, to represents a characteristic aspect of the disease process ("mapping can be performed in order to compare the tissue and cell type specific response, different time points, drug dosage; different patients from the same cohort, etc. For instance, we have compared gene expression patterns from mammary gland duct epithelium of two breast cancer patients, one from pre-invasive DSIC stage, another with invasive cancer. Both data sets were used for building the initial networks, and then visualized separately"; col 19, In 34-46); and generating a computer-implemented second causal relationship network model relating the expression levels of the one or more genes in the comparison cells, the lipidomics data for the comparison cells, the metabolomics data for the comparison cells, or the combination of the aforementioned, and the functional activity or cellular response of the comparison cells based on the first comparison data set and the second comparison data set using the programmed computing system; and wherein identifying the causal relationship unique in the biological system based on the computer-implemented first causal relationship network model comprises: generating a computer-implemented differential causal relationship network from the first causal relationship network model and the second causal relationship network model using a computing device; and identifying the causal relationship unique in the biological system from the generated differential causal relationship network (pathways are connected to each other and linked to relevant information to form a functional model; col 8, ln 17-18).
With respect to claims 5 and 6, Nikolskaya does not specifically teach in vitro culture of cells associated with the biological system subject to perturbation. However, Nikolskaya teaches a network system by which the system allows building system level models of biochemistry in which models can be used as skeletons for further integration of high-throughput data in efforts of prediction of disease and toxicity from exposures (column 8, lines 13-25). Further Nikolskaya discloses that experimental adjustments may be made by choosing experiment specific interactions and removing or adding specific interaction mechanisms, for example (col. 19, ln. 16-25). As such, perturbation would be prima facie obvious in view of experimental parameter design. Further to recited assays or techniques in the instant claims, said assays are not performed but rather are the characteristics of said data are obtained.
With respect to claim 14, Nikolskaya discloses identification of only differentially expressed genes and analysis of a network only applied to said set for network build (col. 33, ln. 24-33).
With respect to claim 54, Nikolskaya further discloses associations between node determinations for a relationship network and forming a differentially related network (col. 32, ln. 50-67).
With respect to claim 55, Nikolskaya discloses relationships as defined by closest proximity interactions (col. 33, ln. 17-23).
With respect to claim 56, Nikolskaya discloses graphical representations of networks (e.g., Figures 44, 46B, 46C).
With respect to claims 57 and 58, Nikolskaya disclose representation of normal networks (col. 4, ln. 37-43).
With respect to Independent claim 16, Nikolskaya discloses
A method of generating a causal relationship network model of a disease process for identification of a modulator of the disease process, the method comprising (drug discovery in terms of target prioritization and identification of multi-gene/multi-proteins biomarkers; col 18, ln 34-36; with respect to disease, col. 4, ln. 28-43)
(1) obtaining a first data set from disease-related cells, the first data set representing measured expression levels of one or more genes in the disease-related cells, measured lipidomics data for the disease-related cells, measured metabolomics data for the disease-related cells, or a combination of the aforementioned (establishing a model for the biological system (System Model; col 4, ln 25), using cells associated with the biological system (cellular response; col 18, ln 55-58, fine mapping can be performed in order to compare the tissue and cell type specific response; col 19, ln 38- 40; global gene expression profiles, proteomics or metabolomics profiles; col 18, In 34-37, fine mapping can be performed in order to compare the tissue and cell type specific response, different time points, drug dosage...etc.; col 19, ln 38-41; further systems include those of disease such as Examples 1, 3, 5, 8, 10, 11, 12);
(2) obtaining a second data set from the disease-related cells, the second data set representing a measured functional activity or a measured cellular response of the disease related-cells (active proteins perform certain cellular functions {such as a metabolic transformation of malonyl into acetyl-CoA in this example}, which can be presented as one-step interactions in the space of thousands of metabolic transformations regulated at multiple levels from the cell membrane receptors to transcription factors. The intersection of the experimental data with the interactions content on the networks {derived from experimental literature} provides the closest possible view of the activated cellular machinery in a cell-either signaling or metabolism; col 18, ln 63-col 19, ln 6);
(3) generating a computer-implemented causal relationship network model relating the expression levels of one or more genes in the disease-related cells, the lipidomics data for the disease-related cells, the metabolomics data for the disease-related cells, or the combination of the aforementioned and the functional activity or cellular response of the disease-related cells based on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code (pathways are connected to each other and linked to relevant information to form a functional model; col 8, ln 17-18) using a programmed computing system (computational reconstruction of relevant metabolic networks; col 4, ln 32-33);
(4)identifying a causal relationship unique in the disease process based on the computer-implemented causal relationship network model, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of the disease process (a collection of human tissue-specific and condition-specific biochemical pathways are linked by common intermediates into maps or models. These models serve as a framework to integrate complementary types of high-throughput data and to establish mechanisms underlying clinical manifestations of diseases"; col 8 ln 5-11; "comparing System Reconstructions made for normal and diseased organs or tissues, thus providing important information about possible regulatory mechanisms and potential drug targets"; col 5, ln 6-9). Although Nikolskaya does not explicitly recite wherein the model is based solely on the first and second data sets, Nikolskaya teaches wherein "mapping can be performed in order to compare the tissue and call type specific response, different time points, drug dosage; different patients from the same cohort, etc. For instance, we have compared gene expression patterns from mammary gland duct epithelium of two breast cancer patients, one from pre-invasive DSIC stage, another with invasive cancer. Both data sets were used for building the initial networks, and then visualized separately" (col 19, ln 34-46). It would have been prima facie obvious to a person of ordinary skill in the art at the time of the invention to limit the network-building data to that acquired from samples processed using a single system in a single experiment or set of experiments in order to enable comparisons of the data to each other, and to avoid potential errors in target identification by using data from outside the experimental scope, based on the teaching of Nikolskaya.
With respect to claims 17 and 18, Nikolskaya further discloses comparison of gene expression patterns from mammary gland duct epithelium of two breast cancer patients, one from pre-invasive DSIC stage, another with invasive cancer. Both data sets were used for building the initial networks, and then visualized separately"; col 19, ln 34-46, thus disclosing breast cancer.
2. Claims 10 and 12 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over US 8,000,949 (Nikolskaya) as pertains to claim 1 above, in view of US 2011/0131027 (Solomon).
With respect to claim 1, the prior art to Nikolskaya discloses the limitations as described above.
Regarding claims 10 and 12, Nikolskaya does not specifically indicate wherein the method is carried out by an artificial intelligence (AI)-based informatics platform.
However, the prior art to Solomon teaches a bioinformatics system for functional proteomics modeling (para [0025]), including modeling protein network interactions (para [0070], [0071]), using artificial intelligence systems ("artificial neural networks which learn and adapt for data mining, data search and pattern matching in large databases and development of self-organizing maps"; para [0073]). It would have been prima facie obvious to a person of ordinary skill in the art at the time of invention to use artificial intelligence platforms, such as artificial neural networks, as taught by Solomon, to enable the generation of the networks taught by Nikolskaya in a computational system. A person of skill in the art would have recognized the utility of a self-adaptive programming system that responds to the input data, such as a neural network, for the generation of the models without undue experimentation.
Further regarding claim 12, although neither Nikolskaya nor Solomon specifically recites wherein the AI-based informatics platform receives all data input from the first and second data sets without applying a statistical cut-off point, it would have been prima facie obvious to a person of ordinary skill in the art at the time of the invention to opt to apply or not apply a statistical cut-off point to the data, based on experimental results and the capacity of the system to function in the absence of the use of a data statistical cut-off point. Said options reflect design choice only and given the nature of the problem to be solved one would have had a reasonable expectation of success in choosing appropriate (or not) cut-off points for the operational algorithm for meaningful representation of the relationship network using the principles of artificial intelligence design.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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1.Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-22 of U.S. Patent No. 9,886,545. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are directed to
A method of generating a causal relationship network model of a biological system for identification of a modulator of the biological system, the method comprising:
(1) obtaining a first data set from cells associated with the biological system, the first data set representing measured expression levels of one or more genes in the cells associated with the biological system, measured lipidomics data for the cells associated with biological system, measured metabolomics data for the cells associated with the biological system, or a combination of the aforementioned;
(2) obtaining a second data set from the cells associated with the biological system, the second data set representing a measured functional activity or a measured cellular response of the cells associated with the biological system;
(3) generating a computer-implemented causal relationship network model relating the expression levels of the one or more genes in the cells associated with the biological system, the lipidomics data for the cells associated with the biological system, the metabolomics data for the cells associated with the biological system, or the combination of the aforementioned and the functional activity or cellular response of the cells associated with the biological system based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code;
(4) identifying a causal relationship unique in the biological system based on the computer-implemented causal relationship network model, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of the biological system (claim 1). Said method further includes wherein generating the computer-implemental causal relationship network comprises:(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the measured expression levels of the one or more genes, the lipidomics data, the metabolomics data, or the combination of the aforementioned, and the functional activity or cellular response in the cells associated with the biological system; (ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and (iii) globally optimizing the ensemble of trial networks by evolving at least some of the trial networks in parallel using the plurality of processors (claim 47); wherein one or more first processors in the plurality of processors used to evolve a first trial network are different from one or more second processors in the plurality of processors used to evolve a second trial network (claim 48); wherein evolving a trial network includes adding a network fragment from the list to the trial network or replacing a network fragment in the trial network with a network fragment from the list and determining whether the addition or replacement improves a total probabilistic score for the trial network (claim 49).
The claims of the ‘545 patent are directed to:
A method for identifying a modulator of a disease process, said method comprising:
(1) obtaining a first data set representing measured expression levels of a plurality of genes in an in vitro culture of disease related cells;
(2) obtaining a first control data set representing measured expression levels of a plurality of genes in an in vitro culture of control cells;
(3) obtaining a second data set representing a measured functional activity or a measured cellular response of the in vitro culture of disease related cells;
(4) obtaining a second control data set representing a measured functional activity or a measured cellular response of the in vitro culture of control cells;
(5) generating a computer-implemented first causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code, wherein generating the computer-implemented first causal relationship network model comprises:
(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the expression levels of the plurality of genes and the functional activity or cellular response in the in vitro culture of the disease related cells;
(ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and
(iii) globally optimizing the ensemble of trial networks by evolving the trial networks in parallel using the plurality of processors, wherein one or more first processors in the plurality of processors used to evolve a first trial network are different from one or more second processors in the plurality of processors used to evolve a second trial network, and wherein evolving a trial network includes adding a network fragment from the list to the trial network or replacing a network fragment in the trial network with a network fragment from the list and determining whether the addition or replacement improves a total probabilistic score for the trial network;
wherein the generation of the first causal relationship network model is not based on any known biological relationships other than the first data set and the second data set;
(6) generating a computer-implemented second causal relationship network model relating the expression levels of the plurality of genes and the functional activity or cellular response of the control cells based solely on the first control data set and the second control data set using the programmed computing system, wherein generating the computer-implemented second causal relationship network model comprises:
(i) creating a second list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first control data set and the second control data set, wherein the variables correspond to the expression levels of the plurality of genes and the functional activity or cellular response in the in vitro culture of control cells;
(ii) creating a second ensemble of trial networks, each trial network constructed from a different subset of the second list of network fragments; and
(iii) globally optimizing the second ensemble of trial networks by evolving the trial networks in parallel using the plurality of processors, wherein one or more first processors in the plurality of processors used to evolve a first trial network are different from one or more second processors in the plurality of processors used to evolve a second trial network, wherein evolving a trial network includes adding a network fragment from the second list to the trial network or replacing a network fragment in the trial network with a network fragment from the second list and determining whether the addition or replacement improves a total probabilistic score for the trial network;
(7) generating a computer-implemented differential causal relationship network from the first causal relationship network model and the second causal relationship network model using a computing device by steps including:
i) for each relationship between two nodes in a selected one of the first causal relationship network model and the second causal relationship network model, determining if the other causal relationship network model includes a relationship between the same two nodes, and, where the other causal relationship network model includes a relationship between the same two nodes, determining if the relationship between the same two nodes in the other causal relationship network model has at least one significantly different parameter than that of the relationship in the selected causal relationship network model; and
ii) forming the differential causal relationship network by including the relationships in the selected causal relationship network model that are absent from the other causal relationship network model and including the relationships in the selected causal relationship network model that have at least one significantly different parameter in the other causal relationship network model; and
(8) identifying a causal relationship unique in the disease process from the generated differential causal relationship network, wherein a gene associated with the unique causal relationship is identified as a modulator of the disease process.
Each of the independent claims in combination with the dependent claims herein are directed to overlapping subject matter with the claims of the ‘545 patent and thus are obvious variants one of the other.
2. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-27 of U.S. Patent No. 10,061,887. Although the claims at issue are not identical, they are not patentably distinct from each other because are directed to:
A method of generating a causal relationship network model of a biological system for identification of a modulator of the biological system, the method comprising:
(1) obtaining a first data set from cells associated with the biological system, the first data set representing measured expression levels of one or more genes in the cells associated with the biological system, measured lipidomics data for the cells associated with biological system, measured metabolomics data for the cells associated with the biological system, or a combination of the aforementioned;
(2) obtaining a second data set from the cells associated with the biological system, the second data set representing a measured functional activity or a measured cellular response of the cells associated with the biological system;
(3) generating a computer-implemented causal relationship network model relating the expression levels of the one or more genes in the cells associated with the biological system, the lipidomics data for the cells associated with the biological system, the metabolomics data for the cells associated with the biological system, or the combination of the aforementioned and the functional activity or cellular response of the cells associated with the biological system based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code;
(4) identifying a causal relationship unique in the biological system based on the computer-implemented causal relationship network model, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of the biological system (claim 1). Said method further includes wherein generating the computer-implemental causal relationship network comprises:(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the measured expression levels of the one or more genes, the lipidomics data, the metabolomics data, or the combination of the aforementioned, and the functional activity or cellular response in the cells associated with the biological system; (ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and (iii) globally optimizing the ensemble of trial networks by evolving at least some of the trial networks in parallel using the plurality of processors (claim 47); wherein one or more first processors in the plurality of processors used to evolve a first trial network are different from one or more second processors in the plurality of processors used to evolve a second trial network (claim 48); wherein evolving a trial network includes adding a network fragment from the list to the trial network or replacing a network fragment in the trial network with a network fragment from the list and determining whether the addition or replacement improves a total probabilistic score for the trial network (claim 49); wherein the computer-implemented causal relationship network model based on measurements from cells associated with the biological system is a first computer-implemented causal relationship network model; and wherein the method further comprises: obtaining a first comparison data set from comparison cells, the first comparison data set representing measured expression levels of one or more genes in the comparison cells, measured lipidomics data for the comparison cells, measured metabolomics data for the comparison cells, or a combination of the aforementioned; obtaining a second comparison data set for the comparison cells, the second comparison data set representing a measured functional activity or a measured cellular response of the comparison cells; and generating a computer-implemented second causal relationship network model relating the expression levels of the one or more genes in the comparison cells, the lipidomics data for the comparison cells, the metabolomics data for the comparison cells, or the combination of the aforementioned, and the functional activity or cellular response of the comparison cells based on the first comparison data set and the second comparison data set using the programmed computing system; and wherein identifying the causal relationship unique in the biological system based on the computer-implemented first causal relationship network model comprises: generating a computer-implemented differential causal relationship network from the first causal relationship network model and the second causal relationship network model using a computing device; and identifying the causal relationship unique in the biological system from the generated differential causal relationship network (claim 53); wherein generating the computer-implemented differential causal relationship network from the first causal relationship network model and the second causal relationship network model comprises: i) for each relationship between two nodes in a selected one of the first causal relationship network model and the second causal relationship network model, determining if the other causal relationship network model includes a relationship between the same two nodes, and, where the other causal relationship network model includes a relationship between the same two nodes, determining if the relationship between the same two nodes in the other causal relationship network model has at least one significantly different parameter than that of the relationship in the selected causal relationship network model; and ii) forming the differential causal relationship network by including the relationships in the selected causal relationship network model that are absent from the other causal relationship network model and including the relationships in the selected causal relationship network model that have at least one significantly different parameter in the other causal relationship network model (claim 54).
Claims of the ‘887 patent are directed to:A method for identifying a modulator of a biological system, the method comprising:
(1) obtaining a first data set from a model for the biological system, wherein the model comprises cells associated with the biological system, and wherein the first data set represents global proteomic changes in the cells associated with the biological system;
(2) obtaining a second data set from the model, wherein the second data set represents one or more functional activities or cellular responses of the cells associated with the biological system, and wherein said one or more functional activities or cellular responses of the cells comprises global enzymatic activity and/or an effect of the global enzyme activity on the enzyme metabolites or substrates in the cells associated with the biological system;
(3) generating a computer implemented first causal relationship network model among the global proteomic changes and the one or more functional activities or cellular responses based solely on the first and second data sets using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code, wherein generating the computer-implemented first causal relationship network model comprises:
(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the global proteomic changes, and the one or more functional activities or cellular responses of the cells associated with the biological system including the global enzymatic activity and/or effect of the global enzyme activity on the enzyme metabolites or substrates in the cells associated with the biological system;
(ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and
(iii) globally optimizing the ensemble of trial networks by evolving the trial networks in parallel using the plurality of processors, wherein one or more first processors in the plurality of processors used to evolve a first trial network are different from one or more second processors in the plurality of processors used to evolve a second trial network, and wherein evolving a trial network includes adding a network fragment from the list to the trial network or replacing a network fragment in the trial network with a network fragment from the list and determining whether the addition or replacement improves a total probabilistic score for the trial network;
wherein the generation of the first causal relationship network model is not based on any known biological relationships other than the first and second data sets;
(4) generating a computer-implemented differential causal relationship network from the first causal relationship network model and a second computer-implemented causal relationship network model based on control cell data using a computing device by steps including:
(i) for each relationship between two nodes in a selected one of the first causal relationship network model and the second causal relationship network model, determining if the other causal relationship network model includes a relationship between the same two nodes, and, where the other causal relationship network model includes a relationship between the same two nodes, determining if the relationship between the same two nodes in the other causal relationship network model has at least one significantly different parameter than that of the relationship in the selected causal relationship network model; and
(ii) forming the differential causal relationship network by including the relationships in the selected causal relationship network model that are absent from the other causal relationship network model and including the relationships in the selected causal relationship network model that have at least one significantly different parameter in the other causal relationship network model; and
(5) identifying a causal relationship unique in the biological system from the differential causal relationship network, wherein at least one enzyme associated with the unique causal relationship is identified as a modulator of the biological system.
Each of the independent claims in combination with the dependent claims herein are directed to overlapping subject matter with the claims of the ‘545 patent and thus are obvious variants one of the other.
3. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-27 of U.S. Patent No. 11,456,054. Although the claims at issue are not identical, they are not patentably distinct from each other because are directed to:
A method of generating a causal relationship network model of a biological system for identification of a modulator of the biological system, the method comprising:
(1) obtaining a first data set from cells associated with the biological system, the first data set representing measured expression levels of one or more genes in the cells associated with the biological system, measured lipidomics data for the cells associated with biological system, measured metabolomics data for the cells associated with the biological system, or a combination of the aforementioned;
(2) obtaining a second data set from the cells associated with the biological system, the second data set representing a measured functional activity or a measured cellular response of the cells associated with the biological system;
(3) generating a computer-implemented causal relationship network model relating the expression levels of the one or more genes in the cells associated with the biological system, the lipidomics data for the cells associated with the biological system, the metabolomics data for the cells associated with the biological system, or the combination of the aforementioned and the functional activity or cellular response of the cells associated with the biological system based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code;
(4) identifying a causal relationship unique in the biological system based on the computer-implemented causal relationship network model, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of the biological system (claim 1). Said method further includes wherein generating the computer-implemental causal relationship network comprises:(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the measured expression levels of the one or more genes, the lipidomics data, the metabolomics data, or the combination of the aforementioned, and the functional activity or cellular response in the cells associated with the biological system; (ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and (iii) globally optimizing the ensemble of trial networks by evolving at least some of the trial networks in parallel using the plurality of processors (claim 47).
The claims of the ‘054 patent are directed to:
A method for identifying a modulator of a disease process, said method comprising:
(1) obtaining a first data set from disease-related cells, wherein the first data set represents measured expression levels of one or more genes in the disease-related cells, measured lipidomics data for the disease-related cells, measured metabolomics data for the disease-related cells, or a combination of the aforementioned;
(2) obtaining a second data set from the disease-related cells, wherein the second data set represents a measured functional activity or a measured cellular response of the disease-related cells;
(3) generating a computer-implemented first causal relationship network model relating the expression levels of the one or more genes in the disease-related cells, the lipidomics data for the disease-related cells, the metabolomics data for the disease-related cells, or the combination of the aforementioned and the functional activity or cellular response of the disease-related cells based on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code, wherein generating the computer-implemented first causal relationship network comprises:
(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the expression levels of the one or more genes, the lipidomics data, the metabolomics data, or the combination of the aforementioned and the functional activity or cellular response in the disease related cells;
(ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and
(iii) globally optimizing the ensemble of trial networks by evolving the trial networks in parallel using the plurality of processors;
wherein relationships in the first causal relationship network model and causality in the first causal relationship network model are determined based on the first data set and the second data set and not based on previously identified or known biological relationships between variables;
(4) identifying, from the computer-implemented causal relationship network model, a causal relationship unique in the disease process, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of a disease process.
Each of the independent claims in combination with the dependent claims herein are directed to overlapping subject matter with the claims of the ‘545 patent and thus are obvious variants one of the other.
4. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-43 of U.S. Patent No. 12,437,835, in view of Nikolskya (cited above). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are directed to:
A method of generating a causal relationship network model of a biological system for identification of a modulator of the biological system, the method comprising:
(1) obtaining a first data set from cells associated with the biological system, the first data set representing measured expression levels of one or more genes in the cells associated with the biological system, measured lipidomics data for the cells associated with biological system, measured metabolomics data for the cells associated with the biological system, or a combination of the aforementioned;
(2) obtaining a second data set from the cells associated with the biological system, the second data set representing a measured functional activity or a measured cellular response of the cells associated with the biological system;
(3) generating a computer-implemented causal relationship network model relating the expression levels of the one or more genes in the cells associated with the biological system, the lipidomics data for the cells associated with the biological system, the metabolomics data for the cells associated with the biological system, or the combination of the aforementioned and the functional activity or cellular response of the cells associated with the biological system based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code;
(4) identifying a causal relationship unique in the biological system based on the computer-implemented causal relationship network model, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of the biological system (claim 1). Said method further includes wherein generating the computer-implemental causal relationship network comprises:(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the measured expression levels of the one or more genes, the lipidomics data, the metabolomics data, or the combination of the aforementioned, and the functional activity or cellular response in the cells associated with the biological system; (ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and (iii) globally optimizing the ensemble of trial networks by evolving at least some of the trial networks in parallel using the plurality of processors (claim 47); wherein one or more first processors in the plurality of processors used to evolve a first trial network are different from one or more second processors in the plurality of processors used to evolve a second trial network (claim 48); wherein evolving a trial network includes adding a network fragment from the list to the trial network or replacing a network fragment in the trial network with a network fragment from the list and determining whether the addition or replacement improves a total probabilistic score for the trial network (claim 49); wherein the computer-implemented causal relationship network model based on measurements from cells associated with the biological system is a first computer-implemented causal relationship network model; and wherein the method further comprises: obtaining a first comparison data set from comparison cells, the first comparison data set representing measured expression levels of one or more genes in the comparison cells, measured lipidomics data for the comparison cells, measured metabolomics data for the comparison cells, or a combination of the aforementioned; obtaining a second comparison data set for the comparison cells, the second comparison data set representing a measured functional activity or a measured cellular response of the comparison cells; and generating a computer-implemented second causal relationship network model relating the expression levels of the one or more genes in the comparison cells, the lipidomics data for the comparison cells, the metabolomics data for the comparison cells, or the combination of the aforementioned, and the functional activity or cellular response of the comparison cells based on the first comparison data set and the second comparison data set using the programmed computing system; and wherein identifying the causal relationship unique in the biological system based on the computer-implemented first causal relationship network model comprises: generating a computer-implemented differential causal relationship network from the first causal relationship network model and the second causal relationship network model using a computing device; and identifying the causal relationship unique in the biological system from the generated differential causal relationship network (claim 53); wherein generating the computer-implemented differential causal relationship network from the first causal relationship network model and the second causal relationship network model comprises: i) for each relationship between two nodes in a selected one of the first causal relationship network model and the second causal relationship network model, determining if the other causal relationship network model includes a relationship between the same two nodes, and, where the other causal relationship network model includes a relationship between the same two nodes, determining if the relationship between the same two nodes in the other causal relationship network model has at least one significantly different parameter than that of the relationship in the selected causal relationship network model; and ii) forming the differential causal relationship network by including the relationships in the selected causal relationship network model that are absent from the other causal relationship network model and including the relationships in the selected causal relationship network model that have at least one significantly different parameter in the other causal relationship network model (claim 54).
Claims of the ‘835 patent are directed to:A method for identifying a modulator of angiogenesis, said method comprising:
(1) obtaining a first data set from a model for angiogenesis that uses cells associated with angiogenesis to represent a characteristic aspect of angiogenesis, wherein the first data set represents one or more of genomic data, lipidomic data, proteomic data, metabolomic data, transcriptomic data, and single nucleotide polymorphism (SNP) data characterizing the cells associated with angiogenesis;
(2) obtaining a second data set from the model for angiogenesis, wherein the second data set represents one or more functional activities or cellular responses of the cells associated with angiogenesis;
(3) generating a first causal relationship network model among the one or more of genomic data, lipidomic data, proteomic data, metabolic data, transcriptomic data, and single nucleotide polymorphism (SNP) data characterizing the cells associated with angiogenesis, and the one or more functional activities or cellular responses of the cells associated with angiogenesis based on the first data set and the second data set using a programmed computing system including a plurality of processors, wherein generating the first causal relationship network comprises:
(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the one or more of genomic data, lipidomic data, proteomic data, metabolomic data, transcriptomic data, and single nucleotide polymorphism (SNP) data and the one or more functional activities or cellular responses of the cells associated with angiogenesis;
(ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and
(iii) globally optimizing the ensemble of trial networks by evolving the trial networks in parallel using the plurality of processors;
wherein relationships in the first causal relationship network model and causality in the first causal relationship network model are determined based on the first data set and the second data set and not based on previously identified or known biological relationships between variables;
(4) generating a differential causal relationship network from the first causal relationship network model and a second causal relationship network model based on control cell data using a computing device by steps including:
(i) for each relationship between two nodes in a selected one of the first causal relationship network model and the second causal relationship network model, determining if the other causal relationship network model includes a relationship between the same two nodes, and, where the other causal relationship network model includes a relationship between the same two nodes, determining if the relationship between the same two nodes in the other causal relationship network model has at least one significantly different parameter than that of the relationship in the selected causal relationship network model; and
(ii) forming the differential causal relationship network by including the relationships in the selected causal relationship network model that are absent from the other causal relationship network model and including the relationships in the selected causal relationship network model that have at least one significantly different parameter in the other causal relationship network model; and
(5) identifying, from the differential causal relationship network, a causal relationship unique in angiogenesis, wherein a gene, lipid, protein, metabolite, transcript, or SNP associated with the unique causal relationship is identified as a modulator of angiogenesis.
Each of the independent claims in combination with the dependent claims herein are directed to overlapping subject matter with the claims of the ‘545 patent and thus are obvious variants one of the other. Further to the specifics of angiogenesis, it would have been prima facei obvious in view of the teachings of Nikolskya to include mechanisms of angiogenesis as the pathway of interest wherein Nikolskya discloses cancer and disease processes See above rejection under 35 USC 103).
5. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 11,694,765, in view of Nikolskya (cited above). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are directed to:
A method of generating a causal relationship network model of a biological system for identification of a modulator of the biological system, the method comprising:
(1) obtaining a first data set from cells associated with the biological system, the first data set representing measured expression levels of one or more genes in the cells associated with the biological system, measured lipidomics data for the cells associated with biological system, measured metabolomics data for the cells associated with the biological system, or a combination of the aforementioned;
(2) obtaining a second data set from the cells associated with the biological system, the second data set representing a measured functional activity or a measured cellular response of the cells associated with the biological system;
(3) generating a computer-implemented causal relationship network model relating the expression levels of the one or more genes in the cells associated with the biological system, the lipidomics data for the cells associated with the biological system, the metabolomics data for the cells associated with the biological system, or the combination of the aforementioned and the functional activity or cellular response of the cells associated with the biological system based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code;
(4) identifying a causal relationship unique in the biological system based on the computer-implemented causal relationship network model, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of the biological system (claim 1). Said method further includes wherein generating the computer-implemental causal relationship network comprises:(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the measured expression levels of the one or more genes, the lipidomics data, the metabolomics data, or the combination of the aforementioned, and the functional activity or cellular response in the cells associated with the biological system; (ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and (iii) globally optimizing the ensemble of trial networks by evolving at least some of the trial networks in parallel using the plurality of processors (claim 47); wherein one or more first processors in the plurality of processors used to evolve a first trial network are different from one or more second processors in the plurality of processors used to evolve a second trial network (claim 48); wherein evolving a trial network includes adding a network fragment from the list to the trial network or replacing a network fragment in the trial network with a network fragment from the list and determining whether the addition or replacement improves a total probabilistic score for the trial network (claim 49); wherein the computer-implemented causal relationship network model based on measurements from cells associated with the biological system is a first computer-implemented causal relationship network model; and wherein the method further comprises: obtaining a first comparison data set from comparison cells, the first comparison data set representing measured expression levels of one or more genes in the comparison cells, measured lipidomics data for the comparison cells, measured metabolomics data for the comparison cells, or a combination of the aforementioned; obtaining a second comparison data set for the comparison cells, the second comparison data set representing a measured functional activity or a measured cellular response of the comparison cells; and generating a computer-implemented second causal relationship network model relating the expression levels of the one or more genes in the comparison cells, the lipidomics data for the comparison cells, the metabolomics data for the comparison cells, or the combination of the aforementioned, and the functional activity or cellular response of the comparison cells based on the first comparison data set and the second comparison data set using the programmed computing system; and wherein identifying the causal relationship unique in the biological system based on the computer-implemented first causal relationship network model comprises: generating a computer-implemented differential causal relationship network from the first causal relationship network model and the second causal relationship network model using a computing device; and identifying the causal relationship unique in the biological system from the generated differential causal relationship network (claim 53); wherein generating the computer-implemented differential causal relationship network from the first causal relationship network model and the second causal relationship network model comprises: i) for each relationship between two nodes in a selected one of the first causal relationship network model and the second causal relationship network model, determining if the other causal relationship network model includes a relationship between the same two nodes, and, where the other causal relationship network model includes a relationship between the same two nodes, determining if the relationship between the same two nodes in the other causal relationship network model has at least one significantly different parameter than that of the relationship in the selected causal relationship network model; and ii) forming the differential causal relationship network by including the relationships in the selected causal relationship network model that are absent from the other causal relationship network model and including the relationships in the selected causal relationship network model that have at least one significantly different parameter in the other causal relationship network model (claim 54).
Claims of the ‘765 patent are directed to:
A method for identifying a modulator of drug-induced toxicity, said method comprising:
(1) obtaining a first data set from a model for drug-induced toxicity that uses cells associated with drug-induced toxicity and represents a characteristic aspect of drug-induced toxicity, wherein the first data set represents one or more of measured genomics, lipidomics, proteomics, metabolomics, transcriptomics, and single nucleotide polymorphism (SNP) data characterizing the cells associated with drug-induced toxicity;
(2) obtaining a second data set from the model for drug-induced toxicity, wherein the second data set represents a measured functional activity or a measured cellular response of the cells associated with drug-induced toxicity;
(3) obtaining a third data set from comparison cells, wherein the third data set represents one or more of measured genomics, lipidomics, proteomics, metabolomics, transcriptomics, and SNP data characterizing the comparison cells;
(4) obtaining a fourth data set from the comparison cells, wherein the fourth data set represents a measured functional activity or a measured cellular response of the comparison cells;
(5) generating a computer-implemented first causal relationship network among the one or more of measured genomics, lipidomics, proteomics, metabolomics, transcriptomics, and SNP data and the measured functional activity or cellular response based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code; wherein the generation of the first causal relationship network is not based on any known biological relationships other than the first data set and the second data set; and wherein generating the computer-implemented first causal relationship network comprises:
(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the one or more of measured genomics, lipidomics, proteomics, metabolomics, transcriptomics, and SNP data and the measured functional activity or cellular response in the cells associated with drug-induced toxicity;
(ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and
(iii) globally optimizing the ensemble of trial networks by evolving the trial networks in parallel using the plurality of processors;
(6) generating a computer-implemented second causal relationship network among the one or more of measured genomics, lipidomics, proteomics, metabolomics, transcriptomics, and SNP data and the measured functional activity or cellular response based solely on the third data set and the fourth data set using the programmed computing system, wherein the generation of the second causal relationship network is not based on any known biological relationships other than the third data set and the fourth data set; and
(7) identifying, from a computer-implemented comparison of the first causal relationship network and the second causal relationship network, a causal relationship unique in drug-induced toxicity, wherein a gene, lipid, protein, metabolite, transcript, or SNP associated with the unique causal relationship is identified as a modulator of drug-induced toxicity;
wherein the comparison of the first causal relationship and the second causal relationship includes generating a differential causal relationship network from the first causal relationship network and the second causal relationship network;
wherein the differential causal relationship network includes one or more of:
at least one relationship present in the first causal relationship network and absent in the second causal relationship network,
at least one relationship present in the second causal relationship network and absent in the first causal relationship network;
at least one relationship having a different directionality in the first causal relationship network than in the second causal relationship network; or
at least one relationship having at least one significantly different parameter in the first causal relationship network than in the second causal relationship network.
Each of the independent claims in combination with the dependent claims herein are directed to overlapping subject matter with the claims of the ‘765 patent and thus are obvious variants one of the other. Further to the specifics of drug-toxicity, it would have been prima facie obvious in view of the teachings of Nikolskya to include mechanisms for drug toxicity as the pathway of interest wherein Nikolskya discloses drug toxicity at least at (col. 8, ln. 15-25).
6. Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 16-35 of copending Application No. 18/197,673 (reference application), in view of Nikolskya (cited above). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are directed to:
A method of generating a causal relationship network model of a biological system for identification of a modulator of the biological system, the method comprising:
(1) obtaining a first data set from cells associated with the biological system, the first data set representing measured expression levels of one or more genes in the cells associated with the biological system, measured lipidomics data for the cells associated with biological system, measured metabolomics data for the cells associated with the biological system, or a combination of the aforementioned;
(2) obtaining a second data set from the cells associated with the biological system, the second data set representing a measured functional activity or a measured cellular response of the cells associated with the biological system;
(3) generating a computer-implemented causal relationship network model relating the expression levels of the one or more genes in the cells associated with the biological system, the lipidomics data for the cells associated with the biological system, the metabolomics data for the cells associated with the biological system, or the combination of the aforementioned and the functional activity or cellular response of the cells associated with the biological system based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code;
(4) identifying a causal relationship unique in the biological system based on the computer-implemented causal relationship network model, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of the biological system (claim 1).
Application ‘673 claims are directed to:
A method for identifying a modulator of drug-induced toxicity, said method comprising:
(1) establishing a model for drug-induced toxicity, using cells associated with drug- induced toxicity, to represents a characteristic aspect of drug-induced toxicity;
(2) obtaining a first data set from the model for drug-induced toxicity, wherein the first data set represents one or more of genomics, lipidomics, proteomics, metabolomics, transcriptomics, and single nucleotide polymorphism (SNP) data characterizing the cells associated with drug-induced toxicity;
(3) obtaining a second data set from the model for drug-induced toxicity, wherein the second data set represents a functional activity or a cellular response of the cells associated with drug-induced toxicity;
(4) generating a consensus causal relationship network among the expression levels of the one or more of genomics, lipidomics, proteomics, metabolomics, transcriptomics, and single nucleotide polymorphism (SNP) data and the functional activity or cellular response based solely on the first data set and the second data set using a programmed computing device, wherein the generation of the consensus causal relationship network is not based on any known biological relationships other than the first data set and the second data set; (5) identifying, from the consensus causal relationship network, a causal relationship unique in drug-induced toxicity, wherein a gene, lipid, protein, metabolite, transcript, or SNP associated with the unique causal relationship is identified as a modulator of drug-induced toxicity.
Each of the independent claims in combination with the dependent claims herein are directed to overlapping subject matter with the claims of the ‘673 application and thus are obvious variants one of the other. Further to the specifics of drug-toxicity, it would have been prima facie obvious in view of the teachings of Nikolskya to include mechanisms for drug toxicity as the pathway of interest wherein Nikolskya discloses drug toxicity at least at (col. 8, ln. 15-25).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
7. Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-49, 61-68, 81-107, and 109-110 of copending Application No. 19/308,148 (reference application), in view of the prior art to Nikolskya (cited above). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are directed to:
A method of generating a causal relationship network model of a biological system for identification of a modulator of the biological system, the method comprising:
(1) obtaining a first data set from cells associated with the biological system, the first data set representing measured expression levels of one or more genes in the cells associated with the biological system, measured lipidomics data for the cells associated with biological system, measured metabolomics data for the cells associated with the biological system, or a combination of the aforementioned;
(2) obtaining a second data set from the cells associated with the biological system, the second data set representing a measured functional activity or a measured cellular response of the cells associated with the biological system;
(3) generating a computer-implemented causal relationship network model relating the expression levels of the one or more genes in the cells associated with the biological system, the lipidomics data for the cells associated with the biological system, the metabolomics data for the cells associated with the biological system, or the combination of the aforementioned and the functional activity or cellular response of the cells associated with the biological system based solely on the first data set and the second data set using a programmed computing system including storage holding network model building code and a plurality of processors configured to execute the network model building code;
(4) identifying a causal relationship unique in the biological system based on the computer-implemented causal relationship network model, wherein a gene, a lipid, or a metabolite associated with the unique causal relationship is identified as a modulator of the biological system (claim 1).
The claims of the ‘148 application are directed to:
A method for identifying a modulator of a biological system, the method comprising:
establishing a model for the biological system, using cells associated with the biological system, to represents a characteristic aspect of the biological system;
obtaining a first data set from the model, wherein the first data set represents global proteomic changes in the cells associated with the biological system;
obtaining a second data set from the model, wherein the second data set represents one or more functional activities or cellular responses of the cells associated with the biological system, wherein said one or more functional activities or cellular responses of the cells comprises global enzymatic activity and/or an effect of the global enzyme activity on the enzyme metabolites or substrates in the cells associated with the biological system;
generating a consensus causal relationship network among the global proteomic changes and the one or more functional activities or cellular responses based solely on the first and second data sets using a programmed computing device, wherein the generation of the consensus causal relationship network is not based on any known biological relationships other than the first and second data sets; and
identifying, from the consensus causal relationship network, a causal relationship unique in the biological system, wherein at least one enzyme associated with the unique causal relationship is identified as a modulator of the biological system (claim 1 as representative).
Each of the independent claims in combination with the dependent claims herein are directed to overlapping subject matter with the claims of the ‘148 application and thus are obvious variants one of the other. Further to the specifics of drug-toxicity, it would have been prima facie obvious in view of the teachings of Nikolskya to include multiple mechanisms for the pathway of interest wherein Nikolskya discloses metabolites (Figure 35A); gene regulatory networks (Figure 40); disease processes (Figure 42A); toxicity (col. 8, ln. 15-25) and the like.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
Claims 1-3, 5-6, 9-10, 12, 14, 16-18, 45-46, and 51-58 are rejected herein.
Claims 13 and 47-50 meet the eligibility requirements under 35 USC 101 because the claims include recitations that provide for “creating an ensemble of trial networks…and globally optimizing the ensemble of trial networks by evolving the trial networks in parallel using a plurality of processors” which, at the time of the invention are steps not directed to mere abstract mental/mathematical processes, as said steps require parallel computing operations. Further, said operations within the computing environment were not routine, well-known and conventional and provide for improvement to computing time when analyzing large network interactions with a large set of variables.
Claims 13 and 47-50 appear to be free from the prior art because the prior art of Nikolskaya or Nikolskaya and Solomon fail to teach or fairly suggest steps of “creating an ensemble of trial networks…and globally optimizing the ensemble of trial networks by evolving the trial networks in parallel using a plurality of processors”.
Inquiries
Papers related to this application may be submitted to Technical Center 1600 by facsimile transmission. Papers should be faxed to Technical Center 1600 via the PTO Fax Center. The faxing of such papers must conform to the notices published in the Official Gazette, 1096 OG 30 (November 15, 1988), 1156 OG 61 (November 16, 1993), and 1157 OG 94 (December 28, 1993) (See 37 CFR § 1.6(d)). The Central Fax Center Number is (571) 273-8300.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lori A. Clow, whose telephone number is (571) 272-0715. The examiner can normally be reached on Monday-Thursday from 11:00AM to 9:00PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached on (571) 272-9047.
Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to (571) 272-0547.
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/Lori A. Clow/ Primary Examiner, Art Unit 1687
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