DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are currently pending and under exam herein.
Claims 1-20 are rejected.
Priority
The instant application does not claim benefit to any preceding applications. Therefore, the effective filing date of claims 1-20 is 28 February 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 28 February 2023 complies with 37 CFR 1.98. Accordingly, all references listed have been considered by the examiner.
Drawings
The drawings filed on 28 February 2023 have been received and are accepted.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 6, 13, and 20 recite the elements “SNPs,” “mRNA,” and “microRNA” without previously defining the acronyms within the claims. This rejection can be overcome by (1) amending the claims to spell out the acronym upon first appearance in the claims (although applicant is reminded that no new matter may be added to the application), or (2) removing the recitations of “SNPs,” “mRNA,” and “microRNA” from the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract ideas and natural phenomenon) without significantly more. Under MPEP § 2106, subject matter is patent eligible when the claimed invention is to one of the four statutory categories of invention [Step 1], and the claim is not directed to a judicial exception [Step 2A] unless the claim as a whole includes additional limitations amounting to significantly more than the exception [Step 2B].
Step 1
Claims 1-20 describe inventions that are to one of the statutory categories. In Step 1, a claim must fall within one of the four enumerated categories of statutory subject matter (process, machine, manufacture, or composition of matter); a claim falling outside these categories is ineligible without further analysis. See MPEP § 2106.03. Claims 1-7 are properly to one of the four statutory categories because the claimed invention is a system, which falls into the machine category [Step 1: Yes]. Claims 8-14 are properly to one of the four statutory categories because the claimed invention is a method, which falls into the process category [Step 1: Yes]. Claims 15-20 are properly to one of the four statutory categories because the claimed invention is a computer program product comprising program instructions on a computer readable storage medium, which is defined in the specification at para. [0051] to exclude transitory signals per se, falling into the manufacture category [Step 1: Yes].
Step 2A
Under Step 2A, a claim is directed to a judicial exception if, under the broadest reasonable interpretation, it recites an abstract idea, law of nature, or natural phenomena [Prong One] without the claim as a whole integrating the exception into a practical application [Prong Two]. Abstract ideas include mathematical concepts, mental processes, and certain methods of organizing human activity. Mathematical concepts encompass mathematical relationships, formulas, equations, and mathematical calculations. See MPEP § 2106.04(a)(2)(I). Mental processes involve concepts that can be performed in the human mind or by a human with the aid of pen and paper, such as observations, evaluations, judgments, or opinions. See MPEP § 2106.04(a)(2)(III). Certain methods of organizing human activity include fundamental economic principles, commercial or legal interactions, and managing personal behavior or relationships. See MPEP § 2106.04(a)(2)(II). Laws of nature and natural phenomena, include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature. See MPEP § 2106.04(b)-(c).
Prong One
A claim recites a judicial exception when it sets forth or describes a law of nature, natural phenomenon, or abstract idea. Claims 1-20 recite abstract ideas that fall into the groupings of mathematical concepts and mental processes.
Independent claims 1, 8, and 15 recite the following limitations, which describe abstract ideas:
Claim 1 recites a cumulant-based network analysis tool configured to ingest a network of multi-modal biomarkers relating to a complex disease and produce a graphical representation of the network with nodes representing the multi-modal biomarkers and edges representing interactions between the multi-modal biomarkers; and
wherein the user interface is configured for queries relating to one or more phenotypes associated with the complex disease and answers to the queries are visualized on the display as subgraphs of the graphical representation of the network, wherein multi-modal biomarkers of the network that are associated with the one or more queried phenotypes are displayed as highlighted nodes on the subgraphs and interactions of the multi-modal biomarkers that are associated with the one or more queried phenotypes are highlighted as edges on the subgraphs.
Claim 8 recites ingesting a network of multi-modal biomarkers relating to a complex disease into a cumulant-based network analysis (CuNA) tool that produces a graphical representation of the network comprising nodes representing multi-modal biomarkers and edges representing interactions between the multi-modal biomarkers; and
entering one or more queries into the interactive user interface and generating subgraphs in response to the one or more queries, wherein multi-modal biomarkers of the network that are associated with the one or more queried phenotypes are displayed as highlighted nodes on the subgraphs and interactions of the multi-modal biomarkers that are associated with the one or more queried phenotypes are highlighted as edges on the subgraphs.
Claim 15 recites ingesting a network of multi-modal biomarkers relating to a complex disease and producing a graphical representation of the network as nodes representing the multi-modal biomarkers and edges representing interactions between the multi-modal biomarkers; and
building an interactive dashboard comprising a user interface for queries relating to one or more phenotypes associated with the complex disease and a display for visualizing answers to the queries as subgraphs of the graphical representation of the network, wherein multi-modal biomarkers of the network that are associated with the one or more queried phenotypes are displayed as highlighted nodes on the subgraphs and interactions of the multi-modal biomarkers that are associated with the one or more queried phenotypes are highlighted as edges on the subgraphs.
The limitations related to a cumulant-based network analysis tool ingesting a network of multi-modal biomarkers involves specific mathematical/statistical techniques for modeling higher-order interactions, which constitutes an abstract idea within the mathematical concepts grouping. See MPEP § 2106.04(a)(2)(I). For small or simple network problems, cumulant-based network analysis can be performed mentally or with the aid of pen and paper, constituting an abstract idea within the mental processes grouping. See MPEP § 2106.04(a)(2)(III). The limitations related to phenotype queries to display a subgraph where associated nodes and edges are highlighted involves analyzing associations and identifying relationships in the network, which could be performed by a human mentally or with pen and paper after receiving the phenotype(s) of interest, constituting an abstract idea within the mental processes grouping. See MPEP § 2106.04(a)(2)(III). Additionally, providing a subgraph with highlighted nodes and edges that are associated with the queried phenotype is a form of filtering content, which constitutes an abstract idea within the organizing human activity grouping. See MPEP § 2106.04(a)(2)(II)(C).
Dependent claims 2-7, 9-14, and 16-20 recite the following limitations, which narrow or describe abstract ideas:
Claims 2, 9, and 16 recite wherein a neighborhood of multi-modal biomarkers is established for the complex disease by measuring the shortest edge distance between any node pair within a subgraph.
Claims 3, 10, and 17 recite wherein node importance is determined based upon an aggregate of centrality measures selected from the group consisting of degree, eigenvector centrality, betweenness centrality, information centrality, voterank, and combinations thereof.
Claims 4, 11, and 18 recite wherein an overlapping node is a single node highlighted by two or more queries.
Claims 5, 12, and 19 recite wherein an overlapping node spanning two or more subgraphs indicates a significant multi-modal biomarker for the complex disease.
Claims 6, 13, and 20 recite wherein the multi-modal biomarkers are selected from the group consisting of genes, SNPs, mRNA, microRNA, proteins, metabolites, enzymes, imaging-derived phenotypes, continuous phenotypes, binary phenotypes, and combinations thereof.
Claims 7 and 14 recite wherein the complex disease is selected from the group consisting of cardiovascular diseases, neurological diseases, cancer, bacterial diseases, and viral diseases.
The limitations of claims 2, 9, and 16 describe using the shortest path algorithm to build a neighborhood or determine a local cluster, which can be performed mentally or with pen and paper, constituting an abstract idea within the mathematical concepts and mental processes groupings. See MPEP §§ 2106.04(a)(2)(I) & (III). The limitations of claims 3, 10, and 17 describe using centrality calculations to determine node importance, which can be performed mentally or with pen and paper, constituting an abstract idea within the mathematical concepts and mental processes groupings. See MPEP §§ 2106.04(a)(2)(I) & (III). The limitations of claims 4, 11, and 18 narrow the phenotype query limitation of the independent claims by specifying that an overlapping node occurs when a node is common to two or more queries. The limitations of claims 5, 12, and 19 describe identifying significant biomarkers for a disease based on overlapping nodes, which constitutes an abstract idea within the mental processes grouping. See MPEP § 2106.04(a)(2)(III). The limitations of claims 6-7, 13-14, and 20 narrow the abstract ideas of the independent claims by specifying the biomarkers and diseases being analyzed.
Therefore, claims 1-20 recite abstract ideas within the groupings of mathematical concepts, organizing human activity, and mental processes [Step 2A, Prong One: Yes].
Prong Two
Claims 1-20 as a whole do not integrate the recited judicial exception into a practical application. A claim that recites a judicial exception [Prong One] is deemed to be directed to a judicial exception [Step 2A] unless the claim as a whole contains additional elements that integrate the exception into a practical application [Prong Two]. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See MPEP §§ 2106.04(d) and 2106.05(e). A claim does not integrate a judicial exception into a practical application by reciting insignificant extra-solution activity, generally linking the exception to a particular technological environment or field of use, merely reciting to apply the exception, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP § 2106.04(d)(I). Insignificant extra-solution activities are nominal or tangential additions to a claim that are incidental to the primary process or product, including both pre-solution and post-solution activity (e.g. pre-solution data gathering for use in a process). If integrated into a practical application, the claim is eligible; otherwise, it is directed to the judicial exception, necessitating further analysis at Step 2B.
Independent claims 1, 8, and 15 recite the following limitations, which are additional elements:
Claim 1 recites an interactive dashboard comprising a user interface and a display.
Claim 8 recites wherein the CuNA tool is associated with an interactive dashboard comprising a user interface for queries relating to one or more phenotypes associated with the complex disease and a display for visualizing answers to the queries as subgraphs of the graphical representation of the network.
Claim 15 recites program instructions on one or more computer readable storage media.
The additional elements are generic computer components used to apply the mathematical/analytical process, amounting to mere instructions to apply the abstract manual process on a computer, which does not integrate the judicial exceptions into a practical application. See MPEP § 2106.05(f). Claims 2-7, 9-14, and 16-20 do not include any additional elements.
The claims as a whole merely recite abstract ideas implemented on generic computer components without meaningful limitations that tie it to a specific technological improvement. Therefore, claims 1-20 do not contain additional elements that integrate the recited abstract ideas into a practical application [Step 2A, Prong Two: No].
Step 2B
Claims 1-20 do not include additional elements, whether considered individually or in combination, that are sufficient to amount to significantly more than the judicial exception itself. Under Step 2B, the claim is analyzed to determine whether there are any additional elements that, individually or in combination, constitute an “inventive concept" sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. See MPEP § 2106.05; and Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217-18, 110 USPQ2d 1976, 1981 (2014).
Independent claims 1, 8, and 15 recite the following limitations, which are additional elements:
Claim 1 recites an interactive dashboard comprising a user interface and a display.
Claim 8 recites wherein the CuNA tool is associated with an interactive dashboard comprising a user interface for queries relating to one or more phenotypes associated with the complex disease and a display for visualizing answers to the queries as subgraphs of the graphical representation of the network.
Claim 15 recites program instructions on one or more computer readable storage media.
The additional elements are generic computer implementation of network analysis, data querying, and graphical visualization, which were well-understood, routine, and conventional before the effective filing date of the claimed invention. See MPEP §§ 2106.05(d) & (f); Mathieu Bastian and Sebastien Heymann, Gephi: An Open Source Software for Exploring and Manipulating Networks, 3(1) Proceedings of the International AAAI Conference on Web and Social Media 361-62, Abstract (2009); and Paul Shannon et al., Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks, 13(11) Genome Res. 2498-504, Abstract (November 2003). Thus, there is no inventive concept sufficient to amount to significantly more than the judicial exceptions themselves.
Overall, claims 1-20 amount to no more than implementing abstract ideas on conventional computers in a routine way. Therefore, claims 1-20 are rejected for failing to set forth patent eligible subject matter under 35 U.S.C. 101 because the claimed invention recites abstract ideas [Step 2A, Prong One: Yes] and the additional elements do not integrate the judicial exception into a practical application [Step 2A, Prong Two: No] and do not amount to claiming significantly more than the recited exception [Step 2B: No].
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Aritra Bose et al., CuNA: Cumulant-based Network Analysis of genotype-phenotype associations in Parkinson’s Disease, medRxiv 2021.08.02.21261457 (preprint 5 August 2021) (IDS document) (hereinafter “Bose”) in view of Melissa S Cline et al., Integration of biological networks and gene expression data using Cytoscape, 2 Nat Protoc 2366-82 (27 September 2007) (hereinafter “Cline”), as evidenced by Geno4SD, Getting Started (2022), https://biomedsciai.github.io/-Geno4SD/source/-installation.html#install-geno4sd (hereinafter “Geno4SD”).
Independent Claims
Regarding claim 1, Bose discloses CuNA, a cumulant-based network analysis tool designed to find higher order genotype-phenotype interactions by integrating genes implicated in the disease and the associated phenotypes or clinical features. At ll.32-34 (a cumulant-based network analysis tool). Bose teaches using CuNA to study Parkinson’s Disease (PD) by inputting genes, SNPs, and binary phenotypes related to PD. At ll.65-66; 123-124 (configured to ingest a network of multi-modal biomarkers relating to a complex disease). CuNA builds graphical representations of the networks with nodes representing the input features and edges representing the weighted interactions between features. At ll.156-158; Figure 2 (produce a graphical representation of the network with nodes representing the multi-modal biomarkers and edges representing interactions between the multi-modal biomarkers). Bose teaches that the resulting network contains patterns where a specific phenotype may be associated with genotypes or other -omic data using standard methods to reveal logical relationships among features. At ll.560-563.
Bose fails to disclose an interactive dashboard comprising a user interface and a display, wherein the user interface is configured for phenotype queries associated with the complex disease and answers to the queries are visualized on the display as subgraphs of the graphical representation of the network, wherein associated multi-modal biomarkers are displayed as highlighted nodes on the subgraphs and associated interactions of the multi-modal biomarkers are highlighted as edges on the subgraphs. However, Cline discloses a protocol using Cytoscape for data-to-visual attribute mapping that allows biologists to synoptically view multiple types of data in a network context. At 2367 col.1 para.1. Cytoscape uses a graphical user interface to allow users to interact with the system. At 2369 para.5 (an interactive dashboard comprising a user interface and a display). The protocol begins by obtaining network data depicting interactions between features. At 2368 col.1 para.4 – col.2 para.4. Cline teaches that users can analyze network features by selecting an expression experiment or a subset of experiments. At 2376 para.4 – 2377 para.2 (wherein the user interface is configured for queries relating to one or more phenotypes associated with the complex disease). The nodes and edges corresponding to the user-selected expression experiment are selected in the Cytoscape canvas to be displayed as a separate network within the larger network. At 2377 para.6; Figure 5 (answers to the queries are visualized on the display as subgraphs of the graphical representation of the network). The corresponding nodes within the subnetwork are highlighted according to their predicted importance, while edges are highlighted according to the type of interaction represented. At 2378 para.1; Figures 5-6 (wherein multi-modal biomarkers of the network that are associated with the one or more queried phenotypes are displayed as highlighted nodes on the subgraphs and interactions of the multi-modal biomarkers that are associated with the one or more queried phenotypes are highlighted as edges on the subgraphs).
A person having ordinary skill in the art could combine CuNA with Cytoscape, and in combination, the platforms would perform the same function as they do separately. One of ordinary skill in the art would use CuNA to input multi-modal data and build networks of interactions before inputting those networks into Cytoscape for visualization and pattern exploration. One of ordinary skill in the art would recognize that the results of the combination are predictably an improved system for network analysis that allows users to explore interactions related to phenotypes of interest. Combining prior art elements according to known methods to yield predictable results is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, A.
Regarding claim 8, Bose discloses inputting genes, SNPs, and binary phenotypes related to PD into CuNA to produce a graphical representations of the networks with nodes representing the input features and edges representing the weighted interactions between features. At ll. 65-66; 123-124; 156-158; Figure 2 (a computer implemented method comprising: ingesting a network of multi-modal biomarkers relating to a complex disease into a cumulant-based network analysis (CuNA) tool that produces a graphical representation of the network comprising nodes representing multi-modal biomarkers and edges representing interactions between the multi-modal biomarkers). In combining CuNA with Cytoscape, CuNA is associated with a graphical user interface for phenotypic queries that displays the answer as a subnetwork of the larger network. Cline at 2369 para.5; 2377 para.6; Figure 5 (wherein the CuNA tool is associated with an interactive dashboard comprising a user interface for queries relating to one or more phenotypes associated with the complex disease and a display for visualizing answers to the queries as subgraphs of the graphical representation of the network). In utilizing the combined CuNA- Cytoscape system, a user would select phenotypes of interest and generate a subnetwork of corresponding nodes and edges. Cline at 2377 para.6; Figure 5 (entering one or more queries into the interactive user interface and generating subgraphs in response to the one or more queries). The corresponding nodes within the subnetwork are highlighted according to their predicted importance, while edges are highlighted according to the type of interaction represented. Cline at 2378 para.1; Figures 5-6 (wherein multi-modal biomarkers of the network that are associated with the one or more queried phenotypes are displayed as highlighted nodes on the subgraphs and interactions of the multi-modal biomarkers that are associated with the one or more queried phenotypes are highlighted as edges on the subgraphs).
Regarding claim 15, Bose discloses CuNA, a cumulant-based network analysis tool designed to find higher order genotype-phenotype interactions by integrating genes implicated in the disease and the associated phenotypes or clinical features. At ll.32-34 (a computer program product for discovery of multi-modal biomarkers for complex diseases comprising). CuNA is an open-source algorithm implemented in Python, which is publicly available for researchers to download and use. At l.144; see also Geno4SD, § Install Geno4SD (program instructions on one or more computer readable storage media). Bose discloses inputting genes, SNPs, and binary phenotypes related to PD into CuNA to produce a graphical representations of the networks with nodes representing the input features and edges representing the weighted interactions between features. At ll. 65-66; 123-124; 156-158; Figure 2 (ingesting a network of multi-modal biomarkers relating to a complex disease and producing a graphical representation of the network as nodes representing the multi-modal biomarkers and edges representing interactions between the multi-modal biomarkers). Cline discloses that Cytoscape is an open-source software freely available for researchers to download and use. At 2367 col.1 para.1; Box 2: Cytoscape Installation (program instructions on one or more computer readable storage media for building an interactive dashboard). Cytoscape uses a graphical user interface for phenotypic queries that displays the answer as a subnetwork of the larger network. Cline at 2369 para.5; 2377 para.6; Figure 5 (a user interface for queries relating to one or more phenotypes associated with the complex disease and a display for visualizing answers to the queries as subgraphs of the graphical representation of the network). The nodes corresponding to the phenotype of interest within the subnetwork are highlighted according to their predicted importance, while edges are highlighted according to the type of interaction represented. At 2378 para.1; Figures 5-6 (wherein multi-modal biomarkers of the network that are associated with the one or more queried phenotypes are displayed as highlighted nodes on the subgraphs and interactions of the multi-modal biomarkers that are associated with the one or more queried phenotypes are highlighted as edges on the subgraphs).
Dependent Claims
Regarding claims 2, 9, and 16, Cline discloses that users can select the search strategy for establishing high-scoring modules, one of which uses the shortest path between node pairs within a subnetwork. At 2377 para.4 (claims 2 & 9: wherein a neighborhood of multi-modal biomarkers is established for the complex disease by measuring the shortest edge distance between any node pair within a subgraph; claim 16: further comprising program instructions for measuring the shortest edge distance between any node pair within a subgraph in order to identify a neighborhood of multi-modal biomarkers for the one or more queried phenotypes).
Regarding claims 4, 11, and 18, Bose discloses that two distinct patterns that yield the same features are called redescriptions. At ll.557-558 (claims 4 & 11: wherein an overlapping node is a single node highlighted by two or more queries; claim 18: further comprising program instructions for highlighting when a single node is included in answer to two or more queries).
Regarding claims 5, 12, and 19, Bose teaches that redescriptions can reveal logical relationships among features that may reflect underlying biological pathways. At ll.559-563 (wherein an overlapping node spanning two or more subgraphs indicates a significant multi-modal biomarker for the complex disease).
Regarding claims 6, 13, and 20, Bose teaches using CuNA by inputting genes, SNPs, and binary phenotypes related to PD. At ll.65-66; 123-124 (wherein the multi-modal biomarkers are selected from the group consisting of genes, SNPs, mRNA, microRNA, proteins, metabolites, enzymes, imaging-derived phenotypes, continuous phenotypes, binary phenotypes, and combinations thereof).
Regarding claims 7 and 14, Bose teaches using CuNA to study Parkinson’s Disease, which is a complex neurological disorder. At l.13 (wherein the complex disease is selected from the group consisting of cardiovascular diseases, neurological diseases, cancer, bacterial diseases, and viral diseases).
Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bose and Cline as applied to claims 1-2, 4-9, 11-16, and 18-20 above, and further in view of Sergio Gómez, Centrality in Networks: Finding the Most Important Nodes, in Moscato, P., de Vries, N. (eds) Business and Consumer Analytics: New Ideas (31 May 2019) (hereinafter “Gómez”) and Giovanni Scardoni et al., CentiScaPe, Cytoscape App Store (21 April 2017) (hereinafter “Scardoni”).
Regarding claims 3, 10, and 17, Bose and Cline teach independent claims 1, 8, and 15 (see rejections above). Bose and Cline fail to teach wherein node importance is determined based upon an aggregate of centrality measures selected from the group consisting of degree, eigenvector centrality, betweenness centrality, information centrality, voterank, and combinations thereof. However, Gómez discloses determining node importance based on an aggregate of eigenvector centrality. At 418 paras.2-3. Gómez teaches that Cytoscape does not directly calculate centralities, but there are plug-ins which can be used to find centralities. At 429 para.5. Scardoni discloses one such plug-in, CentiScaPe, which computes specific centrality parameters describing the network topology. At para.2.
Based on the teachings of Gómez, a person having ordinary skill in the art would modify the combined system of Bose and Cline to include the CentiScaPe Cytoscape plug-in to determine node importance based on an aggregate of eigenvector centrality. One of ordinary skill in the art would reasonably expect success in this modification because CentiScaPe is designed to determine node importance based on centrality within the Cytoscape platform. Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, USPQ2d 1385, 1395 – 97 (2007); and MPEP § 2143, G.
Conclusion
No claim is allowed.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mohamed Abdel-Hafiz et al., Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification, 5 Front Big Data. (22 June 2022). Discloses methods for detecting significant subgraphs in networks and techniques to visually compare subgraphs produced by the methods.
Mathieu Bastian and Sebastien Heymann, Gephi: An Open Source Software for Exploring and Manipulating Networks, 3(1) Proceedings of the International AAAI Conference on Web and Social Media 361-62 (2009). Discloses Gephi, an open source software for graph and network exploration and manipulation.
Sunil Nagpal et al., NetConfer: a web application for comparative analysis of multiple biological networks, 18 BMC Biol (19 May 2020). Discloses NetConfer, a graphical user interface based web application that implements multiple network comparison methodologies and presents them in the form of organized analysis workflows.
Fang Zheng et al., Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks, 2018:5670210 Biomed Res Int (30 July 2018). Discloses a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network.
Vivek Sriram et al., NETMAGE: A human disease phenotype map generator for the network-based visualization of phenome-wide association study results, 11 Gigascience (15 February 2022). Discloses NETMAGE, a web-based tool that produces interactive disease-disease network visualizations. Users can search the map by various attributes and select nodes to view related phenotypes, associated variants, and various network statistics.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily Ann Darrigrand whose telephone number is (571) 272-1098. The examiner can normally be reached Monday-Thursday 7:00AM-4:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry Riggs, can be reached at (571) 270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.A.D./ Examiner, Art Unit 1686
/LARRY D RIGGS II/ Supervisory Patent Examiner, Art Unit 1686
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