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 .
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 1-20 are pending.
Claims 1-20 are rejected.
Priority
Applicant's claim for the benefit of a prior-filed application, PCT/GB2020/052288, filed Sep 22 2020, is acknowledged.
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to App. No. GB1913690.2, filed Sep 23 2019. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Accordingly, each of claims 1-20 are afforded the effective filing date of Sep 23 2019.
Information Disclosure Statement
The information disclosure statements (IDS) filed on Mar 23 2022, Jan 2 2024, and Apr 16 2024 are in compliance with the provisions of 37 CFR 1.97 and have therefore been considered. Signed copies of the IDS documents are included with this Office Action.
Drawings
The Drawings submitted Mar 23 2022 are accepted.
Specification
The amended abstract submitted on Mar 23 2022 is accepted.
The disclosure is objected to for the following informalities. It is noted that for purposes of the instant Office Action, any reference to the specification pertains to the specification as originally filed on Mar 23 2022.
Hyperlinks
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. Non-limiting examples include paragraphs [0109]. Applicant will note that this is exemplary and other instances may exist. It is requested that all instances be corrected.
Appropriate correction for all objections to the specification is required.
Claim Rejections - 35 USC § 112
35 U.S.C. 112(b)
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.
Claims 8-9 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 8 recites “wherein the reference abundance value for each node…” and claim 9 recites “the representative abundance value for each node…”. However, claim 1 recites only associating a corresponding differential abundance value for the gene or protein to which that node corresponds, the differential abundance value derived from a comparison of a representative abundance value for the gene or protein in a biological sample exhibiting the molecular phenotype and a reference abundance value for the gene or protein. Claim 1 does not recite a reference abundance value or a representative abundance value for each node. Therefore, there is insufficient antecedent basis for this limitation in the claim as there is no previous recitation of a reference abundance value or a representative abundance value for each node. For compact examination, it is assumed that claim 8 intends to refer to the reference abundance value only and claim 9 intends to refer to the representative abundance value only. The rejection may be overcome by clarifying the antecedent basis of the limitation.
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.
A. Claims 12 and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because they recite “computer-readable medium having instructions stored thereon…”. Therefore the claims read on carrier waves and include transitory propagating signals. (In re Nuijten, Federal Circuit, 2006). It is noted that the recitation of a "non-transitory computer-readable medium" would overcome the rejection with respect to this issue under 101.
B. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
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 methods and apparatuses, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03]. It is noted that claims 12 and 16 do not recite one of the four statutory categories as described above. However, they will be examined with regards to the 35 USC 101 analysis in the interest of compact examination.
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).
The claims also recite a law of nature or a natural phenomenon. The MPEP at 2106.04(b) further explains that 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.
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 1 and 13-15: associating, with each node of the biological interaction network, a corresponding differential abundance value for the gene or protein to which that node corresponds, the differential abundance value derived from a comparison of a representative abundance value for the gene or protein in a biological sample exhibiting the molecular phenotype and a reference abundance value for the gene or protein;
using the differential abundance values of the nodes of the biological interaction network, performing a hill-climbing algorithm to partition the biological interaction network into clusters; and
determining, from the topology of the clusters, a signature of the molecular phenotype.
Independent claims 1 and 14 also recite “the biological interaction network comprising a plurality of nodes, each node associated with a corresponding gene or protein”, which is interpreted only to recite the data which is acted upon by the method steps of the claim but does not constitute an additional element.
Independent claims 14-15: comparing the signature with a reference signature of a known molecular phenotype.
Claim 12: a method of claim 1 to be performed.
Claim 16: a method of claim 14 to be performed.
Dependent claims 2 and 18: associating, with each edge of the plurality of edges, a weight.
Dependent claim 7: determining the corresponding differential abundance value.
Dependent claims 2-5, 8-11, and 17-20 recite further steps that limit the judicial exceptions in the independent claims and, as such, also are directed to those abstract ideas. For example, claims 2-3, 5, 8-9, 17, and 20 further limit the biological interaction network and/or the representative and reference abundance values associated with the nodes; claims 2, 10-11, and 18 further limit performing the hill-climbing algorithm; and claims 4 and 19 further limit the molecular phenotype.
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 determine a signature of a molecular phenotype or compare that signature with a reference signature. Without further detail as to the methodology involved in “associating”, “performing”, “determining”, and “comparing”, under the BRI, one may simply, for example, use pen and paper to associate differential abundance values and weights with nodes and edges in a network, separate the nodes into clusters based on the values and/or weights, determine a signature based on the clusters, and compare the signature to reference signatures. It is noted that the instant specification discloses an example of a biological network that comprises 20 genes or proteins and demonstrates the process of performing the steps as recited in the claims to determine a molecular phenotype, which supports the interpretation of the recited steps as being able to be performed mentally or with pen and paper.
The steps of “performing a hill-climbing algorithm” in the independent claims and claims 2, 10-11, and 18, and “determining the corresponding differential abundance values” in claim 7 require mathematical techniques as the only supported embodiments, as is disclosed in the specification at: a hill-climbing algorithm is a technique in numerical analysis for optimizing a target function in an iterative manner [0014; 0057-0066]; a differential abundance value may be derived from a comparison of the representative abundance value and the reference abundance value to represent up-regulated or down-regulated values [0013].
The claims also recite a natural relationship between the abundance value of genes or proteins and the molecular phenotype in a biological sample. Therefore, the claims recite a law of nature or a natural phenomenon.
Therefore, claims 1-20 recite an abstract idea and a law of nature/natural phenomenon [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:
Dependent claim 6: receiving data representative of the biological interaction network.
Dependent claim 7: receiving… the corresponding differential abundance value.
The claims also include non-abstract computing elements. For example, independent claims 1 and 14 include that the method is computer-implemented; independent claims 13 and 15 include an apparatus comprising: one or more memory devices configured to store a biological interaction network, the biological interaction network comprising a plurality of nodes, each node associated with a corresponding gene or protein, and a plurality of edges, each edge connecting a pair of nodes and indicative of an interaction between the genes or proteins to which each node of that associated pair of nodes corresponds; and one or more processors configured to perform the method; and claims 12 and 16 include a computer-readable medium having instructions stored thereon.
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 “receiving” data, perform functions of collecting 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)). Additionally, the biological interaction network as recited in the claims functions only in the claims as the data upon which the recited judicial exceptions act, and therefore do not integrate the judicial exceptions into a practical application.
Further steps directed to the additional non-abstract computing elements of the independent claims of 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. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that 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 discloses that the method allows for a molecular phenotype to be characterized according to the shape of global gene expression at [0008], 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 claims 1-20 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 prior art review to Miryala et al. (Gene, 2018, 642:84-94; newly cited) discloses that biological interaction networks comprising a plurality of nodes, each node associated with a corresponding gene or protein and a plurality of edges, each edge connecting a pair of nodes and indicative of an interaction between the genes or proteins to which each node of that associated pair of nodes corresponds, is a data structure that is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, the abstract, although the entire document is relevant. The specification also notes that a biological interaction network may be any suitable network that applies to a biological system, for which the nodes of the network may be taken to represent genes or proteins and the edges can represent interactions between the genes or proteins at [0010], indicating that the biological interaction network is not particularly limited and is well-known in the field. Further, 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 1-20, 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 also notes that computer processors and systems, as example, are commercially available or widely used at [0090]. 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, claims 1-20 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.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mathews et al. (dx.doi.org/10.1101/328054, May 24 2018, newly cited; corresponds to NPL 4 cited on the Mar 23 2022 IDS) in view of Nicolau et al. (PNAS, 2011, 108(17):7265-7270; newly cited) and evidenced by Strazzeri et al. (arXiv:1806.06142v1, Jun 16 2018; newly cited; corresponds to NPL 3 cited on the Mar 23 2022 IDS).
Claims 1 and 14 disclose a computer-implemented method of characterizing a molecular phenotype of a biological sample using a biological interaction network, the biological interaction network comprising a plurality of nodes, each node associated with a corresponding gene or protein. Claims 12 and 16 disclose a computer-readable medium having instructions stored thereon which, when executed by a processor, causes the method of claims 1 and 14. Claims 13 and 15 disclose an apparatus for characterizing a molecular phenotype of a biological sample, the apparatus comprising: one or more memory devices configured to store a biological interaction network, the biological interaction network comprising: a plurality of nodes, each node associated with a corresponding gene or protein; and a plurality of edges, each edge connecting a pair of nodes and indicative of an interaction between the genes or proteins to which each node of that associated pair of nodes corresponds; and one or more processors.
The prior art to Mathews discloses an unsupervised data analysis methodology that operates in the setting of a multivariate dataset and a network which expresses influence between the variables of the given set, specifically analyzing gene expression profiles, to discern coherent states or signatures displayed by the gene expression profiles (i.e., characterizing a molecular phenotype) (abstract). Mathews teaches a network whose nodes correspond to genes whose presence or absence constitutes participation in a coordinated function or process of interest, and whose edges represent the coordinating relationships (i.e., a biological interaction network) (p. 4, section 2.2). Mathews teaches the use and development of various algorithms (see at least the abstract), which reads on computer-implemented methods. Although Matthews does not explicitly teach the general purpose computer apparatus of claims 13 and 15 and the computer-readable medium of claims 12 and 16, it would have been obvious to include those features to provide an automatic means for performing the method and obtaining the same result as if the method were performed manually. The courts have held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art (In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958)) (see MPEP 2144.04(III)). Mathews, 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.
The method steps of claims 1 and 14 and those performed by the processors of claims 13 and 15 comprise:
associating, with each node of the biological interaction network, a corresponding differential abundance value for the gene or protein to which that node corresponds, the differential abundance value derived from a comparison of a representative abundance value for the gene or protein in a biological sample exhibiting the molecular phenotype and a reference abundance value for the gene or protein;
Matthews teaches that their primary input is a gene expression quantification sample set S formatted as a point cloud (i.e., abundance values for the genes) and an influence, regulation, or pathway network relating the genes (i.e., a biological interaction network) (p. 3, par. 4; p. 4-7, sections 2.1-2.4). Although Matthews teaches normalizing the gene expression values (p. 4, section 2.1), Matthews does not teach differential abundance values for the genes. See below for teachings by Nicolau regarding differential abundance values.
Matthews teaches restricting or projecting (i.e., associating) the genes of S to the genes appearing in the network of genes with a coordinated function or process of interest (p. 4, section 2.2).
using the differential abundance values of the nodes of the biological interaction network, performing a hill-climbing algorithm to partition the biological interaction network into clusters;
Matthews teaches mapping the point clouds of normalized gene expression values onto the network by calculating network-based distance metrics between samples and using the Mapper algorithm to cluster the network (p. 3, par. 3; p. 4-7, sections 2.3-2.5; Figure 3). Matthews teaches that the Mapper algorithm is a type of Morse-theoretic analysis (p. 3, par. 3).
Morse-theoretic analysis is considered to read on a hill-climbing algorithm as evidenced by Strazzeri. Strazzeri teaches that Morse theory describes the flow of data in an “uphill” direction in order to cluster a graph (p. 2, par. 5; p. 15, par. 5; p. 23, par. 5; p. 26, par. 7). Additionally, the specification as published sets forth that performing the hill-climbing algorithm may comprise performing a Morse theory algorithm [0014; 0034].
and determining, from the topology of the clusters, a signature of the molecular phenotype.
Matthews teaches using the output of the Mapper algorithm, a topological data analysis method, to identify coherent states or molecular phenotypes (p. 3, par. 5; p. 8-9, section 2.7-2.8; p. 9-13, section 3; Figure 5).
Claims 14 and 15 further comprise comparing the signature with a reference signature of a known molecular phenotype, where claim 15 also adds to determine a molecular phenotype of the biological sample.
Matthews teaches comparing the pattern of activation and inactivation for particular coherent states in the context of the gene network, by, for example, examining components A and B in states 1 and 2 (i.e., reference signature compared to the signature produced by the method) (p. 8-9, section 2.7-2.8). Matthews teaches comparing the coherent states of the KEGG p53 signaling network, where each of the coherent states consist mainly of certain different types of samples.
Matthews does not teach differential abundance values for genes.
However, the prior art to Nicolau discloses a method that extracts information from high-throughput microarray data and, by using topology, provides greater depth of information than current analytic technique (abstract). Nicolau teaches beginning with a data matrix of transcriptional microarray data from diseased tissue (i.e., biological sample exhibiting the molecular phenotype) and normal tissue (i.e., reference abundance value) and transforming the data into matrices of deviation from healthy state in the normal tissue (p. 7267, col. 1, par. 3-4). Nicolau teaches only including genes that show a significant deviation from the healthy state and using a filter function to determine deviation of in positive or negative directions (i.e. differential abundance) (p. 7267, col. 1, par. 5-6 and col. 2, par. 1). Nicolau teaches applying the Mapper algorithm to the transformed, filtered data (p. 7267, col. 1, par. 2 and col. 2, par. 2).
Regarding claims 1 and 12-16, 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, Matthews and Nicolau because both references disclose methods for using the Mapper algorithm to perform topological analysis of gene expression data. Matthews motivates one of ordinary skill in the art to look to Nicolau because Matthews teaches largely following Nicalau’s method (p. 3, par. 3) and explicitly teaches that a reasonable choice for the filter function of the Mapper algorithm is a deviation function devised in comparison with a control dataset (p. 7, par. 3; see also p. 3, par. 4). It would have been obvious of one of ordinary skill in the art to combine the methods of Matthews and Nicolau to examine the differential response of genes in a disease vs. healthy state, as taught by Nicolau, in the biologically-motivated pathways, as taught by Matthews, to obtain the predictable result of examining the disease state of genes in specific pathways.
Regarding claims 2 and 17-18, Matthews in view Nicolau and as evidenced by Strazzeri teaches the method of claim 1, the apparatus of claim 13, and the method of claim 14 as described above. Claims 2 and 17 further add that the biological interaction network comprises a plurality of edges, each edge connecting a pair of nodes and indicative of an interaction between the genes or proteins to which each node of that associated pair of nodes corresponds. Claims 2 and 18 further add associating, with each edge of the plurality of edges, a weight; and wherein performing the hill-climbing algorithm comprises performing the hill- climbing algorithm using the weights of the edges.
Mathews teaches a network whose nodes correspond to genes whose presence or absence constitutes participation in a coordinated function or process of interest, and whose edges represent the coordinating relationships (i.e., interaction) (p. 4, section 2.2). Matthews teaches that calculated distance between the connections between components or pairs are weighted in order to build a weighted graph with weighted edges connecting the genes that is input into the Mapper algorithm (p. 4-6, sections 2.3-2.4).
Regarding claim 3, Matthews in view Nicolau and as evidenced by Strazzeri teaches the method of claim 1. Claim 3 further adds that each node of the plurality of nodes is associated with a corresponding gene; the representative abundance value for the gene comprises a gene expression value for the gene; the reference abundance value for the gene comprises a reference gene expression value for the gene; and the differential abundance value comprises a differential gene expression value, the differential gene expression value derived from a comparison of the representative gene expression value and the reference gene expression value.
Matthews teaches a network whose nodes correspond to genes (p. 4, section 2.2). Matthews teaches that the genes have expression values (p. 4, section 2.2; p. 8-9, section 2.7), and the use of a control dataset with similar values (i.e., reference abundance values) (p. 7, par. 3). Matthews does not teach differential abuandance.
However, Nicolau is considered to teach determining differential abundance as described above (p. 7267, col. 1, par. 3-6 and col. 2, par. 1).
Regarding claims 4 and 19, Matthews in view Nicolau and as evidenced by Strazzeri teaches the method of claim 1 and the apparatus of claim 13 as described above. Claims 4 and 19 further add that the molecular phenotype of the biological sample comprises a disease state of the biological sample.
Matthews teaches examining samples with respect to some process, including a disease process (p. 3, par. 4; p. 13, par. 3).
Regarding claims 5 and 20, Matthews in view Nicolau and as evidenced by Strazzeri teaches the method of claim 1 and the apparatus of claim 13 as described above. Claims 5 and 20 further add that the biological network comprises a biological pathway.
Matthews teaches that the network can be a known pathway (abstract; p. 1, par. 1 through p. 2, par. 1; p. 3, par. 3-4; p. 4-5, sections 2.2-2.3; p. 12).
Regarding claim 6, Matthews in view Nicolau and as evidenced by Strazzeri teaches the method of claim 1. Claim 6 further adds, prior to the associating, receiving data representative of the biological interaction network.
Matthews teaches that the network can be a known pathway (abstract; p. 1, par. 1 through p. 2, par. 1; p. 3, par. 3-4; p. 4-5, sections 2.2-2.3; p. 12), which reads receiving data representative of the biological interaction network as instantly claimed because Matthews would have to obtain the data regarding the known pathways to use it in their method.
Regarding claim 7, Matthews in view Nicolau and as evidenced by Strazzeri teaches the method of claim 1. Claim 7 further adds, for each node of the biological network, receiving or determining the corresponding differential abundance value.
Matthews does not teach this claim.
However, Nicolau is considered to teach determining differential abundance as described above (p. 7267, col. 1, par. 3-6 and col. 2, par. 1).
Regarding claims 8-9, Matthews in view Nicolau and as evidenced by Strazzeri teaches the method of claim 1. Claim 8 further adds that the reference abundance value for each node comprises an average of abundance values for a plurality of biological samples. Claim 9 further adds that the representative abundance value for each node comprises an average of abundance values for a plurality of biological samples exhibiting the molecular phenotype.
Matthews does not teach this claim.
However, Nicolau teaches using samples of normal tissue and diseased tissue from multiple patients in a matrix with columns of vectors of each of the patients, with rows as genes (p. 7267, col. 1, par. 3 and col. 2, par. 1). As Nicolau teaches that the filter function measure the overall deviation from the null hypothesis (p. 7267, col. 1, par. 6), which is whether the expression of the genes in the disease state differ from those in the healthy samples, it is considered that Nicolau fairly teaches an average of abundance values for a plurality of biological samples for both the reference abundance values and the representative abundance value as instantly claimed.
Regarding claim 10, Matthews in view Nicolau and as evidenced by Strazzeri teaches the method of claim 1. Claim 10 further adds performing the hill-climbing algorithm comprises performing a Morse theory algorithm.
Matthews teaches that the Mapper algorithm is a version of Morse-theoretic analysis (p. 3, par. 3).
Regarding claim 11, Matthews in view Nicolau and as evidenced by Strazzeri teaches the method of claim 1. Claim 11 further adds for each node of the biological interaction network, determining, for each neighboring node of all neighboring nodes connected to the node, a score based on the differential abundance value of the neighboring node; determining, out of all neighboring nodes connected to the node, the neighboring node associated with the highest or lowest score; and determining that the node and the neighboring node associated with the highest or lowest score are of the same cluster.
Matthews teaches that the Mapper algorithm divides the point cloud (i.e., nodes) into overlapping slices by binning the values of the filter function, clustering the points of each slice, and linking pairs of clusters depending on the amount of overlap (i.e., highest score) between clusters (p. 7, section 2.5). Matthews does not teach differential abundance values.
However, Nicolau is considered to teach determining differential abundance as described above (p. 7267, col. 1, par. 3-6 and col. 2, par. 1).
Conclusion
No claims are allowed.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Schofield et al. (bioRxiv, 9 January 2019, XP055753228, DOI: 10.1101/516328; cited on the Mar 23 2022 IDS), Schofield et al. (JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY, 28 March 2019, 144(1):70-82; cited on the Mar 23 2022 IDS), Schofield et al. (European Respiratory Journal, 52, abstract; newly cited), and Perotin et al. (European Respiratory Journal, 2019; newly cited) each disclose methods of performing topological data analysis on patient and gene or protein expression data. However, each reference teaches clustering the networks based on the patients, and produces clusters where patients are represented by nodes, rather than by genes. Genetic data is then mapped across the networks because each node contains information about all the genes. The references therefore do not teach “the biological interaction network comprising a plurality of nodes, each node associated with a corresponding gene or protein” in claims 1 and 13-15 and “the biological interaction network comprises a plurality of edges, each edge connecting a pair of nodes and indicative of an interaction between the genes or proteins to which each node of that associated pair of nodes corresponds” as in claims 2, 13, 15, and 17.
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/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685