DETAILED ACTION
Notice of 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.
Status of the Claims
Claims 1-11 are pending.
Claims 1-3, 6 and 10-11 are objected to.
Claims 1-11 are rejected.
Priority
This application US 18/126,189 (03/24/2023) claims benefit of Foreign Application IT 102022000005861 (03/24/2022) as reflected in the filing receipt mailed on 04/25/2023. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-11 is 03/24/2022.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/24/2023, 10/11/2023 and 11/27/2023 were considered.
Specification Objections
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code in pg. 8 para. 6, pg. 11 para. 4, pg. 13 para. 4, pg. 15 para. 1 and pg. 16 para. 1 and para. 3. 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.
Claim objections
Claims 1-3, 6 and 10-11 are objected to because of the following informalities. Appropriate correction is required.
In claims 1 and 10-11, the recited "the respective variables" (line 4 of second generating step and line 3 of third generating step) should read "the respective plurality of variables" for proper claim language. Claim 2 repeats the issue above for the first, second and third generating step.
In claims 1 and 10-11, the recited "the respective variables" (line 4 of second generating step and line 3 of third generating step) should read "the respective plurality of variables" for proper claim language. Claim 2 repeats the issue above for the first, second and third generating step.
In claims 1 and 10-11, the recited "the respective variables" (line 4 of second generating step and line 3 of third generating step) should read "the respective plurality of variables" for proper claim language. Claim 2 repeats the issue above for the first, second and third generating step.
In claims 1 and 10-11, the recited "the features" (line 3 of the fourth generating step) should read "the one or more features" for proper claim language.
In claims 1 and 10-11, the recited "said features" (line 2 of the training step, line1 of the third calculating step and line 2 of the estimating step) should read "said one or more features" for proper claim language.
In claims 1 and 10-11, the recited "associating to each variable of said first dataset of omics data a respective node" should read "associating, to each variable of said first dataset of omics data, a respective node" for proper grammar.
In claims 1 and 10-11, the recited "and" between the second generating step and the first calculating step should be removed and said calculating step should be presented in a newline and indented. As set forth in 37 CPR 1.75, each element or step of the claim should be separated by a line indentation (608.01(m) Form of Claims). Sub-steps / elements should be indented from their parent step / element. This rule should be applied throughout the claims as needed.
claims 1 and 10-11 repeat the issue above between the third generating step and the second calculating step and again between the fourth generating step and the third calculating step.
claims 1 and 10-11 repeat the issue above between the fourth generating step and the third calculating steps.
claims 1 and 10-11 repeat the issue above between the determining and storing steps.
claim 2 repeats the issue above between the first generating step and first calculating step.
claim 2 repeats the issue above between the second generating step and second calculating step.
In claims 1 and 10-11, the recited "determining, via a feature-extraction method, for each community one or more respective features" (determining step) should read "determining, via a feature-extraction method, for each community, one or more respective features" for proper grammar.
In claims 1 and 10-11, the recited "generating a training dataset by obtaining for each reference patient a respective value of said variable of interest" (generating step) should read "generating a training dataset by obtaining, for each reference patient, a respective value of said variable of interest" for proper grammar.
In claims 1 and 10-11, the recited "calculating for each reference patient the respective values" (fourth calculating step) should read "calculating, for each reference patient, the respective values" for proper grammar.
In claim 2, the recited "third layer" (last line of the claim) is missing a comma after the recited term for proper grammar.
In claim 3, the recited "for example" is missing a comma after the recited term for proper grammar.
In claim 6, the recited "calculating for each pair of nodes a respective weight" should read "calculating, for each pair of nodes, a respective weight" for proper grammar.
Claim Rejections - 35 USC § 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 1-11 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
The following recitations require but lack antecedent basis, rendering their claims indefinite because there is no previous recitations of the followings terms as written:
claims 1 and 10-11, "the same genes" (line 3 of the first receiving step)
claims 1 and 10-11, "the values" (line 2 of the first receiving step)
claims 1 and 10-11, "the similarity value" (last line of the first and second calculating steps)
claims 1 and 10-11, "the weights" (line 2 of the pruning step)
claims 1 and 10-11, "the mapping rules" (line 1 of the storing step)
claim 3, "the gene expression" (first claim element)
claim 3, "the variation of the number of copies" (second claim element)
claims 4-5, "the biweight-midcorrelation" (first claim element)
claim 4, "the normalized mutual information" (second claim element)
claim 5, "the point-biserial correlation" (second claim element)
claim 6, "the exponent β"
claim 7, "the Noise-Corrected method"
claim 7, "the Infomap method"
claim 7, "the Uniform-Manifold Approximation-and- Projection method"
claim 8, "the severity of said disease" (second claim element)
In claims 1 and 10-11, the recited "the similarity value" (last line of the second calculating step) is indefinite. It is unclear if the recited term refers to the recited "a respective similarity value" (line 3 of the second generating step) or the "a respective similarity value" (line 2 of the third generating step). To overcome the rejection, the claims may be amended to clarify the recited "the similarity value" (last line of the second calculating step).
In claims 1 and 10-11, the recited "each pair of nodes" (line 1 of the second generating step, line 1 of the first calculating step, line 1 of the third generating step and line 1 of the second calculating step); "respective pair of nodes" (last line of the first and second calculating steps); "two nodes" (last line of the second and third generating steps); and "the nodes" (lines 3, 4 and 5 of the pruning step and line 3 of the determining step) are indefinite. It is unclear if all the recited nodes refer to the same nodes of different ones. To overcome the rejection, the claims may be amended to clarify the use of said nodes. Claim 2 repeats the issue above.
Claim 3 recites "for example" which is indefinite because it recites exemplary claim language (see MPEP 2173.05(d)).
Claim 3 recites "optionally said third dataset of omics data are chosen from among," which is indefinite because there is no "third dataset" recited in claim 1. To overcome this rejection, claim 3 must depend on claim 2.
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 1-11 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed 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.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
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 (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a method (claims 1-9), a system (claim 10) and a CRM (claim 11); each of which falls within one of the categories of statutory subject matter.
[Step 1: claims 1-11: Yes]
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 (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
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 mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "generating a multi-layer network comprising a first layer and a second layer" (independent claim 1 and 10-11);
• "generating intra-omics connections by calculating for each pair of nodes of the first layer and each pair of nodes of the second layer a respective similarity value as a function of the data of the respective variables associated to the two nodes" (independent claim 1 and 10-11);
• "calculating, for each pair of nodes of the first layer and each pair of nodes of the second layer, a respective weight associated to the connection between the respective nodes as a function of the similarity value of the respective pair of nodes" (independent claim 1 and 10-11);
• "generating inter-omics connections by calculating for each pair of nodes between the first layer and the second layer a respective similarity value as a function of the data of the respective variables associated to the two nodes" (independent claim 1 and 10-11);
• "calculating, for each pair of nodes between the first layer and the second layer, a respective weight associated to the connection between the respective nodes as a function of the similarity value of the respective pair of nodes" (independent claim 1 and 10-11);
• "pruning non-salient intra-omics connections and inter-omics connections of said multi-layer network by applying, to the weights associated to the intra-omics connections between the nodes of said first layer, to the weights associated to the intra-omics connections between the nodes of said second layer, and to the weights associated to the inter-omics connections between the nodes of said first layer and said second layer, a backboning method" (independent claim 1 and 10-11);
• "identifying a plurality of communities of said multi-layer network" (independent claim 1 and 10-11);
• "determining, via a feature-extraction method, for each community one or more respective features as a function of the values of the variables associated to the nodes that belong to the respective community" (independent claim 1 and 10-11);
• "generating a training dataset by obtaining for each reference patient a respective value of said variable of interest" (independent claim 1 and 10-11);
• "calculating for each reference patient the respective values of the features associated to said communities as a function of the respective values of the variables of the reference patient by using said mapping rules" (independent claim 1 and 10-11);
• "training a classifier configured for estimating the value of said variable of interest as a function of the values of said features using said training dataset" (independent claim 1 and 10-11);
• "calculating for said patient the values of said features as a function of the respective values of the variables of the patient using said mapping rules" (independent claim 1 and 10-11);
• "estimating by means of said trained classifier the value of said variable of interest as a function of said values of said features calculated for said patient" (independent claim 1 and 10-11);
• "generating intra-omics connections by calculating, for each pair of nodes of the third layer, a respective similarity value as a function of the data of the respective variables associated to the two nodes" (claim 2);
• "generating inter-omics connections by calculating, for each pair of nodes between the first layer and the third layer and each pair of nodes between the second layer and the third layer, a respective similarity value as a function of the data of the respective variables associated to the two nodes" (claim 2);
• "calculating, for each pair of nodes between the first layer and the third layer and each pair of nodes between the second layer and the third layer, a respective weight associated to the connection between the respective nodes as a function of the similarity value of the respective pair of nodes" (claim 2); and
• "pruning the non-salient intra-omics connections and inter-omics connections of said multi-layer network by applying to the weights associated to the intra-omics connections between the nodes of said third layer, to the weights associated to the inter-omics connections between the nodes of said first layer and said third layer and to the weights associated to the inter-omics connections between the nodes of said second layer and said third layer a backboning method" (claim 2).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed by mathematical operation. The step directed to “executing an algorithm to estimate a variable of interest” requires mathematical techniques as the only supported embodiments because it describes a mathematical technique (MPEP 2106.04(a)(2) pertains). Further support for the mathematical techniques used in the claims is provided in the specification at pg. 18 para. 3, which discloses an algorithm implemented via mathematical functions to estimate a variable of interest, and at pg. 15 para. 1-4, which discloses mathematical operations to determine sub-communities of the multilayer network. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "associating to each variable of said first dataset of omics data a respective node in said first layer and to each variable of said second dataset of omics data a respective node in said second layer" (independent claims 1 and 10-11) and
• "associating to each variable of said third dataset of omics data a respective node in a third layer" (claim 2).
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 (i.e. concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) or because the method only requires a user to manually determine action based on an added number. Under the BRI, the recited limitations are mental processes because a human mind is also sufficiently capable of evaluate and correlate variables.
Dependent claims 4-9 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, claims 4-5 recite further details about the generating data connections; claims 6-8 recite further details about the calculating steps; and claim 9 recites further details about the disease.
[Step 2A Prong One: claims 1-11: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way 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, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1-11 recite additional elements that are not abstract ideas:
• "processor" (independent claims 1 and 10-11);
• "receiving a first dataset of omics data and a second dataset of omics data, where each dataset of omics data comprises the values of a respective plurality of variables that refer to the same genes for each reference patient of a plurality of reference patients" (independent claims 1 and 10-11);
• "storing the mapping rules used to generate said one or more features as a function of the values of the variables" (independent claims 1 and 10-11);
• "receiving the values of the variables of said first dataset of omics data and said second dataset of omics data for a patient" (independent claims 1 and 10-11); and
• "receiving a third dataset of omics data, wherein said third dataset of omics data comprises the values of a respective plurality of variables that refer to said genes for said reference patient" (claim 2).
Dependent claim 3 recites further details about the first dataset of omics data, said second dataset of omics data and optionally said third dataset.
Considerations under Step 2A, Prong Two
The recited limitations in claims 1-11 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
Further steps directed to additional non-abstract elements of a computing device/computer 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 are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions.
The judicial exceptions in the claims are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C. Additionally, claims 1-10 and 16-20 do not recite an additional element, and therefore there is nothing in the claims to provide a practical application at Step 2A, Prong 2, or significantly more at Step 2B.
Claims directed to "receiving" and "storing" read on receiving or transmitting data over a network -Symantec, 838 F.3d at 1321 - MPEP 2106.05(a) pertains; which constitutes just necessary data gathering and therefore correspond to insignificant extra-solution activity.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-11: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
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 the 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 examination 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).
Claims 1-11 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Further, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a 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), Versa ta 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)(Il)(i)).
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-11: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
A. Claims 1-3 and 7-11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Li ("MoGCN: a multi-omics integration method based on graph convolutional network for cancer subtype analysis." Frontiers in Genetics 13:806842 (2022) - Published 02/01/2022) in view of Azevedo ("Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits." NPJ systems biology and applications 7.1:24 (2021)) in view of Coscia ("Network backboning with noisy data." 2017 IEEE 33rd international conference on data engineering (ICDE). IEEE, 2017) – as cited in the 10/11/2023 IDS) in view of Ma ("OmicsMapNet: Transforming omics data to take advantage of Deep Convolutional Neural Network for discovery." arXiv preprint arXiv:1804.05283 (2018)), as cited on the attached Form PTO-892.
Claim 1 recites a method comprising steps. Claim 10 recites a device, comprising: a processing system comprising instructions that, when executed by at least one hardware processor included with the processing system, cause the at least one hardware processor to perform operations comprising said steps. Claim 11 recites a computer program product stored in a non-transitory computer-readable medium and configured to be loaded into a memory of at least one processor, computer program comprising portions of software code that are configured to cause the at least one process to perform operations said steps.
The prior art to Li discloses a method and a system related to a combination of a patient similarity network and graph convolutional network to establish a complete pipeline for multi-omics biological data (pg. 5 col. 2 para. 1); wherein a non-transitory computer-readable medium is provided via software for the analysis (pg. 5 col. 1 para. 1).
The steps performed by the method of claim 1, a device of claim 10, and a CRM of claim 11 comprise:
during a training phase:
receiving a first dataset of omics data and a second dataset of omics data, where each dataset of omics data comprises the values of a respective plurality of variables that refer to the same genes for each reference patient of a plurality of reference patients
• Li teaches a combination of a patient similarity network and graph convolutional network to establish a complete pipeline for multi-omics biological data (pg. 5 col. 2 para. 1) based on genomics, transcriptomics and proteomics datasets (i.e. reading on first, second and third datasets) (pg. 1 Abstract) to integrate multi-omics datasets for cancer subtyping classifications efficiently (pg. 2 col. 2 para. 2).
generating a multi-layer network comprising a first layer and a second layer via the following operations:
associating to each variable of said first dataset of omics data a respective node in said first layer and to each variable of said second dataset of omics data a respective node in said second layer
• Li teaches a multi-omics integration model (i.e. reading on multi-layer network) based on graph convolutional network developed for cancer subtype classification and analysis based on genomics, transcriptomics and proteomics datasets (i.e. reading on first, second and third datasets) (pg. 1 Abstract); wherein the network is built by stacking multiple convolutional layers where W is the weight matrix learned from training and one input is the multiomics feature matrix X with n being the number of nodes and d being the number of features (pg. 4 col. 1 para. 2) (i.e. reading on associating to each variable of said first dataset of omics data a respective node in said first layer and to each variable of said second dataset of omics data a respective node in said second layer).
generating intra-omics connections by calculating for each pair of nodes of the first layer and each pair of nodes of the second layer a respective similarity value as a function of the data of the respective variables associated to the two nodes, and
calculating, for each pair of nodes of the first layer and each pair of nodes of the second layer, a respective weight associated to the connection between the respective nodes as a function of the similarity value of the respective pair of nodes
• Li teaches a multi-omics integration model (i.e. reading on multi-layer network) based on graph convolutional network developed for cancer subtype classification and analysis based on genomics, transcriptomics and proteomics datasets (i.e. reading on first, second and third datasets – hence three layers of omics data) (pg. 1 Abstract).
• Li does not teach "intra-omics connections." However, Azevedo teaches a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome (pg. 1 Abstract) to model the human transcriptome and biological mechanisms of complex diseases and traits (pg. 1 Title ) and to estimate the extent to which each community’s gene expression profile was predictive of each of the tissues (pg. 11 col. 1 para. 1); wherein, in the multiplex network analysis of the transcriptome (i.e. reading on analysis of relationships within a single omics layer namely transcriptome – hence intra-omics), the global multiplexity index to represent the number of times that two given genes are clustered in the same community and a high value of global multiplexity index indicates a greater level of connectivity and greater functional similarity, as they appear multiple times in the same community across different layers (i.e. reading on generating intra-omics connections by calculating for each pair of nodes of the first layer and each pair of nodes of the second layer a respective similarity value as a function of the data of the respective variables associated to the two nodes) (pg. 11 col. 2 para. 5); wherein layers represent different tissues, nodes represent genes, and edges between two nodes are weighted according to the correlation weights (i.e. reading on calculating, for each pair of nodes of the first layer and each pair of nodes of the second layer, a respective weight associated to the connection between the respective nodes as a function of the similarity value of the respective pair of nodes) (pg. 11 col. 2 para. 4).
generating inter-omics connections by calculating for each pair of nodes between the first layer and the second layer a respective similarity value as a function of the data of the respective variables associated to the two nodes, and
calculating, for each pair of nodes between the first layer and the second layer, a respective weight associated to the connection between the respective nodes as a function of the similarity value of the respective pair of nodes
• Li teaches a multi-omics integration model (i.e. reading on multi-layer network) based on graph convolutional network developed for cancer subtype classification and analysis based on genomics, transcriptomics and proteomics datasets (i.e. reading on first, second and third datasets and inter omics network) (pg. 1 Abstract); wherein the network is built by stacking multiple convolutional layers where W is the weight matrix learned from training and one input is the multiomics feature matrix X with n being the number of nodes and d being the number of features (pg. 4 col. 1 para. 2); wherein similarity network fusion algorithm integrates different types of omics data , creating a network for each data type, and ultimately establishing a comprehensive view of the disease or biological process computing patient-patient similarity matrices for each data type and constructs patient-patient similarity networks (pg. 3 col. 1 para. 3) (i.e. reading on generating inter-omics connections by calculating for each pair of nodes between the first layer and the second layer a respective similarity value as a function of the data of the respective variables associated to the two nodes, and calculating, for each pair of nodes between the first layer and the second layer, a respective weight associated to the connection between the respective nodes as a function of the similarity value of the respective pair of nodes).
pruning non-salient intra-omics connections and inter-omics connections of said multi-layer network by applying, to the weights associated to the intra-omics connections between the nodes of said first layer, to the weights associated to the intra-omics connections between the nodes of said second layer, and to the weights associated to the inter-omics connections between the nodes of said first layer and said second layer, a backboning method
• Li does not teach the recitation above. However, Coscia teaches a network backboning method to extract the latent structure from noisy networks by pruning non-salient edges (pg. 1 Abstract) solving the problem of extracting the backbone from a dense complex network (pg. 3 col. 1 para. 4); wherein said method accurate estimates the amount of noise in the edge weights (pg. 3 col. 2 para. 1); wherein Nij is the weight of the edge connecting nodes i and j (pg. 3 col. 1 para. 3); wherein the proposed methodology can be applied to consider multilayer networks, where nodes in different layers are coupled together and where these couplings influence the backbone structure (pg. 11 col. 2 para. 4) (i.e. reading on pruning non-salient intra-omics connections and inter-omics connections of said multi-layer network by applying, to the weights associated to the intra-omics connections between the nodes of said first layer, to the weights associated to the intra-omics connections between the nodes of said second layer, and to the weights associated to the inter-omics connections between the nodes of said first layer and said second layer, a backboning method).
identifying a plurality of communities of said multi-layer network;
determining, via a feature-extraction method, for each community one or more respective features as a function of the values of the variables associated to the nodes that belong to the respective community
• Li does not teach the recitation above. However, Azevedo teaches a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome (pg. 1 Abstract); wherein tissue-dependent communities are generated, analysed, tested for enrichment for known biological processes (i.e. reading on identifying a plurality of communities of said multi-layer network), and exploited towards identification of new functional gene sets wherein Uniform Manifold Approximation and Projection (i.e. reading on feature-extraction method) embeddings of gene expression data defined by the communities and the persistence of the global structure are evaluated to identify biologically-meaningful clusters (pg. 2 Fig. 1) (i.e. reading on identifying a plurality of communities of said multi-layer network and determining, via a feature-extraction method, for each community one or more respective features as a function of the values of the variables associated to the nodes that belong to the respective community).
storing the mapping rules used to generate said one or more features as a function of the values of the variables
• Li does not teach the recitation above. However, Ma teaches OmicsMapNet – a method that aggregates related omics data molecular features together to improve their collective contrast between phenotypes while at the same time harvesting the power of deep learning frameworks (pg. 1 para. 1); wherein a treemap is used to rearrange omics data based on knowledge of hierarchical mapping and functional annotation of genes (pg. 3 para. 1) and allocates spatially genes as points in a 2D plane based on some types of “closeness” measures (e.g., correlations) (pg. 3 para. 2) (i.e. reading on storing the mapping rules used to generate said one or more features as a function of the values of the variables).
generating a training dataset by obtaining for each reference patient a respective value of said variable of interest
• Li teaches a combination of a patient similarity network and graph convolutional network to establish a complete pipeline for multi-omics biological data (pg. 5 col. 2 para. 1) based on genomics, transcriptomics and proteomics datasets (i.e. reading on first, second and third datasets) (pg. 1 Abstract) with samples collected from each omics level to make-up a the training sets to train the model and the testing set to test the model's performance (pg. 2 col. 2 para. 4) (i.e. reading on generating a training dataset by obtaining for each reference patient a respective value of said variable of interest).
calculating for each reference patient the respective values of the features associated to said communities as a function of the respective values of the variables of the reference patient by using said mapping rules
• Li teaches a combination of a patient similarity network and graph convolutional network to establish a complete pipeline for multi-omics biological data (pg. 5 col. 2 para. 1) computing patient-patient similarity matrices for each data type and constructs patient-patient similarity networks (pg. 3 col. 1 para. 3); wherein expression features were extracted via an autoencoder model and used as multi-omics expression datasets from patients (pg. 2 col. 2 para. 2); wherein the visualization of the patient similarity network provides an intuitive explanation for the clinical diagnosis of patient subtyping (pg. 4 col. 1 para. 3) (i.e. reading on calculating for each reference patient the respective values of the features associated to said communities as a function of the respective values of the variables of the reference patient).
• Li does not teach "using mapping rules." However, Ma teaches OmicsMapNet – a method that aggregates related omics data molecular features together to improve their collective contrast between phenotypes while at the same time harvesting the power of deep learning frameworks (pg. 1 para. 1); wherein a treemap is used to rearrange omics data based on knowledge of hierarchical mapping and functional annotation of genes (pg. 3 para. 1) and allocates spatially genes as points in a 2D plane based on some types of “closeness” measures (e.g., correlations) (pg. 3 para. 2) (i.e. reading on using mapping rules).
training a classifier configured for estimating the value of said variable of interest as a function of the values of said features using said training dataset
• Li teaches a combination of a patient similarity network and graph convolutional network to establish a complete pipeline for multi-omics biological data (pg. 5 col. 2 para. 1) computing patient-patient similarity matrices for each data type and constructs patient-patient similarity networks (pg. 3 col. 1 para. 3); wherein expression features were extracted via an autoencoder model and used as multi-omics expression datasets from patients (pg. 2 col. 2 para. 2); wherein the visualization of the patient similarity network provides an intuitive explanation for the clinical diagnosis of patient subtyping (pg. 4 col. 1 para. 3) (i.e. reading on features calculated for said patient); wherein the model's accuracy represents the proportion of all samples judged correctly by the trained classifier (pg. 4 col. 2 para. 4); wherein the classifier method is applied for subtype prediction (pg. 5 col. 2 para. 1) (i.e. reading on training a classifier configured for estimating the value of said variable of interest as a function of the values of said features using said training dataset).
during an estimation phase:
receiving the values of the variables of said first dataset of omics data and said second dataset of omics data for a patient
• Li teaches a combination of a patient similarity network and graph convolutional network to establish a complete pipeline for multi-omics biological data (pg. 5 col. 2 para. 1) based on genomics, transcriptomics and proteomics datasets (pg. 1 Abstract) to integrate multi-omics datasets for cancer subtyping classifications efficiently (pg. 2 col. 2 para. 2); wherein samples collected from each omics level make-up a the training sets to train the model and the testing set to test the model's performance (pg. 2 col. 2 para. 4) (i.e. reading on receiving the values of the variables of said first dataset of omics data and said second dataset of omics data for a patient).
calculating for said patient the values of said features as a function of the respective values of the variables of the patient using said mapping rules
• Li teaches a combination of a patient similarity network and graph convolutional network to establish a complete pipeline for multi-omics biological data (pg. 5 col. 2 para. 1) computing patient-patient similarity matrices for each data type and constructs patient-patient similarity networks (pg. 3 col. 1 para. 3); wherein expression features were extracted via an autoencoder model and used as multi-omics expression datasets from patients (pg. 2 col. 2 para. 2); wherein the visualization of the patient similarity network provides an intuitive explanation for the clinical diagnosis of patient subtyping (pg. 4 col. 1 para. 3) (i.e. reading on features calculated for said patient).
• Li does not teach "using mapping rules." However, Ma teaches OmicsMapNet – a method that aggregates related omics data molecular features together to improve their collective contrast between phenotypes while at the same time harvesting the power of deep learning frameworks (pg. 1 para. 1); wherein a treemap is used to rearrange omics data based on knowledge of hierarchical mapping and functional annotation of genes (pg. 3 para. 1) and allocates spatially genes as points in a 2D plane based on some types of “closeness” measures (e.g., correlations) (pg. 3 para. 2) (i.e. reading on using mapping rules).
estimating by means of said trained classifier the value of said variable of interest as a function of said values of said features calculated for said patient
• Li teaches a combination of a patient similarity network and graph convolutional network to establish a complete pipeline for multi-omics biological data (pg. 5 col. 2 para. 1) computing patient-patient similarity matrices for each data type and constructs patient-patient similarity networks (pg. 3 col. 1 para. 3); wherein expression features were extracted via an autoencoder model and used as multi-omics expression datasets from patients (pg. 2 col. 2 para. 2); wherein the visualization of the patient similarity network provides an intuitive explanation for the clinical diagnosis of patient subtyping (pg. 4 col. 1 para. 3) (i.e. reading on features calculated for said patient); wherein the model's accuracy represents the proportion of all samples judged correctly by the trained classifier (pg. 4 col. 2 para. 4); wherein the classifier method is applied for subtype prediction (pg. 5 col. 2 para. 1) (i.e. reading on estimating by means of said trained classifier the value of said variable of interest as a function of said values of said features calculated for said patient).
Claim 2 recites:
receiving a third dataset of omics data, wherein said third dataset of omics data comprises the values of a respective plurality of variables that refer to said genes for said reference patient, and wherein said generating a multi-layer network comprises
• Li teaches a combination of a patient similarity network and graph convolutional network to establish a complete pipeline for multi-omics biological data (pg. 5 col. 2 para. 1) based on genomics, transcriptomics and proteomics datasets (i.e. reading on first, second and third datasets – hence three layers of omics data) (pg. 1 Abstract) to integrate multi-omics datasets for cancer subtyping classifications efficiently (pg. 2 col. 2 para. 2).
associating to each variable of said third dataset of omics data a respective node in a third layer
• Li teaches a multi-omics integration model (i.e. reading on multi-layer network) based on graph convolutional network developed for cancer subtype classification and analysis based on genomics, transcriptomics and proteomics datasets (i.e. reading on first, second and third datasets) (pg. 1 Abstract); wherein the network is built by stacking multiple convolutional layers where W is the weight matrix learned from training and one input is the multiomics feature matrix X with n being the number of nodes and d being the number of features (pg. 4 col. 1 para. 2) (i.e. reading on associating to each variable of said third dataset of omics data a respective node in a third layer).
generating intra-omics connections by calculating, for each pair of nodes of the third layer, a respective similarity value as a function of the data of the respective variables associated to the two nodes and
generating intra-omics connections by calculating, for each pair of nodes of the third layer, a respective similarity value as a function of the data of the respective variables associated to the two nodes
• Li teaches a multi-omics integration model (i.e. reading on multi-layer network) based on graph convolutional network developed for cancer subtype classification and analysis based on genomics, transcriptomics and proteomics datasets (i.e. reading on first, second and third datasets – hence three layers of omics data) (pg. 1 Abstract).
• Li does not teach "intra-omics connections." However, Azevedo teaches a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome (pg. 1 Abstract) to model the human transcriptome and biological mechanisms of complex diseases and traits (pg. 1 Title ) and to estimate the extent to which each community’s gene expression profile was predictive of each of the tissues (pg. 11 col. 1 para. 1); wherein, in the multiplex network analysis of the transcriptome (i.e. reading on analysis of relationships within a single omics layer namely transcriptome – hence intra-omics), the global multiplexity index to represent the number of times that two given genes are clustered in the same community and a high value of global multiplexity index indicates a greater level of connectivity and greater functional similarity, as they appear multiple times in the same community across different layers (i.e. reading on generating intra-omics connections by calculating, for each pair of nodes of the third layer, a respective similarity value as a function of the data of the respective variables associated to the two nodes) (pg. 11 col. 2 para. 5); wherein layers represent different tissues, nodes represent genes, and edges between two nodes are weighted according to the correlation weights (i.e. reading on generating intra-omics connections by calculating, for each pair of nodes of the third layer, a respective similarity value as a function of the data of the respective variables associated to the two nodes) (pg. 11 col. 2 para. 4).
generating inter-omics connections by calculating, for each pair of nodes between the first layer and the third layer and each pair of nodes between the second layer and the third layer, a respective similarity value as a function of the data of the respective variables associated to the two nodes, and
calculating, for each pair of nodes between the first layer and the third layer and each pair of nodes between the second layer and the third layer, a respective weight associated to the connection between the respective nodes as a function of the similarity value of the respective pair of nodes
• Li teaches a multi-omics integration model (i.e. reading on multi-layer network) based on graph convolutional network developed for cancer subtype classification and analysis based on genomics, transcriptomics and proteomics datasets (i.e. reading on first, second and third datasets and inter omics network) (pg. 1 Abstract); wherein the network is built by stacking multiple convolutional layers where W is the weight matrix learned from training and one input is the multiomics feature matrix X with n being the number of nodes and d being the number of features (pg. 4 col. 1 para. 2); wherein similarity network fusion algorithm integrates different types of omics data , creating a network for each data type, and ultimately establishing a comprehensive view of the disease or biological process computing patient-patient similarity matrices for each data type and constructs patient-patient similarity networks (pg. 3 col. 1 para. 3) (i.e. reading on generating inter-omics connections by calculating for each pair of nodes between the first layer and the second layer a respective similarity value as a function of the data of the respective variables associated to the two nodes, and calculating, for each pair of nodes between the first layer and the second layer, a respective weight associated to the connection between the respective nodes as a function of the similarity value of the respective pair of nodes).
pruning the non-salient intra-omics connections and inter-omics connections of said multi-layer network by applying to the weights associated to the intra-omics connections between the nodes of said third layer, to the weights associated to the inter-omics connections between the nodes of said first layer and said third layer and to the weights associated to the inter-omics connections between the nodes of said second layer and said third layer a backboning method
• Li does not teach the recitation above. However, Coscia teaches a network backboning method to extract the latent structure from noisy networks by pruning non-salient edges (pg. 1 Abstract) solving the problem of extracting the backbone from a dense complex network (pg. 3 col. 1 para. 4); wherein said method accurate estimates the amount of noise in the edge weights (pg. 3 col. 2 para. 1); wherein Nij is the weight of the edge connecting nodes i and j (pg. 3 col. 1 para. 3); wherein the proposed methodology can be applied to consider multilayer networks, where nodes in different layers are coupled together and where these couplings influence the backbone structure (pg. 11 col. 2 para. 4) (i.e. reading on pruning non-salient intra-omics connections and inter-omics connections of said multi-layer network by applying, to the weights associated to the intra-omics connections between the nodes of said first layer, to the weights associated to the intra-omics connections between the nodes of said second layer, and to the weights associated to the inter-omics connections between the nodes of said first layer and said second layer, a backboning method).
Claim 3 recites:
wherein said first dataset of omics data, said second dataset of omics data and optionally said third dataset of omics data are chosen from among:
a dataset of transcriptomic data, where each variable corresponds to the gene expression of a particular gene, for example expressed in transcripts per million;
a dataset of copy number variation data, where each variable corresponds to the variation of the number of copies of a particular gene; and
a dataset of mutation data, where each variable corresponds to the mutation data of a particular gene
• Li teaches a combination of a patient similarity network and graph convolutional network to establish a complete pipeline for multi-omics biological data (pg. 5 col. 2 para. 1) based on genomics, transcriptomics and proteomics datasets (i.e. reading on first, second and third datasets) (pg. 1 Abstract) to integrate multi-omics datasets for cancer subtyping classifications efficiently (pg. 2 col. 2 para. 2); wherein for each omics layer, a stable set of essential genes is obtained (i.e. reading on a dataset of transcriptomic data, where each variable corresponds to the gene expression of a particular gene) (pg. 4 col. 1 para. 1)
Claim 7 recites:
wherein
said backboning method is the Noise-Corrected method;
said identifying a plurality of communities of said multi-layer network comprises performing a plurality of executions of the Infomap method; and/or
said feature-extraction method is the Uniform-Manifold Approximation-and- Projection method
• Li does not teach the recitation above. However, Coscia teaches a backboning method wherein Noise-Corrected backbone is applied, assuming the edge weights are drawn from a binomial distribution, to extract non-salient connections (pg. 2 col. 1 para. 4).
Claim 8 recites:
wherein
said variable of interest indicates:
information whether the respective patient has developed said disease,
the severity of said disease which the respective patient has developed, or
a disease-free survival time of the respective patient
Claim 9 recites:
wherein said given disease is a neoplasm or cancer, such as a non-small-cell lung cancer
• Li teaches a combination of a patient similarity network and graph convolutional network to establish a complete pipeline for multi-omics biological data (pg. 5 col. 2 para. 1) computing patient-patient similarity matrices for each data type and constructs patient-patient similarity networks (pg. 3 col. 1 para. 3); wherein expression features were extracted via an autoencoder model and used as multi-omics expression datasets from patients (pg. 2 col. 2 para. 2); wherein the visualization of the patient similarity network provides an intuitive explanation for the clinical diagnosis of patient subtyping (pg. 4 col. 1 para. 3); wherein a Kaplan-Meier 10-years overall survival analysis (i.e. reading on disease-free survival time of the respective patient as in claim 8) was performed to validate the prognostic value of genes for the validation breast cancer cohort – n=1880 (i.e. reading on wherein said given disease is a neoplasm or cancer as in claim 9) (pg. 5 col. 1 para. 2)
Rationale for combining (MPEP §2142-2143)
Regarding claims 1-3 and 7-11, 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, the methods of Li in view of Azevedo, Coscia and Ma because all references disclose methods for processing data to extract relevant information. The motivation would have been to:
• catalyze research into inter-tissue regulatory mechanisms, and their downstream consequences on human disease (pg. 1 Abstract Azevedo);
• incorporate a more realistic model that simultaneously considers the propensity of nodes to send and receive connections (pg. 1 Abstract Coscia) and
• process omics data and improve the contrast between molecular features and phenotypes (pg. 1 para. 1 Ma).
Therefore it would have been obvious to one of ordinary skill in the art to substitute method for processing data to extract relevant information of Li to the methods by Azevedo, Coscia and Ma because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for processing data to extract relevant information.
B. Claim 4 is rejected under 35 U.S.C. 103(a) as being unpatentable over Li, Azevedo, Coscia and Ma as applied to claims 1 and 3 above further in view of Butler ("Integrating single-cell transcriptomic data across different conditions, technologies, and species." Nature biotechnology 36.5:411-420 (2018)), as cited on the attached Form PTO-892.
Claim 4 recites:
wherein said generating intra-omics connections comprises:
in the case where the respective dataset of omics data comprises transcriptomic data or copy number variation data, calculating the respective similarity values)-via the biweight-midcorrelation metric; and/or
in the case where the respective dataset of omics data comprises mutation data, calculating the respective similarity values (sg)-via the normalized mutual information
• Neither Li or Azevedo or Coscia or Ma teach the recitation above. However, Butler teaches an analytical strategy for integrating single cell RNA-seq data sets (i.e. reading on transcriptomics) based on common sources of variation, enabling the identification of shared populations across data sets (i.e. reading on intra-omics conncetions) (pg. 411 col. 1 para. 1); wherein biweight midcorrelation, a median based similarity metric, was used to identify genes whose expression robustly correlates with each projection vector in both data sets, and therefore drive shared sources of variation (pg. 423 col. 1 para. 2).
Rationale for combining (MPEP §2142-2143)
Regarding claim 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, the methods of Li, Azevedo, Coscia and Ma in view of Butler because all references disclose methods for processing data to extract relevant information. The motivation would have been to facilitate the comparison of RNA-seq data sets, deepening the understanding of how distinct cell states respond to disease (pg. 411 col. 1 para. 1 Butler).
Therefore it would have been obvious to one of ordinary skill in the art to substitute method for processing data to extract relevant information of Li, Azevedo, Coscia and Ma to the methods by Butler because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for processing data to extract relevant information.
C. Claim 5 is rejected under 35 U.S.C. 103(a) as being unpatentable over Li, Azevedo, Coscia and Ma as applied to claims 1 and 3 above further in view of Tang ("Multi-omics integrative analysis of acute and relapsing malaria in a non-human primate model of P. vivax infection." BioRxiv 564195 (2019)), as cited on the attached Form PTO-892.
Claim 5 recites:
wherein said generating inter-omics connections comprises:
- in the case where the respective dataset of omics data comprise transcriptomic data and copy number variation data, calculating the respective similarity values (sg)-via the biweight-midcorrelation metric; and/or
- in the case where the respective dataset of omics data comprise other data, calculating the respective similarity values esg)-via the point-biserial correlation
• Neither Li or Azevedo or Coscia or Ma teach the recitation above. However, Tang teaches integrated transcriptomics, metabolomics, and lipidomics data (i.e. reading on inter-omics connections) to distinguish between acute infections, relapse infections, and uninfected NHP in an unsupervised manner to an extent that was not possible based on the individual data types alone (pg. 2 para. 2); wherein pairwise biweight midcorrelation was applied to identify correlations between Multi-Omics Relatedness Networks (via their eigenfeatures) and phenotypic measurements (immunophenotyping, plasma cytokine profiles, and clinical traits) (pg. 6 para. 2).
Rationale for combining (MPEP §2142-2143)
Regarding claim 5, 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, the methods of Li, Azevedo, Coscia and Ma in view of Tang because all references disclose methods for processing data to extract relevant information. The motivation would have been to incorporate the analysis of relationships across multiple biological layers using a mutual information-based approach (pg. 1 Summary Tang).
Therefore it would have been obvious to one of ordinary skill in the art to substitute method for processing data to extract relevant information of Li, Azevedo, Coscia and Ma to the methods by Tang because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for processing data to extract relevant information.
D. Claim 6 is rejected under 35 U.S.C. 103(a) as being unpatentable over Li, Azevedo, Coscia and Ma as applied to claim 1 above further in view of Zhang ("A general framework for weighted gene co-expression network analysis." Statistical applications in genetics and molecular biology 4.1:1128 (2005)), as cited on the attached Form PTO-892.
Claim 6 recites:
wherein said calculating for each pair of nodes a respective weight wij associated to the connection between the respective nodes comprises applying the following equation:
PNG
media_image1.png
30
63
media_image1.png
Greyscale
, where sij is the respective similarity value and the exponent β is chosen between 1 and 10
• Li teaches a multi-omics integration model (i.e. reading on multi-layer network) based on graph convolutional network developed for cancer subtype classification and analysis based on genomics, transcriptomics and proteomics datasets (i.e. reading on first, second and third datasets and inter omics network) (pg. 1 Abstract); wherein the network is built by stacking multiple convolutional layers where W is the weight matrix learned from training and one input is the multiomics feature matrix X with n being the number of nodes and d being the number of features (pg. 4 col. 1 para. 2); wherein similarity network fusion is performed to enhance strong connections and remove weak connections (pg. 3 col. 2 para. 1).
• Li does not teach the recited equation
PNG
media_image1.png
30
63
media_image1.png
Greyscale
. However, Zhang teaches a weighted gene co-expression network for ‘soft’ thresholding that assigns a connection weight to each gene pair (pg. 1 Abstract); wherein the power adjacency function is disclosed as
PNG
media_image2.png
60
283
media_image2.png
Greyscale
(pg. 5 para. 6).
Rationale for combining (MPEP §2142-2143)
Regarding claim 6, 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, the methods of Li, Azevedo, Coscia and Ma in view of Zhang because all references disclose methods for processing data to extract relevant information. The motivation would have been to introduce several node connectivity measures and provide empirical evidence that said measures can be important for predicting the biological significance of a gene. (pg. 1 Abstract Zhang).
Therefore it would have been obvious to one of ordinary skill in the art to substitute method for processing data to extract relevant information of Li, Azevedo, Coscia and Ma to the methods by Zhang because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for processing data to extract relevant information.
Conclusion
No claims are allowed.
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/F.F.L./Examiner, Art Unit 1685
/JANNA NICOLE SCHULTZHAUS/Examiner, Art Unit 1685
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