DETAILED ACTION
Applicant’s response, filed May 27 2026, has been fully considered. Rejections and/or objections not reiterated from previous Office Actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Claim Status
Claims 38-53 are pending.
Claims 1-37 are canceled.
Claim 53 is newly added.
Claims 38 and 51-52 are objected to.
Claims 38-53 are rejected.
Priority
The instant Application claims domestic benefit to US provisional application 62/941,557, filed Nov 27 2019.
Applicant's claim for the benefit of a prior-filed application, PCT/US2020/061787, filed Nov 23 2020, is acknowledged.
Accordingly, each of claims 38-53 are afforded the effective filing date of Nov 27 2019.
Drawings
The replacement drawing sheets submitted May 27 2026 are accepted and the outstanding objections from the previous Office Action are withdrawn.
Specification
The amendments to the specification submitted May 27 2026 are accepted and the outstanding objections from the previous Office Action are withdrawn.
Claim Objections
The outstanding objections to the claims are withdrawn in view of the amendments submitted herein.
The claims are objected to because of the following informalities. The instant objection is newly stated and is necessitated by claim amendment.
Claims 38 and 51-52 recite, in the second to last line, “the generated network in according with the filtering”, which should be amended to recite “accordance”.
Claim Rejections- 35 USC § 112
The outstanding rejections to the claims are withdrawn in view of the amendments submitted herein.
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 38-53 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. Any newly recited portions are necessitated by claim amendment.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, 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
Step 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
With respect to Step 1: yes, the claims are directed to a system, a method, and a non-transitory computer readable storage medium, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of 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 Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) as well as a law of nature or a natural phenomenon are as follows:
Independent claims 38 and 51-52: identifying respective microbes and/or genes in the microbiome data stored in the database;
wherein the database of said microbiome data represents microbiomes, in a plurality of samples acquired at respective times, from each of a plurality of groups of animals including at least one control group and at least one treatment group (claims 51-52);
generating a network comprising nodes interconnected by edges in the memory, each node representing one or more identified microbes or one or more microbial metabolites, and each edge of the network representing an association between a respective pair of the one or more identified microbes or a reaction mediated between two metabolites by an enzyme encoded in the one or more identified genes, wherein at least some nodes and edges of the network are each associated with a condition attribute identifying one of said plurality of groups from which a respective sample was acquired and/or a timestamp attribute identifying a time of one of said samples;
…display (of the graphical representation of the generated network) thereon, wherein at least one of the at least some nodes and edges of the graphical representation are rendered with at least one visual property;
responsive to interactive input, dynamically updating the graphical representation of the generated network being displayed in accordance with a filtering, of the microbiome data, based at least on the condition attribute and/or the timestamp attribute associated with respective nodes and/or edges in the network; and
wherein the dynamic updating comprises… changing the at least one visual property of at least one of the at least some nodes and/or edges to reflect a difference between the generated network prior to the filtering based on the condition attribute and/or the timestamp attribute and the generated network in according with the filtering based on the condition attribute and/or the timestamp attribute.
Dependent claim 42: calculate a correlation, in the microbiome data, of the microbes represented by the first and second nodes; and
indicate at least some of the calculated correlation in the displayed network.
Dependent claim 47: in response to receiving interactive input, performing taxonomic restructuring of the network by applied condition.
Dependent claim 48: in response to receiving interactive input, perform said taxonomic restructuring providing identification of conditions to selectively increase or decrease relative abundance of selected organisms in the microbiome.
Dependent claim 49: in response to receiving interactive input, perform said taxonomic restructuring providing identification of conditions that maximize commensal-istic conditions that benefit a host of the sample or minimize competition that causes conditions detrimental to the host.
Dependent claim 53: dynamically transition the graphical representation from a first network state to a second network state, wherein the first network state corresponds a first selected condition attribute or timestamp attribute, wherein the second network state corresponds to a second selected condition attribute or timestamp attribute, wherein a visual property of one or more of the nodes and/or edges differs from the first network state to the second network state.
Dependent claims 39-41, 43-44, 47, and 50 recite further steps that limit the judicial exceptions in independent claim 38 and, as such, also are directed to those abstract ideas. For example, claim 39 further limits what microbes the generated network includes; and claims 40-41, 43-44, 47, and 50 further limit what the nodes of the network represent.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually update a displayed network after filtering based on an attribute. Without further detail as to the methodology involved in “identifying”, “generating”, “displaying, “updating”, “transitioning”, “filtering”, “calculating”, and “performing taxonomic reconstruction”, under the BRI, one may simply, for example, use pen and paper to identify microbes and/or genes in data, generate a network comprising nodes and edges of the identified microbes or metabolites/genes, filtering the network based on attributes associated with the nodes and/or edges, updating the network after filtering or transitioning the network from a first to a second network based on selected conditions or timestamps, calculate and display a correlation in the network, and performing taxonomic reconstruction based on a condition. The display of an abstract idea (i.e., displaying a network) is considered to recite that same abstract idea (see Interval Licensing LLC v AOL, Inc., 896 F.3d 1335, 1344-45 (Fed. Cir. 2018), which recognized that information “is an intangible” and that “the collection, organization, and display of two sets of information on a generic display device is abstract absent a specific improvement to the way computers operate”; also Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) for reciting a mental process in a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind (MPEP 2106.04(a)(2)(III)(A))). Some of these steps, such as calculate a correlation, require mathematical techniques as the only supported embodiments, as is disclosed in the specification as published at least at [0069; 0081; 0087-0088].
Therefore, claim 38 and 51-52 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions 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 judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claim 38: storing a database of said microbiome data representing microbiomes, in a plurality of samples acquired at respective times, from each of a plurality of groups of animals including at least one control group and at least one treatment group.
Independent claims 38 and 51-52: outputting a graphical representation of the generated network to the display…; and
It is noted that “outputting” information to the display reads on outputting instructions by a computer.
…automatically changing….
Dependent claims 45-46 recite steps that further limit the recited additional elements in the claims. For example, claim 45-46 further limits the stored microbiome data;
The claims also include non-abstract computing elements. For example, independent claim 38 includes a system comprising a memory, a display, and a processor; claim 51 includes a display; and claim 52 includes a non-transitory computer readable storage medium storing instructions, which, when executed by one or more processors of a computer, causes the computer to perform operations, and a display.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “storing a database”, and data outputting, such as “outputting a graphical representation of the generated network to the display for display thereon”, perform functions of collecting and outputting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of the computing system in claims 38 and 51-52 do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Additionally, the limitation directed to “automatically changing” the displayed network reads on the use of a computer to change the information displayed based on “interactive input”. Hence, these are mere instructions to apply the judicial exceptions using a computer in response to a user’s input, and therefore the claim does not integrate the judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
The specification as published discloses that there is a need for systems and methods capable of visualizing digitized microbiome data from animals in a format that allows a scientist to interpret the data and reach actionable conclusions, including, but not limited to the identification of biomarkers and therapeutic targets at [0005], but does not provide a clear explanation for how the additional elements provide these improvements. Therefore, the additional elements do not clearly improve the functioning of a computer, or comprise an improvement to any other technical field. Further, the additional elements do not clearly affect a particular treatment; they do not clearly require or set forth a particular machine; they do not clearly effect a transformation of matter; nor do they clearly provide a nonconventional or unconventional step (MPEP2106.04(d)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: 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 courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)). As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 38 and 51-52 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)). The specification as published also notes that computer processors and systems, as example, are commercially available or widely used at [0040-0041; 0200-0208]. 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 the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Response to Applicant Arguments
At p. 18-19, Applicant submits that the claims otherwise integrate any recited judicially excepted subject matter into a practical application because the claims are similar to Example 37 of the USPTO’s patent eligibility examples. Applicant submits that in claim 37, “automatically moving” was considered an additional element which provided a specific improvement by using usage information to arrange icons. Applicant submits that the instant claims include outputting a graphical representation which are automatically updated, which should be included as an additional element. Applicant submits that these limitations provide an improvement through the graphical representation that includes a visual property tied to the node/edge and an associated attribute and how that graphical representation can then be dynamically updated in response to user input, and how the properties of the nodes or edges are automatically changed as part of that dynamic update, which Applicant considers distinct and improved from prior art systems which are generally static.
It is respectfully submitted that this is not persuasive. In Example 37, the claims are drawn to automatically moving the most used icons, an additional element which provided a specific improvement by using usage information to arrange icons, as asserted by Applicant. However, the facts of the instant claims and those at issue in Example 37 are not the same. The instant claims are not directed to automatically moving icons based on usage, but to displaying different parts of a network based on a user input. Such actions reflect the mere selection of data that is desired to be viewed, and the application of a computer to automatically perform such a display. In Example 37, the improvement provided to the user was in using the GUI, whereas the purported improvement in the instant claims is in viewing data displayed on a screen. Such a purported improvement is not considered to actually provide an improvement in an additional element because the display of the data is considered to recite the abstract idea of the generated, selected, and filtered data, as discussed in the above rejection.
Further, it is not considered that the mere updating of a displayed network to show a relevant part of the network is an improvement over the state of the art, as evidenced by at least DeHaven et al. (US 2016/0019335), which discloses that: the nodal network may be searched according to at least one attribute or search characteristic of one of the metabolites, the nodes, the relationships, and the annotations, and the results of the search may be graphically displayed in relation to the nodal network [0021; 0026-0028] using, for example, a relational database and toggling between different functionalities [0024] or using different filters [0032]; and when metabolite profiles are available for a single, a group, or multiple groups of patients at different time points, aspects of the systems, methods, and computer program products of the present disclosure may allow the user to browse these profiles directly mapped on the pathway/relationship/association networks (see, e.g., FIG. 10) via dynamic frames that appear when a node is selected [0033]. While the additional elements of the claims are examined at Step 2B for conventionality, an improvement to the state of the art at Step 2A, Prong 2, does not require the same proof of widespread adoption in the field. It is considered that the disclosure of DeHaven of automatically displaying only desired portions of a network provides evidence that the instant claims do not recite an improvement to the state of the art at the time of filing, despite the discussion in the specification compared to conventional static systems. It is considered that the disclosure in the specification is not a comprehensive disclosure of the entire field at the time of filing.
At p. 20, Applicant submits that the specific way in which the visual output of the biological data is being rendered results in the claims being patent eligible under the Federal Circuit's Core Wireless case because the display and automatic change do not merely statically present or generally display information, but use non-generic condition/timestamp labels that are structural attributes of a biological network for display. Applicant submits that this is a specific technical improvement to the display function.
It is respectfully submitted that this is not persuasive. In Core Wireless Licensing S.A.R.L., v. LG Electronics, Inc., 880 F.3d 1356, 1362-63, 125 USPQ2d 1436, 1440-41 (Fed. Cir. 2018), the claims provided an improved user interface for electronic devices that displays an application summary of unlaunched applications, where the particular data in the summary is selectable by a user to launch the respective application, which is considered to recite an improvement to the functioning of a computer (see MPEP 2106.05(a)(I). However, the instant claims do not recite the same improvement because the facts of the instant claims and those in Core Wireless are different. In the instant claims, the user does not do anything with the displayed information as in Core Wireless. Contrary to Applicant’s assertion that using “structural attributes of a biological network for display” provides a technical improvement to the display function, such selection, filtering, and display of the data does not change the actual function of the computer or the display itself, but merely recites the display of manipulated data in an automated way by a computer.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
A. Claims 38-43 and 45-53 are rejected under 35 U.S.C. 103 as being unpatentable over DeHaven et al. (US 2016/0019335; previously cited) in view of Ma et al. (Scientific Reports, 2016, 6(28048):1-13; cited on the May 25 2022 IDS). The instant rejection is maintained from the previous Office Action and any newly recited portions are necessitated by claim amendment.
The prior art to DeHaven discloses a method for analyzing metabolite data in a sample (abstract). DeHaven, indicated by the open circles, teaches the instant features, indicated by the closed circles, as follows. Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims.
Claim 38 discloses a system for visualizing microbiome data, comprising a memory storing a database, a display, and a processor configured to perform method steps. Claim 51 discloses a method for visualizing microbiome data. Claim 52 discloses a non-transitory computer readable storage medium storing instructions, which, when executed by one or more processors of a computer, causes the computer to perform operations. DeHaven teaches a method, system, and computer program product for analyzing metabolomics data for a plurality of metabolites in a sample ([0003]; FIG. 16), where the present disclosure is directed to implementing a system that provides storage, query-tools, and visualization of a biochemical knowledgebase [0020].
The method and operations of claims 38 and 51-52 comprise:
identifying respective microbes and/or genes in microbiome data stored in a database, wherein the database of said microbiome data represents microbiomes, in a plurality of samples acquired at respective times, from each of a plurality of groups of animals including at least one control group and at least one treatment group;
DeHaven teaches storing extensive and, in some instances, complete, biochemical pathway information including, for example, biochemicals, reactants, products, cofactors, directionality, intra-pathway relationships, combinations thereof, and/or any other suitable relationships or associations related to the biochemical pathway information [0020]. DeHaven teaches storing attributes in a database [0024], where the attributes related to additional types of -omics data (e.g., genomics (i.e., genes), transcriptomics, proteomics, DNA copy number, etc.) besides the metabolomics data [0024]. DeHaven teaches that information such as organism, species, and metadata can be associated with the data in storage [0024]. DeHaven teaches accessing metabolite profiles for a single, a group, or multiple groups (e.g., control vs. disease (i.e., a plurality of groups comprising control and treatment groups)) of patients at different time points [0033].
generating a network comprising nodes interconnected by edges in the memory, each node representing one or more identified microbes or one or more microbial metabolites, and each edge of the network representing an association between a respective pair of the one or more identified microbes or a reaction mediated between two metabolites by an enzyme encoded in the one or more identified genes, wherein at least some nodes and edges of the network are each associated with a condition attribute identifying one of said plurality of groups from which a respective sample was acquired and/or a timestamp attribute identifying a time of one of said samples; and
DeHaven teaches assigning each of a plurality of metabolites to a node, connecting corresponding nodes according to a defined relationship between corresponding metabolites to form and graphically display a nodal network (FIG. 1, 3, 4-5, 10-11; [0020]). DeHaven teaches that metabolites and/or enzymes may be assigned to nodes, and the reaction between metabolites may be assigned to edges [0024]. DeHaven teaches that at least one of the nodes and one of the relationships is annotated with at least one of empirical information associated therewith and relational information associated with other nodes and relationships [0020; 0039]. DeHaven teaches accessing metabolite profiles for a single, a group, or multiple groups (e.g., control vs. disease) of patients at different time points [0033]. As DeHaven teaches accessing metabolite profiles for specific groups at specific time points and annotating nodes with relationships, it is considered that DeHaven fairly teaches attributes for a respective sample and timestamps as instantly claimed.
outputting a graphical representation of the generated network to the display for display thereon, wherein at least one of the at least some nodes and edges of the graphical representation are rendered with at least one visual property;
DeHaven teaches that the nodal network is visually/graphically displayed such that at least a portion of the nodes and the relationships therebetween are visible in a single view (i.e., rendering with at least one visual property) [0020]. DeHaven teaches that at least one of the nodes and one of the relationships is annotated with at least one of empirical information associated therewith and relational information associated with other nodes and relationships [0020], which also reads on rendering with at least one visual property. DeHaven teaches visualizing the networks by display on a monitor or screen of a computer device [0032].
responsive to interactive input, dynamically updating the graphical representation of the generated network being displayed in accordance with a filtering, of the microbiome data, based at least on the condition attribute and/or the timestamp attribute associated with respective nodes and/or edges in the network; and wherein the dynamic update comprises automatically changing the at least one visual property of at least one of the at least some nodes and/or edges to reflect a difference between the generated network prior to the filtering based on the condition attribute and/or the timestamp attribute and the generated network in according with the filtering based on the condition attribute and/or the timestamp attribute.
DeHaven teaches that the nodal network may be searched according to at least one attribute or search characteristic of one of the metabolites, the nodes, the relationships, and the annotations, and the results of the search may be graphically displayed in relation to the nodal network (i.e., reflecting a difference between the generated network prior to the filtering) [0021; 0026-0028] using, for example, a relational database and toggling between different functionalities (i.e., dynamically updating the network in response to interactive input) [0024] or using different filters [0032]. DeHaven teaches a manipulation engine or tool configured to allow a user to run queries on the metabolite data and/or provide visualization of that data, where a user may visualize results in a graphical environment or as a graphic depicting relevant nodes and relationships therebetween (i.e., automatically changing at least one visual property of the nodes and edges) [0026]. DeHaven teaches that when metabolite profiles are available for a single, a group, or multiple groups (e.g., control vs. disease) of patients at different time points, aspects of the systems, methods, and computer program products of the present disclosure may allow the user to browse these profiles directly mapped on the pathway/relationship/association networks (see, e.g., FIG. 10) via dynamic frames that appear when a node is selected, in order that users may be directed to metabolite nodes that include the most significant profile variations for facilitating the analysis (i.e., reflecting a difference between the generated network prior to the filtering) [0033].
DeHaven does not explicitly teach visualizing microbiome data as instantly claimed.
However, the prior art to Ma discloses the effects of Hodgkin’s lymphoma and the chemotherapy for treating the disease on the human milk microbiome through integrated network and community diversity analyses (abstract). Ma teaches displaying bacterial species interaction networks for each of the sample groups comprised of nodes as microbial species and edges as interactions (Figures 1-10), including metabolite-OTU interaction networks built based on the correlation between the metabolite abundance and OTU, or bacterial species, abundance (p. 2, par. 4).
Regarding claims 38 and 51-52, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, DeHaven and Ma because both reference disclose methods for visualizing -omics level data. The prior art to Ma shows that microbiome data which comprises metabolite and microbiome species content is a known type of data which lends itself to network analysis. Therefore, it would have been obvious to one of ordinary skill in the art to substitute or include the data analyzed by Ma in the method of DeHaven, because one of ordinary skill in the art would have been able to carry out such a substitution and would have reasonably expected predictable dynamic network analysis of that data.
Regarding claim 39, DeHaven in view of Ma teaches the system of claim 38 as described above. Claim 39 further adds that the network includes all microbes identified for the microbiome in the microbiome data, each microbe in the microbiome data being represented by a node of the network.
DeHaven teaches visualizing large scale networks such as the human interactome and mapping images or annotations to those large scale networks [0029].
While DeHaven does not teach including microbes in the networks as discussed above, Ma teaches displaying bacterial species interaction networks for each of the sample groups comprised of nodes as microbial species and edges as interactions (Figures 1-10; p. 2, par. 4). It would have been prima facie obvious to one of ordinary skill in the art to include all identified microbes in the microbiome data of Ma as a large scale network as taught by DeHaven, because one could have merely combined each of the elements of Ma and DeHaven and each element would have performed the same function as it did separately with the predictable result of displaying a large scale microbial interaction network.
Regarding claims 40 and 43, DeHaven in view of Ma teaches the system of claim 38 as described above. Claim 40 further adds that each node of the network represents one of an operational taxonomic unit (OTU), a microbe ID, a taxonomy, or a metabolite. Claim 43 further adds that a first node and a second node in the network each represents a respective metabolite and an edge between the first and second nodes represents a gene annotation, sequence, or a reaction between two metabolites.
DeHaven teaches nodes as metabolites (see at least [0024] and FIG. 1, 3-5, 7, 10-11). DeHaven teaches that metabolites and/or enzymes may be assigned to nodes, and the reaction between metabolites may be assigned to edges [0024], which reads on claim 43.
Further, Ma teaches nodes as OTUs (Figure 1-6), which reads on OTU, microbe ID, or taxonomy as recited in claim 40.
Regarding claims 41-42, DeHaven in view of Ma teaches the system of claim 38 as described above. Claim 41 further adds that a first node and a second node in the network each represents a respective microbe and an edge between the first and second nodes represents a statistical correlation, observation, or a characteristic associating organisms together. Claim 42 further adds calculating a correlation, in the microbiome data, of the microbes represented by the first and second nodes; and indicating at least some of the calculated correlation in the displayed network.
DeHaven teaches that attributes can be statistical comparison of biochemical/metabolite levels [0022-0023; 0044], where the results of statistical procedures may be realized in association with the visually displayed results of the user query [0032], but DeHaven does not teach making networks of microbes.
However, Ma teaches displaying bacterial species interaction networks for each of the sample groups comprised of nodes as microbial species and edges as interactions (Figures 1-10; p. 2, par. 4). Ma teaches that networks were built based on pair-wise correlation between OTU abundances, community diversities and metabolite abundances, and metabolite abundances and OTU abundances (i.e., calculating correlations) (p. 2, par. 4 through p. 3, par. 1). Ma teaches displaying positive and negative correlations as edge colors and most abundant nodes based on color and size (i.e., indicating at least some of the calculated correlation in the displayed network) (Figures 1-10).
Regarding claim 45, DeHaven in view of Ma teaches the system of claim 38 as described above. Claim 45 further adds that the microbiome data includes taxonomic data derived from 16S marker gene surveys, metagenomic sequencing, or another technique allowing identification, delineation, and counting of separate organisms.
DeHaven does not teach microbiome data.
However, Ma teaches using 16S rRNA and metabolite datasets of the breast milk microbiomes (p. 2, par. 3).
Regarding claim 46, DeHaven in view of Ma teaches the system of claim 38 as described above. Claim 46 further adds that the microbiome data includes metabolic pathway data derived from predicted metagenomes, shotgun metagenomic sequencing, or another technique that allows identification, delineation, and counting of separate genes.
DeHaven teaches analyzing genetic data [0031; 0039] and performing statistical comparisons of biochemical data [0044], which reads on data generated by a technique that allows identification, delineation, and counting of separate genes.
Regarding claims 47-50, DeHaven in view of Ma teaches the system of claim 38 as described above. Claim 47 further adds that nodes in the network each represents a respective microbe, and wherein the processor is further configured to, in response to receiving interactive input, perform taxonomic restructuring of the network based on an applied condition. Claim 48 further adds, in response to receiving interactive input, performing said taxonomic restructuring providing identification of conditions to selectively increase or decrease relative abundance of selected organisms in the microbiome. Claim 49 further adds, in response to receiving interactive input, performing said taxonomic restructuring providing identification of conditions that maximize commensal-istic conditions that benefit a host of the sample or minimize competition that causes conditions detrimental to the host. Claim 50 further adds that nodes in the network each represents a respective microbe and, in response to receiving interactive input, performing network restructuring of the network.
DeHaven teaches that users may examine relationship between various -omics data types and between biological experiments by rank-ordering metabolic pathways maps using an enrichment fold change calculation, and bipartite networks connecting a node representing an experiment or statistical comparison to nodes representing either (1) biochemicals, (2) metabolic pathway maps, (3) pathway ontologies, or (4) keyword ontologies [0058], which is considered to read on restructuring a network based on an applied condition as in claim 47 and claim 50.
DeHaven does not teach nodes representing microbes as in claims 47-50 or performing taxonomic restructuring as in claims 47-49.
However, Ma teaches displaying bacterial species interaction networks for each of the sample groups comprised of nodes as microbial species and edges as interactions (Figures 1-10; p. 2, par. 4). Ma teaches displaying bacterial species interaction networks for each of the sample groups comprised of nodes as microbial species and edges as interactions (Figures 1-10), including metabolite-OTU interaction networks built based on the correlation between the metabolite abundance and OTU, or bacterial species, abundance (p. 2, par. 4).
Regarding claims 47-50, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, DeHaven and Ma because both reference disclose methods for visualizing -omics level data. It would have been obvious to one of ordinary skill in the art to include all identified microbes in the microbiome data of Ma as a large scale network as taught by DeHaven, because one could have merely combined each of the elements of Ma and DeHaven and each element would have performed the same function as it did separately with the predictable result of displaying a large scale microbial interaction network. It would have further been obvious to one of ordinary skill in the art to the select any attribute of the data shown in the large scale microbial interaction network to view only the portion of data relevant to that attribute and to generate new connections between the microbes (i.e., taxonomic restructuring) based on the selected attributes, to arrive at the networks disclosed by Ma, because Ma teaches that the networks produced by each of the conditions are different (Figures 1-3). Therefore, one of ordinary skill in the art would have been motivated to examine how the different conditions affect the microbial interaction networks using the dynamic method taught by DeHaven. As Ma teaches that the microbial abundances are used for each condition to build the networks (p. 2, par. 4), it is considered that DeHaven in view of Ma fairly teaches selectively increasing or decreasing the relative abundance of selected organisms in the microbiome through the taxonomic reconstruction as recited in claim 48. As Ma teaches identifying changes in the abundance of beneficial and potentially harmful metabolites and bacteria associated with breastfeeding in humans (i.e., a host) in Figure 10 (p. 6, par. 3 through p. 10, par. 1) based on health condition and treatment time point of the subjects, it is considered that DeHaven in view of Ma fairly teaches identifying conditions that maximize commensal-istic conditions that benefit a host of the sample or minimize competition that causes conditions detrimental to the host as recited in claim 49.
Regarding claim 53, DeHaven in view of Ma teaches the system of claim 38 as described above. Claim 53 further adds dynamically transitioning the graphical representation from a first network state to a second network state, wherein the first network state corresponds a first selected condition attribute or timestamp attribute, wherein the second network state corresponds to a second selected condition attribute or timestamp attribute, wherein a visual property of one or more of the nodes and/or edges differs from the first network state to the second network state.
B. Claim 44 is rejected under 35 U.S.C. 103 as being unpatentable over DeHaven in view of Ma, as applied to claim 38 as above, and in further view of Borenstein et al. (PNAS, 2007, 105(38:14482-14487; previously cited). The instant rejection is maintained from the previous Office Action and any newly recited portions are necessitated by claim amendment.
Regarding claim 44, DeHaven in view of Ma teaches the system of claim 38 as described above. Claim 44 further adds that nodes of the network are constrained to exist within separate metabolic clusters representing respective organisms such that respective multiple metabolic networks each represent a different microbe and connections between metabolic clusters are connected through metabolite nodes deemed as extracellular.
DeHaven does not teach nodes representing microbes.
However, Ma teaches displaying metabolite-OTU networks (Figure 10), where metabolite nodes are distinguished based on groups and are associated with certain bacterial nodes (p. 6, par. 3 through p. 10, par. 1), which reads on metabolic clusters representing respective organisms such that respective multiple metabolic networks each represent a different microbe and connections between metabolic clusters are connected through metabolite nodes as instantly claimed. Ma does not teach extracellular metabolites as instantly claimed.
However, the prior art to Borenstein discloses the identification of a seed set of a metabolic network, the set of compounds that, based on the network topology, are exogenously acquired (i.e., extracellular metabolites) (abstract; Fig. 1; entire document is relevant). Borenstein teaches displaying the metabolic network of an organism with the seed compounds indicated in a different color (Fig. 1C). Borenstein also teaches constructing a phylogenetic tree based on seed compounds content, where the phylogenetic tree contains species as nodes connected based on exogenous compounds (Fig. 3; p. 14485, col. 1, par. 4).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, DeHaven in view of Ma with Borenstein because each reference discloses methods for performing network analysis. Ma already teaches networks of microbes clustered by metabolites. It would have been obvious to use the method of Borenstein to identify extracellular metabolites and identify which microbe clusters are connected by extracellular metabolites. The motivation to examine extracellular metabolites in the network of DeHaven in view of Ma would have been to understand interactions between the microbes and their environment, as taught by Borenstein (p. 1482, col. 1, par. 2 through col. 2, par. 2).
Response to Applicant Arguments
At p. 20-21, Applicant submits that the previous Office Action relied upon Ma for visualization aspects, but that Ma does not teach dynamically updating any graphical representations or automatically changing a visual property.
It is respectfully submitted that this is not persuasive. The previous and current rejections rely upon Ma for analysis and visualization of microbiome data. Ma is not relied upon for teaching dynamically updating any graphical representations or automatically changing a visual property. The previous and current rejections rely upon DeHaven for dynamically updating network visualizations of metabolomics data, and the rejection sets forth that it would be obvious to incorporate the microbiome data analyzed and visualized by Ma in the visualization system of DeHaven, especially given that DeHaven teaches analyzing metabolite data that is associated with information such as organism, species, and metadata can be associated with the data in storage [0024]. Further, DeHaven is considered to teach the new limitation directed to “automatically changing” as set forth in the above rejection.
Conclusion
No claims are allowed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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